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InVEST 1.004 Beta User's Guide: - High

1. Finally we define the Budyko dryness index where R values that are greater than one denote pixels that are potentially arid Budyko 1974 as follows k ETo xj E P where ETo is the reference evapotranspiration from pixel x and ky is the plant evapotranspiration coefficient associated with the LULC j on pixel x ETo represents an index of climatic demand while kx is largely determined by x s vegetative characteristics Allen et al 1998 Water Scarcity Model The Water Scarcity Model calculates the water scarcity value along a flow path based on water yield and water consumptive use in a watershed s of interest The user inputs how much water is consumed by each land use land cover type in a table format For example in an urban area consumptive use can be calculated as the product of population density and per capita consumptive use These land use based values only relate to the consumptive portion of demand some water use is non consumptive such water used for cooling or other industrial processes that return water to the stream after use For simplicity each pixel in the watershed is either a contributing pixel which contributes to hydropower production or a use pixel which uses water for other consumptive uses This assumption implies that land use associated with consumptive uses will not contribute any yield for downstream use The amount of water that actually reaches the reservoir 78 for dam d realized sup
2. a MK 1000 1500 300 250 SK SS 40K 450 Ae D 00 1000 1500 2000 2 K SKK 3500 4000 4500 5000 Max Distance of Threat m Max Distance of Threat m Figure 1 An example of the relationship between the distance decay rate of a threat and the maximum effective distance of a threat under A linear and B exponential 3 The third landscape factor that may mitigate the impact of threats on habitat is the level of legal institutional social physical protection from disturbance in each cell Is the grid cell in a formal protected area Or is it inaccessible to people due to high elevations Or is the grid cell open to harvest and other forms of disturbance The model assumes that the more legal institutional social physical protection from degradation a cell has the less it will be affected by nearby threats no matter the type of threat Let 2 0 1 indicate the level of accessibility in grid cell x where 1 indicates complete accessibility As 2 decreases the impact that all threats will have in grid cell x decreases linearly It is important to note that while legal institutional social physical protections often do diminish the impact of extractive activities in habitat such as hunting or fishing it is unlikely to protect against other sources of degradation such as air or water pollution habitat fragmentation or edge effects If the threats considered are not mitigated by legal institutional social physical prope
3. The final PC coefficient used in the model should be P x C x 1000 to ensure integer values 6 Watersheds The watersheds should be delineated by the user based on the location of reservoirs or other points of interest Exact locations of specific structures such as reservoirs should be obtained from the managing entity or may be obtained on the web at sites such as the National Inventory of Dams http crunch tec army mil nidpublic webpages nid cfm Watersheds that contribute to the points of interest must be generated If known correct watershed maps exist they should be used Otherwise watersheds can be generated in ArcMap using a hydrologically correct digital elevation model Due to limitations in ArcMap geoprocessing the maximum size of a watershed that can be processed by the Nutrient Retention tool is approximately the equivalent of 4000x4000 cells If the whole watershed contributing to a point of 132 interest is larger than this size it will need to be divided into sub watersheds that are each smaller If the whole watershed is smaller then it does not need to be divided Sub watersheds will be mosaicked back together into whole watersheds for the final output See the Working with the DEM section of this manual for more information on generating watersheds and sub watersheds 7 Sediment table The estimated sediment removal cost from the reservoirs will ideally be based on the characteristics of each reservoir and re
4. and place all your input files here It s not necessary to place input files in the workspace but advisable so you can easily see the data you use to run your model Or if this is your first time using the tool and you wish to use sample data you can use the data provided in InVEST Setup exe If you unzipped the InVEST files to your C drive as described in the Getting Started chapter you should see a folder called Invest timber This folder will be your workspace The input files are in a folder called Invest timber input and in Invest base_data e Open an ARCMAP document to run your model e Find the INVEST toolbox in ARCTOOLBOX ARCTOOLBOX is normally open in ARCMAP but if it is not click on the ARCTOOLBOX symbol See the Getting Started chapter if you don t see the InVEST toolbox and need instructions on how to add it e You can run this analysis without adding data to your map view but usually it is recommended to view your data first and get to know them Add the data for this analysis to your map using the ADD DATA button and look at each file to make sure it is formatted correctly Save your ARCMApP file as needed e Click once on the plus sign on the left side of the INVEST toolbox to see the list of tools expand Double click on TIMBER 142 gt InVEST mxd ArcMap Arcinfo BR gile Edit View Insert Selection Tools window Help Jo A fd ES x K A mE Ife ry 5 K eae gt 4 w Task Gose
5. start _ date 1 Bio _ HWP _ fut Cut _ cur xru 2 F x c H req _ cur en_cur i Ee ci tel OB t i ii yr_ fut yr_cur Cut _ fut xru 2 x Freq _ fut C_den_ fut However for parcels that were harvested on the current landscape but are not expected to be harvested on the future landscape the mass of wood removed from a parcel from Start_date to yr_fut is given by the first term of equation C8 yr_ fut yr_cur Bio_ HWP _ fut Cut_cur xr 2 start_ da 1 x Freq_cur J C_den_ cur C9 For parcels that were not harvested on the current landscape but are expected to be harvested on the future landscape the mass of wood removed from a parcel from Start_date to yr_fut is given by second term of equation C8 fut Fit yr_ fut yr_cur Bio_ HWP _ fit Cut_ fut xr 2 x C10 Freq_ tut C_den_ fut Finally The volume of the of wood that has been removed from a parcel from Start_date to yr_fut is given by 56 yr_ fut yr_cur Vol_ HWP _ fut Cut_cur x ru 2 fut Gls yr_ tut yr_cur Cut_ fut x ru 2 x x Freq_ fut C_den_ fut BCEF_ cur yr_ fut yr_cur _ start _date i i gt C12 Vol _ HWP _ fut Cut _cur x ru 2 x x C11 start_ date Freq_cur x 1 x i C_den_cur BCEF _cur Freq _cur C_den_cur BCEF _cur or ee yr _ fut yr _cur f C13 Vol_HWP _ fut Cut _ fut x ru 2 x x Freq _ fut C_den_ fut BCEF _cur depending on the combination o
6. where Parcl_area is the area ha of parcel x TNPV Parcl_area x NPV 7 The last table entry BCEF is used to transform the total volume of wood removed from a parcel from yr_cur or yr_fut to T years later TBiomass If Immed_harv 1 then Perc_ harv T TBiomass Parcl_ area x _ x Harv_ mass x r 8 100 Freq_ harv Otherwise if Immed_harv 0 then Perc_ harv T 9 TBiomass Parcl_ area x x Harv_ mass x tu _ 9 100 i Freq_ harv and 1 TVolume TBiomass x 10 BCEF Example Landscape with timber production in five parcels In this example the first two timber parcels are managed for timber production on a 45 year even age rotation 1 45 of the stand is harvested and then replanted each year in perpetuity but have different mixes of species and different management costs Each managed timber parcel is 1000 hectares The third timber parcel has the same species mix as the second but 4 of the parcel is harvested every 20 years and it will only be managed for at least another 50 years The fourth polygon is a clear cut of a 500 ha natural forest that is slated to become a shopping mall The fifth parcel represents a portion of a mature primary forest The parcel in the larger forest that will be used for timber production is 500 ha It will be systematically clear cut over the next ten years and then managed as a single species pla
7. DEM required A GIS raster dataset with an elevation value for each cell Make sure the DEM is corrected by filling in sinks and if necessary burning hydrographic features into the elevation model recommended when you see unusual streams See the Working with the DEM section of this manual for more information Name File can be named anything but avoid spaces DEM is simplest Format Standard GIS raster file e g ESRI GRID or IMG with elevation value for each cell given in meters above sea level Sample data set InVEST Base_Data dem 2 Soil depth required A GIS raster dataset with an average soil depth value for each cell The soil depth values should be in millimeters Name File name can be anything but avoid spaces Format Standard GIS raster file with an average soil depth in millimeters for each cell Sample data set InVEST Base_Data soil_depth 3 Precipitation required A GIS raster dataset with a non zero value for average annual precipitation for each cell The precipitation values should be in millimeters Name File can be named anything but avoid spaces Format Standard GIS raster file e g ESRI GRID or IMG with precipitation values for each cell 101 Sample data set InVEST Base_Data precip 4 Plant Available Water Content required A GIS raster dataset with a plant available water content value for each cell Plant Available Water Content fraction PAWC is the fractio
8. IPCC 2006 Canadell and Raupach 2008 Capoor and Ambrosi 2008 Hamilton et al 2008 Pagiola 2008 The social value of a sequestered ton of carbon is equal to the social damage avoided by not releasing the ton of carbon into the atmosphere Tol 2005 Stern 2007 Calculations of social cost are 44 complicated and controversial see Weitzman 2007 and Nordhaus 2007b but have resulted in value estimates that range from USD 9 55 to 84 55 per metric ton of CO released into the atmosphere Nordhaus 2007a and Stern 2007 respectively In addition to the social value of carbon sequestration and storage there are several emerging markets for carbon based on both regulation and voluntary demand The Kyoto Protocol the current treaty addressing international climate change includes a mechanism for establishing projects that sequester carbon to earn credits which they then can sell to others needing to offset their own CO emissions As a result of the Kyoto Protocol the European Union Emissions Trading Scheme EU ETS emerged to allow the regulated firms of the EU to trade their emissions allowances The Chicago Climate Exchange CCX emerged in the United States which is not a signatory party of the Kyoto Protocol The CCX allows interested parties to trade emissions offsets that have been certified on a voluntary basis The EU ETS and the CCX had prices of around 25 Euros and USD 6 per metric ton of CO respectively as of April 2008 In ad
9. If the harvest is expected to be an immediate one time clear cut T 1 If a series of clear cuts in a natural forest are occurring or are expected T can be no greater than the number of years that harvest of the natural stand can continue given Perc_harv and Freq_harv For example if a natural stand is going to be replanted as a single species plantation or allowed to regenerate naturally before being harvested again in the future T for the harvest of the natural stand can be no larger than 7 if Perc_harv 33 3 and Freq_harv 3 assuming a harvest pi o 138 takes place in years 1 yr_cur or yr_fut depending on the associated LULC map 4 and 7 j Immed_harv This attribute answers whether a harvest occurs immediately whether a harvest occurs in yr_cur or whether the user is evaluating a forest parcel associated with a future LULC scenario occurring in yr_fut Answer yes entered as YES or Y or no entered as No or N to whether a harvest should be calculated for yr_cur or yr_fut If yes then the NPV of harvest in the parcel includes a harvest in yr_cur otherwise the first harvest accounted for in the parcel s NPV occurs Freg_harv years into the into time interval T k BCEF An expansion factor that translates the mass of harvested wood into volume of harvested wood The expansion factor is measured in Mg of dry wood per m of wood The expansion factor is a function of stand type and stand age this factor is know as t
10. J Regetz S Polasky H Tallis D Cameron K Chan G Daily J Goldstein P Kareiva E Lonsdorf R Naidoo TH Ricketts and R Shaw 2008 Modeling Multiple Ecosystem Services and Tradeoffs at Landscape Scales Frontiers in Ecology and the Environment Forthcoming Nordhaus W 2007a Critical Assumptions in the Stern Review on Climate Change Science 317 5835 201 202 Nordhaus W 2007b A Review of the Stern Review on the Economics of Global Warming Journal of Economic Literature 45 686 702 Pagiola S 2008 Payments for environmental services in Costa Rica Ecological Economics 65 4 712 724 Plantinga AJ and RA Birdsey 1994 Optimal Forest Stand Management When Benefits are Derived from Carbon Natural Resource Modeling 8 4 373 387 72 Polasky S E Nelson D Pennington and K Johnson 2010 The Impact of Land Use Change on Ecosystem Services Biodiversity and Returns to Landowners A Case Study in the State of Minnesota Environmental and Resource Economics in press Post WM WR Emanuel PJ Zinke and AG Stangenberger 1982 Soil carbon pools and world life zones Nature 298 156 159 Post WM KC Kwon 2000 Soil carbon sequestration and land use change processes and potential Global Change Biology 6 317 327 Raich JW AE Russell K Kitayama WJ Parton and PM Vitousek 2006 Temperature influences carbon accumulation in moist tropical forests Ecology 87 76 87 Ruesch A and HK Gibbs 2008 New IPCC tier
11. K Texture is the principal factor affecting K but soil profile organic matter and permeability also contribute It varies from 70 100 for the most fragile soil and 1 100 for the most stable soil It is measured on bare reference plots 22 2 m long on 9 slopes tilled in the direction of the slope and having received no organic matter for three years Values of 0 0 6 are reasonable while higher values should be given a critical look K may be found as part of standard soil data maps Coarse yet free global soil characteristic data is available at http www ngdc noaa gov seg cdroms reynolds reynolds reynolds htm In the United States free soil data is available from the U S Department of Agriculture s NRCS in the form of two datasets SSURGO http soils usda gov survey geography ssurgo and STATSGO http soils usda gov survey geography statsgo Where available SSURGO data should be used as itis much more detailed than STATSGO Where gaps occur in the SSURGO data STATSGO can be used to fill in the blanks The soil erodibility should be calculated as the average of all horizons within a soil class component and then a weighted average of the components should be estimated This can be a tricky GIS analysis In the US soil categories each soil property polygon can contain a number of soil type components with unique properties and each component may have different soil horizon layers also with unique properties Processing req
12. M Grasso B Hannon K Limburg S Naeem RV O Neill J Paruelo RG Raskin P Sutton and M van den Belt 1997 The value of the world s ecosystem services and natural capital Nature 387 253 260 Free JB 1993 Insect pollination of crops Academic Press London Greenleaf SS NM Williams R Winfree and C Kremen 2007 Bee foraging ranges and their relationship to body size Oecologia 153 589 596 Greenleaf SS and C Kremen 2006 Wild bee species increase tomato production and respond differently to surrounding land use in Northern California Biological Conservation 133 81 87 Klein AM BE Vaissiere JH Cane I Steffan Dewenter SA Cunningham C Kremen and T Tscharntke 2007 Importance of pollinators in changing landscapes for world crops Proceedings of the Royal Society B Biological Sciences 274 303 313 Kremen C NM Williams RL Bugg JP Fay and RW Thorp 2004 The area requirements of an ecosystem service crop pollination by native bee communities in California Ecology Letters 7 1109 1119 Lonsdorf E C Kremen T Ricketts R Winfree N Williams and SS Greenleaf 2009 Modelling pollination services across agricultural landscapes Annals of Botany 1 12 online http aob oxfordjournals org cgi content abstract 103 9 1589 160 Lonsdorf E TH Ricketts CM Kremen NM Williams and S Greenleaf in press Pollination services in P Kareiva TH Ricketts GC Daily H Tallis and S Polasky eds The theory
13. The systems are designed to account for annual variability in water volume given the likely levels for a given watershed but are vulnerable to extreme variation caused by land use and land cover LULC changes LULC changes can alter hydrologic cycles affecting patterns of evapotranspiration infiltration and water retention and changing the timing and volume of water that is available for hydropower production World Commission on Dams 2000 Ennaanay 2006 Changes in the landscape that affect annual average water yield upstream of hydropower facilities can increase or decrease hydropower production capacity Maps of where water yield used for hydropower is produced can help avoid unintended impacts on hydropower production or help direct land use decisions that wish to maintain power production while balancing other uses such 75 as conservation or agriculture Such maps can also be used to inform investments in restoration or management that downstream stakeholders such as hydropower companies make in hopes of improving or maintaining water yield for this important ecosystem service In large watersheds with multiple reservoirs for hydropower production areas upstream of power plants that sell to a higher value market will have a higher value for this service Maps of how much value each parcel contributes to hydropower production can help managers avoid developments in the highest hydropower value areas understand how much value will be l
14. The use of these equations requires knowledge of the distribution of tree size in a given stand Some researchers have made use of these equations a bit easier by first relating a stand s distribution of different sized trees to its age and then mapping the relationship between age and aboveground biomass i e VOB x WD x BEF For example Silver et al 2000 have estimated 65 aboveground biomass as a function of stand age i e years since afforestation reforestation or previous LULC for native forest types in tropical ecosystems Smith et al 2006 take the transformation of allometric equations one step further by relating age to total biomass carbon belowground plus aboveground directly for various US forests When using IPCC data or other similar broad data sources one final issue to consider is how the level of anthropogenic disturbance affects carbon stocks The aboveground C stock of highly disturbed areas will likely be lower than the stocks of undisturbed areas It is not clear what type of disturbance levels IPCC or other such sources assume when reporting aboveground biomass estimates If forest disturbance is an issue in the demonstration site LULC types should be stratified by levels of disturbance For an example of such stratification see Table 2 5 page 14 of ECCM 2007 The effect of this disturbance on C storage in harvested wood products HWPs is discussed below Finally we generally do not treat aboveground h
15. Water Resources Research vol 37 pp 701 708 96 WATER PURIFICATION NUTRIENT RETENTION Summary Water purification is an essential service provided by ecosystems InVEST estimates the contribution of vegetation and soil to purifying water through the removal of nutrient pollutants from runoff The biophysical model uses data on water yield land use and land cover nutrient loading and filtration rates and water quality standards if they exist to determine nutrient retention capacity for current and future land use scenarios The valuation model uses data on water treatment costs and a discount rate to determine the value contributed by the natural system to water purification It does not address chemical or biological interactions besides filtration by terrestrial vegetation such as in stream processes and is less relevant to locations with extensive tile drainage or ditching strong surface water ground water interactions or hydrology dominated by infiltration excess dry regions with flashy rains Introduction Clean water is a vital service provided by healthy streams watersheds and river basins Polluted water is especially harmful to human health In fact waterborne illnesses are the leading cause of human disease and death around the world killing more than 3 4 million people annually World Health Organization Clean water also provides habitat for aquatic life in streams rivers and lakes but these habitats require
16. and practice of ecosystem service valuation Losey JE and M Vaughan 2006 The economic value of ecological services provided by insects Bioscience 56 311 323 Nabhan GP and SL Buchmann 1997 Services provided by pollinators Pages 133 150 in GC Daily ed Nature s services Island Press Washington D C Priess JA M Mimler AM Klein S Schwarze T Tscharntke and I Steffan Dewenter 2007 Linking deforestation scenarios to pollination services and economic returns in coffee agroforestry systems Ecological Applications 17 407 417 Ricketts TH 2004 Tropical forest fragments enhance pollinator activity in nearby coffee crops Conservation Biology 18 1262 1271 Ricketts TH NM Williams and MM Mayfield 2006 Connectivity and ecosystem services crop pollination in agricultural landscapes Pages 255 289 in M Sanjayan and K Crooks eds Connectivity for Conservation Cambridge University Press Cambridge UK Roubik DW and M Aluja 1983 Flight ranges of Melipona and Trigona in tropical forest Journal of the Kansas Entomological Society 56 217 222 Southwick EE and L Southwick 1992 Estimating the economic value of honey bees Hymenoptera Apidae as agricultural pollinators in the United States Journal of Economic Entomology 85 621 633 Winfree R J Dushoff EE Crone CB Schultz RV Budny NM Williams and C Kremen 2005 Testing simple indices of habitat proximity American Naturalist 165 6 707 717 161
17. j x A ArcToobox 30 Analyst Tools Analysis Tools Cartography Tools Conversion Tools Coverage Toas Date Interoperability Tools Q Date Management Tools F Geocoding Tools Geostatisticd Snalyst Tools InvEST E Pollution Control amp Sodversity Sedmentation hulc_samp_owr roads Ss E roads M ctes B MW bic_sanp_or em EJ gt GE AR FA amp Timber a O ches 0 roads 2 EEN E kicsap or LJ Ia gt ee amp Polination F hulc_seenp_our Linear Referencing Tools Mukidimension Tools Network Analyst Tools Samples Server Tools J Spatial Analyst Tools Q Spatial Statistics Tools amp Tracking Analyst Tools es ao 2 4 gt pwin OPOYA Z oj Aia Jio B z ul Ar arrn 474624 43 4956720 512 Meters e An interface will pop up like the one below The tool indicates default file names but you can use the file buttons to browse instead to your own data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface In addition refer to the Data Needs section above for information on data formats 143 S Timber Workspace Workspace C InVEST Timber Managed area map Folder that contains all the C MInVEST Timber nput plantation shp e will by etau e sav omnis Plantation Production Table a location C InYEST Timber Inputiplant_table dbf M
18. on its map then all threats should be mapped with the presence absence scale The impact of threats on habitat in a grid cell is mediated by four factors 1 The first factor is the relative impact of each threat Some threats may be more damaging to habitat all else equal and a relative impact score accounts for this see Table 1 for a list of possible threats For instance urban areas may be considered to be twice as degrading to any nearby habitats as agricultural areas A degradation source s weight w indicates the relative destructiveness of a degradation source to all habitats The weight w can take on any value from 0 to 1 For example if urban area has a threat weight of 1 and the threat weight of roads is set equal to 0 5 then the urban area causes twice the disturbance all else equal to all habitat types To reiterate if we have assigned species group specific habitat suitability scores to each LULC then the threats and their weights should be specific to the modeled species group 2 The second mitigating factor is the distance between habitat and the threat source and the impact of the threat across space In general the impact of a threat on habitat decreases as distance from the degradation source increases so that grid cells that are more proximate to threats will experience higher impacts For example assume a grid cell is 2 km from the edge of an urban area and 0 5 km from a highway The impact of these two threat so
19. 3 for grassland etc The LULC class codes should be in the grid value column Sample data set Invest Base_data lulc_samp_bse 34 4 Threat data required A table of all threats you want the model to consider The table contains information on the each threat s relative importance or weight and its impact across space Name file can be named anything File Type dbf or xls if using ArcGIS 9 3 Rows each row is a degradation source Columns each column contains a different attribute of each degradation source and must be named as follows a THREAT the name of the specific threat Threat names must not exceed 8 characters b MAX_DIST the maximum distance over which each threat affects habitat quality measured in km The impact of each degradation source will decline to zero at this maximum distance c WEIGHT the impact of each threat on habitat quality relative to other threats Weights can range from 1 at the highest to 0 at the lowest d DECAY Indicates whether the impact of the threat decreases linearly or exponentially across space Value can be either 0 or 1 A value of indicates a linear decline in impact while 0 indicates an exponential decline Sample Data Set Invest Biodiversity Input threats_samp dbf Example Hypothetical study with three threats Agriculture degrades habitat over a larger distance than roads do and has a greater overall magnitude of impact Further paved roads attr
20. B Diagana 2003 Creating Incentives for the Adoption of Sustainable Agricultural Practices in Developing Countries The Role of Soil Carbon Sequestration American Journal of Agricultural Economics 85 1178 1184 Baer SG DJ Kitchen JM Blair and CW Rice 2002 Changes in Ecosystem Structure and Function along a Chronosequence of Restored Grasslands Ecological Applications 12 1688 1701 Bernoux M MDS Carvalho B Volkoff and CC Cerri 2002 Brazil s soil carbon stocks Soil Science Society of America Journal 66 888 896 Brown SL PE Schroeder and JS Kern Spatial distribution of biomass in forests of the eastern USA Forest Ecology and Management 123 1999 81 90 Brown S 2002 Measuring carbon in forests current status and future challenges Environmental Pollution 116 363 372 Brown S Estimating Biomass and Biomass Change of Tropical Forests a Primer FAO Forestry Department 1997 Report for FAO Forestry Paper 134 Brown S and PE Schroeder 1999 Spatial patterns of aboveground production and mortality of woody biomass for eastern US forests Ecological Applications 9 968 980 Cairns MA PK Haggerty R Alvarez BHJ De Jong and I Olmsted 2000 Tropical Mexico s recent land use change A region s contribution to the global carbon cycle Ecological Applications 10 1426 1441 Cairns MA S Brown EH Helmer and GA Baumgardner 1997 Root biomass allocation in the world s upland forests Oecologia 111 1 11 Canadell JG and
21. CALCULATIONS Management Costs Market ve N Future Value CO j P Figure 1 Conceptual diagram of the Managed Timber Production model Parameters represented in color are included in the model while those in gray are not map is given by yr_cur or with some future LULC scenario map where the year associated with the future LULC map is given by yr_fut If the timber parcel map is associated with the current LULC map the model calculates for each timber parcel the net present value NPV of harvests that occurred between the current year and some user defined date assuming that harvest practices and prices are static over the time interval modeledl If the timber parcel map is associated with a future scenario LULC map the model calculates for each timber parcel the NPV of harvests that occurred between the future date and some user defined date again assuming that harvest practices and prices do not change over the user defined time interval The model produces the NPV of harvests in the currency of either the current year or future year depending on whether the user inputs a current or future LULC map For example if the selected year for the future scenario is 2050 and the dollar is the currency used to value timber harvests then the NPV of harvests from 2050 to some user defined later than 2050 is given in year 2050 dollars 136 Limitations and simplifications This model assumes that the percent of the forest harvested
22. Ehrlich M Quesada A Miranda VJ Jaramillo F Garc a Oliva A Mart nez Yrizar H Cotler J L pez Blanco A P rez Jim nez A B rquez C Tinoco G Ceballos L Barraza R Ayala and J Sarukh n 2005 Ecosystem services of tropical dry forests insights from long term ecological and social research on the Pacific Coast of Mexico Ecology and Society 10 17 N ez D L Nahuelhual and C Oyarz n 2006 Forests and water The value of native temperate forests in supplying water for human consumption Ecological Economics 58 606 616 Ricketts TH 2004 Tropical Forest Fragments Enhance Pollinator Activity in Nearby Coffee Crops Conservation Biology 18 1262 1271 Sohngen B and S Brown 2006 The influence of conversion of forest types on carbon sequestration and other ecosystem services in the South Central United States Ecological Economics 57 698 708 145 CROP POLLINATION Summary Seventy five percent of globally important crops rely either in part or completely on animal pollination The InVEST pollination model focuses on wild bees as a key animal pollinator It uses estimates of the availability of nest sites and floral resources and bee flight ranges to derive an index of bee abundance nesting on each cell on a landscape i e pollinator supply It then uses flight range information to estimate an index of bee abundance visiting each agricultural cell If desired the model then calculates a simple index of t
23. Format standard GIS raster file e g ESRI GRID or IMG where the Value field contains a unique integer ID value for each sub watershed Sample data set C Invest Base_Data subwatersheds 6 Watersheds required A GIS raster dataset that contains one watershed for each point of interest for the sediment valuation model These points represent reservoirs or other types of structures where sedimentation is an issue of concern The watersheds are delineated for these points of interest See Sub watersheds above for more information on watershed size requirements Name File can be named anything but no spaces in the name and less than 13 characters Format standard GIS raster file e g ESRI GRID or IMG where the Value field contains a unique integer ID value for each watershed which corresponds to the watershed reservoir ID field in the Sediment Reservoir input table Sample data set C Invest Base_Data watersheds 7 Biophysical table required A table containing model information corresponding to each of the land use classes NOTE these data are attributes of each LULC class not each cell in the raster map Name Table names should only have letters numbers and underscores no spaces File type dbf or mdb Rows Each row is a land use land cover class Columns Each column contains a different attribute of each land use land cover class and must be named as follows a lucode Land use code Unique integer for each LUL
24. Nat Acad Sci 105 9471 9476 Noss R F M A Connell and D D Murphy 1997 The science of conservation planning habitat conservation under the endangered species act Island Press Prugh L K Hodges A Sinclair and J Brashares 2008 Effect of habitat area and isolation on fragmented animal populations Proceedings of the National Academy of Sciences 105 20770 Ricketts T H 2001 The Matrix Matters Effective Isolation in Fragmented Landscapes American Naturalist 158 87 99 Vitousek P M H A Mooney J Lubchenco and J M Melillo 1997 Human Domination of Earth s Ecosystems Science 277 494 Wilcove D S D Rothstein J Dubow A Phillips and E Losos 1998 Quantifying Threats to Imperiled Species in the United States Bioscience 48 607 615 43 CARBON STORAGE AND SEQUESTRATION Summary Terrestrial ecosystems which store more carbon than the atmosphere are vital to influencing carbon dioxide driven climate change The INVEST model uses maps of land use and land cover types and data on wood harvest rates harvested product degradation rates and stocks in four carbon pools aboveground biomass belowground biomass soil dead organic matter to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time Additional data on the market or social value of sequestered carbon and its annual rate of change and a discount rate can be used in an optional model that
25. a habitat LULC to non habitat LULC reduces the size and continuity of neighboring habitat patches Edge effects refer to changes in the biological and physical conditions that occur at a patch boundary and within adjacent patches For example adjacent degraded non habitat LULC parcels impose edge effects on habitat parcels and can have negative impacts within habitat parcels by for example facilitating entry of predators competitors invasive species or toxic chemicals and other pollutants Another example in many developing countries roads are a threat to forest habitat quality on the landscape because of the access they provide to timber and non timber forest harvesters Each threat source needs to be mapped on a raster grid A grid cell value on a threat s map can either indicate intensity of the threat within the cell e g road length in a grid cell or cultivated area in a gird cell or simply a 1 if the grid cell contains the threat in a road or crop field cover and 28 0 otherwise Let 0 indicate threat r s score in grid cell y where r 1 2 R indexes all modeled degradation sources All mapped threats should be measured in the same scale and metric For example if one threat is measured in density per grid cell then all degradation sources should be measured in density per grid cell where density is measured with the same metric unit e g km and km Or if one threat is measured with presence absence 1 0
26. a raster grid that displays the present value in currency per pixel of sediment retention on the landscape In other words it is the avoided cost of sediment removal at a downstream reservoir over the reservoirs projected lifetime due to the ability of the landscape to keep sediment in place This raster grid provides valuable information to the decision maker on the relative importance of each part of the landscape in determining the cost of sediment removal for a particular reservoir This output allows managers to see which parts of the landscape are providing the greatest value in terms of avoided sediment removal costs They may want to protect or at least avoid serious land use change in these areas Similarly when scenarios of future land management are analyzed with this model the Value of Sediment Removal layer can be used to identify where the benefits of avoided maintenance costs will be lost maintained or improved across the landscape Summarizing this layer across the landscape can also give an overall sense of the total costs that will be avoided given a particular landscape configuration The user should keep in mind that the Tier 1 model may not accurately depict the sedimentation process in the user s watershed of interest Furthermore the model is based on parameterization of several different equations and each parameter describes a stochastic process Due to the uncertainty inherent in the processes being modeled here the u
27. a soil class component and then a weighted average of the components should be estimated This can be a tricky GIS analysis In the US soil categories each soil property polygon can contain a number of soil type l Droogers P and R G Allen 2002 Estimating reference evapotranspiration under inaccurate data conditions Irrigation and Drainage Systems 16 33 45 i Hargreaves G H 1994 Defining and using reference evapotranspiration J Irrig and Drain Engrg ASCE 120 6 1132 1139 3 Allen R G Pereira L S Raes D amp Smith M 1998 Crop evapotranspiration Guidelines for computing crop requirements Irrigation and Drainage Paper No 56 FAO Rome Italy 91 components with unique properties and each component may have different soil horizon layers also with unique properties Processing requires careful weighting across components and horizons The Soil Data Viewer http soildataviewer nrcs usda gov a free ArcMap extension from the NRCS does this soil data processing for the user and should be used whenever possible Ultimately a grid layer must be produced Data gaps such as urban areas or water bodies need to be given appropriate values Urban areas and water bodies can be thought of having zero soil depth A good product would be to determine the minimum of depth to bedrock and typical water table depth d Plant available water content PAWC Plant available water content is a fraction obtained from most standard
28. and land cover LULC map showing both natural and managed land types This map is divided into a regular grid of square cells each of which is assigned a single LULC type For each type the model requires estimates of both nesting site availability and flower availability e g for bee food nectar and pollen These data can be supplied from quantitative field estimates or from expert opinion and are expressed in the form of relative indices between 0 and 1 Flower availability can be supplied separately for different seasons if important and the availability of nesting substrates can be estimated separately for multiple nesting guilds e g ground nesters cavity nesters Because bees are proficient flyers they integrate over several elements of a landscape moving between nesting habitats and foraging habitats Ricketts et al 2006 The distances they typically fly affect both their persistence and the level of service they deliver to farms The model therefore 147 requires a typical foraging distance for each pollinator species These data can be supplied from quantitative field estimates e g Roubik and Aluja 1983 proxies such as body size Greenleaf et al 2007 or from expert opinion Using these data the model first estimates the abundance index of each pollinator species in every cell in the landscape based on the available nesting sites in that cell and the flowers i e food in surrounding cells Flowers in nearby c
29. be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file e g ESRI GRID or IMG with unique integers for each watershed in the Value field Sample data set InVEST Base_Data watersheds 8 Biophysical Table required A table of land use land cover LULC classes containing data on biophysical coefficients used in this tool NOTE these data are attributes of each LULC class rather than attributes of individual cells in the raster map Sample data set InVEST Base_Data Water_Tables mdb Biophysical_Models Name Table names should only have letters numbers and underscores no spaces Format dbf or mdb Rows Each row is an LULC class Columns Each column contains a different attribute of each land use land cover class and must be named as follows a lucode Land use code Unique integer for each LULC class e g 1 for forest 3 for grassland etc must match the LULC raster above b LULC_desc Descriptive name of land use land cover class optional c root_depth The maximum root depth for vegetated land use classes given in integer millimeters Non vegetated LULCs should be given a value of 0 d etk The evapotranspiration coefficient for each LULC class used to obtain potential evapotranspiration by using plant energy transpiration characteristics to modify the reference evapotranspiration which is based on alfalfa Coefficients should be multiplied by 1000 so that
30. better overall quality habitat Scores for certain areas on a landscape could also be compared For example we could compare aggregate habitat quality scores in areas of the landscape that are known to be in the geographic ranges of species of interest For example suppose we have geographic range maps of 9 species and have submitted current and future LULC scenario maps to the Tier 1 biodiversity model In this case we would determine 18 aggregate habitat quality scores once for each modeled species under each scenario Let Gs cur indicate the set of grid cells on the current landscape that are in s range Then the average habitat quality score in species s range on the current landscape is given by b3 i k Qy _cur a 9 Sagur Q cur ica where Q _cur indicates the habitat quality score on parcel x in LULC j on the current landscape and Oy cur 0 if qual_cur for x is No Data The average range normalized habitat quality score for all 9 species on the current landscape would be given by Our E aQ aw 9 10 Then we would repeat for the future landscape with the grid cells in set G _f for each species s and the set of Oy fur References Ando A J Camm S Polasky and A Solow 1998 Species distributions land values and efficient conservation Science 279 2126 2128 Czech B P R Krausman and P K Devers 2000 Economic Associations among Causes of Species Endangerment in the United States Bioscienc
31. carbon storage over time The assumption of a constant rate of change will tend to undervalue the carbon sequestered as a nonlinear path of carbon sequestration is more socially valuable due to discounting than a linear path Fig 2 Figure 2 The model assumes a linear change in carbon storage the solid line while the actual path to the year T s carbon storage level may be non linear like the dotted line In this case t can indicate the year of the current landscape and T the year of the future landscape With positive discounting the value of the modeled path the solid line is less valuable than the actual path Therefore if sequestration paths tend to follow the dotted line the modeled valuation of carbon sequestration will underestimate the actual value of the carbon sequestered Carbon sequestered ae w d O Ww Z e 2 hen W 49 Data needs The model uses five maps and tables of input data two are required and three are optional This section outlines the map and data tables required by the model including the economic data that the tool interface will prompt the user to enter See Appendix for detailed information on data sources and pre processing 1 Current land use land cover LULC map required A GIS raster dataset with a LULC code for each cell The dataset should be projected in meters and the projection used should be defined Name file can be named anything but avoid spaces Fo
32. check your input files tot_C_fut This file shows the total amount of carbon that will be stored in each parcel under your future landscape scenario It is a sum of all the carbon pools for which you have included data The values are in Mg per grid cell Again the lowest value can be 0 sequest This file maps the difference in carbon stored between the future landscape and the current landscape or the carbon that is sequestered during the entire given time period i e this is a rate per the total time period elapsed yr_fut yr_cur not per year The values are in Mg per grid cell In this map some values may be negative and some positive 61 Positive values indicate sequestered carbon whereas negative values indicate carbon that was lost Areas with large negative or positive values should have the biggest changes in LULC or harvest rates Remember that carbon emissions due to management activities tractors burning fuel fertilizer additions etc on a parcel are NOT included in this assessment value_stor This file maps the economic value of carbon stock in the current landscape The relative differences between parcels should be identical to tot_C_cur but the values are in US dollars per grid cell instead of Mg per grid cell Some of the values may be quite high compared to the sequestration value but keep in mind that currently there is not a market for carbon storage as modeled in this map value_seq This file maps the
33. con isnull the DEM different_DEM theDEM 21 O E Verify the stream network If the stream network generated from the DEM does not correctly match reality burning a correct stream network into the DEM might be necessary Here are the basic steps for ArcMap 1 Create the stream network from the DEM using the Hydrology gt Flow Accumulation tool and compare it to a known correct stream layer If the generated stream network does not look correct continue with the following steps 2 If starting with a vector stream layer convert it to a grid that has the same cell size and extent as the DEM 3 Assign the stream grid a cell value of 1 where there are streams and 0 elsewhere 4 Subtract a multiple of this stream grid from the DEM If using ArcHydro create the stream network from the DEM using Terrain Preprocessing gt Stream Definition and compare it to a known correct stream layer If the generated stream network does not look correct burn the correct stream layer in using the Terrain Preprocessing gt DEM Manipulation gt DEM Reconditioning function Identify sinks in the DEM and fill them From the ESRI help on How Sink works A sink is a cell or set of spatially connected cells whose flow direction cannot be assigned one of the eight valid values in a flow direction raster This can occur when all neighboring cells are higher than the processing cell or when two cells flow into each oth
34. described above Right click on the model name in the InVEST toolbox and click on PROPERTIES For example click on the plus sign to the left of the InVEST toolbox and then right click on the Carbon model The PROPERTIES dialog appears Select the SOURCE tab from the top The path to the python script associated with this tool is shown 17 Carbon Properties General Source Parameters Validation Help Script File iIn vESTipython Carbon p P r V Run Python script in process Cancel Setting source for the script e Edit this path to point to the location of the python script It is easiest to click on the folder button at the right of the box to browse to the script and select it e Click OK Changing default variables When you double click on an InVEST model such as Carbon or Timber an interface will appear These interfaces show default values and path names to sample data described more fully in the service chapters You can edit these pathnames to point to data Another way to change the default path and file names is to Right click on the model name and click on PROPERTIES in the InVEST toolbox The dialog below appears Select the PARAMETERS tab at the top to see a list of input parameters for the model Select an input parameter from the top window which will set its properties on the lower window including your desired default values and pathnames 18 Click OK after setting the de
35. fact for several important crops e g blueberries native species are more efficient and effective pollinators than honeybees Cane 1997 These native bees in addition to feral honeybees living in the wild can benefit crops without active management of captive hives This is the pollination service associated with habitat conservation For bees to persist on a landscape they need two things suitable places to nest and sufficient food provided by flowers near their nesting sites If provided these resources pollinators are available to fly to nearby crops and pollinate them as they collect nectar and pollen The model therefore uses information on the availability of nesting sites and flower resources as well as flight ranges of bees to map an index of bee abundance across the landscape In a second step the model uses this map and bee flight ranges again to predict an index of the number of pollinators likely visiting crops in each agricultural cell on the landscape If you opt to also estimate value indices the model then takes a third and fourth step In the third step it uses a simplified yield function to translate bee abundance into crop value on each agricultural cell And in the fourth step it attributes these cell values back to cells supplying these bees These steps are laid out in more detail below and the full model description can be found in Lonsdorf et al in press How it works The model is based on a land use
36. folders will automatically be created in your workspace you will have the opportunity to define this file path Intermediate where temporary files are written and which is deleted after each tool run Service where results that show ecosystem services are saved such as sediment retention and Output where non service biophysical results are saved such as sediment export Before running the Avoided Reservoir Sedimentation Model make sure that the InVEST toolbox has been added to your ArcMap document as described in the Getting Started chapter of this manual Second make sure that you have prepared the required input data files according to the specifications in Data Needs Identify workspace If you are using your own data you need to first create a workspace or folder for the analysis data on your computer hard drive The entire pathname to the workspace should not have any spaces All your output files will be saved here For simplicity you may wish to call the folder for your workspace Sediment and create a folder in your workspace called Input and place all your input files here It s not necessary to place input files in the workspace but advisable so you can easily see the data you use to run your model Or if this is your first time using the tool and you wish to use sample data you can use the data provided in InVEST Setup exe If you unzipped the INVEST files to your C drive as described
37. for a given module View the attribute table by right clicking on the layer and selecting OPEN ATTRIBUTE TABLE You can change the symbology of an input layer by right clicking on the layer name in the TABLE OF CONTENTS and selecting PROPERTIES then clicking on the SYMBOLOGY tab Note Some of the models make changes to the data tables as they run Such models will not run correctly if the tables are added to the map as the data will be locked Double click the model you wish to run e g Carbon and complete the required parameters in the dialogue box that appears Invest 3 L R amp Pollution Control F amp Sedimentation SB Biodiversity goe ADA FE tool or ollination x a O SB Timber M The Carbon dialog is shown below as an example Fields for which the entered path leads to a non existent file will be marked with a red x next to the space for that variable You can run the model with sample data as shown by the default paths or navigate the paths to your data Instructions specific for each model are in subsequent chapters 15 Workspace C InvEST Carbon cs Current land cover map C INVEST Base_Data lulc_samp_cur Year of current land cover 2000 Resolution desired cell size to use in meters optional 2000 Carbon pools and decay rates CAINVEST Carbon Input carbon _pools_samp dbf Current harvest rate map optional Year of future land cover opt
38. for additional ecosystem services including for coastal and marine services will be released as they become available InVEST is a freely available open source product and as such the source code of each model can be inspected and modified by users INVEST is subject to standard open source license and attribution requirements which are described and must be agreed to in the installation process As in other open source projects it is hoped that users will submit improvements error fixes and suggestions to the Natural Capital Project so that improvements can be made to future versions This guide This guide will help you understand the basics of the INVEST models and start using them The next chapter leads you through the installation process and provides general information about the tool and interface The remaining chapters present the ecosystem service models Each chapter v briefly introduces a service and suggests the possible uses for InVEST results v explains intuitively how the model works including important simplifications assumptions and limitations v describes the data needed to run the model which is crucial because the meaning and value of InVEST results depend on the input data v provides step by step instructions for how to input data and interact with the tool v offers guidance on interpreting InVEST results v includes an appendix of information on relevant data sources and data preparation advice thi
39. how water is distributed through the landscape Actual_ET describes the evapotranspiration portion of the hydrologic cycle showing how much water precipitation is lost annually to evapotranspiration across the watershed The wyield raster shows how much water is yielded from each parcel of land This raster can be used to determine which parcels of land are most important to total annual water yield although at this step the user still will now know how much of that water is benefiting downstream users of any type The consumptive use grid then shows how much water is used for consumptive activities such as drinking bottling etc each year across the landscape The rsupply realized supply grid calculates the difference between cumulative water yield and cumulative consumptive use This grid demonstrates where the water supply for hydropower production is abundant and where it is most scarce The user needs to remember that the consumptive use grid may not truly represent where water is taken only where it is demanded This may cause some misrepresentation of the scarcity in certain locations but this grid offers a general sense of the water balance and whether there is a lack of or abundance of water in the area of interest 89 The hp_yield hp_energy and hp_value grids ares the most relevant model outputs for prioritizing the landscape for investments that wish to maintain water yield for hydropower production The hp_value grid co
40. in Data Needs Create a workspace on your computer hard drive if you are using your data The pathname to the workspace should not have spaces All your output files will be saved here For simplicity you could create a folder in your workspace called Input and place all your input files here It is not necessary to place input files in the workspace but this will make it easier to view the data you use to run your model If this is your first time using InVEST and you wish to use sample data you can use the data provided in InVEST Setup exe If you unzipped the InVEST files to your C drive as described in the Getting Started chapter you should see a folder named InVEST WP_Nutrient_Retention This folder will be your workspace The input files are in InVEST Base_Data Open an ARCMAP document to run the model Locate the INVEST toolbox in ARCTOOLBOX ARCTOOLBOX should be open in ARCMAP but if it is not click on the ARCTOOLBOX symbol See the Getting Started chapter if you do not see the InVEST toolbox Click the plus sign on the left side of the INVEST toolbox to expand the list of tools Double click on Nutrient_Retention Three options will appear Water Yield Nutrient Removal and Valuation Water Yield must be run first Nutrient Removal second and Valuation last The scripts MUST be run in this order because the output from a previous script is required for the next script 105 Click on Water Yield gt 1
41. in the Getting Started chapter you should see a folder called Invest Sediment This folder will be your workspace The input files are in a folder called Invest Sediment Input and in Invest Base_Data Open an ArcMap document to run your model Find the InVEST toolbox in ARCToolbox ARCToolbox is normally open in ARCMap but if it is not click on the ARCToolbox symbol See the Getting Started chapter if you don t see the InVEST toolbox and need instructions on how to add it 124 You can run this analysis without adding data to your map view but usually it is recommended to view your data first and get to know them Add the data for this analysis to your map using the ADD DATA button and look at each file to make sure it is formatted correctly Save your ARCMap file as needed Click once on the plus sign on the left side of the INVEST toolbox to see the list of tools expand Next click on the plus sign next to the Avoided Reservoir Sedimentation toolset Within the toolset are two tools Soil Loss and Valuation You will need to run Soil Loss first to generate layers that will feed into Valuation Double click on Soil Loss An interface will pop up like the one below The tool shows default file names but you can use the file buttons to browse instead to your own data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface Click Show Help if the description
42. load_n for phosphorus supply values in load_p The potential for terrestrial loading of water quality impairing constituents is based on nutrient export coefficients The nutrient loading values are given as integer values by multiplying the export coefficients by 1000 giving units of 1000 Kg Ha yr d eff_n eff_p The vegetation filtering value per pixel size for each LULC class as an integer percent between zero and 100 If nitrogen is being evaluated supply values in eff_n for phosphorus supply values in eff_p This field identifies the capacity of vegetation to retain nutrient as a percentage of the amount of nutrient flowing into a cell from upslope For example if the user has data describing that wetland of 5000 m retains 82 of nitrogen then the retention efficiency that the he should input into this filed for eef_n is equal to 103 82 5000 cell size In the simplest case when data for each LULC type are not available high values 60 to 80 may be assigned to all natural vegetation types such as forests natural pastures wetlands or prairie indicating that 60 80 of nutrient is retained An intermediary value also may be assigned to features such as contour buffers All LULC classes that have no filtering capacity such as pavement can be assigned a value of zero Sample data set InVEST Base_Data Water_Tables mdb Biophysical_Models Example Case with 6 LULC categories where potential evapotranspiration root de
43. management with new Cut and Freq values Cut_cur Cut_fut and Freq_cur Freq_fut We assume these new management conditions begin r_cur yr _ fut 2 yr_cur yr _ fut 2 harvest activity given in current harvest rate map for parcel 2 ends in 2020 In addition the future harvest rate map includes a new harvested parcel given by FID 5 We assume that harvest begins there in 2020 as well In parcels 3 and 4 harvest management does not change across the current and future landscapes Note that we retained the FID values across the two maps here this is not necessary as the ArcGIS program will perform the necessary spatial matches in the year 2020 given by 2 Parcel 2 is not expected to be harvested at and yr_fut Therefore the model assumes that the any point between FID Cut_fut Freq_fut Decay_fut C_den_fut BCEF_fut 1 50 10 30 0 5 1 3 50 5 50 0 5 1 4 45 1 1 0 5 1 5 25 2 15 0 5 1 Below we describe exactly how the future harvest values are calculated If a parcel was harvested on the current landscape and is expected to be harvested on the future landscape yr_cur yr _ fut 2 due to harvest from parcel x in the future year is given by HWP _ fut Cut _cur x yr_ fut yr_ cur D futtyr o rart _date M t Freq _ cur Cut _ fut x yr ut yr _ cur yr _ fu TATE pe t Freq where the function fis as before Recall that if yr_cur yr_fut 2 results in a frac
44. net present value calculations Floating point value 84 Running the Model The Hydropower model maps the water yield water consumption energy produced by water yield and corresponding energy value over the landscape This model is structured as a toolkit which has three tools The first tool Water Yield calculates the surface water yield and actual evapotranspiration across the landscape This output feeds into the next portion of the model the Water Scarcity tool which calculates water consumption supply and realized supply which is yield minus consumption The third tool Valuation calculates energy production and the value of that energy as it can be attributed to cells on the watershed landscape By running the tool three folders will automatically be created in your workspace you will have the opportunity to define this file path Intermediate where temporary files are written and which is deleted after each tool run Service where results that show ecosystem services are saved and Output where non service biophysical results are saved Before running the Hydropower Model make sure that the InVEST toolbox has been added to your ArcMap document as described in the Getting Started chapter of this manual Second make sure that you have prepared the required input data files according to the specifications in Data Needs e Identify workspace If you are using your own data you need to first create a w
45. of streams in the watershed of interest he she should compare it the V_stream map output of the model This value also needs to be well estimated in watersheds where ditches are present This threshold expresses where hydraulic routing is discontinued and where retention stops and the remaining pollutant will be exported to the stream 9 Slope threshold required An integer slope value describing landscape characteristics such as slope management practices including terracing and slope stabilization techniques It depends on the DEM resolution and the terracing practices used in the region In so many cases around the world farmers cultivate slopes without any terracing or slope stabilization but after certain slope limit terraces become necessary This limit could be taken as this slope threshold A good understanding knowledge and familiarity with the regional landscape will help the user to determine this parameter The model s default value is 25 This threshold could a subject for calibration 10 Sediment table optional required for valuation A table containing valuation information for each of the reservoirs There must be one row for each watershed in the Watersheds layer Name Table names should only have letters numbers and underscores no spaces File type dbf or mdb Rows Each row is a reservoir or structure that corresponds to the watersheds raster Columns a wshed_id watershed ID Unique integer value for ea
46. or to help design permitting and mitigation programs that sustain nature s benefits to society Conservation organizations could use InVEST to better align their missions to protect biodiversity with activities that improve human livelihoods Corporations such as bottling plants timber companies and water utilities could also use InVEST to decide how and where to invest in natural capital to ensure that their supply chains are preserved Following are examples of questions InVEST can help answer v Where do ecosystem services originate and where are they consumed v How does a proposed forestry management plan affect timber yields biodiversity water quality and recreation v Which parts of a watershed provide the greatest carbon sequestration biodiversity and tourism values v Where would reforestation achieve the greatest downstream water quality benefits while maintaining or minimizing losses in water flows y How will climate change and population growth impact ecosystem services and biodiversity Introduction to INVEST The InVEST 1 004 Beta package described in this guide offers models for carbon sequestration crop pollination managed timber production water purification for nutrients reservoir hydropower production and sediment retention for reservoir maintenance It also includes a biodiversity model so that tradeoffs between biodiversity and ecosystem services can be assessed Models will be released soon for other servi
47. provided to O farms from each m cell PSm as ae i PS gt V m Ve o P 5 where V represents the crop value in farm cell o The result is a map of pollinator service value that estimates the relative index of economic value of pollinators for agricultural areas If the simple saturating yield function is deemed too simplistic one may link this pollination model to InVEST s agricultural production model that includes other factors such as fertilizer irrigation labor etc The integration of these two models will give a more appropriate representation of the multiple inputs to agricultural production It will also be possible to more specifically derive the amount of crop yield provided by wild pollinators yield contribution and the net present value of that additional yield See Lonsdorf et al 2009 and Lonsdorf et al in press for equations that determine the pollinator supply farm abundance and pollinator service value maps 149 Limitations and simplifications The model predicts an abundance index of wild pollinators on agricultural fields cells within a landscape based on the pattern of land cover types and the resources they are estimated to contain for bees It also converts this abundance into indices of production value and attributes this value to the source cells for pollinators Like other InVEST models the Pollination model is extremely simple but it makes reasonably accurate predictions when compared to
48. qual_fut suffix in the output folder as well Further if you have submitted a baseline LULC map input 3 as well you will also see the raster rarity_fut suffix in the output folder If you have entered a baseline map input 3 and threat layers for the baseline input 4 then you will find the rasters degrad_bse suffix AND qual_bse suffix in the output folder Recall if you are setting H for all LULC j on a continuum between 0 and 1 based on the habitat suitability for a particular species group then these results are only applicable to that species group Modifying output and creating a landscape biodiversity score The model output doesn t provide landscape level quality and rarity scores for comparing the baseline current and future LULC scenarios Instead the user must summarize habitat extent and quality and rarity scores for each landscape At the simplest level a habitat quality landscape score for a LULC scenario is simply the aggregate of all grid cell level scores under the scenario In other words we can sum all grid level quality scores on the qual_bse suffix if available qual_cur suffix and qual_fut suffix if available maps and then compare scores A map may 41 have a higher aggregate quality score for several reasons For one it may just have more habitat area However if the amount of habitat across any two scenarios is approximately the same then a higher landscape quality score is indicative of
49. row is a timber parcel Columns Each parcel should be identified with a unique ID The production table data containing attributes of the parcel can be included as part of the shapefile s attribute 137 table or as a separate table that is joined or related to the shapefile Either way the variables and parameters to include in the data table are described below Sample data set Invest Timber Input plantation shp 2 Production table required A table of information about the timber parcels on the landscape This is a separate data table that can be joined to the polygon dataset in 1 Name file can be named anything File type dbf or an attribute table as part of the timber parcel map Rows each row is a different parcel Columns contain an attribute for each parcel and must be named as follows a Parcel_ID Same as timber parcel ID in 1 IDs must match the parcel IDs used in the polygon map User must select this field as a model input b Parcl_area The area of the timber parcel in hectares c Perc_harv The proportion of the timber parcel area that is harvested each harvest period units are integer percent d Harv_mass The mass of wood harvested per hectare in metric tons Mg ha in each harvest period e Freq_harv The frequency of harvest periods in years for each parcel f Price The marketplace value of the wood harvested from the parcel Mg This price should reflect what is paid to the harvesters at mills o
50. the Getting Started chapter you should see a folder called Invest carbon This folder will be your workspace The input files are in a folder called Invest carbon input and in Invest base_data e Open an ARCMAP document to run your model e Find the INVEST toolbox in ARCTOOLBOX ARCTOOLBOX is normally open in ARCMAP but if it is not click on the ARCTOOLBOX symbol See the Getting Started chapter if you don t see the InVEST toolbox and need instructions on how to add it e You can run this analysis without adding data to your map view but usually it is recommended to view your data first and familiarize yourself Add the data for this analysis to your map using the ADD DATA button and look at each file to make sure it is formatted correctly Save your ARCMAp file as needed e Click once on the sign on the left side of the INVEST toolbox to expand the list of tools Double click on Carbon 59 e Carbon tool dialog Carbon Workspace CINVESTiCarbon a Current land cover map C InvEST Base_Datal luic_samp_ouwr 5 Year of current land cover 2000 Resoktion desired cel sze to use in meters optional 2000 Carbon pools C InvEST Carbon Input carbon_pools_samp dbf Current harvest rate map optional C InVESTiCarbon nput harv_samp_cur shp S Future land cover map optional CHINVEST Base_Datalluic_samp_Put S Year of future land cover 2030 Future harvest rate map optional CINVES
51. the data provided in InVEST Setup exe If you unzipped the nVEST files to your C drive as described in the Getting Started chapter you should see a folder called Invest biodiversity This folder should be your workspace The input files are in a folder called Invest biodiversity input and in Invest base_data 38 e Open an ARCMApP document to run your model e Find the INVEST toolbox in ARCTOOLBOX ARCTOOLBOX should be open in ARCMAP but if it is not click on the ARCTOOLBOX symbol See the Getting Started chapter if you do not see the InVEST toolbox e Click once on the plus sign on the left side of the INVEST toolbox to see the list of tools expand Double click on Biodiversity NatCap mxd ArcMap ArcInfo File Edit View Bookmarks Insert Selection Tools Window Help ImageConnect 3D Analyst Laver FR lulc ill cur is S 1glhl li Editor mh Task Create New Feature mara x a a QQxur0 esa Bc hk OMS 2 AD Fawn B e H ES H e wf J Des Sex ee efron ye SOR x amp Biodiversity ArcToolbox O roads 4 3D Analyst Tools O access _will 3 Analysis Tools E m Cartography Tools O oregon_state 3 Conversion Tools O lule_will_fut Value High 95 i O fire Value High 1 a B Biodiversity Low 0 B Carbon H O lule will cur B Open Access el 3 Pollination LULC_GROUP B Timber Mag Network Analyst Tools Mi built S Samples Gil Forest amp Server Tools
52. the final etk values given in the table are integers ranging between and 1500 some crops evapotranspire more than alfalfa in some very wet tropical regions and where water is always available 83 9 Demand Table required A table of LULC classes showing consumptive water use for each landuse landcover type Consumptive water use is that part of water used that is incorporated into products or crops consumed by humans or livestock or otherwise removed from the watershed water balance Name Table names should only have letters numbers and underscores no spaces Format dbf or mdb Rows Each row is a landuse landcover class Columns Contain water demand values per LULC class and must be named as follows a lucode Integer value of land use land cover class e g 1 for forest 3 for grassland etc must match LULC raster described above b demand The estimated average consumptive water use for each landuse landcover type Water use should be given in integer cubic meters per year 10 Hydropower Table optional required for valuation A table of hydropower stations with associated model values Name Table names should only have letters numbers and underscores no spaces Format dbf or mdb Rows Each row is a hydropower station Columns Each column contains an attribute of each hydropower station and must be named as follows a station_id Unique integer value for each watershed which must correspon
53. the output grids into ARCMap using the ADD DATA button You can change the symbology of a layer by right clicking on the layer name in the table of contents selecting PROPERTIES and then SYMBOLOGY There are many options here to change the way the file appears in the map You can also view the attribute data of output files by right clicking on a layer and selecting OPEN ATTRIBUTE TABLE Now run the Valuation Tool One output from the Soil Loss model Total Sediment Retained tot_retain is an input to this model and is found in the Service folder The interface is below 2 Valuation B Help Workspace C InvEST Sedimentaton 5 2 Valuation Calculates the value of the DEM c invEST Base_Data dem x landscape for keeping sediment out of a reservoir Total sediment retained C INVEST SedmentationiServiceitot_retan X Watersheds C InVEST Base_Data watersheds ba a Sedmert table C InVEST Base_DatelWater_Tables mdb Sediment S Resolution Mmmm of Inputs 7 E Suffix optional OK Cancel Envionmerts lt 4 Hide Help 126 When the script completes running they will also be placed into the Service folder A description of the files is below Since this model is open source the user can edit the scripts to modify update and or change equations by right clicking on the script s name and selecting Edit The script will then open in a text editor After making
54. the parameters for all model runs Running the Biodiversity Model Before running the Biodiversity Model first make sure that the InVEST toolbox has been added to your ARCMAP document as described in the Getting Started chapter of this manual Second make sure that you have prepared the required input data files according to the specifications in Data Needs Specifically you will need 1 a current LULC raster file showing the location of different LULC types in the landscape 2 a future LULC raster if you wish to project future habitat quality and rarity across the landscape 3 a baseline LULC map if you wish to express habitat rarity on the current and future landscapes or measure habitat extent and quality on the baseline landscape 4 a threat data table denoting the intensity and distance over which a degradation source occurs 5 grids showing the spatial distribution of each threat on each submitted map current future and baseline 6 a shapefile indicating the relatively accessibility to an area based on protection 7 a table indicating the habitat suitability for each LULC and the sensitivity of each habitat type to each threat and 8 a numeric value indicating the half saturation constant e Create a workspace You must create a folder in your workspace called input and place all your input files here including all your threat maps If this is your first time using InVEST and you wish to use sample data you can use
55. the volume and species of wood removed in each harvest period timber prices and management costs The Model The model is designed for cases where an entity e g a government a tribe a community a private timber company has a formally recognized right to harvest roundwood from a forest According to FAOSTAT http faostat fao org roundwood is wood in its natural state as felled or otherwise harvested with or without bark round split roughly squared or in other forms It comprises all wood obtained from removals This model s output maps the net present values of forests legally recognized harvests over some user defined time interval This model is very 134 simple and designed for cases where little data on harvest practices and tree stand management exists If you have access to detailed harvest and forest management data you may want to use an alternative model Timber harvest by entities that do not have a formally recognized harvesting right is not accounted for in this model This type of wood harvest whether it is illegal or occurs in forest areas where property rights are either not defined or not well enforced is dealt with in the Open Access Timber and Non Timber Products Model to be released soon How it works This model can be used in one of two ways First it can be used to model the expected value of a stream of harvests from a forest plantation over a user defined time interval A forest plantation
56. threat habitats leads to C InVEST1004 Biodiversity Input sensitivity_samp dbf us protection of their component species Half saturation constant populations and 30 other small scale ecological processes Resolution desired cell size to use in meters optional 1000 Results suffix optional v OK Cancel Environments lt lt Hide Help Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires you to enter some data After entering all values as required click on OK The script will run and its progress will be indicated by a Progress dialogue Upon successful completion of the model you will see new folders in your workspace called intermediate and output These folders contain several raster grids which are described in the next section Load the output grids into ARCMAP using the ADD DATA button You can change the SYMBOLOGY of a layer by right clicking on the layer name in the table of contents selecting PROPERTIES and then SYMBOLOGY There are many options here to change the file s appearance You can also view the attribute data of output files by right clicking on a layer and selecting OPEN ATTRIBUTE TABLE 40 Interpreting Results Parameter Log Each time the model is run a text file will appear in the output folder The file will list the parameter values for that run and will be named according to the ser
57. weights are normalized so that the sum across all threats weights equals 1 By normalizing weights such that they sum to 1 we can think of D as the weighted average of all threat levels in grid cell x The map of Dy will change as the set of weights we use change Please note that two sets of weights will only differ if the relative differences between the weights in each set differ For example set of weights of 0 1 0 1 and 0 4 are the same as the set of weights 0 2 0 2 and 0 8 A grid cell s degradation score is translated into a habitat quality value using a half saturation function where the user must determine the half saturation value As a grid cell s degradation score increases its habitat quality decreases Let the quality of habitat in parcel x that is in LULC j be given by Q where D Qj m i d a 4 and z we hard code z 2 5 and k are scaling parameters or constants Qx is equal to 0 if H 0 Qj increases in H and decreases in D Q can never be greater than 1 The k constant is the half saturation constant and is set by the user The parameter k is equal to the D value where D D K i I E 1 l D 4 0 5 For example if k 5 then 1 vhs i 4 0 5 when D 5 In the biodiversity model interface we set k 30 but the user can change it see note in Data Needs section 8 If you are doing scenario analyses whatever value you chose for k the first landscape you run the model on that same k m
58. 1 global biomass carbon map for the year 2000 Available http cdiac ornl gov epubs ndp global_carbon carbon_documentation html Accessed 2008 Jul 7 Schuman GE HH Janzen and JE Herrick 2002 Soil carbon dynamics and potential carbon sequestration by rangelands Environmental Pollution 116 391 396 Sedjo RA and B Sohngen Carbon Credits for Avoided Deforestation Washington DC Resources for the Future 2007 October 2007 Report for RFF DP 07 47 Silver WL R Ostertag and AE Lugo 2000 The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands Restoration Ecology 8 394 407 Skog KE K Pingoud and JE Smith 2004 Method Countries Can Use to Estimate Changes in Carbon Stored in Harvested Wood Products and the Uncertainty of Such Estimates Environmental Management 33 Supplement 1 S65 S73 Smith JE LS Heath KE Skog RA Birdsey Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States Newtown Square PA US Department of Agriculture Forest Service Northeastern Research Station 2006 Report for NE 343 Socolow RH 2005 Can We Bury Global Warming Scientific American 293 49 55 Socolow RH and SW Pacala 2006 A Plan to Keep Carbon in Check Scientific American 295 50 57 Sohngen Brent RH Beach and Kenneth Andrasko 2008 Avoided Deforestation as a Greenhouse Gas Mitigation Tool Economic Issues J
59. 53 Hertfordshire UK Quest Environmental 114 AVOIDED RESERVOIR SEDIMENTATION MODEL Summary Reservoirs are linked to a number of ecosystem services a including the generation of energy through reservoir hydropower production irrigation of crops and recreational activities Erosion and sedimentation of watersheds can lead to decreased hydropower output structural damage to reservoirs and other water infrastructure and flooding InVEST estimates the capacity of a land parcel to retain sediment using data on geomorphology climate vegetation and management practices These estimates are combined with data on sediment removal costs reservoir design and a discount rate to calculate the avoided cost of sediment removal Limitations of the model include negligence of mass erosion inadequate information about sediment removal costs and simplified LULC classifications Introduction Erosion and sedimentation are natural processes that contribute to healthy ecosystems but too much may have severe consequences Excessive erosion can reduce agricultural productivity increase flooding and pollutant transport and threaten bridges railroads and power infrastructures Erosion can lead to sediment build up which strains water infrastructures such as reservoirs and flood control systems and increases water treatment costs Sedimentation is particularly problematic for reservoirs which are designed to retain sediment as water is releas
60. 55 74 RESERVOIR HYDROPOWER PRODUCTION Summary Hydropower accounts for twenty percent of worldwide energy production most of which is generated by reservoir systems InVEST estimates the annual average quantity and value of hydropower produced by reservoirs and identifies how much water yield or value each part of the landscape contributes annually to hydropower production The model has three components water yield water consumption and hydropower valuation The first two components use data on average annual precipitation annual reference evapotranspiration and a correction factor for vegetation type soil depth plant available water content land use and land cover root depth elevation saturated hydraulic conductivity and consumptive water use The valuation model uses data on hydropower market value and production costs the remaining lifetime of the reservoir and a discount rate The biophysical models do not consider surface ground water interactions or the temporal dimension of water supply The valuation mode assumes that energy pricing is static over time Introduction The provision of fresh water is an ecosystem service that contributes to the welfare of society in many ways including through the production of hydropower the most widely used form of renewable energy in the world Most hydropower production comes from watershed fed reservoir systems that generally deliver energy consistently and predictably
61. 700 850 1000 DEM data is available for any area of the world although at varying resolutions Free raw global DEM data is available on the internet http www worldwildlife org freshwater hydrosheds cfm or for a final product may be purchased relatively inexpensively at sites such as MapMart www mapmart com The DEM used in the model must be hydrologically correct meaning that sinks were filled 4 See SWAT Soil Water Assessment Tool manual 94 i Consumptive water use The consumptive water use for each land use land class type should be estimated based on agricultural forestry and hydrology literature and or consultation with local professionals in these fields The value used in the table is an average for each land use type For crops water use can be calculated using information on crop water requirements and scaling up based on area covered by crops In more general agricultural areas water use by cattle agricultural processing etc must be considered For forestry a similar calculation can be made based on estimates of water use by different forest types In urban areas water use may be calculated based on an estimated water use per person and multiplied by the approximate population area per raster cell Industrial water use must also be considered where applicable For all of these calculations it is assumed that the crops trees people etc are spread evenly across each land use class j Hydropower Wa
62. C class e g 1 for forest 3 for grassland etc must match the LULC raster above b LULC_desc Descriptive name of land use land cover class optional c usle_c Cover and management factor for the USLE This value is given in the table as an integer by multiplying the C factor by 1000 d usle_p Management practice factor for the USLE This value is given in the table as 122 an integer by multiplying the P factor by 1000 c sedret_eff The sediment retention value for each LULC class as an integer percent between zero and 100 This field identifies the capacity of vegetation to retain sediment as a percentage of the amount of sediment flowing into a cell from upslope In the simplest case when data for each LULC type are not available a value of 100 may be assigned to all natural vegetation types such as forests natural pastures wetlands or prairie indicating that 100 of sediment is retained An intermediary value also may be assigned to features such as contour buffers All LULC classes that have no filtering capacity such as pavement can be assigned a value of zero Sample data set C Invest Base_Data Water_Tables mdb Biophysical_Models 8 Threshold flow accumulation required The number of upstream cells that must flow into a cell before it s considered part of a stream Used to define streams from the DEM The model s default value is 1000 This value is to define and create the stream lines If the user has a map
63. C type j Subtracting this ratio from one the model derives an index that represents the rarity of that LULC class on the landscape of interest N R 1 _ 5 j baseline where N is the number of grid cells of LULC j on the current or projected map and N baseline gives the number of grid cells of LULC j on the baseline landscape The calculation of R requires that the baseline current and or projected LULC maps are all in the same resolution In this scoring system the closer to 1 a LULC s R score is the greater the likelihood that the preservation of that LULC type on the current or future landscape is important to biodiversity conservation If LULC j did not appear on the baseline landscape then we set R 0 Once we have a R measure for each LULC type we can quantify the overall rarity of habitat type in grid cell x with R D oR where o 1 if grid cell x is in LULC j on a current or projected landscape and equals 0 otherwise Limitations and simplifications In this model all threats on the landscape are additive although there is evidence that in some cases the collective impact of multiple threats is much greater than the sum of individual threat levels would suggest Because the chosen landscape of interest is typically nested within a larger landscape it is important to recognize that a landscape has an artificial boundary where the habitat threats immediately outside of the study boundary have been clipp
64. Forest Reserve Tanzania Journal of Tropical Forest Science 7 230 242 McLauchlan KK SE Hobbie and WM Post 2006 Conversion From Agriculture To Grassland Builds Soil Organic Matter On Decadal Timescales Ecological Applications 16 143 153 Miner R 2006 The 100 Year Method for Forecasting Carbon Sequestration in Forest Products in Use Mitigation and Adaptation Strategies for Global Change On line only http www springerlink com content 2167274 11736675 1 fulltext pdf Mollicone D F Achard S Federici H Eva G Grassi A Belward F Raes G Seufert H Stibig G Matteucci and E Schulze 2007 An incentive mechanism for reducing emissions from conversion of intact and non intact forests Climatic Change 83 477 493 Munishi PKT and TH Shear 2004 Carbon Storage in Afromontane Rain Forests of the Eastern Arc Mountains of Tanzania their Net Contribution to Atmospheric Carbon Journal of Tropical Forest Science 16 78 93 Murphy JM et al 2004 Quantification of modelling uncertainties in a large ensemble of climate change simulations Nature 430 768 772 Murray B B Sohngen and M Ross 2007 Economic consequences of consideration of permanence leakage and additionality for soil carbon sequestration projects Climatic Change 80 127 143 Nascimento HEM and WF Laurance 2002 Total aboveground biomass in central Amazonian rainforests a landscape scale study Forest Ecology and Management 168 311 321 Nelson E G Mendoza
65. Harv 3 Spatial Analyst Tools Unkn amp Spatial Statistics Tools WB Water 3 Tracking Analyst Tools H A amp Pollination E Source Selection Favorites Index Searcta ofo nje u 4 Bl Drawing C O AY r I Arial 10 x B Z U Ae aw 27 R 484048 601 4943943 04 Meters e An interface will pop up like the one above that indicates default file names but you can use the file buttons to browse to your data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface In addition refer to the Data Needs section above for information on data formats 39 Biodiversity Workspace C InVEST1004 Biodiversity us Current land cover map This model the C MIn EST1004 Biodiversity Input lulc_samp_cur v us simplest biodiversity model Tier 1 Future land cover map optional combines S information on land cover and threats to Baseline land cover map optional pr a erje r s quality and rarity C InVEST1004 Base_Data base_samp maps This approach often Threats data referred to as C InYEST1004Biodiversity Inputithreats_samp dbf X S coarse filter for its focus on broad land Accessibilty to threats optional categories assumes C InVEST1004 Biodiversity Input access_samp shp 7 that protecting a variety of high quality Sensitivity of land cover types to each
66. LC types in sub Saharan Africa e South America Bernoux et al 2002 estimated soil C stocks to a depth of 30 cm for different soil type vegetation associations in Brazil For example the soil C stock in HAC soils under 14 different land cover categories including Amazon forest and Brazilian Cerrado range from 2 to 116 kg C m Important Note In most research that estimates carbon storage and sequestration rates on a landscape soil pool measures only include soil organic carbon SOC in mineral soils Post and Kwon 2000 However if the ecosystem being modeled has a lot of organic soils e g wetlands or paramo it is critical to add this component to the mineral soil content In landscapes where the conversion of wetlands into other land uses is common carbon releases from organic soils should also be tracked closely IPCC 2006 2 4 Carbon stored in dead organic matter 67 If local or regional estimates of carbon stored in dead organic matter aren t available default values from the IPCC 2006 can be assigned Table 2 2 p 2 27 gives default carbon stocks for leaf litter in forested LULC types For non forested types litter is close to 0 Grace et al 2006 estimate the average carbon stored in litter for major savanna ecosystems around the world Table 1 It is not clear if their total above ground biomass estimates include deadwood or not Deadwood stocks are more difficult to estimate in general and we have located
67. MR Raupach 2008 Managing Forests for Climate Change Mitigation Science 320 1456 1457 70 Cline WR 1992 The economics of global warming Instuitute for International Economics Washington D C Coomes DA RB Allen NA Scott C Goulding and P Beets 2002 Designing systems to monitor carbon stocks in forests and shrublands Forest Ecology and Management 164 89 108 Conte MN and MJ Kotchen 2010 Explaining the price of voluntary carbons offsets Climate Change Economics forthcoming Capoor K and P Ambrosi State and Trends of the Carbon Market 2008 Washington D C World Bank Institute 2008 May Delaney M S Brown AE Lugo A Torres Lezama and NB Quintero 1998 The quantity and turnover of dead wood in permanent forest plots in six life zones of Venezuela Biotropica 30 2 11 Detwiler RP 1986 Land Use Change and the Global Carbon Cycle The Role of Tropical Soils Biogeochemistry 2 67 93 Dias AC M Louro L Arroja and I Capela 2007 Carbon estimation in harvested wood products using a country specific method Portugal as a case study Environmental Science amp Policy 10 3 250 259 Edinburgh Centre for Carbon Management The Establishing Mechanisms for Payments for Carbon Environmental Services in the Eastern Arc Mountains Tanzania 2007 May 2007 Fargione J J Hill D Tilman S Polasky and P Hawthorne 2008 Land Clearing and the Biofuel Carbon Debt Science 319 1235 1238 Feng H 2005 The dyn
68. Makundi 2001 Thus 2 hectares x 25 years x 17 82 Mg ha yr 891 Mg of timber has been removed from the plantation annually for 50 years If we assume the carbon content of the plantation s trees are 0 48 Makundi 2001 then 891 x 0 48 427 68 metric tons of C are in the aboveground biomass of forest stand removed each year from the plantation or 8 6ha yr Ascertaining dates in which harvesting began in each parcel may be difficult If it is you could assign an early date of initial harvest to all parcels which essentially assumes that the carbon in the pool of harvested wood products has reached steady state i e does not change year to year Assume a date such that the time since first harvest is more than twice the half life of carbon in the harvested wood products e g if the half life of carbon in wood products is 20 years choose a date of initial harvest that is 40 years before the current landscape map used 5 Economic inputs carbon price and discount rates 68 Recent estimates suggest that the social cost of carbon SCC or the marginal damage associated with the release of an additional Mg of C into the atmosphere ranges from 32 per metric ton of C Nordhaus 2007a to 326 per metric ton of C Stern 2007 in 2010 US dollars The value of this damage can also be considered the monetary benefit of an avoided release Tol 2009 provides a comprehensive survey of SCC estimates reporting median values of 66 and 130 per metri
69. Now you are ready to run Nutrient Removal Follow the same steps as for Water Yield Note that an output from Water Yield Service wyield is a required input to Nutrient Removal Make sure to select one of the Nutrient Type boxes the model needs one of 106 the two to be checked to run sometime you will see optional after Nitrogen or Phosphorus but you still need to check the box of the nutrient you are interested in The interface is below S 2 Nutrient Removal DEOR er a fe IDWEST WP ute Reteraen la 2 Nutrient Removal y Determines the amount of nutrient CAINMEST ase Deealdem 1 o runof that is retained by the landscape and the amount that is tater yiek ms exported to the stream CUInvESTINeP_Mutrient_Retentionisermcelwycld gt i gt g INVEST WBase_Datallonduse_ 90 J 3 CANES T Base Dotalsubvatorsheds 5 i CAInvEST B0e0_Detalwotersieds J TlInvESTWBase_Daca Water aties mcb Biephyacal Models J C inWESTWBose_Data Water_Tatles meb Water_Punfcaton lg When the script completes running its results will be saved in the Output and Service folders Load the output grids into ARCMAP using the ADD DATA button Finally you have the option to run Valuation Two outputs from Nutrient Removal are required Service w_retain and Output watershed_nutrient dbf The interface is below 107 3 Valuation aa E Help Workspace Fa CAnESTIWP Mutriert Retention E 3 Valuation Calculates the value of
70. R R should be obtained from published values as calculation is very tedious For calculation R equals E the kinetic energy of rainfall times 30 maximum intensity of rain in 30 minutes in cm hr Roose 1996 found that for Western Africa R a precipitation where a 0 5 in most cases 0 6 near the sea 0 3 to 0 2 in tropical mountain areas and 0 1 in Mediterranean mountain areas The following equation is widely used to calculate the R index http www fao org docrep t1765e t1765e0e htm R E 130 210 89 logio130 130 E kinetic energy of rainfall expressed in metric MJx m ha cm of rainfall 130 maximum intensity of rain in 30 minutes expressed in cm per hour In the United States national maps of the erosivity index can be found through the United States Department of Agriculture USDA and Environmental Protection Agency EPA websites The USDA published a loss handbook http www epa gov npdes pubs ruslech2 pdf that contains a hard copy map of the erosivity index for each region Using these maps requires creating a new line feature class in GIS and converting to raster Please note that conversion of units is also required multiply by 17 02 The EPA has created a digital map that is available at http www epa gov esd land sci emap_west_browser pages wemap_mm_ sl rusle_r_gt htm The map is in a shapefile format that needs to be converted to raster along with an adjustment in units 130 3 Soil erodibility
71. S minus ULSE watershed_sediment dbf Table containing the total sediment exported Tot_Sed to the outlet of each watershed This Tot_Sed will be compared to any observed sediment loading at the outlet of the watershed Knowledge of the hydrologic regime in the watershed and the contribution of the sheetwash yield into total sediment yield help adjust and calibrate this model Output s_retain Sediment Removed tons Raster containing the sediment removed 127 by each pixel on the landscape from the sediment loadings of the pixel upstream This grid is the output of the sediment removal through routing filtration It has the unit Output s_export tons Raster containing the sediment load that will reach the stream from each pixel on the landscape The cumulative of this raster values is equal to the Tot_Sed Output s_cum_exp tons Raster containing the cumulative of s_export in the watershed of interest This map shows areas that contribute the most to the sediment loading within the watershed Output ws_sedexp tons Raster containing the total sediment loading exported to the outlet of the watershed when summed equal to Tot_Sed Service Tot_retain tons Raster containing the total sediment retained by each pixel on the landscape It is the sum of and cell_ret and s_retain This raster is THE MEASURE OF THIS ECOSYSTEM SERVICE IN BIOPHYSCIAL TERMS This raster is used in the valuation step Service sed_value Value of Sediment Re
72. Size of landscape If your landscape is very large e g gt 3 million cells then you may experience problems Consider either entering a larger resolution than the original resolution of the image or cropping your image to a smaller extent o Resolution The cell size chosen for the model run determines the effective number of cells that the model has to handle Select this carefully depending on the pollinator flight distances 157 o Foraging distances Alpha If the Alphas of the pollinators are large gt 1000m then the distance matrix becomes large which results in a long run time or potential crashing o Number of pollinator species Since the model processes each pollinator in turn the more species you have the longer it takes to complete the run o Your computer The memory and speed of your computer will determine the success and speed of your run It is preferable to have at least 2GB memory and enough free disk space o On a 3GB memory computer with a 3 5 million cells and 56m resolution 4 pollinators with alphas between 100m and 2000m the model takes up to 3 hours to run e Upon successful completion of the model you will see two new folders in your workspace called output for final maps and intermediate for intermediate results The folders should contain several raster grids described in the next section e Load these grids into ARCMAP using the ADD DATA button The next section further describes what thes
73. T Carbon tInput hary_semp_fut shp 5 I Compute Economic Valuation optonal Price of Carbon per metric ton optional Annual rate of change in the price of carbon optional Market discount rate optional i Resuks Suffix optional d Carbon This model calculates the standing stock of Carbon and amount of carbon sequestered over time using four fundamental carbon pools aboveground biomass belowground biomass soil and dead organic matter It also computes the amount of Carbon stored in harvested wood products and values this stock and sequestered carbon OK Cancel Environments lt lt HdeHebp Tool Help e An interface will pop up like the one above The tool shows default file names but you can use the file buttons to browse instead to your own data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface In addition refer to the Data Needs section above for information on data formats 60 Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires you to enter some data If you choose to run the optional economic valuation all optional inputs below the checkbox become required After you ve entered all values as required click on OK The script will run and its progress will be indicated by a Progress dialogue Upon successful completion of the mod
74. The Universal Soil Loss Equation USLE provides the foundation of the biophysical step of the InVEST sediment retention model USLE RxKxLSxCxP from Wischmeier amp Smith 1978 whereR is the rainfall erosivity K is the soil erodibility factor LS is the slope length gradient factor C is the crop vegetation and management factor and P is the support practice factor The Slope Length Factor LS is one of the most critical parameters in the USLE Slope length is the distance from the origin of overland flow along its flow path to the location of either concentrated flow or deposition It reflects the indirect relationship between slope and land management terracing ditches buffers barriers The LS factor is essentially the distance that a drop of rain sediment runs until its energy dissipates It represents a ratio of soil loss under given conditions compared to a reference site with the standard slope of 9 and slope length of 72 6 feet The steeper and longer the slope is relative to the conditions of the reference site the higher the risk for erosion will be for more information see http www omafra gov on ca english engineer facts 00 001 htm The estimates of slope length are based on methodology in a model called N SPECT such that abrupt changes in slope result in length cutoffs Adjustments are necessary when slope is greater than 9 and slope length is different than 72 6 feet 22 12m In the model different LS equations are auto
75. ULC class code for each cell e g 1 for forest 3 for grassland etc These codes must match LULC codes in the Biophysical table see below Sample data set C Invest Base_Data landuse_90 5 Sub watersheds required This is a GIS raster dataset that contains one watershed or corresponding multiple sub watersheds for each point of interest for the sediment valuation model These points represent reservoirs or other types of structures where sedimentation is an issue of concern The sub watersheds are delineated within each watershed contributing to these points of 121 interest Due to limitations in ArcMap geoprocessing the maximum size of a watershed or sub watershed to be processed in this model is approximately the equivalent of 4000x4000 cells with cell size equal to the smallest cell size of your input layers If the whole watershed contributing to a point of interest is larger than this size it will need to be divided into sub watersheds that are each smaller Then the resulting sub watershed layer should be entered here and the whole watershed layer should be used in the Watersheds input If the whole watershed is smaller then it does not need to be divided and the same watershed layer should be entered for both Sub watersheds and Watersheds inputs Sub watersheds will be mosaiced back together into whole watersheds for the final output Name File can be named anything but no spaces in the name and less than 13 characters
76. WF eccoregional planning The InVEST Habitat Quality and Rarity model is most relevant to coarse filter or habitat based approaches The reasons for modeling biodiversity alongside ecosystem services are simple and powerful Doing so allows us to compare spatial patterns of biodiversity and ecosystem services and to identify win win areas i e areas where conservation can benefit both natural systems and human economies as well as areas where these goals are not aligned Further it allows us to analyze trade offs between biodiversity and ecosystem services across differing scenarios of future land use change Land use land cover LULC patterns that generate greater ecosystem service production may not always lead to greater biodiversity conservation Nelson et al 2008 and modeling future options today can help identify and avoid tradeoffs The Model The InVEST biodiversity model Tier 1 combines information on LULC and threats to biodiversity to produce habitat quality and rarity maps This approach generates two key sets of information that are useful in making an initial assessment of conservation needs the relative extent and degradation of different types of habitat types in a region and changes across time This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status If habitat changes are taken as representative of genetic species or ecosystem changes
77. Water Yield g Help Workspace CA nvesTivP_Nutriert_Retenson 1 Water Yield Preoptaton Calculates an approximate water fe INVEST Base_Datalpreop 7 yield and actual evapotranspiration for each raster cell in a landscape Potentislevapotranspration based on Zhang et al 2001 and ciurvestWase Daalem iY Milly et al 1994 Soi Depth Co InvVESTBase_Datajsol_ceoth Plant available water Fraction C MInvESTiBase_Dakalpawic Lond use fe INVES TIBase_Datajlanduse_90 Watersheds Co INVESTBase_Dota watersheds Biophyscel table Co InvESTiBase_Data yeater_Tabtles mob Acphyscal_Models Output resolution C i ml Resuks suffix op3ona Cancel Erwionments lt Hide Help An interface will appear like the one above that indicates default sample data file names but you can use the file buttons or drop down arrows to browse to your data When you place your cursor in each space you can read a brief description of the data requirements in the right side of the interface Refer to the Data Needs section for information on data formats Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires data After entering all required data click OK The script will run and its progress will be indicated by a Progress dialogue To view the attribute data of output files right click a layer and select OPEN ATTRIBUTE TABLE
78. a The Nature G P oe Q Woons INSTITUTE Conservancy Ky STANFORD UNIVERSITY Protecting nature feini life WWF InVEST 1 004 Beta User s Guide Integrated Valuation of Ecosystem Services and Tradeoffs Editors Heather Tallis and Taylor Ricketts Contributing Authors Erik Nelson Driss Ennaanay Stacie Wolny Nasser Olwero Kari Vigerstol Derric Pennington Guillermo Mendoza Juliann Aukema John Foster Jessica Forrest Dick Cameron Katie Arkema Eric Lonsdorf Christina Kennedy Citation Tallis H T Ricketts T Nelson E Ennaanay D Wolny S Olwero N Vigerstol K Pennington D Mendoza G Aukema J Foster J Forrest J Cameron D Arkema K Lonsdorf E Kennedy C 2010 InVEST 1 004 beta User s Guide The Natural Capital Project Stanford TABLE OF CONTENTS THE NEED FOR A NEW TOOL The Need for a New Tool Who should tise INVEST sbeinn tarrata aie e a aa aae ee a aE i aa oiia 6 Introduction to InVEST A work in progress This guide GETTING STARTED Getting Started with InVEST Installing the InVEST tool and data on your computer Adding the InVEST toolbox to ArcMap Using Sample Data Formatting Your Data Running the models Changing default paths in scripts Changing default variables Support Information Model run checklist Reporting errors Working with the DEM BIODIVERSITY HABITAT QUALITY amp RARITY Introduction The Model How it works Limitations and simplifications Data needs Runni
79. a landscape DEM G MInWESTBase_Oatojdem 5a for retaining and removing nitrogen or phosphorus pollution Pollutant reteined C nVEST WP_Nlutrienk_Retertion Service wy_retein E Watersheds EAEnvEST Base_Detalvretersheds Watershed loads table C InVEST WP _Nutrient_Retention Output watershed_nutrient cbf Water Purification table C INVEST Base DetalWater_Tables mndbi Water Purification Resolution Care Enviionments lt lt Hide Help When the script completes running its results will be saved in the Service folder Load the output grids into ARCMAP using the ADD DATA button To view the attribute data of output files right click a layer and select OPEN ATTRIBUTE TABLE Interpreting Results Parameter Log Each time the model is run a text file will appear in the Output folder The file will list the parameter values for that run and will be named according to the service the date and time and the suffix Final Results Final results are found in the Output and Service folders within the working directory set up for this model 108 runoff_idx Runoff Index raster Describes the potential for surface and subsurface runoff across the landscape Based on the water yield and runoff coming from upstream pixels to the pixel hss Hydrologic Sensitivity Score raster The model normalizes the runoff index based on its mean and assigns landscape pixels HSS scores This HSS will adjust pixels loading pi
80. a proper nutrient balance If nutrients and toxins accumulate in water fish and other aquatic creatures may be poisoned along with the people consuming them Many of these harmful conditions are caused by non point source pollution which occurs when a pollution source is distributed over an area or discharged into the atmosphere and incorporated into hydrological flows through rainfall and runoff There are numerous sources of non point source pollution including fertilizer used in agriculture and residential landscaping and oil that leaks from cars onto roads When it rains or snows water flows over the landscape carrying pollutants from these surfaces into streams rivers lakes and the ocean One way to reduce non point source pollution is to reduce the amount of pollutants that enter the water body If this is not possible ecosystems can provide this service by retaining some non point 97 source pollutants For instance vegetation can remove pollutants by storing them in tissue or releasing them back to the environment in another form Soils can also store and trap some soluble pollutants Wetlands can slow flow long enough for pollutants to be taken up by vegetation Riparian vegetation is particularly important in this regard often serving as a last defense against pollutants entering a stream Land use planners from government agencies to environment groups need information regarding the contribution of ecosystems to mitigating wate
81. able of land cover attributes required A table containing data on each class in the LULC map as described above in 1 Data needed are relative indices 0 1 not absolute numbers Data can be summarized from field surveys or obtained by expert assessment if field data is unavailable Name file can be named anything File type dbf Excel worksheets xls xlsx or Ms Access tables mdb accdb If using ArcGIS 9 2x then you will need to use xls or mdb files Excel 2007 xIsx and Ms Access 2007 accdb files will only work with ArcGIS 9 3x Rows each row is a different LULC class Columns each column contains a different attribute of each LULC class and must be named as follows a LULC Land use and land cover class code LULC codes match the values column in the LULC raster and must be numeric in consecutive order and unique b LULCname Descriptive name of LULC class optional c N_nestl N_nest2 etc Relative index of the availability of nesting type 1 2 etc within each LULC type on a scale of 0 1 values do not need to sum to 1 across nesting types Set the LULC type with the greatest availability of nesting habitat at 1 and give all other land classes a value in proportion to this maximum value The italicized parts of names must match those in NS_nest etc in the Table of pollinator species or guilds described in input 2 above d F_seasonl F_season2 etc Relative abundance 0 1 of flowers i
82. act more traffic than dirt roads and thus are more destructive to nearby habitat than dirt roads THREAT MAX _DIST WEIGHT DECAY dirt_rd 2 0 1 1 Paved_rd 4 0 4 1 Agric 8 1 0 Sources of threats s required GIS raster file of the distribution and intensity of each individual threat You will have as many of these maps as you have threats Each cell in the raster contains a value that indicates the density or presence of a threat within it e g area of agriculture length of roads or simply a 1 if the grid cell is a road or crop field and 0 otherwise All threats should be measured in the same scale and units i e all measured in density terms or all measured in presence absence terms and not some combination of metrics The extent and resolution of these raster datasets does not need to be identical to that of the scenario maps the LULCs map from inputs 1 2 or 3 In cases where the threats and LULC map resolutions vary the model will use the resolution and extent of the LULC cover map InVEST will not prompt you for these rasters in the tool interface It will instead automatically find and use each one based on names in the Threats data table input 4 Therefore these threat maps need to be in a file named input that is one level below the workspace identified in the model interface see below 35 Please do not leave any area on the threat maps as No Data If an area has not
83. age values or expert opinion about the whole pollinator community Name file can be named anything File Type dbf Excel worksheets xls xlsx or Ms Access tables mdb accdb If using ArcGIS 9 2x then you will need to use xls or mdb files Excel 2007 xlsx and Ms Access 2007 accdb files will only work with ArcGIS 9 3x Rows each row is a unique species or guild of pollinator Columns columns contain data on each species or guild Column order doesn t matter but columns must be named as follows italicized portions of names can be customized for meaning but must be consistent with names in other tables a Species Name of species or guild Note species names can be numerical codes or names The model will produce outputs coded by the first 4 characters of each species name e g Andr for Andrena nivalis thus each species or guild should be uniquely identifiable at 4 characters If species or guild are not uniqueluely identifiable at 4 characters then the model will truncate the names at 3 and at a digit b NS_nestl NS_nest2 etc Nesting guilds of each pollinator Values should be entered either as 0 or 1 with 1 indicating a nesting type that is utilized and 0 indicating a non utilized nest type If a pollinator falls within multiple nesting guilds then indicate 1s for all compatible nest types Nesting types might be ground nests tree cavities etc c FS_seasonl FS_season2 etc Pollinator activity by flora
84. alculate aboveground biomass and therefore carbon stocks from timber inventories which are often done by forestry ministries on a set of plots Use the following formula to estimate the aboveground carbon stock in a forest stand that has been inventoried for its merchantable volume VOB x WD x BEF x CF where VOB is the per hectare volume of trees in cubic meters measured from tree stump to crown point the merchantable portion of the tree WD is the wood density of trees dry biomass per unit of tree volume BEF is the ratio of total aboveground dry biomass to dry biomass of inventoried volume and CF is the ratio of elemental carbon to dry biomass by mass Brown 1997 The biomass expansion factor BEF accounts for C stored in all other portions of the tree aboveground e g branches bark stems foliage etc the non merchantable portions of the tree In most cases WD for a plot is approximated with values for dominant species Brown 1997 provides a table of WD values for many tree species in Appendix 1 of section 3 and a method for calculating BEF Equation 3 1 4 See ECCM 2007 for an application of this FAO method to forest inventory data from eastern Tanzania IPCC 2006 also presents estimates of WD x BEF where BEF values for hardwood pine conifer and natural forest stands by eco region are given in Table 4 5 and WD values for many species are given in Tables 4 13 and 4 14 Use the BCEF values in Table 4 5 that are subscripted
85. allocation of energy production and hydropower value since it is assumed that water consumed along flow paths is drawn equally from every pixel upstream As a result water scarcity energy production patterns and hydropower values may be incorrectly estimated Fifth a single variable ya is used to represent multiple aspects of water resource allocation which may misrepresent the complex distribution of water among uses and over time Finally the model assumes that hydropower production and pricing remain constant over time It does not account for seasonal variation in energy production or fluctuations in energy pricing which may affect the value of hydropower Even if sub annual production or energy prices change however the relative value between parcels of land in the same drainage area should be accurate 81 Data needs Here we outline the specific data used by the model See the appendix for detailed information on data sources and pre processing For all raster inputs the projection used should be defined and the projection s linear units should be in meters Nine data layers are required and one is optional required for valuation 1 Digital elevation model DEM required A GIS raster dataset with an elevation value for each cell Make sure the DEM is corrected by filling in sinks and if necessary burning hydrographic features into the elevation model recommended when you see unusual streams See the Working wit
86. amics of carbon sequestration and alternative carbon accounting with an application to the upper Mississippi River Basin Ecological Economics 54 23 35 Gaston G S Brown M Lorenzini and KD Singh 1998 State and change in carbon pools in the forests of tropical Africa Global Change Biology 4 97 114 Glenday J 2006 Carbon storage and emissions offset potential in an East African tropical rainforest Forest Ecology and Management 235 72 83 Grace J J San Jose P Meir HS Miranda and RA Montes 2006 Productivity and carbon fluxes of tropical savannas Journal of Biogeography 33 387 400 Green C V Avitabile EP Farrell and KA Byrne 2006 Reporting harvested wood products in national greenhouse gas inventories Implications for Ireland Biomass and Bioenergy 30 2 105 114 Gibbs HK S Brown JO Niles and JA Foley 2007 Monitoring and estimating tropical forest carbon stocks making REDD a reality Environmental Research Letters 2 045023 Hamilton K M Sjardin T Marcello and G Xu Forging a Frontier State of the Voluntary Carbon Markets 2008 Washington D C Ecosystem Marketplace and New Carbon Finance 2008 Hope CW 2006 The social cost of carbon what does it actually depend on Climate Policy 6 565 572 Houghton RA 2005 Tropical deforestation as a source of greenhouse gas emissions In Tropical Deforestation and Climate Change Moutinho and Schwartzman eds Instituto de Pesquisa Ambiental da Amazonia and Env
87. an algorithm that aggregates phosphorus nitrates and other constituents Alternatively a manager may begin using values from EPA table as a starting point to generate discussion and then alter values based on local expert opinion and stakeholder feedback 6 Vegetation Filtering Value eff_n eff_p These values are used to incorporate the effects of natural vegetation that buffer potential water quality impairment downhill from sources To develop these values all land class pixels that contain natural vegetation such as forests natural pastures wetlands or prairie are assigned high values and vegetation that has no or little filtering value receives a value of zero All values should fall between 0 and 100 Consult with a hydrologist if not certain about assignment of specific values 7 Calibration Data Calib Calibration data is needed for ensuring that the Tier 1 Water Purification Nutrient Retention model results match well with reality Most often calibration data may be obtained from water quality monitoring that is already in place If the point of interest is a water supply intake the drinking water entity will most likely collect water quality at the point of intake If the point of interest is in a stream or lake the water quality may have been tested by a public agency Most likely if the location is of interest in terms of meeting a water quality standard data should be available In the U S the user may contact or look
88. ar species group then use Os and 1s where a 1 indicates habitat Otherwise if sufficient information is available on a species group s habitat preferences assign LULC a relative habitat suitability score from 0 to 1 where 1 indicates the highest habitat suitability For example a grassland songbird may prefer a native prairie habitat above all other habitat types prairie is given a Habitat score of 1 for grassland birds but will also use a managed hayfield or pasture in a pinch managed hayfield and pasture is given a Habitat score of 0 5 for grassland birds d L_THREATI L_THREAT2 etc The relative sensitivity of each habitat type to each threat You will have as many columns named like this as you have threat and the italicized portions of names must match row names in the Threat data table noted above input 4 Values range from 0 to 1 where 1 represents high sensitivity to a threat and 0 represents no sensitivity Note Even if the LULC is not considered habitat do not leave its sensitivity to each threat as Null or blank instead enter a 0 and the model will convert it to NoData Sample data set Invest Biodiversity Input sensitivity_samp dbf Example A hypothetical study with four LULC and three threats In this example we treat woodlands and forests as absolute habitat and bare soil and cultivated areas as absolute non habitat Forest mosaic is the most sensitive least resistant habitat type and is more
89. arefully to be aware of potential data problems or to determine why the model did not produce an output 16 ects Caten J lt lt Deals F Close this dialog when completed successfully Executing Carbon 1 Carbon C NatCap Carbon lulc will cur 1990 500 carbon pools will harv will cur lulc will fut 2030 harv vill fut true 43 5 5 Start Time Thu Jul 03 15 52 45 2006 Running script Carbon Resampling to 500 Computing first 4 pools current Progress dialog e The model creates two folders in the workspace you selected intermediate and output After your script completes successfully you can view the results by adding them from the folders to your ARCMAP document using the ADD DATA button View the attribute table and change SYMBOLOGY by right clicking on the layer name in the TABLE OF CONTENTS and selecting PROPERTIES then clicking on the SYMBOLOGY tab Changing default paths in scripts If you extracted the files from InVEST Setup exe to the default location InVEST the INVEST toolbox will work after you load it to ARCMAP If you extracted the contents of the folder to a different location it will work as long as you maintain the internal structure of the InVEST folder If you moved the python scripts out of the InVEST folder however the python scripts associated with the InVEST toolbox still need to be correctly referenced To do this Add the InVEST toolbox to your ARCMAP document as
90. arket Discount Rate Results suffix 1 OK Cancel Environments lt lt Hide Help Tool Help Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires you to enter some data After you ve entered all values as required click on OK The script will run and its progress will be indicated by a Progress dialogue Upon successful completion of the model you will see new folders in your workspace called intermediate and output These folders contain several raster grids These grids are described in the next section Load the output grids into ARCMAP using the ADD DATA button You can change the symbology of a layer by right clicking on the layer name in the table of contents selecting PROPERTIES and then SYMBOLOGY There are many options here to change the way the file appears in the map You can also view the attribute data of output files by right clicking on a layer and selecting OPEN ATTRIBUTE TABLE 144 Interpreting results Parameter Log Each time the model is run a text file will appear in the output folder The file will list the parameter values for that run and will be named according to the service the date and time and the suffix Final Results Final results are found in the output folder of the workspace for this model The model produces two main output files 1 Timber_suffix shp The attribute table has three colum
91. assigned a value 1 These polygons can be land management units or a regular array or hexagons or grid squares Any cells not covered by a polygon will be assumed to be fully accessible and assigned values of 1 File type GIS polygon shapefile Name file can be named anything 36 Rows each row is a specific polygon on the landscape Columns a ID unique identifying code for each polygon FID also works b Access values between 0 and 1 for each parcel as described above Sample data set Invest access_samp shp Habitat types and sensitivity of habitat types to each threat required A table of LULC types whether or not they are considered habitat and for LULC types that are habitat their specific sensitivity to each threat Name file can be named anything File type dbf or xls if using ArcMAP 9 3 Rows each row is a LULC type Columns columns contain data on land use types and their sensitivities to threatss Columns must be named according to the naming conventions below a LULC numeric code for each LULC type Values must match the codes used in the LULC maps submitted in inputs 1 through 3 All LULC types that appear in the current future or baseline maps inputs 1 through 3 need to appear as a row in this table b NAME the name of each LULC c HABITAT Each LULC is assigned a habitat score Hj from 0 to 1 If you want to simply classify each LULC as habitat or not without reference to any particul
92. asure directly but can be reliably estimated from online tools tables or equations The Potential evapotranspiration could be also calculated monthly and annually using the Hamon equation Hamon 1961 Wolock and McCabe 1999 PET amon 13974 D W where d is the number of days in a month D is the mean monthly hours of daylight calculated for each year in units of 12 hours and Wt is a saturated water vapor density term calculated by _ 495 roel W 100 where T is the monthly mean temperature in degrees Celsius Potential evapotranspiration was set to zero when mean monthly temperature was below zero Then for each year during the time periods analyzed the monthly calculated PET values at each grid cell were summed to calculate a map of the annual PET for each year c Soil depth Soil depth may be obtained from standard soil maps Coarse yet free global soil characteristic data is available at http www ngdc noaa gov seg cdroms reynolds reynolds reynolds htm In the United States free soil data is available from the U S Department of Agriculture s NRCS in the form of two datasets SSURGO http soils usda gov survey geography ssurgo and STATSGO http soils usda gov survey geography statsgo Where available SSURGO data should be used as it is much more detailed than STATSGO Where gaps occur in the SSURGO data STATSGO can be used to fill in the blanks The soil depth should be calculated as the max depth of all horizons within
93. at map s period File name cannot be longer than 7 characters if using a GRID format Format standard GIS raster file e g ESRI GRID or IMG with a relative degradation source value for each cell from that particular degradation source The Value column indicates the relative degradation source that cell shows File location files must be saved in a folder titled input within the model s workspace see below Sample data sets Invest Biodiversity Input crp_c crp_f rr_c rr_f urb_c urb_f rot_c rot_f prds_c prds_f srds_c srds_f lrds_c lrds_f By using these sets of inputs we are running a habitat quality and rarity analysis for the current and future LULC scenario maps A habitat quality map will not be generated for the baseline map because we have not submitted any threat layers for the baseline map The name crp refers to cropland rr to rural residential urb to urban rot to rotation forestry prds to primary roads srds to secondary roads and Irds to light roads Accessibility to sources of degradation optional A GIS polygon shapefile containing data on the relative protection that legal institutional social physical barriers provide against threats Polygons with minimum accessibility e g strict nature reserves well protected private lands are assigned some number less than 1 while polygons with maximum accessibility e g extractive reserves are
94. at the INVEST toolbox has been added to your ARCMAP document as described in the Getting Started chapter Second make sure that you have prepared the required input data files according to the specifications in Data Needs Specifically you will need 1 a land cover raster file showing the location of different land cover and land use types in the landscape and 2 a carbon pools file which denotes the amount of aboveground belowground and soil carbon and carbon from dead biomass by land cover type Optionally you may also include 1 a map of harvest rates 2 economic data on the value of carbon and 3 future land use land cover and harvest rate data to project future carbon scenarios e Identify workspace If you are using your own data you need to first create a workspace or folder for the analysis data on your computer hard drive The entire pathname to the workspace should not have any spaces All your output files will be dumped here For simplicity you may wish to call the folder for your workspace carbon and create a folder in your workspace 58 called input and place all your input files here It s not necessary to place input files in the workspace but advisable so you can easily see the data you use to run your model Or if this is your first time using the tool and you wish to use sample data you can use the data provided in nVEST Setup exe If you unzipped the InVEST files to your C drive as described in
95. ata change this to suit your GDAL install location 6 Install the GDAL python bindings Download the appropriate package from this location http pypi python org pypi GDAL 1 6 1 Browse to the bottom of that page and select a version that matches your python version Make sure that you have prepared the required input data files according to the specifications in Data Needs Specifically you will need a land cover raster file depicting the different land cover and land use types in the landscape a Table of Land Cover Attributes describing the suitability of the land cover types to nesting and floral resources and a Table of Pollinator Species or Guilds describing the nesting and seasonal behavior and crop visitation of different pollinators Create a workspace on your computer hard drive if you are using your data The pathname to the workspace should not have spaces All your output files will be dumped here For simplicity you could create a folder in your workspace called input and place all your input files here It is not necessary to place input files in the workspace but this will make it easier to view the data you use to run your model If this is your first time using InVEST and you wish to use sample data you can use the data provided in InVEST Setup exe If you unzipped the InVEST files to your C drive as described in the Getting Started chapter you should see a folder called Invest pollination This folder shoul
96. ated by the USLE equation USLE RxKxLSxCxP Avoided erosion sediment retention on the parcel is then calculated by subtracting USLE from RKLS Vegetation does not only keep sediment from eroding where it grows It also traps sediment that has eroded upstream The USLE equation overlooks this component of sediment dynamics so we attempt to account for it as follows All soil that the USLE equation estimates will erode is routed downstream via a flowpath We estimate how much of the sediment eroded on all parcels will be trapped by downstream vegetation based on the ability of vegetation in each parcel to capture and retain sediment The model also determines the total sediment load exported that reaches the stream from each pixel on the landscape The table below describes how the removal of sediment by vegetation along hydrologic flowpaths is done Cell Vegetation USLE Retained by Cell Outflow quantity OQ from retention retained Cell Gi 1 Ei 1 El USLE1 0 USLE1 2 E2 USLE2 USLE1 E2 USLE1 G2 USLE2 3 E3 USLE3 USLE1 G2 USLE2 E3 USLE1 G2 USLE2 G3 USLE3 4 E4 USLE4 USLE1 G2 G3 E4 USLE1 G2 G3 G4 USLE2 G3 E4 USLE2 G3 G4 USLE3 E4 USLE3 G4 USLE4 The total retained sediment tot_retain is equal to the sum of the sediment removed by the pixel itself and the sediment removed through routing filtration The reservoir dead volume is designed to be filled completely at the end of the l
97. ation may be available through public sources and may be accessible online In particular if the hydropower plant is located in the United States information may be found on the internet The first place to check is the National Inventory of Dams http crunch tec army mil nidpublic webpages nid cfm If a hydropower dam is owned by the Bureau of Reclamation they should have information on the reservoir on their Dataweb http www usbr gov dataweb Similar information may be found online at other websites for reservoirs owned or operated by other government agencies or energy companies g Calibration For calibration data are needed on how much water actually reaches each hydropower station on an average annual basis Data should be available from 95 the managing entity of the hydropower plant In absence of information available directly from the hydropower operators data may be available for a stream gage just upstream of the hydropower station Gages in the U S may be managed by the USGS the state fish and wildlife agency the state department of ecology or by a local university The model user should consider whether the gage measures natural or managed streamflow and adjust measurements as necessary The drainage area downstream of the gage and upstream of the hydropower station must also be considered when comparing gaged flow with modeled flow h Time_period The design life span of each hydropower station can be obtained fro
98. by S Finally Brown et al 1989 give BEF for tropical broadleaf forests under three land uses undisturbed logged and nonproductive Brown 1997 attaches several caveats to the use of the above equation First the equation VOB x WD x BEF x CF is designed for inventoried stands that are closed as opposed to open forests with sparser canopy coverage such as oak savanna Second VOB estimates should be a function of all tree species found in the stand not just the economically most valuable wood Third trees with diameters as low as 10 centimeters at breast height DBH 10 need to be included in the inventory if this aboveground biomass carbon equation is to be as accurate as possible Brown 2002 also notes that the use of a single BEF value is a simplification of the actual biomass growth process These caveats lead Brown 2002 to recommend the use of allometric biomass equations to estimate woody aboveground biomass if available These equations give the estimated relationship between a stand s distribution of different sized trees and the stand s aboveground biomass Brown 1997 and Brown and Schroeder 1999 provide general aboveground biomass allometric equations for all global eco regions and the eastern US respectively Cairns et al 2000 provide aboveground biomass allometric equations for LULC types in southern Mexico Nascimento and Laurance 2002 estimate Amazonian rainforest aboveground biomass using allometric curves
99. c ton in 2010 US dollars values differ because of different assumptions regarding discounting of time Other recent estimates can be found in Murphy et al 2004 Stainforth et al 2005 and Hope 2006 An alternative method for measuring the cost of an emission of a metric ton of C is to set the cost equal to the least cost alternative for sequestering that ton The next best alternative currently is to capture and store the C emitted from utility plants According to Socolow 2005 and Socolow and Pacala 2007 the cost of this technology per metric ton captured and stored is approximately 100 Finally while we do not recommend this approach market prices can be used to set the price of sequestered carbon The Chicago Climate Exchange CCX and the European Climate Exchange ECX provide values 24 and 153 per metric ton of C on May 14 2008 respectively The difference in these prices illustrates the problem with using markets to set values The CCX and ECX are different in structure scope and the public policy that grounds each institution This leads to different market fundamentals and different prices for reasons unrelated to the social value of carbon sequestration We do not recommend the use of market prices because they usually only apply to additional carbon sequestration sequestration above and beyond some baseline sequestration rate Further carbon credit values from carbon markets such as the Chicago or European Cl
100. carbon stock for the aboveground pool C_above_fut the carbon stock for the aboveground pool for the future scenario C_below_cur the current carbon stock for the belowground pool C_below_fut the carbon stock for the belowground pool for the future scenario C_soil_cur the current carbon stock in soil C_soil_fut the carbon stock in soil for the future scenario C_dead_cur the current carbon stock in dead organic matter C_dead_fut the carbon stock in dead organic matter for the future scenario C_HWP_cur carbon stored in harvested wood products for current land cover C_HWP_fut carbon stored in harvested wood products for future scenario Bio_HWP_cur biomass of wood removed since start_date for current land cover Bio_HWP_fut biomass of wood removed since start_date for future land cover Vol_HWP_cur volume of wood removed since start_date for current land cover Vol_HWP_fut volume of wood removed since start_date for future land cover Ic_res_cur the current LULC map at the resolution chosen by the user Ic_res_fut the future LULC map at the resolution chosen by the user 62 e Carbon_dateandtime_suffix txt a text file that summarizes the parameter data used to run the Carbon Storage and Sequestration Model Appendix data sources This is a rough compilation of data sources and suggestions for finding compiling and formatting data This section should be used f
101. cel that is expected be harvested at some yr _cur yr _ fut point between the year given by and yr_fut where yr_fut indicates the year associated with the future land cover map e g if yr_cur is 2000 and fut_yr is 2050 then yr _cur yr_ fut 2 landscape until the year halfway between the current and future years The harvest variables yr _cur yr_ fut 2 2025 This means that current harvest rate map conditions hold on the for the future will be applied in the year Note that any fraction is round yr _cur yr_ fut 2 harvest rate map does not have to retain any spatial semblance to the current harvest rate map Nor do parcels that are harvested on the current and future maps have to have a common FID 2026 The future down e g if yr_cur is 2000 and fut_yr is 2053 then Sample data files for future scenarios are future land cover C InVEST Base_Data lule_samp_fut and future harvest rate map C InVEST Carbon harv_samp_fut shp Example A hypothetical study of future carbon storage in HWP for four forest parcels Continuing with current harvest rate map 2005 described above assume the future LULC 54 map corresponds to the year 2035 Three of the four forest parcels that have wood removed on the current landscape keep their boundaries in the future and continue to have wood removed into the future parcels with FID 1 3 and 4 on the current harvest rate map However the first parcel changes its
102. ces These will include models for flood mitigation irrigation agricultural production open access harvest of timber and non timber products recreation and tourism and cultural benefits InVEST is most effectively used within a decision making process that starts with a series of stakeholder consultations illustrated in Figure 1 Through discussion questions of interest to policy makers communities and conservation groups are identified These questions may concern service delivery on a landscape today and how these services may be affected by new programs policies and conditions in the future For questions regarding the future stakeholders develop scenarios to explore the consequences of expected changes on natural resources These scenarios typically include a map of future land use and land cover which is a critical input in all InVEST models Stakeholder Engagement Choices Change in Management Climate Population Biophysical Models gt Maps Tradeoffs Balance Sheets Economic Models gt Figure 1 Schematic of the decision making process in which InVEST is embedded Stakeholders create scenarios that are assessed for ecosystem service value by biophysical and economic models that produce several types of outputs Following stakeholder consultations and scenario development InVEST can estimate the amount and value of ecosystem services that are provided on the current landscape or under future
103. cessfully produce fruits or seeds Klein et al 2007 provides a list of crops and their pollination requirements that can help identify whether crops in a region of interest may benefit from wild animal pollinators Decision makers can use information on crop pollinators their abundance across a landscape and the pollination services they provide to crops in several ways First with maps of pollinator 146 abundance and crops that need them land use planners could predict consequences of different policies on pollination services and income to farmers for an example see Priess et al 2007 Second farmers could use these maps to locate crops intelligently given their pollination requirements and predictions of pollinator availability Third conservation organizations or land trusts could use the tool to optimize conservation investments that benefit both biodiversity and farmers Finally governments or others proposing payment schemes for ecosystem services could use the results to estimate who should pay whom and how much The Model A wide range of animals can be important pollinators e g birds bats moths and flies but bees are the most important group for most crops Free 1993 As a result the InVEST Pollination model focuses on the resource needs and flight behaviors of wild bees Many people think of honeybees managed in artificial hives when they think of pollinators but wild bees also contribute to crop pollination In
104. ch pixel It has three components which run sequentially in InVEST First it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration The model does not differentiate between surface subsurface and baseflow but assumes that all water yield from a pixel reaches the point of interest via one of these pathways Second it calculates the proportion of surface water that is used for hydropower production by subtracting the surface water that is consumed for other uses Third it estimates the energy produced by the water reaching the hydropower reservoir and the value of this energy over the reservoir s lifetime The water yield model is based on the Budyko curve and annual average precipitation First we determine annual water yield Y for each pixel on the landscape indexed by x 1 2 X as follows AEFT Y 1 i P xj x x 76 where AET is the annual actual evapotranspiration on pixel x with LULC j and P is the annual precipitation on pixel x Transpiration Precipitation Evaporation a O aed O O i Groundwater Recharge Figure 1 Conceptual diagram of the water balance model used in the hydropower production model The water cycle is simplified including only the parameters shown in color and ignoring the parameters shown in gray Yield as calculated by this step of the model is then adjusted for other consum
105. ch reservoir which must correspond to values in the Watersheds raster b cost Cost of sediment removal in Currency m removed Floating point value c dead_vol The volume of water below the turbine It is a design dimension below which water is not available for any use and it s designed to store deposit sediment without hindering turbine and reservoir hydropower functions d time_span Integer time period to be used in calculating Present Value PV of removal costs This time period should be the remaining designed lifetime of the structure For instance if you are using an LULC map for the year 2000 and a reservoir of interest 123 was designed in 1950 for a 100 year lifetime the time period entered here should be 50 years e discount The rate of discount over the time span used in net present value calculations Floating point value Sample data set C Invest Base_Data Water_Tables mdb Sediment Running the Model The Avoided Reservoir Sedimentation model maps the soil loss sediment exported sediment retained and value of sediment retention on the landscape This model is structured as a toolkit which has two tools The first tool Soil Loss produces multiple outputs including USLE sediment retained by the landscape and sediment exported to the stream One of these output values feed into the next portion of the model the Valuation tool which calculates sediment retention value By running the tool three
106. changes click File Save to save your new script Interpreting Results The following is a short description of each of the outputs from the Avoided Reservoir Sedimentation model all of which are automatically saved into the Outputs folder in your workspace Output LS m Depicts length slope factor Values of 0 to 7 8 are typical for uninterrupted slope lengths of 900 m for percent slopes of 0 10 For slopes greater than 20 LS values should not be higher than 45 m Values should be tested and verified to the real world landscape characteristics Knowledge of the hydrologic processes and the watershed terrain characteristics will help test these values Multiple runs by changing slope threshold Length slope power variable Length slope multiplication variable and stream threshold will help adjust LS Output RKLS tons Potential soil loss This is the potential loss under the natural conditions for bare soil where vegetation is absent This value will represent the impacts of natural conditions soil properties climate rain characteristics and slope on soil erosion Units are tons ha Output USLE tons Actual potential soil loss taking into account land use and cultivation practices Average annual soil loss in tons ha The mean USLE for the whole watershed will be an output field in the Sediment table USLE will be less than RKLS Output Cell_Ret tons the sediment retained by LULC on the pixel itself This is equal to RKL
107. characters Format Standard GIS raster file e g ESRI GRID or IMG with available water content values for each cell Sample data set InVEST Base_Data pawe 5 Average Annual Potential Evapotranspiration required A GIS raster dataset with an annual average evapotranspiration value for each cell Potential evapotranspiration is the potential loss of water from soil by both evaporation from the soil and transpiration by healthy Alfalfa or grass if sufficient water is available The evapotranspiration values should be in millimeters 82 Name File can be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file e g ESRI GRID or IMG with potential evapotranspiration values for each cell Sample data set InVEST Base_Data eto 6 Land use land cover required A GIS raster dataset with an LULC code for each cell The LULC code should be an integer Name File can be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file e g ESRI GRID or IMG with an integer LULC class code for each cell e g 1 for forest 3 for grassland etc These codes must match LULC codes in the Biophysical table Sample data set InVEST Base_Data landuse_90 7 Watersheds required A GIS raster dataset This is a layer of watersheds such that each watershed contributes to a point of interest where hydropower production will be analyzed Name File can
108. d be your workspace The input files are in a folder called Invest pollination input and in invest base_data Open an ARCMAP document to run your model Locate the INVEST toolbox in ARCTOOLBOX ARCTOOLBOX should be open in ARCMAP but if it is not click on the ARCTOOLBOX symbol See the Getting Started chapter if you do not see the InVEST toolbox 155 e Click once on the plus sign on the left side of the INVEST toolbox to see the list of tools expand Double click on Pollination Ble Edt Wen is r Gslecton Lods iwindews tele Oaks B 1 447 40 2 O bh Spskalane Layer fise samaca 8 sexe P ten fe tee 6 5 o A Te ay Ped iies SF Layers ArcTodbx 3 WY lir_samp cu 4 3D Analyst Toots i Anebysis Tools Cartography Tools Comversion Teale i Data Interoasranty Took Data Management Togs Geceoding Tools Geestariscd Oratyst Tock E InvEST Potton 3 Liwer Reversing Too Futdmension Tools Network Andyst Tools Sames Sever Tous Sosial Analyst loos Spatiel Statistics Tock Tracking Analyst Tonis 7 55242 002 4966151 874 Maters e An interface will appear like the one below that indicates default file names but you can use the file buttons to browse to your data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface Refer to the Data Needs section for information on data for
109. d of this attribute is used to relate it to the current LULC map c Start_date The first year the carbon removed from a forest will be accounted for in the HWP pool The first year should coincide with a year in which wood was actually harvested from the parcel If wood was harvested from a parcel in 1995 2000 and 2005 and the LULC map being evaluated is from 2005 then St_date can equal 1995 2000 or 2005 it is your choice d Freq_cur The frequency in years with which the Cut_cur amount is harvested If the value is 1 then the Cut_cur amount is removed annually from the parcel if 5 then every 5 years etc Decay_cur The half life of wood products harvested measured in years f C_den_cur The carbon density in the harvested wood MgC Mg of dry wood Typically the statistic ranges between 0 43 and 0 55 see table 4 3 of IPCC 2006 If C_den_cur is not known for a parcel set it equal to 0 5 g BCEF_cur An expansion factor that translates the mass of harvested wood into volume of harvested wood Biomass Conversion Expansion Factor The expansion factor is measured in Mg of dry wood per m of wood and is a function of stand type and stand age If you do not have data on this expansion factor you can use the BCEFg row in table 4 5 of IPCC 2006 Otherwise set this expansion factor equal to 1 for each parcel Sample data set Invest Carbon Input harv_samp_cur shp Example A hypothetical study of carbon storage in HWP for four f
110. d to values in the Watersheds raster b station_desc Name of hydropower station optional c calib Annual water yield calibration constant Multiplying this value by the total water supply for a watershed should give the actual total annual water supply observed measured at the point of interest corresponding to the wsupply column of the Scarcity tool s water_scarcity dbf output Floating point value d efficiency The turbine efficiency A number to be obtained from the hydropower plant manager floating point values generally 0 7 to 0 9 e fraction The fraction of inflow water volume that is used to generate energy to be obtained from the hydropower plant manager Managers can release water without generating electricity to satisfy irrigation drinking water environmental demands Floating point value f height The head measured as the average annual effective height of water behind each dam at the turbine intake in meters Floating point value g kw_price The price of one kilowatt hour of power produced by the station in dollars or other currency Floating point value d cost Annual cost of running the hydropower station maintenance and operations costs Floating point value e time_span An integer value of either the expected lifespan of the hydropower station or the period of time of the land use scenario of interest Used in net present value calculations f discount The discount rate over the time span used in
111. d using the following equation Pa P4484 where p4 is power in watts p is the water density 1000 Kg m qa is the flow rate m s g is the gravity constant 9 81 m s and hg is the water height behind the dam at the turbine m In this model we assume that the total annual inflow water volume is released equally and continuously over the course of each year The power production equation is connected to the water yield model by converting the annual inflow volume adjusted for consumption Vin to a per second rate Since electric energy is normally measured in kilowatt hours the power p4 is multiplied by the number of hours in a year All hydropower reservoirs are built to produce a maximum amount of electricity This is called the energy production rating and represents how much energy could be produced if the turbines are 100 efficient and all water that enters the reservoir is used for power production In the real world turbines have inefficiencies and water in the reservoir may be extracted for other uses like irrigation retained in the reservoir for other uses like recreation or released from the reservoir for non power production uses like maintaining environmental flows downstream To account for these inefficiencies and the flow rate and power unit adjustments annual average energy production at dam d is calculated as follows 79 e 0 00272 B y h Vin where amp q is hydropower energy production KWH is th
112. data model that has a more complex and comprehensive set of tools for modeling surface water features This is only intended to be a brief overview of the issues and methods involved in DEM preparation For more detail see the Resources section below 20 O EJ Use the highest quality finest resolution DEM that is appropriate for your application This will reduce the chances of there being sinks and missing data and will more accurately represent the terrain s surface water flow providing the amount of detail that is required for making informed decisions at your scale of interest The Hydro_layers directory When tools are run that use DEM derived layers like slope and flow direction the tool looks for a folder called Hydro_layers located in the same folder as the DEM If this folder does not exist or any of the required derived layers within the folder don t exist the tool will generate them from the input DEM otherwise it uses the layers that already exist In general this is convenient and efficient However if you decide to use a different DEM than the one that was used to generate the files in Hydro_layers and the new DEM is located in the same folder as the old DEM the tool will not realize that it is different and will continue to use the old derived layers So in this case it is necessary to delete the Hydro_layers folder before re running the tool using the new DEM so that the derived laye
113. depth table A valuable review of maximum plant rooting depths is available in Canadell J R B Jackson and H Mooney 1996 Maximum rooting depth of vegetation types at the global scale Oecologia 108 583 595 where 290 observations in the literature are summarized and it is concluded that rooting depths are more consistent than that previously believed among similar biomes and plant species The model determines the minimum of soil depth and rooting depth for an accessible soil profile for water storage Determinations on how to deal with soil less systems such as fractured rock substrates should be based on expert advice Effective maximum root depth must be defined for impermeable landuse land classes such as urban areas or water bodies A rule of thumb is to denote water and urban areas with minimal maximum rooting depths but a zero value should not be used The literature values must be converted to mm and depicted as integer values Maximum root depths by species and biomes Root Depth by Species Root Depth by Biome Trees 7 0 m Cropland 2 1 m Shrubs 5 1 m Desert 9 5 m Herbaceous Plants 2 6 m Sclerophyllous Shrubland amp Forest 5 2 m Tropical Deciduous Forest 3 7 m Tropical Evergreen Forest 7 3 m Grassland 2 6 m Tropical Grassland Savanna 15 m Tundra 0 5 m g Evapotranspiration coefficient table ET Potential Evapotranspiration ET Coefficient x Reference Evapotranspiration ET coefficient values for crops are readily avai
114. dition to these centralized markets there is a substantial over the counter market for voluntary carbon offsets For details about the price of these offsets see Conte and Kotchen 2010 Currently these markets only apply to carbon sequestration i e the additional storage of carbon over time but there is increased interest in financial incentives to avoid release of carbon from ecosystems in the first place so called reduced emissions from deforestation and degradation or REDD Gibbs et al 2007 Mollicone et al 2007 Mackey et al 2008 This option was accepted during the last meeting of the parties to the UN Framework Convention on Climate Change and is likely to be written in to the follow up agreement to the Kyoto Protocol Payments for REDD would financially reward forest owners for reversing their planned deforesting and thinning actions Sedjo and Sohngen 2007 Sohngen et al 2008 Issues of accounting and verification have slowed the emergence of REDD markets but many are anticipating them with private transactions While market prices are one way to estimate the value of CO sequestration these prices will reflect policies subsidies and other factors and therefore will only indicate the true value of this service to society by chance Murray et al 2007 For this reason we recommend that users rely on the avoided damages associated with the emission of COz into the atmosphere rather than prices in existing carbon mar
115. e 50 593 601 Forman R 1995 Land Mosaics The Ecology of landscapes and regions Cambridge Univ Press New York Forman R 2003 Road ecology science and solutions Island Press New York New York Franklin J F and D B Lindenmayer 2009 Importance of matrix habitats in maintaining biological diversity Proceedings of the National Academy of Sciences 106 349 350 Hall L S Krausman P R and Morrison M L 1997 The habitat concept and a plea for standard terminology Wildlife Society Bulletin 25 1 173 182 Lindenmayer D Hobbs R Montague Drake R Alexandra J Bennett A Burgman M Cae P Calhoun A Cramer V Cullen P 2008 A checklist for ecological management of landscapes for conservation Ecology Letters 11 78 91 MacArthur R E 0 Wilson 1967 The theory of island biogeography Princeton University Press Princeton NJ Mckinney M L 2002 Urbanization biodiversity and conservation BioScience 52 883 890 Nelleman C Kullered L Vistnes I Forbes B Foresman T Husby E Kofinas G Kaltenborn B Rouaud J Magomedova M Bobiwash R Lambrechts C Schei P Tveitdal S Grgn O Larsen T 2001 GLOBIO Global methodology for mapping human impacts on the biosphere UNEP DEWA TR 01 3 42 Nelson E S Polasky D J Lewis A J Plantinga E Lonsdorf D White D Bael amp J J Lawler 2008 Efficiency of incentives to jointly increase carbon sequestration and species conservation on a landscape Proc
116. e attribute table This model is open source so you can edit the scripts to modify update and or change equations by right clicking on the script s name and selecting Edit The script will open in a text editor After making changes click File Save to save your new script Interpreting Results The following is a short description of each of the outputs from the Reservoir Hydropower Production tool each of these output files is automatically saved in an Output or Service folder that is saved in the Working Directory that the user specifies 1 Output actual_et Actual evapotranspiration grid This is the annual average actual evapotranspiration fraction of precipitation Actual Evapotranspiration Precipitation across the landscape what is the fraction of precipitation that was evapotranspired 88 2 Service wyield Water yield grid The approximate absolute annual water yield across the landscape calculated as the difference between precipitation and actual evapotranspiration on each land parcel Given in mm 3 Output consump Grid showing water consumption at each pixel where each land use type is mapped to a corresponding value in the Demand Table consump field in m Output supply Cumulative water supply from all pixels upstream in m 5 Output rsupply Cumulative realized supply grid This is a grid over the landscape that shows the difference between water supplied and water demanded from all pixel
117. e files mean e To change the symbology of a layer right click on the layer name in the table of contents select PROPERTIES and then SYMBOLOGY There are many options to change the file s appearance in the map e To view the attribute data of output files right click a layer and select OPEN ATTRIBUTE TABLE Interpreting results Parameter Log Each time the model is run a text file will appear in the output folder This file lists the parameter values for that run and will be named according to the service the date and time and the suffix Final results Final results are found in the output folder within the working directory you set up for this module Final results are found in the output folder within the working directory set up for this model e sup_tot_cur This is a map of pollinator abundance index summing over all bee species or guilds It represents an index of the likely abundance of pollinator species nesting on each cell in the landscape given the availability of nesting sites and of flower food resources nearby e sup_tot_fut The same as above but for the future scenario land cover map if provided 158 e frm_avg_cur This is a map of pollinator abundance on each agricultural cell in the landscape based on the average of all bee species or guilds It represents the likely average abundance of pollinators visiting each farm site e frm_avg_fut The same as above but for the future scenario land co
118. e recommended when comparing simulated loading to observed loading at your point of interest Also comparing V_Stream to stream lines should help in the comparison between model outputs and observed loadings ws_nutexp kg the sum of the exported pollutant to the outlet of the watershed of interest watershed_nutrienst dbf Table containing values for the total nutrient loading at the outlet of the watershed which is the cumulative nutrient load CNL arriving at the point of interest for each watershed equal to Maximum of CNL per watershed wp_value currency This grid shows the economic benefit of filtration by vegetation delivered at the downstream point s of interest THIS OUTPUT REPRESENTS THE ECOSYSTEM SERVICE OF WATER PURIFICATION IN ECONOMIC TERMS It may be useful for identifying areas where investments in protecting this ecosystem service will provide the greatest returns Variation in this output with scenario analyses by running 109 and comparing different LULC scenarios will indicate where land use changes may have the greatest impacts on service provision These outputs provide an interim insight into the dynamics of pollutant loading transport and filtration in a watershed The model will be most informative if it is used in collaboration with experts in hydrology familiar with the watershed In case model coefficients require adjustment and to guard against erroneous data input it is recommended that model outputs ar
119. e turbine efficiency coefficient Ya is the percent of inflow water volume to the reservoir at dam d that will be used to generate energy To convert the annual energy generated by dam d into a net present value NPV of energy produced point of use value we use the following rly NPVH TC d P D Dny where TCy is the total annual operating costs for dam d pe is the market value of electricity per unit of energy consumed provided by hydropower plant at dam d T4 indicates the number of years present landscape conditions are expected to persist or the expected remaining lifetime of the station at dam d set T to the smallest value if the two time values differ and r is the market discount rate The form of the equation above assumes that TC4 Pe Ea and are constant over time Once the total water yield contributing to annual energy production the average amount of energy produced by that inflow volume each year and the net present value of that energy production are determined we need to allocate these results back to each pixel on the landscape in proportion to their contribution to water yield First we determine each pixel s contribution to the total water yield used for hydropower production as where Y is the water yield at each pixel uy is the consumptive use of the pixel x and Vj the inflow water into dam d This output represents the ecosystem service in biophysical terms provision of water
120. e verified with field data mimicking pollutant loading and watershed transport processes Appendix Data Sources This is a rough compilation of data sources and suggestions about finding compiling and formatting data This section should be used for ideas and suggestions only It will be updated as new data sources and methods become available 1 Digital elevation model DEM DEM data is available for any area of the world although at varying resolutions Free raw global DEM data is available on the internet from the World Wildlife Fund http www worldwildlife org freshwater hydrosheds cfm NASA provides free global 30m DEM data at http asterweb jpl nasa gov gdem wist asp Or it may be purchased relatively inexpensively at sites such as MapMart www mapmart com The hydrological aspects of the DEM used in the model must be correct Please see the introduction section to the complete model manual for instructions on how to check for this 2 Soil depth Soil depth may be obtained from standard soil maps Coarse yet free global soil characteristic data are available at http www ngdc noaa gov seg cdroms reynolds reynolds reynolds htm In the United States free soil data is available from the U S Department of Agriculture s NRCS in the form of two datasets SSURGO http soils usda gov survey geography ssurgo and STATSGO http soils usda gov survey geography statsgo Where available SSURGO data should be used as itis much more d
121. each harvest period the mass of timber harvested each harvest period the frequency of each harvest period and harvested related prices and costs remain constant in each timber parcel over the user defined time period In reality each of these variables can change from year to year For example the mix of species harvested from a forest could change from one harvest period to the next and this could affect everything from the amount of wood harvested to the composite price received for the timber In addition un modeled disturbances such as forest fires or disease or occasional managed thinning can have a major impact on harvest levels from a forest parcels Some of these limitations can be addressed by constraining the length of the time period used to assess harvests in parcels For example if the current year is 2000 and only the expected harvests until 2010 are valued any unaccounted changes in timber harvest management or price changes may be minor At this point a future 2010 LULC and timber parcel map could be evaluated with the timber model looking 10 years ahead again from 2010 to 2020 The future timber parcel map could include any changes in timber management and prices that occurred between 2000 and 2010 This process could be repeated for successive decades until for example 2050 Successive model runs with decadal time intervals until 2050 and the ability to change harvesting behavior and prices will better approximate harvesting
122. economic value of carbon sequestered between the current and the future landscape dates yr_cur and yr_fut The relative differences between parcels should be similar but not identical to sequest but the values are in dollars per grid cell instead of Mg per grid cell As with sequest values may be negative indicating the cost of carbon emissions from LULC changes to that parcel Intermediate results These files independently map each of the five carbon pools that contribute to the final results for both current and future landscapes Examining these results can help you determine which of the carbon pools are changing the most between your current and future landscapes and can help you identify areas where your data may need correcting The unit for each of these pool outputs is Mg per grid cell Biomass_HWP_cur and Biomass_HWP_fut are both measured in Mg dry matter per grid cell and Vol_HWP_cur and Vol_HWP_fut are both measured in m of wood per grid cell Ic_res_cur and Ic_res_fut give the current and future LULC maps at the resolution chosen with the model interface Finally Carbon_dateandtime_suffix txt is a text file that summarizes the parameter data you chose when running the Carbon Storage and Sequestration Model The text file s name includes dateandtime which means that the data and time is stamped into the text s file name The text file s name also includes a suffix term that you choose C_above_cur the current
123. ed Regular sediment removal can avoid some of these issues but this involves expensive maintenance costs The magnitude of sediment transport in a watershed is determined by several factors Natural variation in soil properties precipitation patterns and slope create patterns of erosion and sediment runoff Vegetation holds soil in place and captures sediment moving overland However changes in land management practices can alter the sediment retention capacity of land by removing important vegetation There are many clear examples of the effects of LULC change on erosion and sedimentation Forest fires that clear significant areas of vegetation are often followed by mudslides when heavy rains occur Meyer et al 2001 After the fire the vegetation that once held sediment in place no longer exists and the top layers of soil can be carried downstream by overland runoff 115 Deforestation results in a similar process although in some cases it may occur on longer time scales Even in areas where land cover remains the same a change in land use practice can alter the sediment retention capacity of the landscape For example moving from no till to till agriculture has been shown to increase the rate of soil erosion The continuous accumulation of increased sediment loads as a result of changes in LULC can cause serious problems such as increasing siltation rate and increasing dredging costs that were not anticipated during the original design of
124. ed and ignored Consequently threat intensity will always be less on the edges of a given landscape There are two ways to avoid this problem One you can choose a landscape for modeling purposes whose spatial extent is significantly beyond the boundaries of your landscape of interest Then after results have been generated you can extract the results just for the interior landscape of interest Or the user can limit themselves to landscapes where degradation sources are concentrated in the middle of the landscape 33 Data needs The model uses seven types of input data five are required 1 Current LULC map required A GIS raster dataset with a numeric LULC code for each cell The dataset should be in a projection where the units are in meters and the projection used should be defined Name it can be named anything Format standard GIS raster file e g ESRI GRID or IMG with LULC class code for each cell e g 1 for forest 2 for agriculture 3 for grassland etc The LULC class codes should be in the grid s value column The raster should not contain any other data The LULC codes must match the codes in the Sensitivity of land cover types to each threat table below input 7 Sample Data Set Invest Base_Data lulc_samp_cur 2 Future LULC map optional A GIS raster dataset that represents a future projection of LULC in the landscape This file should be formatted exactly like the current LULC map in
125. ediate benefits more than future benefits due to impatience and uncertain economic growth The second discount rate adjusts the social value of carbon sequestration over time This value will change as the impact of carbon emissions on expected climate change related damages changes If we expect carbon sequestered today to have a greater impact on climate change mitigation than carbon sequestered in the future this second discount rate should be positive On the other hand if we expect carbon sequestered today to have less of an impact on climate change mitigation than carbon sequestered in the future this second discount rate should be negative Limitations and simplifications The model greatly oversimplifies the carbon cycle which allows it to run with relatively little information but also leads to important limitations For example the model assumes that none of the LULC types in the landscape are gaining or losing carbon over time Instead it is assumed that all LULC types are at some fixed storage level equal to the average of measured storage levels within that LULC type Under this assumption the only changes in carbon storage over time are due to changes from one LULC type to another or from the harvest of wood products Therefore any grid cell that does not change its LULC type and is at a wood harvest steady state will have a sequestration value of 0 over time In reality many areas are recovering from past land use or are undergoing nat
126. el Ann_Load is the Annual Loading for pollutant of interest allowed before any damage to human health or natural habitat health occur It is the threshold below which service is invaluable CNLis the cumulative pollutant loading estimated by the model at the outlet of your watershed outlet of your watershed point of interest Limitations and Simplifications The model has a number of assumptions The model has a number of assumptions First since the model was developed for watershed and landscapes dominated by saturation excess runoff hydrology it may be less applicable to locations where the hydrology is determined by rainfall intensity in areas where flashy rains are predominant and where infiltration excess runoff occurs This kind of runoff is the result of intense rains that saturate only the top soil layer not the entire profile However the model s use of a runoff index and hydraulic routing should sufficiently adjust for this Second the model can only assess one pollutant per run If the user wishes to model several pollutants but does not have data on loadings and filtration rates for each pollutant choose a pollutant that acts as a surrogate in predicting loadings for other pollutants The most common surrogate is phosphorus because heavy phosphorus loadings are often associated with other pollutants such as nitrogen bacteria and suspended solids however using a pollutant surrogate should be approached with caution Alternat
127. el you will see new folders in your workspace called intermediate and output These folders contain several raster grids These grids are described in the Interpreting Results section Load the output grids into ARCMAP using the ADD DATA button You can change the symbology of a layer by right clicking on the layer name in the table of contents selecting PROPERTIES and then SYMBOLOGY There are many options here to change the way the file appears in the map You can also view the attribute data of output files by right clicking on a layer and selecting OPEN ATTRIBUTE TABLE Interpreting Results Parameter log Each time the model is run a text file will appear in the output folder The file will list the parameter values for that run and will be named according to the service the date and time and the suffix Final results Final results are found in the Output folder within the working directory set up for this model tot_C_cur This file shows the amount of carbon currently stored in Mg in each grid cell at the chosen resolution This is a sum of all of the carbon pools you have included data for above ground below ground soil dead material and harvested wood product The lowest value can be 0 for example paved areas if you don t include the soil beneath the pavement Examine this map to see where high and low values fall Is this what you would expect given the current land use and land cover If not
128. el at a 200m resolution with a 30m resolution LULC map If you leave this line blank the model will perform the analysis at the same resolution of the native LULC map i e the default Note a resolution that is finer than the native resolution of the raster dataset cannot be defined 151 c Agricultural land cover and land use classes optional You can specify LULC classes that represent agricultural parcels dependent upon or that benefit from pollination by bees Doing so will restrict the calculation of pollinator abundance to only the designated farms Enter the LULC values in the format 2 9 13 etc If you do not specify agricultural classes then a farm abundance map will be calculated for the entire landscape the default Refer to Klein et al 2007 for a list of crops and their level of pollinator dependency Sample data set Invest base_data lulc_samp_cur 2 Table of pollinator species or guilds required A table containing information on each species or guild of pollinator to be modeled Guild refers to a group of bee species that show the same nesting behavior whether preferring to build nests in the ground in tree cavities or other habitat features If multiple species are known to be important pollinators and if they differ in terms of flight season nesting requirements or flight distance provide data on each separately If little or no data are available create a single proto pollinator with data taken from aver
129. ells are given more weight than distant cells according to the species average foraging range Since pollinator abundance is limited by both nesting and floral resources the pollinator abundance index on cell x Px is simply the product of foraging and nesting such that M Dm a gt Fie zy m 1 P E N M Dm zee m 1 where N is the suitability of nesting of LULC type j F is the relative amount floral resources produced by LULC type j Dmx is the Euclidean distance between cells m and x and a is the expected foraging distance for the pollinator Greenleaf et al 2007 The result is a map of the abundance index 0 1 for each species which represents a map of pollinator supply i e bees available to pollinate crops In this sense this map represents the potential sources of pollination services but it has not yet incorporated demand In other words the landscape may be rich in pollinator abundance but if there are no bee pollinated crops on that landscape those bees will not be providing the service of crop pollination To make this connection between areas of supply and demand the model calculates an abundance index of visiting bees at each agricultural cell by again using flight ranges of pollinator species to simulate their foraging in nearby cells Specifically it sums pollinator supply values in cells surrounding each agricultural cell again giving more weight to nearby cells This sum created separa
130. er creating a two cell loop Sinks are usually caused by errors in the DEM and they can produce an incorrect flow direction raster Possible by products of this are areas with circular flow direction or a loop or a discontinuous flow network Filling the sinks assigns new values to the anomalous processing cells such that they are better aligned with their neighbors But this process may create new sinks so an iterative process may be required In ArcMap first identify sinks using ArcMap s Hydrology gt Sink tool Fill the resulting sinks with Hydrology gt Fill Do further iterations if there are still sinks that need to be filled In ArcHydro the corresponding tools are Terrain Preprocessing gt DEM Manipulation gt Sink Evaluation and Fill Sinks Flow direction loops If there s a problem in the flow direction raster such as a loop the Water Purification and Sedimentation tools may go into an infinite loop and eventually time out producing this error Error Sub watershed 1 is taking too long 45 minutes This probably indicates that there s a flow direction loop Diagnosing and repairing loops is difficult and is beyond the scope of our tools and built in ArcMap functions However a very rough method of determining whether a loop is being encountered is provided in both of the scripts WP_2_Nutrient_Removal py and 22 O Sediment_1_Soil_Loss py In each of these files look for 3 separate comme
131. erbaceous material as a carbon pool e g grass flowers non woody crops Our working assumption is that this material does not represent a potential source of long term storage like woody biomass belowground biomass and soil Herbaceous material in general recycles its carbon too quickly 2 2 Carbon stored in belowground biomass For LULC categories dominated by woody biomass belowground biomass can be estimated roughly with the root to shoot ratio of belowground to aboveground biomass Default estimates of the root to shoot ratio are given in Table 4 4 on p 4 49 of IPCC 2006 by eco region Broad estimates of this ratio are also given in Section 3 5 of Brown 1997 Some LULC types contain little to no woody biomass but substantial belowground carbon stocks e g natural grasslands managed grasslands steppes and scrub shrub areas In these cases the root to shoot ratio described above does not apply Belowground estimates for these LULC types are best estimated locally but if local data are not available some global estimates can be used The IPCC 2006 lists total biomass aboveground plus belowground and aboveground biomass for each climate zone in table 6 4 p 6 27 The difference between these numbers is a crude estimate of belowground biomass Recently Ruesch and Gibbs 2008 mapped the IPCC 2006 aboveground biomass carbon storage data given year 2000 land cover data Several studies have compiled estimates of belo
132. erent attribute of each watershed and must be named as follows a wshed_id watershed ID Unique integer value for each watershed which must correspond to values in the Watersheds raster b calib Annual watershed loading calibration constant Multiplying this value by the total watershed load output Output ws_nutexp should give the actual total annual load observed measured at the point of interest Floating point value c ann_load The total critical annual nutrient loading allowed for the nutrient of interest at the point of interest Floating point value 104 d cost Annual Cost of nutrient removal treatment in kg removed Floating point value e time_span Number of years for which net present value will be calculated Integer value This could be the time span number of years of either the same LULC scenario or the water treatment plant life span f discount The rate of discount over the time span used in net present value calculations Floating point value Sample data set InVEST Base_Data Water_Tables mdb WaterPurification 0 1 50 24 15 5 1 1 77 24 25 5 2 1 31 24 15 5 Running the Model Before running the Water Purification Nutrient Retention model make sure that the INVEST toolbox has been added to your ARCMAP document as described in the Getting Started chapter of this guide Second make sure that you have prepared the required input data files according to the specifications
133. estimates the value of this ecosystem service to society Limitations of the model include an oversimplified carbon cycle an assumed linear change in carbon sequestration over time and potentially inaccurate discounting rates Introduction Ecosystems regulate Earth s climate by adding and removing greenhouse gases GHG such as CO from the atmosphere In fact forests grasslands peat swamps and other terrestrial ecosystems collectively store much more carbon than does the atmosphere Lal 2002 By storing this carbon in wood other biomass and soil ecosystems keep CO out of the atmosphere where it would contribute to climate change Beyond just storing carbon many systems also continue to accumulate it in plants and soil over time thereby sequestering additional carbon each year Disturbing these systems with fire disease or vegetation conversion e g land use land cover LULC conversion can release large amounts of CO2 Other management changes like forest restoration or alternative agricultural practices can lead to the storage of large amounts of CO2 Therefore the ways in which we manage terrestrial ecosystems are critical to regulating our climate As with all other models for which InVEST provides estimates of value we are focused on the social value of carbon sequestration and storage Terrestrial based carbon sequestration and storage is perhaps the most widely recognized of all ecosystem services Stern 2007
134. etailed than STATSGO Where gaps occur in the SSURGO data STATSGO can be used to fill in the blanks The soil depth should be calculated as the max depth of all horizons within a soil class component and then a weighted average of the components should be estimated This can be a tricky GIS analysis In the US soil categories each soil property polygon can contain a number of soil type components with unique properties and each component may have different soil horizon layers also with unique properties Processing requires careful weighting across components and horizons The Soil Data Viewer http soildataviewer nrcs usda gov a free ArcMap extension from the NRCS does this soil data processing for the user and should be used whenever possible Ultimately a grid layer must be produced Data gaps such as urban areas or water bodies need to be given appropriate values Urban areas and water bodies can be thought of having zero soil 110 depth 4 Land use and land cover A key component for all water models is a spatially continuous land use and land cover raster grid That is within a watershed all land use and land cover categories should be defined Gaps in data that break up the drainage continuity of the watershed will create errors Unknown data gaps should be approximated The more detailed and descriptive these files are the better accuracy and modeling results Global land use data is available from the University of Ma
135. etting the model to read layers and tables from your ARCMAP document rather than from the c drive make sure to clear any selections unless you wish to run your model on the selection Running the models with the input data files open in another program can cause errors Ensure that the data files are not in use by another program to prevent data locking As the models are run it may be necessary to change values in the input tables This can happen within ARCMApP or in an external program Depending on the format of tables used dbf or mdb is recommended you will need an appropriate software program to edit tables To edit tables within ARCMAP you need to start an edit session from the editor toolbar and select the workspace folder or database that contains your data After editing you must save your changes and stop the edit session Some models require specific naming guidelines for data files e g Biodiversity model and field column names Follow these carefully to ensure your dataset is valid Remember to use the sample datasets as a guide to format your data Running the models You are ready to run an InVEST model when you have prepared your data according to the instructions in the relevant chapter and loaded the InVEST toolbox to your ARCMAP document To begin 14 Although not necessary it s often useful to add your input layers to your ARCMAP document to examine them Use the ADD DATA button to add input data
136. ew Toobox _ P Analysis Tools Add Toolbox 0 haw rete future H Ccatogaphy To O 4G Conversion Too Enviroment 3 O kovertest 4 amp Coverace Todls a Data meropere Y Hide Locked Tools ms Q Data Manageme 7 O koverat f 4 r esd Teel eee d a Geostetistical Load Settings ms 3 Lirear Refere Mukidmension Tools H Q Network Analyst Took amp Savples sever Tools 4 Spatial Analyst Tools pata Statistics Tools H Q Tracking Analyst Took QQx2V Ses Br hk I EETA Adding the InVEST toolbox Navigate to the location of InVEST tbx in the InVEST folder Select the toolbox and click OPEN Do not double click on the toolbox icon 11 Add Toolbox Look in InVEST al E Base_Data Biodiversity Carbon Open ccess pollination python Timber Nans Fnvet Show of type Toolboxes 7 Cancel Select InVEST Toolbox The INVEST toolbox should appear in ARCTOOLBOX Click on the plus sign to the left of INVEST to expand it You will see scripts for each InVEST model 12 InVEST mxd ArcMap Arcinfo File Edit Yiew Bookmarks Insert Selection Tools Window Help ImageConnect 3 le QQruT Ses Bok eases ER pace Us Deas SOX ye lf ArcToolbox M roads E 30 Analyst Tools a M cities Analysis Tools lulc_samp_cur Ej S Cartography Tools LULC_GROUP Conversion Tools C g H Coverage Tools E built H 3 Data Interoperability Tools W Forest Data Management T
137. f current and future harvests see above We recommend that the modeler use Bio_HWP_cur and Bio_HWP_fut to refine the current and future LULC maps Specifically if Bio_HWP_cur or Bio_HWP_fut on a portion of the landscape are significant then the modeler should assess whether the LULC types associated with that portion of the current or future landscape accurately reflect the biomass remaining on the landscape For example if the current LULC type on a portion of the landscape that has been heavily harvested in the immediate past is closed conifer it may be more appropriate to reclassify it as thinned conifer or open conifer on the LULC map 5 Economic data optional required for valuation Three numbers are not supplied in a table but instead are input directly through the tool interface a The value of a sequestered ton of carbon in dollars per metric ton of elemental carbon not CO2 which is heavier so be careful to get units right If the social value of COxze is Y per metric ton then the social value of C is 3 67 Y per metric ton Labeled Price of carbon per metric ton optional in the tool interface For applications interested in estimating the total value of carbon sequestration we recommend value estimates based of damage costs associated with the release of an additional ton of carbon the social cost of carbon SCC Stern 2007 Tol 2009 and Nordhaus 2007a present estimates of SCC For exa
138. f trees in a forest die due to disease much of the carbon stored in aboveground biomass becomes carbon stored in other dead organic material Also when trees are harvested from a forest branches stems bark etc are left as slash on the ground The model assumes that the carbon in wood slash instantly enters the atmosphere With respect to its estimates of carbon in HWPs the model is constrained by the fact that users may assign only one harvest rate e g 50 Mg of wood per harvest where a harvest occurs every 2 years and only one decay rate e g the wood harvested from the parcel over the years is always used to make the same product that decays at the same rate to each parcel In reality harvested parcels will exhibit variation in harvest and decay rates over time The model also does not account for the greenhouse gasses GHGs emitted from the transportation of harvested wood from its initial harvest site to its final destination the conversion of raw wood into finished products or agriculture related activities such as from tractors and livestock Annual GHG emissions from agricultural land use can be calculated with the InVEST Agriculture Production Model due to be released soon Finally while most sequestration follows a nonlinear path such that carbon is sequestered at a higher rate in the first few years and a lower rate in subsequent years the model s economic valuation of carbon sequestration assumes a linear change in
139. fications are available e g Anderson et al 1976 and often detailed land cover classification has been done for the landscape of interest A slightly more sophisticated LULC classification could involve breaking relevant LULC types into more meaningful categories For example agricultural land classes could be broken up into different crop types or forest could be broken up into specific species The categorization of land use types depends on the model and how much data is available for each of the land types The user should only break up a land use type if it will provide more accuracy in modeling For instance for the water quality model the user should only break up crops into different crop types if they have information on the difference in nutrient loading between crops Along the same lines the user should only break the forest land type into specific species for the water supply model if information is available on the root depth and evapotranspiration coefficients for the different species Sample Landuse Land class Table ID Land Use Land Class 1 Evergreen Needleleaf Forest 92 N Evergreen Broadleaf Forest 3 Deciduous Needleleaf Forest 4 Deciduous Broadleaf Forest 5 Mixed Cover 6 Woodland 7 Wooded Grassland 8 Closed Shrubland 9 Open Shrubland 10 Grassland 11 Cropland row Crops 12 Bare Ground 13 Urban and Built Up 14 Wetland 15 Mixed evergreen 16 Mixed Forest 17 Orchards Vinyards 18 Pasture f Maximum root
140. field observations of pollinators Lonsdorf et al 2009 Nevertheless with this simplicity come several limitations that must be kept in mind First the model predicts only relative patterns of pollinator abundance and pollination value using indices of 0 1 This is because absolute estimates of nest density resource availability and pollinator abundance are rarely available and yield functions including pollinator abundance for many crops are poorly defined However relying on relative indices limits our ability to estimate absolute economic values to better inform land use planning decision making often based on cost benefit analyses This simplicity is perhaps most limiting in calculating indices of value both on farms and at the source cells of pollinator supply With field samples of absolute pollinator abundance one could calibrate InVEST s relative indices to predict actual pollinator abundances And with specific yield functions one could use these actual abundances to estimate absolute estimates of economic value This would require beyond these additional data custom modeling steps that InVEST does not offer INVEST does produce however the intermediate results necessary to insert these modeling steps Furthermore the logic that increasing pollinator abundance and diversity lead to increased yield is supported by previous research Greenleaf and Kremen 2006 One option for overcoming this limitation is to link this mode
141. for hydropower production The following equations translate this level of ecosystem service provision into energy units and dollar value units Energy production over the lifetime of dam d is attributed to each pixel as follows Er Tiea x Cy where the first term in parentheses represents the electricity production over the lifetime of dam d The second term represents the proportion of water used for hydropower production that comes from pixel x The value of each pixel for hydropower production over the lifetime of dam d is calculated similarly NPVH NPVH xc 80 Limitations and simplifications The model has a number of limitations First it is not intended for devising detailed water plans but rather for evaluating how and where changes in a watershed may affect hydropower production for reservoir systems It is based on annual averages which neglect extremes and do not consider the temporal dimensions of water supply and hydropower production Second the model assumes that all water produced in a watershed in excess of evapotranspiration arrives at the watershed outlet without considering water capture by means other than primary human consumptive uses Surface water ground water interactions are entirely neglected which may be a cause for error especially in areas of karst geology The relative contribution of yield from various parts of the watershed should still valid Third the model does not consider sub annual
142. ge and sequestration Parameters depicted in color are included in the InVEST model while those in gray are not Birdsey 1994 in which case they could use the InVEST timber production model in tandem with the carbon model to assess management options The Model Carbon storage on a land parcel largely depends on the sizes of four carbon pools aboveground biomass belowground biomass soil and dead organic matter Fig 1 The InVEST Carbon Storage and Sequestration model aggregates the amount of carbon stored in these pools according to the land use maps and classifications produced by the user Aboveground biomass comprises all living plant material above the soil e g bark trunks branches leaves Belowground biomass encompasses the living root systems of aboveground biomass Soil organic matter is the organic component of soil and represents the largest terrestrial carbon pool Dead organic matter includes litter as well as lying and standing dead wood A fifth optional pool included in the model applies to parcels that produce harvested wood products HWPs such as firewood or charcoal or more long lived products such as house timbers or furniture Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product Using maps of land use and land cover types and the amount of carbon stored in carbon pools this model estimates the net amount of carbon stored in a land
143. gh amp low ecosystem service production No ecosystem service interactions Produce either absolute values or relative indices TIER 2 Models Incorporate more real processes More data required Daily to monthly time step some temporal dynamics Appropriate spatial extent ranges from parcel to global More precise estimates of ecosystem service delivery Some ecosystem service interactions Produce absolute values TIER 3 Models Most complex dynamic process based models Most data required Daily to monthly time step temporal dynamics with feedbacks and thresholds Appropriate spatial extent ranges from parcel to global More precise estimates of ecosystem service delivery Sophisticated ecosystem service interactions with feedbacks and thresholds Produce absolute values A work in progress The development of InVEST is an ongoing effort of the Natural Capital Project The models included in this Beta release are at a different stages of development and testing however they are all sufficiently developed to be applied To date the Beta models have been applied in several sites and decision contexts including to support policy and conservation planning in the Willamette Basin USA private landowners in Hawaii USA multi stakeholder planning in Tanzania permitting and licensing in Colombia water fund design in Colombia and Ecuador and priority setting for international aid in the Amazon Basin Updated and new models
144. gional cost data The user should consult managers at the individual reservoirs or a local sediment removal expert The technology available at each location may vary and the applicability of the specific technologies depends on the storage capacity mean annual runoff ratio and the storage capacity Annual Sediment yield ratio Once a range of possible technologies has been established for each reservoir the model user should investigate past sediment removal projects to determine appropriate costing This may require calculating to present day value and taking into account that the technology may have improved reducing the relative cost If local information is not available pricing must be estimated using published information Adjust costs to specific requirements location and present day value as needed References Anderson J R Hardy E Roach J and Witmer R 1976 A Land Use and Land Cover Classification System For Use with Remote Sensor Data Geological Survey Professional Paper 964 Edited by NJDEP OIRM BGIA 1998 2000 2001 2002 2005 FAO 2002 FAOSTAT Homepage of Food and Agriculture Organization of the United Nations Online 2008 9 11 Huang Yanhe and Lu Chenglong 1993 Advances in the application of the Universal Soil Loss Equation USLE in China Journal of Fujian Agricultural College Natural Science Edition 22 1 73 77 Roose E 1996 Land Husbandry Components and strategy 70 FAO Soils Bulletin Food a
145. gital copy of the Food and Agriculture Organization of the United Nations FAO eco region map http www fao org geonetwork srv en main home to figure that out Tables 5 1 through 5 3 p 5 9 of IPCC 2006 give estimates for aboveground biomass in agriculture land with perennial woody biomass e g fruit orchards agroforestry etc Tables 4 7 4 8 and 4 12 give aboveground biomass estimates for natural and plantation forest types Recently Ruesch and Gibbs 2008 mapped the IPCC 2006 aboveground biomass carbon storage data given year 2000 land cover data Other general sources of carbon storage estimates can be found For example Grace et al 2006 estimate the average aboveground carbon storage leaf wood for major savanna ecosystems around the world Table 1 Houghton 2005 gives aboveground carbon storage for natural and plantation forest types by continent Tables 1 and 3 Brown et al 1989 give aboveground biomass estimates for tropical broadleaf forests as a function of land use undisturbed logged nonproductive Table 7 Region specific sources of carbon storage data are also available Those we ve found include e Latin America Malhi et al 2006 report aboveground biomass volumes for 227 lowland forest plots in Bolivia Brazil Colombia Ecuador French Guinea Guyana Panama Peru and Venezuela Nascimento and Laurance 2002 estimate aboveground carbon stocks in twenty l ha plots of Amazonian rainfores
146. h C InVEST You can change the location but make sure the pathname does not have spaces or special characters as this will prevent the model from working properly Using Windows Explorer take note of the folder structure and files extracted from InVEST Setup exe Within the InVEST folder you will see the toolbox InVEST tbx The python scripts are in the folder InVEST python There is one script per model and each ends with a py suffix In addition you will see folders for Base Data Biodiversity Hydropower Carbon Pollination Sedimentation Water Purification and Timber These folders contain sample data You will also see an ARCMAP file called InVEST mxd with the InVEST toolbox pre loaded Adding the InVEST toolbox to ArcMap If you are working with sample data you may wish to open InVEST mxd which has the toolbox already loaded Follow these steps if you will be working with your data 10 START ARCMAP Save as a new mxd file Ensure that ARCTOOLBOX is open If not select the toolbox icon from the standard toolbar Right click on an empty part of the ARCTOOLBOX window and select ADD TOOLBOX Or right click on the top most ARCTOOLBOx text see graphic below gt Carbon mxd ArcMap Arcinfo Fle Edt Wew Insert Selection Toa Window Help Georefererning Y Layer icovertest My J E Edt Ho was osasi 2 axi gt o Jv eeow x Layers O hary_rete_current Analyst Tool R
147. h the DEM section of this manual for more information Name File can be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file e g ESRI GRID or IMG with elevation value for each cell given in meters above sea level Sample data set InVEST Base_Data dem 2 Soil depth required A GIS raster dataset with an average soil depth value for each cell The soil depth values should be in millimeters Name File can be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file with an average soil depth in millimeters for each cell Sample data set InVEST Base_Data soil_depth 3 Precipitation required A GIS raster dataset with a non zero value for average annual precipitation for each cell The precipitation values should be in millimeters Name File can be named anything but no spaces in the name and less than 13 characters Format Standard GIS raster file e g ESRI GRID or IMG with precipitation values for each cell Sample data set InVEST Base_Data precip 4 Plant Available Water Content required A GIS raster dataset with a plant available water content value for each cell Plant Available Water Content fraction PAWC is the fraction of water that can be stored in the soil profile that is available for plants use PAWC is a fraction from Oto 1 Name File can be named anything but no spaces in the name and less than 13
148. he biomass expansion factor in the literature If you do not have data on this expansion factor you can use the BCEFg row in table 4 5 of IPCC 2006 Otherwise set this expansion factor equal to 1 for each parcel Sample data set Invest Timber Input plant_table dbf 3 Market Discount Rate optional required for valuation This number is not supplied in a table but instead is input directly through a tool interface Labeled Market discount rate in the tool interface The market discount rate reflects society s preference for immediate benefits over future benefits e g would you rather receive 10 today or 10 five years from now The tool s default value is 7 per year which is one of the rates recommended by the U S government for evaluation of environmental projects the other is 3 However this rate will differ depending on the country and landscape being evaluated It can also be set to 0 if so desired To calculate NPV for a forest parcel a series of equation are used First we calculate the net value of a harvest during a harvest period in timber parcel x VH Perc_ harv 100 where VH is the monetary value ha generated during a period of harvest in x Perc_harv is the percentage of x that is harvested in each harvest period converted to a fraction Price is the market price of a Mg of timber extracted from x Harv_mass is the Mg ha of wood removed from parcel x during a harvest period and Ha
149. he input point data will represent multiple points along the stream network within the main watershed such that a sub watershed will be generated for each In ArcHydro use the Watershed Processing gt Batch Subwatershed Delineation tool with input point data representing multiple points along the stream network within the main watershed such that a sub watershed will be generated for each Resources ArcHydro http www crwr utexas edu giswr hydro ArcHOSS Downloads index cfm For more information on and an alternate method for creating hydrologically correct surfaces see the ESRI help on Hydrologically Correct Surfaces Topo to Raster For more information on sinks see the ESRI help on Creating a depressionless DEM Much more information and tips for all of these processes can be found by searching the ESRI support website http support esri com 24 BIODIVERSITY HABITAT QUALITY amp RARITY Summary Biodiversity is intimately linked to the production of ecosystem services Patterns in biodiversity are inherently spatial and as such can be estimated by analyzing maps of land use and land cover LULC in conjunction with threats InVEST models habitat quality and rarity as proxies for biodiversity ultimately estimating the extent of habitat and vegetation types across a landscape and their state of degradation Habitat quality and rarity are a function of four factors each threat s relative impact the relative sensit
150. he value of these bees to agricultural production and attributes this value back to source cells The results can be used to optimize agriculture and conservation investments Required inputs include a current land use and land cover map land cover attributes species of pollinators present and their flight ranges The model s limitations include exclusion of non farm habitats that may determine pollinator abundance and of the effects of land parcel size The model also does not account for managed pollinators and pollinator persistence over time Introduction Crop pollination by bees and other animals is a potentially valuable ecosystem service in many landscapes of mixed agricultural and natural habitats Allen Wardell et al 1998 Free 1993 Pollination can increase the yield quality and stability of fruit and seed crops as diverse as tomato canola watermelon coffee sunflower almond and cacao Indeed Klein et al 2007 found that 87 of 115 globally important crops benefit from animal pollination a service valued variously in the billions to tens of billions per year globally Costanza et al 1997 Losey and Vaughan 2006 Nabhan and Buchmann 1997 Southwick and Southwick 1992 Despite these numbers it is important to realize that not all crops need animal pollination Some crop plants are wind e g staple grains such as rice corn wheat or self pollinated e g lentils and other beans needing no animal pollinators to suc
151. hen calculating the pollinator service value map This constant converts the pollinator supply into yield and represents the abundance of pollinators required to reach 50 of pollinator dependent yield We suggest that the user apply the default value derived from previous work i e 0 125 Lonsdorf et al 2009 unless there are data to justify changing it The value must be greater than 0 and it is unlikely that the value would be greater than 0 2 Future Scenarios optional To evaluate change in pollination services under a future scenario a Future Land Cover Map needs to be provided for that future time point along with the year depicted The raster dataset needs to be formatted exactly like the current Land Cover Map data input 1 This LULC map could reflect changes in land management policy trends in land use change e g agricultural expansion urbanization increased habitat protection Sample data set Invest Base_data lulc_samp_fut Running the Model Before running the Pollination model make sure that the InVEST toolbox has been added to your ARCMApP document as described in the Getting Started chapter of this guide You will also need two additional python libraries to run the pollination model GDAL and Numpy The versions that you install will depend on the Python version on your computer Installation of these libraries may require you to have admin privileges on the computer Below are the installation instructions The
152. ife time of the reservoir then the reservoir will loose its service in generating hydropower We are assuming that each pixel on the landscape has the allowance of sediment equal to the dead volume divided by the 118 time span of the reservoir and the number of cells in the reservoir watershed max_allowance to export the reservoir So the portion of the sediment retention that the pixel will be valued at is the result of the subtraction of this max_allowance from the total retention The valuation model uses the cost of sediment removal per m entered by the user to determine the avoided cost per tons of sediment retained by the landscape The following equation is used to determine the value each pixel contributes to reservoir maintenance by helping to avoid erosion T SEDREM x MC r PVSR gt where PVSR is the present value of sediment retention on pixel x over T years where T indicates the period of time over which the LULC pattern is constant or the length of the reservoir life length SEDREM is tot_retain minus max_allowance for pixel x and MC is the marginal cost of sediment removal This cost may vary across reservoirs in a single watershed if different technologies are employed for sediment removal at different reservoirs If this is the case the user may input reservoir specific removal costs The marginal cost of sediment removal should be measured in units of monetary currency per cubic meter i e m The d
153. imate Exchanges are largely a function of various carbon credit market rules and regulations and do not necessarily reflect the benefit to society of a sequestered ton of carbon Therefore correct use of market prices would require estimating a baseline rate for the landscape of interest mapping additional sequestration and then determining which additional sequestration is eligible for credits according to market rules and regulations If the user is specifically interested in such an analysis please contact the INVEST team on the message boards at http invest ecoinformatics org We discount the value of future payments for carbon sequestration to reflect society s preference for payments that occur earlier rather than later The US Office of Management and Budget recommends a 7 per annum market discount rate for US based projects OMB 1992 Discount rates vary for other parts of the world The Asian Development Bank uses a rate of 10 to 12 when evaluating projects http www adb org Documents Guidelines Eco_Analysis discount rate asp Canada and New Zealand recommend 10 for their projects Abusah and de Bruyn 2007 Some economists believe that a market or consumption discount rate of 7 to 12 is too high when dealing with the climate change analysis Because climate change has the potential to severely disrupt economies in the future the preference of society to consume today at the expense of both climate stability in the future a
154. imple and often artificial binary approach e g natural versus unnatural to include a broad spectrum of both managed and unmanaged LULC types By using a continuum of habitat suitability across LULC types the user can assess the importance of land use management on habitat quality holistically or consider the potential importance of working or managed landscapes If a continuum of habitat suitability is relevant weights with a roster of LULC on a landscape must be applied in reference to a particular species guild of group For example grassland songbirds may prefer a native prairie habitat above all other habitat types the habitat score for the LULC prairie prarie equals 1 but will also make use of a managed hayfield or pasture in a pinch the habitat score for the LULC hayfield Hpayficia and pasture A pasture equals 0 5 However mammals such as porcupines will find prairie unsuitable for breeding and feeding Therefore if specific data on species group habitat relationships are used the model output refers to habitat extent and quality for the species or group in the modeled set only Besides a map of LULC and data that relates LULC to habitat suitability the model also requires data on habitat threat density and its affects on habitat quality In general we consider human modified LULC types that cause habitat fragmentation edge and degradation in neighboring habitat threats For example the conversion of
155. in the current year 53 We use C_den_cur and BCEF_cur to measure the mass Bio_HWP_cur and volume Vol_HWP_cur of wood that has been removed from a parcel from the start_date to the current year Bio_HWP_cur for parcel x is measured in Mg dry matter ha and is given by Bio __HWP _cur Cut _cur x ru yreur Ear da x l Freq _cur C _den_cur C3 and Vol_HWP_cur for parcel x is measured in m of wood ha and is given by Vol __ HWP _cur Bio_ HWP _cur x oe C4 Vol _exp _cur As mentioned before the model places all parcel level values into a grid cell map that comports with the four pool storage map Future Scenarios optional required for valuation If you have a LULC map data input 1 for a future landscape scenario then expected sequestration rates in the four major carbon pools on the landscape can be measured Similarly sequestration rates in the HWP carbon pool can be measured with a harvest rate map data input 3 for this future landscape A future land cover map a raster dataset should be formatted according to the same specifications as the current land cover map input 1 If you provide a future harvest rate map then the HWP carbon pool can be tracked over time The future harvest rate map should be formatted according to the same specifications as the current harvest rate map a polygon map where values for FID Cut_fut Freq_fut Decay_fut C_den_fut and BCEF_fut are attributed to each par
156. in your analysis If you did not specify agricultural classes then every cell and land cover classes in the LULC map will contain values e frm_ lt beename gt _fut The same as above but for the future scenario land cover map if provided e frm_val_cur This is a map of farm value the relative value of crop production on each agricultural cell due to wild pollinators It is based on a transformation of frm_ave_cur using a simple saturating yield function to translate abundance units into value units It represents in terms of crop production the contribution of wild pollinators Units are not dollars per se but the index is a relative measure of economic value e frm_val_fut The same as above but for future scenario land cover map if provided Appendix Data sources List of globally important crops and their dependence on animal pollinators Klein et al 2007 References Allen Wardell G P Bernhardt R Bitner A Burquez S Buchmann J Cane PA Cox V Dalton P Feinsinger M Ingram D Inouye CE Jones K Kennedy P Kevan and H Koopowitz 1998 The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields Conservation Biology 12 8 17 Cane JH 1997 Lifetime monetary value of individual pollinators the bee habropoda laboriosa at rabbiteye blueberry vaccinium ashei reade Acta Horticulturae 446 67 70 Costanza R R d Arge R de Groot S Farber
157. ion ALV Retained by Cell Outflow quantity OQ from retention retained Cell Gi 1 Ei 1 El ALV1 0 ALV1 2 E2 ALV2 ALV1 E2 ALV1 G2 ALV2 3 E3 ALV3 ALV1 G2 ALV2 E3 ALV1 G2 ALV2 G3 ALV3 4 E4 ALV4 ALV1 G2 G3 E4 ALV1 G2 G3 G4 ALV2 G3 E4 ALV2 G3 G4 ALV3 E4 ALV3 G4 99 ALV4 The model then aggregates the loading that reaches the stream from each pixel to determine a total pollutant load at each point of interest The user can then compare this load to a known observed or simulated using another water quality model measurement and adjust export coefficients and filtration efficiencies as needed until the modeled load matches the measured load for each point of interest The user should consider the likely impact of in stream processes in any calibration work as this model does not include in stream processes Once the total load is determined we can optionally calculate the value of this service provided by each pixel based on the avoided treatment costs the retention by natural vegetation and soil provides We make this calculation as follows Ann _ Load wp _ Value Cost p retained 1 _ _ P p ae N Where Wp_value is the value of retention for pixel x Cost p is the annual treatment cost in currency kg for the pollutant of interest p retained is the total pollutant retained by pixel x of the total amount of upslope pollutant arriving at this pix
158. ion Decay rates can be estimated from literature reports see sources in Appendix or also based on expert opinion if necessary If multiple types of wood products are harvested from a polygon the user should average the rates of decay or focus on the 51 product with the slowest decay rate since that will affect storage the most Because only woody biomass is included in the harvest portion of the model it is not necessary to include harvest or decay rates for herbaceous products If you are unable or uninterested in estimating carbon stored in harvested wood products you do not need to supply this table and the model will ignore this pool Name file can be named anything File type GIS polygon shapefile Rows each row is a specific polygon on the landscape Columns columns contain attributes related to harvested wood products and must be named as follows a FID unique identifying code for each polygon parcels in our vernacular b Cut_cur The amount of carbon typically removed from a parcel during a harvest period measured in Mg ha the model will sum across the area of each parcel This amount should only include the portion of the wood s carbon that is removed from the parcel e g the carbon in the wood delivered to a saw mill In other words the slash and other waste from a wood harvest should be ignored because the model assumes that its carbon content is lost to the atmosphere instantly the cur at the en
159. ion Disease 19 20 Vandalism destruction without 12 0 harvest Adapted from Czech et al 2000 we need to get permission if we decide to include 4 The relative sensitivity of each habitat type to each threat on the landscape is the final factor used when generating the total degradation in a cell with habitat in Kareiva et al 2010 habitat sensitivity is referred to by its inverse resistance Let S 0 1 indicate the sensitivity of LULC habitat type j to threat r where values closer to 1 indicate greater sensitivity The model assumes that the more sensitive a habitat type is to a threat the more degraded the habitat type will be by that threat A habitat s sensitivity to threats should be based on general principles from landscape ecology for conserving biodiversity e g Forman 1995 Noss 1997 Lindenmayer et al 2008 To reiterate if we have assigned 31 species group specific habitat suitability scores to each LULC then habitat sensitivity to threats should be specific to the modeled species group Therefore the total threat level in grid cell x with LULC or habitat type j is given by Dy Dy a We gt Jiss 3 real where y indexes all grid cells on r s raster map and Y indicates the set of grid cells on r s raster map Note that each threat map can have a unique number of grid cells due to variation in raster resolution If S 0 then Dyis not a function of threat r Also note that threat
160. ional 2030 Future harvest rate map optional Future land cover map optional C InvVEST Carbon Input hary_samp_Fut shp I Compute Economic Valuation optional Price of Carbon per metric ton optional 43 Carbon discount rate optional 1 Market discount rate optional 7 Results Suffix optional Carbon This model calculates the standing stock of Carbon and amount of carbon sequestered over time using four fundamental carbon pools aboveground biomass belowground biomass soil and dead organic matter It also computes the amount of Carbon stored in harvested wood products and values this stock and sequestered carbon Cancel Environments lt lt Hide Help Tool Help Note that each tool has a place to enter a suffix to the output filenames Adding a unique suffix prevents overwriting files produced in previous iterations When all required fields are filled in click the OK button on the interface Processing time will vary depending on the script and the resolution and the extent of the datasets in the analysis Every model will open a window showing the progress of the script Be sure to scan the output window for useful messages Normal progress notes will be printed in black font Informative messages that may or may not require changes to the data will be indicated in green font Messages in red font indicate problems that have caused the model not to run Read the green and red messages c
161. ironmental Defense Belem Brazil Houghton RA and JL Hackler 2006 Emissions of carbon from land use change in sub Saharan Africa Journal of Geophysical Research 111 The Intergovernmental Panel on Climate Change IPCC 2006 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 4 Agriculture Forestry and Other Land Use Prepared by the National Greenhouse Gas Inventories Programme Eggleston HS L Buendia K Miwa T Ngara and K Tanabe eds Institute for Global Environmental 71 Strategies IGES Hayama Japan lt http www ipcc nggip iges or jp public 2006 1 vol4 html gt Jenny H 1980 The Soil Resource Springer New York Lal R 2004 Soil Carbon Sequestration Impacts on Global Climate Change and Food Security Science 304 1623 1627 Mackey B Keith H Berry S L Lindenmayer DB Green carbon the role of natural forests in carbon storage Part 1 A green carbon account of Australia s Southeastern Eucalypt forest and policy implications Canberra Australia ANU E Press 2008 Makundi WR 2001 Carbon mitigation potential and costs in the forest sector in Tanzania Mitigation and Adaptation Strategies for Global Change 6 335 353 Malhi Y D Wood TR Baker et al 2006 The regional variation of aboveground live biomass in old growth Amazonian forests Global Change Biology 12 1107 1138 Malimbwi RE B Solberg and E Luoga 1994 Estimation of biomass and volume in miombo woodland at Kitungalo
162. is typically managed in such a way that merchantable or usable wood can be harvested at regular periods over an indefinite period Three characteristics of a plantation forest are 1 species mix has been reduced to a single or a few of the fastest growing species 2 the oldest wood in the plantation is harvested and the rest of the wood is left to mature 3 the areas of a plantation that have been clear cut are replanted with the managed species soon after the clear cut and 4 a more or less even distribution of tree ages e g if the oldest trees in the stand are 20 years old a quarter of the stand is 1 5 year old a quarter of the stand is 6 10 years old a quarter of the stand is 11 15 years old and a quarter of the stand is 16 20 years old Second the InVEST Managed Timber Production Model can be used to calculate the expected value of timber harvests from primary natural forests By primary natural forests we mean areas that at least at the beginning of a harvest cycle retain much of their natural structure and function These could include forests that at least at the beginning of a harvest cycle are being used by local communities and tribes for small scale timber and non timber forest product harvest In some cases these forests may become subject to large scale timber harvest because they are to transition to more managed forests i e forest plantations as described above or some other non forest development that requires a clear c
163. is scant research on the overlap between opportunities to protect biodiversity and to sustain the ecosystem services so critical to these countries economic well being This is precisely the type of challenge that InVEST has been designed to address For managers to understand the patterns of distribution and richness across a landscape individually and in aggregate it is necessary to map the range or occurrences of elements e g 25 species communities habitats The degree to which current land use and management affects the persistence of these elements must also be assessed in order to design appropriate conservation strategies and encourage resource management that maximizes biodiversity in those areas There are a variety of approaches to identifying priorities for conservation with various trade offs among them Each of these approaches focuses on different facets of biodiversity attributes and dynamics including habitat or vegetation based representation i e a coarse filter maximizing the number of species covered by a network of conserved sites for a given conservation budget Ando et al 1998 identifying patterns of richness and endemism CI hotspots and conserving ecological processes There is also a hybrid coarse fine filter approach which selectively includes fine filter elements such as species with unique habitat requirements who may not be adequately protected using a coarse filter approach only TNC and W
164. iscount rate is r Limitations and simplifications Although the USLE method is a standard way to calculate soil loss it has several limitations The USLE method predicts erosion from sheet wash alone erosion from plains in gentle slopes FAO 2002 Rill inter rill gullies and or stream bank erosion deposition processes are not included in this model As such it is more applicable to flatter areas because it has only been verified in areas with slopes of 1 to 20 percent Moreover the relationship between rainfall intensity and kinetic energy may not hold in mountainous areas because it has only been tested in the American Great Plains Finally the equation considers only the individual effect of each variable In reality some factors interact with each other altering erosion rates Another simplification of the model is the grouping of LULC classes because the model s results are highly sensitive to the categorization of LULC classes If there is a difference in land use between two areas within the same broad LULC category it is recommended to create two LULC categories For example if all forest is combined into one LULC class the difference in soil retention between an old growth forest and a newly planted forest is neglected More generally where there is variation across the landscape that affects a USLE parameter the LULC classes should reflect that variation Third the model relies on retention or filtration efficiency values f
165. isn t showing by default In addition refer to the Data Needs section above for information on data formats gt 1 Soil loss e Walp Workspace CANES Sedimentation a 1 Soil loss Uses the Universal Soil Loss DEM C InvEST Base_Data dem z a Equation USLE and calculates the amount of sediment that is retained Erosivity by the landscape and the amount C INvEST SecimertationtInputlerosivity a that is exported to the reservoir Erocinthy C InvesT Secimertation incut erodbity z S Landuse E linvesTiBass_Dataljanduse_90 7 Sub wotersteds CrIN EST Base_Datalsubwatersheds C IAVEST Base_Data watersheds Biophysical table fe INVEST Base_Detal Weber_Tables mdb Biophysical_Models Threshold flow accumulation 1000 Slope threshold 25 Output resolution etre of Inputs isl Results suffix optional OK Cancel Erwiormarts lt lt Hide Help 125 zi Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires you to enter some data After yov ve entered all values as required click on OK The script will run and its progress will be indicated by a Progress dialogue Upon successful completion of the model you will see new folders in your workspace called Intermediate Service and Output These folders contain several raster grids These grids are described in the next section Load
166. ith a name that matches the tool Once you are working with your data you will need to create a workspace and input data folders that are structured like the sample data folders and redirect the tool to access your data 13 Formatting Your Data Before running InVEST it is necessary to format your data Although subsequent chapters of this guide describe how to prepare input data for each model there are several formatting guidelines common to all models Data file names should not have spaces e g a raster file should be named landuse rather than land use Raster dataset names cannot be longer than 13 characters and the first character cannot be a number Spatial data should be projected in meters and all input data for a given tool should be in the same projection If your data is not projected or it is in a projection that is not in meters InVEST will warn you and in some cases stop running Depending on the resolution cell size of your raster data the model could take a long time to run To make the tool run faster enter a desired resolution that is larger than the original resolution This will speed up the execution but will reduce the accuracy of your result It is recommended to initially run models with large cell sizes to increase speed and reduce memory needs Final results can be produced with finer resolution Results will be calculated on selections in tables and feature classes If you are s
167. itivity score that accounts for differences in condition between the fields where the measures were developed and the conditions where the user is applying the model We do this with the following equation ALV HSS pol where ALV is the Adjusted Loading Value at pixel x pol is the export coefficient at pixel x and HSS is the Hydrologic Sensitivity Score at pixel x which is calculated as A HSS WwW S where 4 is the runoff index at pixel x calculated using the following equation and 2y is the mean runoff index in the watershed of interest A ray where yy is the sum of the water yield of pixels along the flow path above pixel x it also U includes the water yield of pixel x Once we know how much pollutant leaves each parcel we can determine how much of that load is retained by each downstream parcel as surface runoff moves the pollutant towards the stream The model routes water down flowpaths determined by slope and allows each parcel downstream from a polluting parcel to retain pollutant based on its land cover type and that land cover type s ability to retain the modeled pollutant We do not account for saturation of uptake By following the pollutant load of each parcel all the way downstream to a water body the model also tracks how much pollutant reaches the stream The table below describes how this removal from routing and hydraulic connectivity is done Cell Vegetat
168. ity is land use and land cover To identify a land parcel s potential soil loss and sediment transport the InVEST Avoided Reservoir Sedimentation model uses the Universal Soil Loss Equation USLE Wischmeier amp Smith 1978 which integrates information on LULC patterns and soil properties as well as a digital elevation model rainfall and climate data Using additional data on reservoir location and the avoided cost of sediment removal it values a land parcel s capacity to retain sediments The avoided cost of sediment removal is the savings due to the reduced need for sediment removal as a result of upland vegetation and watershed land use practices To optimize watershed planning the model allows comparison of avoided sediment removal costs for different land management scenarios How it works First we estimate the potential for soil loss based on geomorphological and climate conditions The model is based on the USLE and represents the first four factors in the equation rainfall erosivity soil erodibility and the length slope factor This part of the model accounts for two key relationships In areas where rainfall intensity is high there is a high chance that soil particles will become detached and transported by overland runoff Also in areas where the soil has a high proportion of sand the erodibility is going to be low which means soil particles are easily detached from the soil pack and transported by overland runoff 116
169. ively the user can run the model multiple times using export values and retention coefficients for each pollutant In general the model can only assess pollutants that are susceptible to export via surface and subsurface flows 100 Third the model does not address any chemical or biological interactions that may occur from the point of loading to the point of interest besides filtration by terrestrial vegetation In reality pollutants may degrade over time and distance through interactions with the air water other pollutants bacteria or other actors Fourth the model assumes that there is continuity in the hydraulic flow path The user should be aware of any discontinuity in the flow path Tile drainage and ditches could create short cuts for pollutant movement and run pollutant directly to streams Finally in some cases the model may provide an inaccurate marginal cost for pollutant removal The full marginal cost of removing a unit volume of pollutants is difficult to estimate due to the complexity of the treatment process The marginal cost may not be a constant value but instead a function of decreasing cost per additional unit volume of pollutant as the total volume increases Also the cost of treatment may change over time as technology improves or water quality standards evolve Data Needs The model uses eleven types of input data See the appendix for information on finding or creating these inputs 1 Digital elevation model
170. ivity of each habitat type to each threat the distance between habitats and sources of threats and the degree to which the land is legally protected Required inputs include a LULC map the sensitivity of LULC types to each threat spatial data on the distribution and intensity of each threat and the location of protected areas The model assumes that the legal protection of land is effective and that all threats to a landscape are additive Introduction A primary goal of conservation is the protection of biodiversity including the range of genes species populations habitats and ecosystems in an area of interest While some consider biodiversity to be an ecosystem service here we treat it as an independent attribute of natural systems with its own intrinsic value we do NOT monetize biodiversity in this model Natural resource managers corporations and conservation organizations are becoming increasingly interested in understanding how and where biodiversity and ecosystem services align in space and how management actions affect both Evidence from many sources builds an overwhelming picture of pervasive biodiversity decline worldwide e g Vitousek et al 1997 Wilcove et al 1998 Czech et al 2000 This evidence has prompted a wide ranging response from both governments and civil society Through the Rio Convention on Biodiversity 189 nations have committed themselves to preserving the biodiversity within their borders Yet there
171. kets to estimate the social value of carbon sequestration and storage Managing landscapes for carbon storage and sequestration requires information about how much and where carbon is stored how much carbon is sequestered or lost over time and how shifts in land use affect the amount of carbon stored and sequestered over time Since land managers must choose among sites for protection harvest or development maps of carbon storage and sequestration are ideal for supporting decisions influencing these ecosystem services Such maps can support a range of decisions by governments NGOs and businesses For example governments can use them to identify opportunities to earn credits for reduced carbon emissions from deforestation and degradation REDD Knowing which parts of a landscape store the most carbon would help governments efficiently target incentives to landowners in exchange for forest conservation Additionally a conservation NGO may wish to invest in areas where high levels of biodiversity and carbon sequestration overlap Nelson et al 2008 A timber company may also want to maximize its returns from both timber production and REDD carbon credits Plantinga and 45 Atmosphere Aboveground biomass SPU Pe se ar a by gies 24 Soil type moisture management Microbes chemistry 5 pools x price ton discounted MARKET VALUE 5 pools x social value ton discounted SOCIAL VALUE Figure 1 Conceptual model of carbon stora
172. l results will be substantially more accurate This will typically take a large sample of plot estimates of carbon storage 2 Carbon stocks Carbon storage data should be set equal to the average carbon storage values for each LULC class The ideal data source for all carbon stocks is a set of local field estimates where carbon storage for all relevant stocks has been directly measured These can be summarized to the LULC map including any stratification by age or other variable If these data are not available however there are several general data sources that can be used Note that several sources including IPCC 2006 report in units of biomass while InVEST uses mass of elemental carbon To convert metric tons of biomass to metric tons of C multiply by a conversion factor which varies typically from 0 43 to 0 51 Conversion factors for different major tree types and climatic regions are listed in Table 4 3 on page 4 48 of IPCC 2006 63 2 1 Carbon stored in aboveground biomass A good but very general source of data for carbon storage is the Intergovernmental Panel on Climate Change s IPCC 2006 methodology for determining greenhouse gas inventories in the Agriculture Forestry and Other Land Use AFOLU sector http www ipcc nggip iges or jp public 2006 1 vol4 html IPCC 2006 To use this set of information from the IPCC you must know your site s climate domain and region use data from Table 4 1 on page 4 46 and a di
173. l season i e flight season Values should be entered on a scale of 0 to 1 with 1 indicating the time of highest activity for the guild or species and O indicating no activity Intermediate proportions indicate the relative seasonal activity Activity level by a given species over all seasons should sum to 1 Create a different column for each season Seasons might be spring summer fall wet dry etc d Alpha average or typical distance each species or guild travels to forage on flowers specified in meters InVEST uses this estimated distance to define the neighborhood of available flowers around a given cell and to weight the sums of floral resources and pollinator abundances on farms You can determine 152 typical foraging distance of a bee species based on a simple allometric relationship with body size see Greenleaf et al 2007 Sample data set Invest pollination input Guild dbf Example A hypothetical study with four species There are two main nesting types cavity and ground Species A is exclusively a cavity nester species B and D are exclusively ground nesters and species C uses both nest types There is only a single flowering season Allyear in which all species are active Typical flight distances specified in meters Alpha vary widely among species Species NS cavity NS ground FS_allyear Alpha A 1 0 1 1490 B 0 1 1 38 C 1 1 1 890 D 0 1 1 84 3 T
174. l with an agricultural production model InVEST or another which will take pollinator abundance as one input to predict and map agricultural yields In formal terms it will use pollination as a factor in a production function that relates yields of a given crop to the quantity and quality of various inputs e g water soil fertility labor chemicals pollination Using these production functions it is possible to estimate the proportion of crop productivity that is due to pollination and thus the economic value of those pollinators Second the model does not include the dynamics of bee populations over time and therefore cannot evaluate whether these populations are sustainable given the current landscape Instead the model simply provides a static snapshot of the number of pollinators on each cell in the landscape given simple estimates of nesting sites and food resources Some of the factors that influence bee populations like habitat disturbances and typical population fluctuations are not captured Third the model does not account for the sizes of habitat patches in estimating abundance For many species there is a minimum patch size under which a patch cannot support that species over the long term There is some evidence that small patches support fewer species of bees Kremen et al 2004 but bees can also survive in surprisingly small areas of suitable habitat Ricketts 2004 150 Fourth pollinators are likely to be i
175. lable from irrigation and horticulture handbooks FAO has an online resource for this Values for other vegetation can be estimated using Leaf Area 93 Index LAD relationships which is a satellite imagery product derived from NDVI analysis A typical LAI ETcoef relationship might look as follows L A I w E T e so0o3e f 1 Evapotranspiration coefficients need to be applied to non vegetated class such as pavement or h amp Ah water bodies As a rule of thumb impermeable surfaces and moving water bodies might be given a low ET coef value no zeros should be defined such as 0 001 to highlight removal of water by drainage Slow or stagnant water bodies might be given an ET coef value of 1 Once evapotranspiration coefficients have been established for all landuse land classes they must be multiplied by 1000 to obtain the integer value i e Int ETceof x 1000 No zero values are allowed Sample ET coef Table ID WN rR Vegetation Type Evergreen Needleleaf Forest Evergreen Broadleaf Forest Deciduous Needleleaf Forest Deciduous Broadleaf Forest Mixed Cover Woodland Wooded Grassland Closed Shrubland Open Shrubland Grassland Cropland row Crops Bare Ground Urban and Built Up Wetland Mixed evergreen Mixed Forest Orchards Vineyards Pasture Sclerophyllous Forests h Digital elevation model DEM etk 1000 1000 1000 1000 1000 1000 1000 398 398 650 650 1000 1000 1000
176. lassic example would be to follow an island ocean model and 27 assume that the managed land matrix surrounding remnant patches of unmanaged land is unusable from the standpoint of species e g MacArthur and Wilson 1967 In this case a 0 would be assigned to managed LULC types in the matrix i e non habitat and a 1 to unmanaged types i e habitat Under this modeling scheme habitat quality scores are not a function of habitat importance rarity or suitability all habitat types are treated equally Model inputs are assumed to not be specific to any particular species or species guild but rather apply to biodiversity generally More recent research suggests that the matrix of managed land that surrounds patches of unmanaged land can significantly influence the effective isolation of habitat patches rendering them more or less isolated than simple distance or classic models would indicate Ricketts 2001 Prugh et al 2008 Modification of the matrix may provide opportunities for reducing patch isolation and thus the extinction risk of populations in fragmented landscapes Franklin and Lindenmayer 2009 To model this a relative habitat suitability score can be assigned to a LULC type ranging from 0 to 1 where 1 indicates the highest habitat suitability A ranking of less than 1 indicates habitat where a species or functional group may have lower survivability Applying this second approach greatly expands the definition of habitat from the s
177. le data set InVEST Base_Data watersheds 9 Model Coefficients Table required A table of land use land cover LULC classes containing data on water quality coefficients used in this tool NOTE these data are attributes of each LULC class rather than attributes of individual cells in the raster map Name File can be named anything File type dbf Rows Each row is an LULC class Columns Each column contains a different attribute of each land use land cover class and must be named as follows a lucode Land use code Unique integer for each LULC class e g 1 for forest 3 for grassland etc must match the LULC raster above b LULC_desc Descriptive name of land use land cover class optional c root_depth The maximum root depth for vegetated land use classes given in integer millimeters Non vegetated LULCs should be given a value of 1 d etk The evapotranspiration coefficient for each LULC class used to obtain actual evapotranspiration by using plant energy transpiration characteristics to modify the reference evapotranspiration which is based on alfalfa or grass Coefficients should be multiplied by 1000 so that the final etk values given in the table are integers ranging between 1 and 1500 some crops evapotranspire more than alfalfa in some very wet tropical regions and where water is always available c load_n load_p The nutrient loading for each land use If nitrogen is being evaluated supply values in
178. ll be the first 4 characters of the Species column in dataset 2 so make sure these 4 characters identify each species or guild uniquely e hn_ lt beename gt _fut The same as above but for the future scenario land cover map if provided e hf_ lt beename gt _cur This is a map of availability of flower resources for each species in the neighborhood around each cell The value for each cell is a sum of surrounding flower values with values from nearer cells given more weight than those from cells further away The sum is taken over a neighborhood with the radius equal to the typical flight range of the bee i e Alpha in dataset 2 o hf_ lt beename gt _fut The same as above but for the future scenario land cover map if provided e sup_ lt beename gt _cur This is a map of the pollinator abundance index for each bee species or guild modeled There will be a different map for each species or guild included in your analysis This map represents the relative likely abundance of a pollinator species nesting on each cell in the landscape given the availability of nesting sites there and of flower food resources nearby e sup_ lt beename gt _fut The same as above but for the future scenario land cover map if provided e frm_ lt beename gt _cur This is a map of the abundance index for each bee species or guild on each agricultural cell in the landscape There will be a different map for each species or 159 guild included
179. ly managed yield a flow of services that are vital to humanity including the production of goods e g food life support processes e g water purification and life fulfilling conditions e g beauty recreation opportunities and the conservation of options e g genetic diversity for future use Despite its importance this natural capital is poorly understood scarcely monitored and in many cases undergoing rapid degradation and depletion To better align ecosystem conservation with economic forces the Natural Capital Project is developing models that quantify and map the values of ecosystem services The modeling suite is best suited for analyses of multiple services and multiple objectives The current Tier 1 models which require relatively little data input can identify areas where investment may enhance human well being and nature We are continuing development of the models and will release new updated versions as they become available Who should use InVEST InVEST is designed to inform decisions about natural resource management Decision makers from governments to non profits to corporations often manage land for multiple uses and inevitably must evaluate trade offs among these uses InVEST s multi service modular design provides an effective tool for evaluating these trade offs For example government agencies could use InVEST to help determine how to manage lands to provide an optimal mix of benefits to people
180. m the station owner or operator Alternative sources may be available online as described above This value may instead represent the time period of a scenario of interest which should be equal to or smaller than the life span of the station i Discount_rate this rate is defined as how much value the currency loses per year References Budyko M I 1974 Climate and Life Academic San Diego California Donohue R J Roderick M L amp McVicar T R 2007 On the importance of including vegetation dynamics in Budyko s hydrological model Hydrology and Earth System Sciences vol 11 pp 983 995 Ennaanay Driss 2006 Impacts of Land Use Changes on the Hydrologic Regime in the Minnesota River Basin Ph D thesis graduate School University of Minnesota Milly P C D 1994 Climate soil water storage and the average annual water balance Water Resources Research vol 3 no 7 pp 2143 2156 Potter N J Zhang L Milly P C D McMahon T A amp Jakeman A J 2005 Effects of rainfall seasonality and soil moisture capacity on mean annual water balance for Australian catchments Water Resources Research vol 41 World Commission on Dams 2000 Dams and development A new framework for decision making The Report of the World Commission on Dams Earthscan Publications LTD London Zhang L Dawes W R amp Walker G R 2001 Response of mean annual evapotranspiration to vegetation changes at catchment scale
181. matically used for slope conditions that differ from the standard reference site conditions of the USLE equation development For low slopes The slope threshold should be specified as a model input and depends on the geomorphology and watershed characteristics a nn 1 4 LS 2a cee a al Jis 22 13 0 09 0 5 slope gt 5 0 4 3 5 lt slope lt 5 0 3 1 lt slope 3 5 0 2 slope lt 1 where flowacc is accumulated water flow to each cell and cellsize is the pixel size or the grid resolution 10m 30m 90m etc For high slopes The slope threshold that the model uses to switch between the equation above and the following equation is specified as a model input and depends on the local geomorphology and watershed characteristics We use the following equation defined by Huang and Lu 1993 for areas with slopes higher than the threshold identified by the user LS 0 08A pret _ slope pein Hlowdir 1 4 160r64 1 4 cellsize otherflowdir 117 where prct_slope is the pixel s percent slope and flowdir is the flow direction of the pixel Calculation of Potential Soil Loss We estimate the ability of vegetation to keep soil in place on a give parcel by comparing erosion rates on that parcel to what erosion rates would be on that parcel with no vegetation present bare soil The bare soil estimate is calculated as follows RKLS RxKxLs Erosion from the parcel with existing vegetation is calcul
182. mats 156 gt Pollination Workspace C InvVESTPallnation Polination S Current land cover map CAINYESTPolination Polination Inout hule_samp_cur aa S vear of current landcover optional Resolution desired cell size to use in meters o 200 Future landcovar map opbonal z vear of future landcover optional Table of pollinator species cr guilds C InvVESTPolination Pollination Input Guid dbf w Table of landcover attributes C InvESTPalnation pollnaton inpue LU dor S 68 71 72 73 74 75 7678 79 80 81 82 83 84 25 88 9091 92 Hal saturation constant 0 125 Results suffix optional F Calculate pollinator servica value gt Cancel Erwironmerts lt lt Hide Help A a Help Agriculture LULC classes optional The LULC classes that should be considered agriculture so the model can estimate pollinator abundances on those cells in the landscape Enter the LULC numbers in the format 2 9 etc If this is left blank it will assume all classes are agnculture so will calculate visitor abundance for all cells Fill in data file names and values for all required prompts Unless the space is indicated as optional inputs are required After entering all required data click OK The script will run and its progress will be indicated by a Progress dialogue The successful running of the model and the time it takes depends on a combination of the following factors o
183. meaning that our estimate of annual reservoir sedimentation will be less than actual sedimentation rates Nonetheless it is possible to use information about the sediment volume in the reservoir at time t V and the volume at which reservoir function will be impacted Vp to estimate the time period over which sediment removal will occur If the user is able to provide accurate estimates of V and Vp then it is likely that information about annual deposition is available as well Let SEDDEP represent the total volume of sediment USLE assumed to reach the reservoir in a given year Then we can model the time path of sediment as V SEDDEP V and we can define the year at which removal will commence as the first period for which V gt Vp In this case let the value of _ he SEDREM x MC EET iar where PVSR is the present value of sediment retention on pixel x over T years where T indicates the period of time over which the LULC pattern is constant or the length of the reservoir life length SEDREM is the sediment removed by the LULC on pixel x MC is the marginal cost of sediment removal ris the discount rate sediment retention on the upstream parcel x be given by PVSR x The accuracy of the sediment retention value is limited by two factors First it is limited by the quality of information of the cost of sediment removal Up to date estimates of sediment removal costs for an area may be difficult to find The user ma
184. moval Raster showing the value Currency per pixel of the landscape for retaining sediment by keeping it from entering the reservoir THIS IS THE MEASURE OF THIS ECOSYSTEM SERVICE IN ECONOMIC TERMS The application of these results depends entirely on the objective of the modeling effort Users may be interested in all of these results or select one or two If sediment removal cost information is not available or valuation is not of interest the user may use a value of one for the cost of sediment removal This forces a unit cost of sediment removal which normalizes the cost across the different reservoirs but still allows a relative comparison of scenarios The following provides more detail on each of the relevant model outputs The length slope factor depends solely on the geometry of the landscape and as the name infers is simply a description of the length of the slopes in the watershed The RKLS is the potential soil loss based on the length slope factor rainfall erosivity and soil erodibility These are factors that generally cannot be altered by human activity as they are inherent to the watershed USLE differs from RKLS in that it takes into account the management practice factor and the cover factor These are factors that can be altered with land use changes or changes in land management Examples of changes that can alter the USLE output are forest clear cuts changing crop type or type of agriculture no till to tilled expan
185. mp Agriculture Organization of the UN Rome Italy Wischmeier W H amp Smith D 1978 Predicting rainfall erosion losses a guide to conservation planning USDA ARS Agriculture Handbook Washington DC 133 MANAGED TIMBER PRODUCTION MODEL Summary timber This model analyzes the amount and volume of legally harvested timber from natural forests and managed plantations based on harvest level and cycle The valuation model estimates the economic value of timber based on the market price harvest and management costs and a discount rate and calculates its economic value Limitations of the model include assumptions that timber harvest production frequency prices and costs are constant over time Introduction Commercial timber production is a valuable commodity provided by forests with the potential to generate significant revenue for those with legal rights to harvest The scale and nature of timber production varies from large privately operated single species plantations to small community managed harvests from natural forest that retains its ecological structure and function Whether timber production occurs on a managed plantation or a natural forest managing the intensity and rate of timber harvest is critical to sustaining this service as well as the supply and value of other services provided by forests such as water purification carbon sequestration and bush meat habitat Maximizing profits requires information about
186. mple two SCC estimates we have used from Tol 2009 are 66 and 130 in 2010 US dollars Polasky et al 2010 For applications interested in estimating the value that could be gained by trading carbon credits in the current markets the value can be taken from the current market prices on the Chicago or European Climate Exchanges b The market discount rate r in the equation below which reflects society s preference for immediate benefits over future benefits labeled Market discount rate optional in the tool interface The default value in the interface is 7 per year which is one of the market discount rates recommended by the U S government for cost benefit evaluation of environmental projects However this rate will depend on the country and landscape being evaluated Philosophical arguments have been made for using a lower discount rate when modeling climate change related 57 dynamics which users may consider using If the rate is set equal to 0 then monetary values are not discounted c The annual rate of change in the price of carbon c in the equation below which adjusts the value of sequestered carbon as the impact of emissions on expected climate change related damages changes over time The default value in the interface is 0 labeled The annual rate of change in the price of carbon optional in the tool interface However setting this rate greater than 0 suggests that the societal value of ca
187. ms or assessing scenarios that address change on a sub annual basis For example scenarios that represent a change in the monthly or seasonal timing of fertilizer application or water extraction in agricultural systems cannot be assessed by Tier 1 models but will be treated well by Tier 2 and Tier 3 models It is expected that users will be able to mix and match Tier 1 2 and 3 models to create the best suite of models given past work existing data and the questions of interest Although the more sophisticated models require substantial data and time to develop once they are parameterized for a certain location they will provide the best available science for new decisions All of the models in INVEST 1 004 are Tier 1 models Tier 2 models for several services have been formulated and documented in a book in press with Oxford University Press due to be published in July 2010 The Natural Capital Project will not develop new Tier 3 models but rather sees these as the sophisticated dynamic models usually developed for individual sites or contexts We will design the Tier 2 software platform as a space where Tier 1 2 and 3 models can be integrated as appropriate for different applications TIER 1 Models Simplest models ignore certain real processes Least data required Annual average time step no temporal dynamics Appropriate spatial extent ranges from sub watershed to global Low precision estimates best for identifying areas of hi
188. n each LULC class for season 1 season 2 etc There are two aspects to consider when estimate relative floral abundance of each LULC class floral abundance or floral coverage as well as the duration of flowering during each season For example a land cover type that comprises 100 of a mass flowering crop that flowers the entire season with an abundance cover of 80 would be given a suitability value of 0 80 A land cover type that flowers only half of the season at 80 floral coverage would be given a floral suitability value of 0 40 Italicized parts of names must match those in FS_nest etc in the Table of pollinator species or guild file described in input 2 above 153 Sample data set Invest pollination input LU dbf Example The same hypothetical study with five LULC classes Class 1 Forest contains the maximum availability of sites for both nesting types cavity and ground The five habitat types vary strongly in flower resources in the single simplified year round flowering season Note matching column heads between this table and the Table of pollinator species or guilds LULC LULCname N_cavity N_ground F_allyear 1 Forest 1 0 1 0 1 0 2 Coffee 0 2 0 1 0 5 3 Pasture grass 0 2 0 1 0 3 4 Shrub undergrowth 0 2 0 1 0 2 5 Open urban 0 2 0 1 0 3 Half saturation constant optional The model will also prompt you to enter a half saturation constant which will be used w
189. n of water that can be stored in the soil profile that is available for plants use Name File can be named anything but avoid spaces Format Standard GIS raster file e g ESRI GRID or IMG with available water content values for each cell Sample data set InVEST Base_Data pawe 5 Average Annual Potential Evapotranspiration required A GIS raster dataset with an annual average evapotranspiration value for each cell Potential evapotranspiration is the potential loss of water from soil by both evaporation from the soil and transpiration by healthy Alfalfa or grass if sufficient water is available The evapotranspiration values should be in millimeters Name File can be named anything but avoid spaces Format Standard GIS raster file e g ESRI GRID or IMG with potential evapotranspiration values for each cell Sample data set InVEST Base_Data eto 6 Land use land cover required A GIS raster dataset with an LULC code for each cell The LULC code should be an integer Name File can be named anything but avoid spaces Format Standard GIS raster file e g ESRI GRID or IMG with an integer LULC class code for each cell e g 1 for forest 3 for grassland etc These codes must match LULC codes in the Model Coefficients table Sample data set InVEST Base_Data landuse_90 7 Sub watersheds required A GIS raster dataset This is a layer of watersheds or sub watersheds which contribute to the points of i
190. n the EPA PLOAD User s Manual http www epa gov waterscience basins b3docs PLOAD_v3 pdf and in the Wetlands Regulatory Assistance Program publication http el erdc usace army mil elpubs pdf tnwrap043 pdf Note that the examples in the EPA guide are in Ibs ac yr and would need to be converted to kg ha yr Phosphorus is a common water quality proxy because it incorporates both dissolved and particulate nutrient loadings are well associated with surface runoff and is usually the limiting nutrient for fresh water systems The table below shows default phosphorus export coefficients largely based on values from USEPA manuals and research studies in the US The bottom three rows are used solely for direct untreated waste water discharge i e untreated sewage piped into water systems from urban areas commonly found in developing countries If local data approximations for Phosphorus export coefficients exist they can be used or replace default values in table Table Example Phosphorus and Nitrogen export coefficients Reckhow et al 1980 Landuse Nitrogen Export Phosphorus Export 111 Coefficient kg ha yr Coefficient kg ha yr Forest 1 8 0 011 Corn 11 1 2 Cotton 10 4 3 Soybeans 12 5 4 6 Small Grain 5 3 1 5 Pasture 3 1 0 1 Feedlot or Dairy 2900 220 Idle 3 4 0 1 Residential 7 5 1 2 Business 13 8 3 Industrial 4 4 3 8 The loading proxy may also aggregate several indicators agreed upon between managers such as
191. nate future landscape can be compared Outputs of the model are expressed as Mg of carbon per grid cell or if desired the value of sequestration in dollars per grid cell We strongly recommend using the social value of carbon sequestration if the user is interested in expressing sequestration in monetary units The social value of a sequestered ton of carbon is the social damage avoided by not releasing the ton of carbon into the atmosphere The market value may be applicable if the user is interested in identifying the value of the landscape for trading under current market conditions The market 47 value of terrestrial based carbon sequestration is the price per metric ton of carbon traded in marketplaces such as the Chicago Climate Exchange ECX The valuation model estimates the economic value of sequestration not storage as a function of the amount of carbon sequestered the monetary value of each unit of carbon a monetary discount rate and the change in the value of carbon sequestration over time Fig 1 Thus valuation can only be done in the carbon model if you have a future scenario Valuation is applied to sequestration not storage because current market prices relate only to carbon sequestration Discount rates are multipliers that typically reduce the value of carbon sequestration over time The first type of discounting the standard economic procedure of financial discounting reflects the fact that people typically value imm
192. nd and dead organic matter pools should reflect this fact For example suppose one of the LULC types is a plantation forest that tends to have one tenth of its area clear cut every year The aboveground and belowground estimates of carbon biomass for this LULC type should reflect the fact that only 9 10 of the area occupied by plantation forests will be covered by trees at any point in time Name file can be named anything File type dbf Rows each row is a LULC class Columns each column contains a different attribute of each LULC class and must be named as follows 50 a LULC code of land use land cover class e g 1 for forest 3 for grassland etc The LULC code should match the LULC codes from the current LULC map dataset 1 above LULC_name descriptive name of LULC class optional C_above amount of carbon stored in aboveground biomass in Mg ha C_below amount of carbon stored in belowground biomass in Mg ha C_soil amount of carbon stored in soil in Mg ha C_dead amount of carbon stored in dead organic matter in Mg ha means Note The unit for all carbon pools is Mg of elemental carbon ha This means that if your data source has information on Mg of CQ stored ha you need to convert those numbers to elemental carbon by multiplying Mg of CO stored ha by 0 2727 Sample data set Invest Carbon Input carbon_pools_samp dbf Example Hypothetical study with five LULC classes Class 1 F
193. nd future generations economic opportunities is seen as unethical by some Cline 1992 Stern 2007 According to this argument analyses of the effects of climate change on society and policies designed to reduce climate change should use low discount 69 rates to encourage greater GHG emission mitigation and therefore compensate for the potentially severe damages incurred by future generations e g r 0 014 in Stern 2007 Recent government policies in several countries have supported the use of a very low discount rate for certain long term projects Abusah and de Bruyn 2007 The carbon discount rate which reflects the greater climatic impact of carbon sequestered immediately over carbon sequestered in the future is discussed in Adams et al 1999 Plantinga et al 1999 Feng 2005 and Nelson et al 2008 References Abusah Sam and Bruyn Clinton de 2007 Getting Auckland on Track Public Transport and New Zealand s Economic Ministry of Economic Development Working Paper Accessed at lt http www med govt nz templates MultipageDocumentTOC_28641 aspx gt Adams DM RJ Alig BA McCarl et al 1999 Minimum cost strategies for sequestering carbon in forests Land Econ 75 360 374 Anderson JR EE Hardy JT Roach RE Witmer A Land Use and Land Cover Classification System for Use with Remote Sensor Data Washington DC United States Government Printing Office 1976 Geological Survey Professional Paper 964 Antle JM and
194. nfluenced by fine scale features in the landscape which are difficult to capture in typical land cover data with typical resolutions of Ikm or even 30m For example small patches of flower resources in an otherwise hostile habitat for bees can provide important food resources but will not be detected by typical land cover maps Some bees are also able to nest in small but suitable areas a single suitable roadside or tree hollow Using average values of nesting site or flower availability for each land cover type along with 30m pixels or larger will therefore not capture these fine scale but important areas of resources Finally the model does not include managed pollinators such as honey bees that are managed in boxed hives and can be moved among fields to pollinate crops InVEST focuses on the ecosystem service of pollination by bees living wild in the landscape Managed pollinators are a technological substitute for this ecosystem service much as a water filtration plant is a substitute for purification services by wetlands and other natural systems Clearly any natural resource assessment needs to consider the costs and benefits of investments in technology filtration plants managed bees alongside those of investments into natural capital wetlands wild bee pollination Data needs The model uses five forms of input data three are required and two are optional 1 Current land cover map required A GIS raster dataset with a land
195. ng the Biodiversity Model Interpreting Results Parameter Log Final Results References CARBON STORAGE AND SEQUESTRATION Summary Introduction The Model How it works Limitations and simplifications Data needs Running the Model Interpreting Results Parameter log Final results Intermediate results Appendix data sources References RESERVOIR HYDROPOWER PRODUCTION Summary Introduction The Model How it works Data needs Running the Model Interpreting Results Appendix Data Sources References WATER PURIFICATION NUTRIENT RETENTION Introduction The Model How it Works Limitations and Simplifications Data Needs Running the Model Interpreting Results Parameter Log Final Results Appendix Data Sources References AVOIDED RESERVOIR SEDIMENTATION MODEL Summary Introduction The Model How it works Limitations and simplifications Data needs Running the Model Interpreting Results Appendix data sources References MANAGED TIMBER PRODUCTION MODEL Introduction The Model How it works Limitations and simplifications Data needs Running the Model Interpreting results Parameter Log Final Results References CROP POLLINATION Introduction The Model How it works Limitations and simplifications Data needs Running the Model Interpreting results Parameter Log Final results Intermediate results Appendix Data sources References THE NEED FOR A NEW TOOL The Need for a New Tool Ecosystems if proper
196. no default data sources Regional estimates e United States Smith et al 2006 estimate carbon storage in litter referred to as Forest Floor C in the document and dead wood the aggregate of C pools referred to as Standing Dead Trees and Down Dead Wood in the document for all major forest types and forest management practices in each region of the U S as a function of stand age e South America Delaney et al 1998 estimate carbon stored in standing and down dead wood in 6 tropical forests of Venezuela According to the authors deadwood is typically 1 10 the amount of biomass as aboveground vegetation 3 Decay rates for harvested wood products For more information on the decay of carbon in HWP and methods for estimating it see Skog et al 2004 Green et al 2006 Miner 2006 Smith et al 2006 chapter 12 Harvested Wood Products of IPCC 2006 and Dias et al 2007 4 Harvest rates and dates harvest began For an example of estimating carbon content in harvested wood products we can use data from Makundi 2001 Assume that a softwood plantation in Tanzania has been producing timber for 50 years on a 5 hectare plot Further the rotation period for this type of plantation is 25 years Makundi 2001 Assume an even age forestry operation Therefore every year 2 hectares with 25 year old trees are clear cut The mean annual increment of the softwood s aboveground biomass is 17 82 Mg ha yr
197. ns The first column gives each timber parcel s TNPV TNPV is the net present economic value of timber production in terms of the user defined currency TNPV includes the revenue that will be generated from selling all timber harvested from yr_cur or yr_fut to T years after yr_cur or yr_fut less harvest and management costs incurred during this period Finally all monetary values are discounted back to yr_cur or yr_fut s present value Negative values indicate that costs management and harvest are greater than income price times harvest levels The TBiomass column gives the total biomass in Mg of harvested wood removed from each timber parcel from yr_cur or yr_fut to T years after yr_cur or yr_fut TBiomass from equation 8 or equation 9 depending on the value of Immed_harv The TVolume column gives the total volume m of harvested wood removed from each timber parcel from yr_cur or yr_fut to T years after yr_cur or yr_fut TVolume from equation 10 2 Timber_dateandtime_suffix txt is a text file that summarizes the parameter data you chose when running the Managed Timber Production Model The text file s name includes dateandtime which means that the data and time is stamped into the text s file name The text file s name also includes a suffix term that you can choose in the model s interface you can also choose to leave it blank References Maass J P Balvanera A Castillo GC Daily HA Mooney P
198. ns are available e g Anderson et al 1976 and often detailed land cover classification has been done for the landscape of interest A slightly more sophisticated LULC classification could involve breaking relevant LULC types into more meaningful types For example agricultural land classes could be broken up into different crop types or forest could be broken up into specific species The categorization of land use types depends on the model and how much data is available for each of the land types The user should only break up a land use type if it will provide more accuracy in modeling For instance for the sediment valuation model the user should only break up crops into different crop types if they have information on the difference in soil characteristics between crop management values 5 P and C coefficients The management practice factor P accounts for the effects of contour plowing strip cropping or terracing relative to straight row farming up and down the slope The cover and management factor C accounts for the specified crop and management relative to tilled continuous fallow Several references on estimating these factors can be found online e U S Department of Agriculture soil erosion handbook http topsoil nserl purdue edu usle AH_537 pdf e USLE Fact Sheet http www omafra gov on ca english engineer facts 00 001 htm e U N Food and Agriculture Organization http www fao org docrep T1765E t1765e0c htm
199. ntains the most information for this purpose as it represents the revenue attributable to each parcel over the expected lifetime of the hydropower station or the number of years that the user has chosen to model This grid accounts for the fact that different hydropower stations within a large river basin may have different customers who pay different rates for energy production If this is the case the hp_value grid will show which portions of which watersheds contribute the highest value water for energy production If energy values do not vary much across the landscape the hp_ yield and hp_energy outputs can be just as useful in planning and prioritization Comparing any of these grids between landuse scenarios allows the user to understand how the role of the landscape may change under different management plans The hydropower output summary table presents the model results in terms of hydropower operation The wyield field provides the total volume of water that arrives at each hydropower plant every year considering water yield and consumption The consump field provides the total volume of water that is consumed demanded in each watershed upstream of the station Total energy produced at each hydropower station is given in the energy field and the corresponding value of that energy is given in the value field This table provides a quick comparison between land use scenarios in a way that complements the spatial representation across
200. ntation indefinitely we do not account for the plantation s expected revenues in this model Parcel_ Parcl_ Perc_ Freq_ Harv_ Price Maint_ Harv_ T Immed_ BCEF ID area harv harv mass cost cost harv 1 1000 2 22 1 80 300 190 50 50 Y 1 2 1000 222 1 70 200 260 124 50 Y 1 3 1000 25 20 70 200 310 225 50 N 1 4 500 100 1 95 350 180 45 1 Y 1 5 500 20 2 95 400 190 105 10 Y 1 141 Running the Model Before running the Timber Model first make sure that the INVEST toolbox has been added to your ARCMAP document as described in the Getting Started chapter of this manual Second make sure that you have prepared the required input data files according to the specifications in Data Needs Specifically you will need 1 a shapefile or raster file showing the locations of different timber management zones in the landscape 2 a table with data on harvest frequency and amount and the price of timber and cost of harvest and 3 the discount rate for timber if other than the 7 US government estimate e Identify workspace If you are using your own data you need to first create a workspace or folder for the analysis data on your computer hard drive The entire pathname to the workspace should not have any spaces All your output files will be dumped here For simplicity you may wish to call the folder for your workspace timber and create a folder in your workspace called input
201. nted out sections of code beginning with Flow direction loop debugging Uncomment the subsequent lines containing references to outfile as directed The next time the tool is run it will write information to the file lt Workspace gt Output wp or sed _loop_debug_ lt current time gt _ lt suffix gt txt This can become a very large file as information is recorded on every cell in the watershed raster as they are processed by moving along flow paths Each line of the debug file has three values the nutrient or sediment load originating on that cell the flow direction and the fraction of nutrient or sediment retained by that land use class as given in the input Biophysical table With the debugging lines of code uncommented run the tool Then look at the end of the debug file if a loop was encountered multiple lines with a particular set of values will be repeated These values can be used to help identify where the loops occur by retaining the lt Workspace gt Intermediate folder comment out the lines at the bottom of the code under Clean up temporary files before doing the debug run adding the Intermediate files frac_removed_ext flowdir_ext and loads_ext to the map and picking out the cells that have the particular set of values that repeated in the debug file the CON tool can be used for this purpose This might produce many different matching areas which would then have to be further investigated to single out the
202. nterest where water quality will be analyzed Due to limitations in ArcMap geoprocessing the maximum size of a watershed or sub watershed is approximately the equivalent of 4000x4000 cells with cell size equal to the smallest cell size of your input layers If the whole watershed contributing to a point of interest is larger than this size it will need to be divided into sub watersheds that are each smaller Then the resulting sub watershed layer should be entered here and the whole watershed layer should be used in the Watersheds input If the whole watershed is smaller then it does not need to be divided and the same watershed layer should be entered for both Sub watersheds and Watersheds inputs Sub watersheds will be mosaicked back together into whole watersheds for the final output Name File can be named anything but avoid spaces 102 Format Standard GIS raster file e g ESRI GRID or IMG with unique integers for each sub watershed in the Value field Sample data set InVEST Base_Data subwatersheds 8 Watersheds required A GIS raster dataset This is a layer of watersheds such that each watershed contributes to a point of interest where water quality will be analyzed See Sub watersheds above for more information on watershed size requirements Name File can be named anything but avoid spaces Format Standard GIS raster file e g ESRI GRID or IMG with unique integers for each watershed in the Value field Samp
203. on the plus sign on the left side of the InVEST toolbox to see the list of tools expand Next click on the plus sign next to the Hydropower toolset Within the toolset are three tools Water Yield Water Scarcity and Valuation You will need to run Water Yield first Water Scarcity second and Valuation last as each tool generates outputs that feed into the next Double click on Water Yield An interface will pop up like the one below The tool shows default file names but you can use the file buttons to browse instead to your own data When you place your cursor in each space you can read a description of the data requirements in the right side of the interface Click Show Help if the description is not displayed In addition refer to the Data Needs section above for information on data formats S 1 Water Yield L Help 1 Water Yield Workspace CHINVEST Hy dropeneer Prenprelion CHAAVESTIBose_Ootajprecp Calculates an approximate water yield and actual svapotranspration for sach raster Polertia avapor arsin cell in a landscape based on C IAWESTI Base Daje Zheng et al 2001 and Milly at al 1994 a e le Soi Depth CHINVEST Base_Oote scd_depth Di Plant avaiable watar fraction CHIAVEST Base_Dote paxc 5 CHINVESTIBse Ontellercuss 90 CHInvESTiBase Oetelwetersheds Biophysical teble CHINVESTiBase DetelWater Tabdles mdbibiootysica Models W le e Output resolution frinn et reas Jak Resuks a4 F
204. ools E Harv E Geocoding Tools E Unkn w S Geostatistical Analyst Tools Mi water Invest 4 amp Pollution Control ag Opendccess E amp S Sedimentation M lule_samp_cur SB Biodiversity oO roads 3 Carbon SB Pollination E amp Timber B Timber O cities E Linear Referencing Tools O roads E Multidimension Tools E lulc_samp_cur w S Network Analyst Tools Samples amp Biodiversity m Server Tools a lulc_samp_cur H E Spatial Analyst Tools roads 4 Spatial Statistics Tools 4 Tracking Analyst Tools amp Pollination a lulc_samp_cur Display Selection it Index Search Results wan e O O ye Aw fo Aria fio B 450930 645 4954387 838 Meters InVEST Toolbox and tools displayed Using Sample Data The InVEST toolbox comes with sample data which may be helpful for becoming familiar with the models and as a guide for formatting your data For instance in preparation for analysis of your data you may wish to test the models by changing input values in the sample data to see how the outputs respond Sample data are found in separate thematic folders in the InVEST folder For example the sample datasets for the Pollination model are found in Invest pollination input and those for the Carbon model in Invest carbon input When opening the models you ll notice that default paths point to these sample datasets You will also notice that the default workspace for each tool is the thematic folder w
205. optional oK Canned Erwicements lt lt Hide Help Fill in data file names and values for all required prompts Unless the space is indicated as optional it requires you to enter some data After yov ve entered all values as required click on OK The script will run and its progress will be indicated by a Progress dialogue 86 e Load the output grids into ArcMap using the ADD DATA button from either Output or Service folders e You can change the symbology of a layer by right clicking on the layer name in the table of contents selecting PROPERTIES and then SYMBOLOGY There are many options here to change the way the file appears in the map You may change the coloring scheme for better visualization e You can also view the attribute data of output files by right clicking on a layer and selecting OPEN ATTRIBUTE TABLE e Now run the tool Water Scarcity One output from the Water Yield model wyield serves as an input to this model and is found in the service folder The interface is below 2 Water Scarcity E Help Workspace CEST Aruropurver Saj 2 Water Scarcity DEM Calculates water consumption Jenn WEST Bess_Delaydem 7 S supply and realized supply supply consumption as Water yiele distributed over a watershed CIWEST Hyd opaa Service eryiatd landscape Land use Ioan WEST Bese Delaiardase_SO 7 Watersheds eau WEST Bese_Delalaters eck gt a Wister demand
206. or each LULC type However there are often few data available locally for filtration rates associated with local LULC types Data from other regions may be applied in these cases but may misrepresent filtration by local 119 LULC types Additionally the model may not accurately depict the sedimentation process in the watershed of interest since the model is based on parameterization of several different equations and each parameter describes a stochastic process Due to the uncertainty inherent in the processes being modeled it is not recommended to make large scale area decisions based on a single run of the model Rather the model functions best as an indicator of how land use changes may affect the cost of sediment removal and like any model is only as accurate as the available input data A more extensive study may be required for managers to calculate a detailed cost benefit analysis for each reservoir site Another assumption is that sediment retention upstream from a reservoir is valuable only if sediment delivery impacts reservoir function which incurs a cost If sediment is not removed from a reservoir the model does not assign a value to the sediment retention service In this case the user may assign a value to upstream sediment retention based on an assumed trajectory of sediment deposition at the reservoir This method is explained below and it not included in this model As noted above we are only modeling sheetwash erosion
207. or ideas and suggestions only This section is updated as new data sources and methods become available 1 Land use land cover map The simplest categorization of LULCs on the landscape involves delineation by land cover only e g cropland temperate conifer forest prairie Several global and regional land cover classifications are available e g Anderson et al 1976 and often detailed land cover classification has been done for the landscape of interest A slightly more sophisticated LULC classification could involve breaking relevant LULC types into broad age categories e g forest of age 0 10 years 11 20 21 40 etc This would allow separate estimates of carbon storage for different ages In scenarios parcels can move from one age class to the next crudely capturing changes in carbon storage over time This approach requires more information however including carbon storage estimates for each age class for all modeled pools of carbon A still more detailed classification could stratify LULC types by variables known to affect carbon storage within a given LULC type e g montane forest 800 1000m montane forest 1001 1200m etc Rainfall temperature and elevation all typically influence carbon storage and sequestration e g Jenny 1980 Coomes et al 2002 Raich et al 2006 If data are available to estimate carbon storage at different elevations or at different levels of rainfall temperature or other climate variables mode
208. orest contains the most carbon in all pools In this example carbon stored in above and below ground biomass differs strongly among land use classes but carbon stored in soil varies less dramatically LULC LULC_name C_above C_below C_soil C_dead 1 Forest 140 70 35 12 2 Coffee 65 40 25 6 3 Pasture grass 15 35 30 4 4 Shrub undergrowth 30 30 30 13 5 Open urban 5 5 15 2 Current harvest rates map optional A GIS shape file of polygons parcels in our vernacular contains data on a Parcel ID b Amount of carbon in the form of woody biomass typically removed from the parcel over the course of a harvest period c Date that the modeler wants to begin accounting for wood harvests in the parcel Frequency of harvest periods in the parcel in the past Average decay rate of products made from the wood harvested from a parcel Average carbon density of the wood removed form the parcel in the past Average tree volume per ton of wood removed form the parcel in the past mpe The GIS polygon map should only delineate parcels that have been harvested all other portions of the landscape should be ignored Note that unlike the current LULC map this file contains multiple data for each individual harvest parcel on the landscape The amount of carbon that is removed on average during each harvest period can be estimated from plot surveys market demand analyses community surveys or based on expert opin
209. orest parcels that have experienced harvests in the past Assume the current LULC map we are using corresponds to the year 2005 Parcels 1 2 and 3 are forests that are managed for timber production Each managed forest experiences a cut every 5 year where Cut_cur gives the amount of carbon Mg ha in the portion of the wood that is removed every fifth year The 4 parcel is a source of firewood and wood is cut from the parcel continuously Thus for this parcel we estimate the annual rate of carbon removed from the forest for firewood For the first three 52 parcels we began to account for carbon removal in 1995 For the final parcel we began accounting for HWP in 2000 Recall that the calculation of HWP_cur Bio_HWP_cur and Vol_HWP_cur does not include the 2005 harvest that carbon is still on the land FID Cut_cur Start_date Freq_cur Decay_cur C_den_cur BCEF_cur 1 75 1995 5 30 0 5 1 2 50 1995 5 35 0 5 1 3 50 1995 5 50 0 5 1 4 45 2000 1 1 0 5 1 We measure the carbon stored in HWP that originated from parcel x on the current landscape with the following equation HWP _ cur Cut _ cur x gt where HWP_cur is measured in Mg ha yr_cur is short for Year of current land cover t indexes the number of harvest periods and ru indicates that any fraction should be rounded up to the next integer value The function r Ee o x dl yr_cur start_ date
210. orkspace or folder for the analysis data on your computer hard drive The entire pathname to the workspace should not have any spaces All your output files will be saved here For simplicity you may wish to call the folder for your workspace Hydropower and create a folder in your workspace called Input and place all your input files here It s not necessary to place input files in the workspace but advisable so you can easily see the data you use to run your model Or if this is your first time using the tool and you wish to use sample data you can use the data provided in InVEST Setup exe If you unzipped the InVEST files to your C drive as described in the Getting Started chapter you should see a folder called Invest Hydropower This folder will be your workspace The input files are in a folder called Invest Base_Data e Open an ArcMap document to run your model e Find the InVEST toolbox in ArcToolbox ArcToolbox is normally open in ArcMap but if it is not click on the ArcToolbox symbol See the Getting Started chapter if you don t see the InVEST toolbox and need instructions on how to add it e You can run this analysis without adding data to your map view but usually it is recommended to view your data first and get to know them Add the data for this analysis to your map using the ADD DATA button and look at each file to make sure it is formatted correctly Save your ArcMap file as needed 85 Click once
211. ornl gov metadata mastdc html nacp daac ornl gov_data_bluangel_harvest_RGED curtis_metadata_climate_monthly evapotranspiration html Reference evapotranspiration 90 depends on elevation latitude humidity and slope aspect There are countless methodologies which range in data requirements and precision If the use of this grid is not possible develop monthly average grids of precipitation and maximum and minimum temperatures www cru uea ac uk which need to incorporate the effects of elevation when interpolating from observation stations Data to develop these monthly precipitation and temperatures grids follow the same process in the development of the Average Annual Precipitation grid with the added monthly disaggregated grids A simple way to determine reference Evapotranspiration is the modified Hargreaves equation which generates superior results than the Pennman Montieth when information is uncertain E 0 Tx0 R x 4 aT 08 DP The modified Hargreaves uses the average of the mean daily maximum and mean daily minimum temperatures Tavg in oC the difference between mean daily maximum and mean daily minimums TD RA is extraterrestrial radiation RA in MJm 2d 1 and precipitation P in mm per month all of which can be relatively easily obtained Temperature and precipitation data are often available from regional charts or direct measurement Radiation data on the other hand is far more expensive to me
212. ost or gained as a consequence of different management options or identify which hydropower producers have the largest stake in maintaining water yield across a landscape The Model The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape offering insight into how changes in land use patterns affect annual surface water yield and hydropower production Modeling the connections between landscape changes and hydrologic processes is not simple e g WEAP model The data required by the most sophisticated models is difficult to come by To accommodate more contexts for which data is readily available data INVEST maps and models the annual average water yield from a landscape used for hydropower production rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale Inste ad InVESTcalculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues How it works Water Yield Model The model runs on a gridded map of regular cells called raster format in GIS It estimates the quantity and value of water used for hydropower production from ea
213. ournal of Environmental Quality 37 1368 1375 Stainforth DA et al 2005 Uncertainty in predictions of the climate response to rising levels of greenhouse gases Nature 433 403 406 Stern N 2007 The Economics of Climate Change The Stern Review Cambridge and New York Cambridge University Press Tiessen H C Feller EVSB Sampaio and P Garin 1998 Carbon Sequestration and Turnover in Semiarid Savannas and Dry Forest Climatic Change 40 105 117 Tilman D J Hill and C Lehman 2006 Carbon Negative Biofuels from Low Input High Diversity Grassland Biomass Science 314 1598 1600 Tol RSJ 2005 The marginal damage costs of carbon dioxide emissions an assessment of the uncertainties Energy Policy 33 2064 2074 Tol RSJ 2009 The Economic Effects of Climate Change Journal of Economic Perspectives 23 29 51 USOMB US Office of Management and Budget 1992 Guidelines and Discount Rates for Benefit Cost Analysis of Federal Programs Circular No A 94 Revised Transmittal Memo No 64 Washington DC US Office of Management and Budget 73 Vagen TG R Lal and BR Singh 2005 Soil carbon sequestration in sub Saharan Africa A review Land Degradation amp Development 16 53 71 Weitzman ML 2007 A review of the Stern Review on the Economics of Climate Change Journal of Economic Literature 45 703 724 Zhang Q and CO Justice 2001 Carbon Emissions and Sequestration Potential of Central African Ecosystems AMBIO 30 351 3
214. parcel over time the total biomass removed from a harvested area of the parcel and the market and social values of the carbon 46 sequestered in remaining stock Limitations of the model include an oversimplified carbon cycle an assumed linear change in carbon sequestration over time and potentially inaccurate discounting rates Biophysical conditions important for carbon sequestration such as photosynthesis rates and the presence of active soil organisms are also not included in the model Fig 1 How it works The model runs on a gridded map of cells called raster format in GIS If the HWP pool is included in the analysis a polygon map of harvest parcels is also modeled Each cell in the raster is assigned a land use and land use and land cover LULC type such as forest pasture or agricultural land Each harvest polygon is assigned harvest type referring to the harvested product harvest frequency and product decay rates After running the model in raster format results can be summarized to practical land units such as individual properties political units or watersheds For each LULC type the model requires an estimate of the amount of carbon in at least one of the four fundamental pools described above If the user has data for more than one pool the modeled results will be more complete The model simply applies these estimates to the LULC map to produce a map of carbon storage in the carbon pools included For the fifth carbon
215. patterns of water delivery timing Water yield is a provisioning function but hydropower benefits are also affected by flow regulation The timing of peak flows and delivery of minimum operational flows throughout the year determines the rate of hydropower production and annual revenue Changes in landscape scenarios are more likely to affect the timing of flows than the annual water yield and are more of a concern when considering drivers such as climate change Modeling the temporal patterns of overland flow requires detailed data that are not appropriate for our approach Still this model provides a useful initial assessment of how landscape scenarios may affect the annual delivery of water to hydropower production Fourth the model describes consumptive demand by LULC type In reality water demand may differ greatly between parcels of the same LULC class Much of the water demand may also come from large point source intakes which are not represented by LULC class The model simplifies water demand by distributing it over the landscape For example the water demand may be large for an urban area and the model represents this demand by distributing it over the urban LULC class The actual water supply intake however is likely much further upstream in a rural location Spatial disparity in actual and modeled demand points may cause an incorrect representation in the scarcity output grid The distribution of consumption is also simplified in the re
216. ply is defined as the difference between total water yield from the watershed and total consumptive use in the watershed Va Y u where ug is the total volume of water consumed in the watershed upstream of dam d and Y is the total water yield from the watershed upstream of dam d If the user has observed data available on actual annual inflow rates to the reservoir for dam d they can be compared to Vin Divide the observed value by the estimated value to derive a calibration constant This can then be entered in to the hydropower station data table and used to make power and value estimates actual rather than relative Hydropower Production and Valuation Model The reservoir hydropower model estimates both the amount of energy produced given the estimated realized supply of water for hydropower production and the value of that energy A present value dollar or other currency estimate is given for the entire remaining lifetime of the reservoir Net present value can be calculated if hydropower production cost data are available The energy produced or the revenue is then redistributed over the landscape based on the proportional contribution of each parcel to energy production Final output maps show how much water yield from each pixel of the landscape contributes to hydropower production and how much energy production or hydropower value can be attributed to that water yield over the lifetime of the reservoir At dam d power is calculate
217. pool HWP model values are defined for each parcel polygon and not for each LULC For each parcel the user indicates the amount of biomass in terms of carbon removed per harvest the frequency of harvests and the rate at which the products that contain carbon degrade With these data the model calculates the amount of stored carbon that originated in a parcel but now resides in finished products such as houses or furniture The model converts parcel level HWP carbon values into a grid cell layer that spatially matches the grid system used for the other four carbon storage pools The model aggregates the carbon in each of the five pools providing an estimate of total carbon storage in each grid cell and across the whole landscape If carbon storage data for a given pool are not mapped then total carbon storage will be underestimated The model also outputs the total biomass and volume of wood removed from each harvested parcel up to the year associated with the modeled landscape If the user provides both a current and future LULC map then the net change in carbon storage over time sequestration and loss and its social value can be calculated To estimate this change in carbon sequestration over time the model is simply applied to the current landscape and a projected future landscape and the difference in storage is calculated map unit by map unit If multiple future scenarios are available the differences between the current and each alter
218. port and access to the user community Several regular training workshops on InVEST may be offered annually subject to funding and demand Information on these trainings will be announced on the support page and can be found at the Natural Capital Project website www naturalcapitalproject org This site is also a good source of general information on InVEST and other activities of the Natural Capital Project 19 Model run checklist Following is a checklist that you should use to ensure that the models run successfully O ArcGIS Version As stated above not all ArcGIS versions are supported Most models are tested in ArcGIS 9 2 SP6 and ArcGIS 9 3 SP1 It is advisable to upgrade to one of these versions O Spatial Analyst extension Most of the models require ArcGIS spatial analyst extension Ensure that this is installed O Regional and Language options Some language settings cause errors while running the models For example settings which use coma for decimals instead of period cause errors in the models To solve this change the regional settings to English O Folder naming ArcGIS is strict about folder naming Avoid spaces and special characters in file and folder names Reporting errors If you experience errors running the models you can get assistance from the discussion list mentioned above Provide the following details in order to get quick help 1 The model in which you encountered the error 2 Your A
219. practices on the landscape than just running the model once from 2000 to 2050 Further given the expected variation in harvest management practices and prices over the modeled time interval it is suggested that the user use mean values for each model input The mean is typically the best summary of the distribution of expected values for a variable For example if it is known that harvests from a timber parcel over time will involve various species it is possible to set the timber price for that parcel equal to the average expected price for all harvested species Data needs The model requires a GIS polygon file a vector database demarcating timber parcels Unique timber parcels can be distinguished by differences in the percent of the parcel harvested each harvest period the mass of wood removed each harvest period the species of trees removed or the costs of managing and harvesting wood from the parcel These attributes along with timber prices and the time interval for analysis can be included as a table in the shapefile or as a separate table 1 Timber parcels required A GIS dataset vector that indicates the different timber parcels on the landscape Each parcel should be given a unique identifier The dataset should be projected in meters and the projection used should be defined Name file can be named anything File type standard GIS polygon file e g shapefile with a unique identifier code for each polygon Rows each
220. problem area Once a loop is found it might help to go back to the DEM and do more sink filling or use the CON tool similarly to how it is used in the Check for missing data section above to assign new values Creating watersheds To create watersheds in ArcMap use the Hydrology gt Watershed tool which requires an input flow direction grid created from the DEM using the Flow Direction tool and point data for the locations of your points of interest which represent watershed outlets reservoirs hydropower stations etc snapped to the nearest stream using the Snap Pour Point tool In ArcHydro there is a more lengthy process which tends to produce more reliable results than the Watershed tool Use the Watershed Processing gt Batch Watershed Delineation tool which requires the creation of a flow direction grid streams catchments and point data for the locations of your points of interest all done within the ArcHydro environment See the ArcHydro documentation for more information After watersheds are generated verify that they represent the catchments correctly Creating sub watersheds If the watersheds of interest are too large greater than 4000 X 4000 pixels for the Water Purification and Sediment models they will need to be broken up into sub watersheds See their User s Guide sections for more information under Data Needs Sub watersheds 23 In ArcMap the Hydrology gt Watershed tool can be used In this case t
221. pth and nutrient both N and P filtration efficiencies do not vary among LULC categories while nutrient loadings do Low Density Residential 1 1 1 7000 0 1000 0 Mid Density Residential 2 1 1 7250 0 1100 0 High Density Residential 3 1 1 7500 0 1200 0 Very High Density Residential 4 1 1 7750 0 1300 0 Vacant 5 1 1 4000 0 100 0 Commercial 6 1 1 18800 0 3000 0 10 Threshold flow accumulation value required Integer value defining the number of upstream cells that must flow into a cell before it s considered part of a stream This is used to generate a stream layer from the DEM The default is 1000 This value is to define and create the stream lines If the user has map of stream lines in the watershed of interest he she should compare it the V_stream map output of the model This value also needs to be well estimated in watersheds where tile drainage and ditches are present This threshold expresses where hydraulic routing is discontinued and where retention stops and the remaining of the pollutant will be exported to the stream 11 Watershed coefficient table optional required for valuation This is a table containing calibration and valuation information for each of the points of interest There must be one row for each watershed in the Watersheds layer Name File can be named anything File type dbf Rows Each row corresponds to a watershed Columns Each column contains a diff
222. ptive uses and applied to hydropower energy and value estimates A The evapotranspiration partition of the water balance is an approximation of the Budyko x curve developed by Zhang et al 2001 77 AET 1 0 R 1 1 R xj where R is the dimensionless Budyko Dryness index on pixel x with LULC j defined as the ratio of potential evapotranspiration to precipitation Budyko 1974 and is a modified dimensionless ratio of plant accessible water storage to expected precipitation during the year As defined by Zhang et al 2001 is a non physical parameter to characterize the natural climatic soil properties where AWC is the volumetric mm plant available water content The soil texture and effective soil depth define AWC which establishes the amount of water that can be held and released in the soil for use by a plant estimated as the product of the difference between field capacity and wilting point and the minimum of soil depth and root depth Z is the Zhang constant that presents the seasonal rainfall distribution and rainfall depths In areas of winter December to April rains we expect to have Z in the order of 10 in areas with humid spread rain events throughout the year and summer rains the Z is in the order of 1 While we calculate w in some cases specific biome values already exist based on water availability and soil water storage Milly 1994 Potter et al 2005 Donohue et al 2007
223. put 1 LULC that appears on the current and future maps should have the same LULC code LULC types unique to the future map should have codes not used in the current LULC map Name it can be named anything Format standard GIS raster file e g ESRI GRID or IMG with LULC class code for each cell e g 1 for forest 3 for grassland etc The LULC class codes should be in the raster s value column Sample data set Invest Base_data lulc_samp_fut 3 Baseline LULC map optional A GIS raster dataset of LULC types on some baseline landscape with a numeric LULC code for each cell This file should be formatted exactly like the current LULC map input 1 The LULCs that are common to the current or future and baseline landscapes should have the same LULC code across all maps LULC types unique to the baseline map should have codes not used in the current or future LULC map If possible the baseline map should refer to a time when intensive mamagement of the land was relatively rare For example a map of LULC in 1851 in the Willamette Valley of Oregon USA captures the LULC pattern on the landscape before it was severely modified to for massive agricultural production Granted this landscape had been modified by American Indian land clearing practices such as controlled fires Name it can be named anything Format standard GIS raster file e g ESRI GRID or IMG with LULC class code for each cell e g 1 for forest
224. r at other timber processing and collection sites If a harvest includes multiple species each with its own price a weighted price should be used where weights are given by the expected relative mix of the species in the harvest Any value derived from pre commercial thins should be included in Maint_cost see below g Maint_cost The annualized cost ha of maintaining the timber parcel if any Costs may include the periodic costs to replant treat and thin the stand plus the cost to harvest treat slash and deliver wood to a processing facility Other costs may include taxes pest treatments etc If commercial thins before the main harvest produce product that has market value the annual ha value of these harvests should be subtracted from Maint_cost If the harvest comes from a natural forest that is not managed for timber production Maint_cost may be 0 Actual stand maintenance costs may vary from year to year in a forest e g in some years portions of a managed stand may have to be thinned prior to harvest and in other years anti pest measures may have to be employed an annualized value smoothes this temporal variation in maintenance costs h Harv_cost The cost ha incurred when harvesting Harv_mass T The number of years from yr_cur or yr_fut that parcel harvests will be valued If the parcel is in an even age rotation managed plantation T can be any number although as we explain below we recommend against large T
225. r pollution Specifically they require information pertaining to the value of every part of a watershed for maintaining water quality so that conservation may be targeted to the areas most important for protecting a safe water supply for people and aquatic life They can also use this information to avoid impacts in areas that currently contribute the most to filtering out pollutants The InVEST Tier 1 model provides this information for non point source pollutants We have designed the model to deal with nutrient pollutants nitrogen and phosphorous but the model can be used for other kinds of contaminants persistent organics pathogens etc if data are available on the loading rates and filtration rates of the pollutant of interest The Model The InVEST Water Purification Nutrient Retention model calculates the amount of nutrients retained on every pixel of a watershed and the economic value retention provides through avoided treatment costs It integrates data on the magnitude of overland flow pollutant loading the capacity of different vegetation types to filter pollutants the cost of water treatment for pollutants of interest and feasibility to meet water quality standards The model s limitations are that it runs on an annual average basis can only assess one pollutant per run does not address chemical or biological interactions besides filtration by terrestrial vegetation and in some cases it may provide an in accurate marginal co
226. rbon sequestered in the future is less than the value of carbon sequestered now It has been widely argued that GHG emissions need to be curtailed immediately to avoid crossing a GHG atmospheric concentration threshold that would lead to a 3 degree Celsius or greater change in global average temperature by 2105 Some argue that such a temperature change would lead to major disruptions in economies across the world Stern et al 2006 Therefore any mitigation in GHG emissions that occurs many years from now may have no effect on whether or not this crucial concentration threshold is passed If this is the case C sequestration in the far future would be relatively worthless and a carbon discount rate greater than zero is warranted Alternatively setting the annual rate of change less than 0 e g 2 suggests that the societal value of carbon sequestered in the future is greater than the value of carbon sequestered now this is a separate issue than the value of money in the future a dynamic accounted for with the market discount rate This may be the case if the damages associated with climate change in the future accelerate as the concentration of GHGs in the atmosphere increases The value of carbon sequestration over time is given by t rm _ fut yr_ cur 1 value _ seq sequost x 2o as C14 fut t t Jr_ fut yr_cur a a h i 100 100 Running the Model Before running the Carbon Storage and Sequestration model make sure th
227. rcGIS version and service pack 3 The error text copy and paste this from the tool dialog including all the progress report in the tool dialog Note that the right click does not work in the dialog so use Ctrl C to copy the error 4 Indicate whether you were running with sample data or your own data Ensure you can successfully run with sample data before you try with your own data This confirms that your system is well setup and ready to run the models 5 It is preferable to include the parameter file The models output a parameters file which indicates your input parameters This can be helpful in troubleshooting 6 Make a distinction between errors and features missing from the model If the issue you are facing is related to the model design give a clear explanation of this and the modeling lead will be able to review and provide support in reasonable time Working with the DEM For the hydrology tools especially Water Purification Nutrient Retention and Avoided Reservoir Sedimentation having a well prepared digital elevation model DEM is critical It must have no missing data or circular flow paths and should correctly represent the surface water flow patterns over the area of interest in order to get accurate results Here are some tips for working with the DEM and creating a hydrologically correct DEM Included is information on using built in ArcMap Spatial Analyst functions as well as ArcHydro see resources below an ArcMap
228. related management schemes the IPCC 2006 assumes no management factors There are alternative global level sources of soil carbon data Post et al 1982 report carbon stocks in the first meter of soil by Holdridge Life Zone Classification System GIS map of these Zones available at http www ngdc noaa gov seg cdroms ged_iia datasets a06 lh htm Silver et al 2000 have estimated soil carbon as a function of years since afforestation reforestation for native forest types in tropical ecosystems Grace et al 2006 estimate the soil carbon for major savanna types around the world Table 1 Detwiler 1986 lists soil carbon for tropical forest soils in Table 2 Several region specific studies also report soil carbon stocks Those we ve found include e North America Smith et al 2006 estimate soil C for every 5 year increment up to 125 years since afforestation reforestation for all major forest types and forest management practices in each region of the U S Others include McLauchlan et al 2006 Tilman et al 2006 Fargione et al 2008 Schuman et al 2002 and Lal 2002 e Africa Houghton and Hackler 2006 give soil C for 5 LULC forest types Rain Forest Moist Forest Dry Forest Shrubland and Montane Forest in sub Saharan Africa that have retained their natural cover and for forest areas that have been converted to croplands shifting cultivation and pasture Vagen et al 2005 provides soil C estimates for various LU
229. reservoir infrastructure maintenance and operation plans To reduce the damages and costs associated with sedimentation land water and reservoir managers require information regarding the extent to which different parts of a landscape contribute to sediment retention and how land use changes may affect this retention Such information can support decisions by government agencies businesses and NGOs For example a power company operating a hydropower reservoir may elect to conserve upstream forests that maintain a sediment retention service if the cost of conserving the forests is less than the costs of reduced hydropower potential sediment removal and dam replacement Maps showing which forest parcels offer the greatest sediment retention benefits would help the power company maximize returns on their investment INVEST aims to provide these kinds of information The outputs from these models will allow planners and managers to consider how LULC change in one area in the watershed can cause sedimentation problems at other locations The Model The Avoided Reservoir Sedimentation model provides the user with a tool for calculating the average annual soil loss from each parcel of land determining how much of that soil may arrive at a particular point of interest estimating the ability of each parcel to retain sediment and assessing the cost of removing the accumulated sediment on an annual basis An important determinant of soil retention capac
230. rmat standard GIS raster file e g ESRI GRID or IMG with LULC class code for each cell e g 1 for forest 3 for grassland etc These codes must match LULC codes in the tables below LULC class codes should be in the LULC column of the dataset Sample data set Invest Base_Data lule_samp_cur The model requires the following two pieces of information about the LULC map which are prompted for in the interface e The year depicted by the LULC map for use in calculating sequestration and economic values labeled Year of current land cover in the interface e The spatial resolution desired cell size in meters at which you would like the model to run labeled Resolution optional You can only define a new resolution that is coarser than the resolution of the LULC map this is the default resolution 2 Carbon pools required A table of LULC classes containing data on carbon stored in each of the four fundamental pools for each LULC class Carbon storage data can be collected from field estimates from local plot studies extracted from meta analyses on specific habitat types or regions or found in general published tables e g IPCC see Appendix If information on some carbon pools is not available pools can be estimated from other pools or omitted by leaving all values for the pool equal to 0 If a forest is regularly harvested for woody biomass the estimates of carbon biomass in the aboveground belowgrou
231. rounded up to the next integer Freq_harv is the frequency in years of harvest periods r is the market discount rate and Maint_cost is the annualized cost ha of managing parcel x Continuing our earlier example where VH 300 if we set Freq_harv 1 a harvest period occurs every year Ty equal to 10 T can be no larger than 10 because the native forest will be completely gone in 10 years given Perc_harv 10 r equal to 7 and Maint_cost equal to 50 ha then the NPV of the stream of VH is 300 NPV gt s 01 07 0 L On the other hand assume Freq_harv 3 a 10 harvest of the timber parcel occurs every 3 years and all other variables are as before then npv k o 300_ z a T 1 07 e 0 a 5 50 e 01 07 In other words a harvest period occurs in years 1 yr_cur or yr_fut 4 7 and 10 with annualized management costs incurred every year where s 0 refers to year 1 s 1 refers to year 4 s 2 refers to year 7 and s 3 refers to year 10 Note that when using equation 3 we always assume a harvest period in yr_cur or yr_fut the next occurs Freq years later the next 2 x Freq years later etc ru 1 NPV 2 Ss z s 0 4 Alternatively if a harvest does not take place in yr_cur or yr_fut and instead the first one is accounted for Freq years into the time interval T then we use the following equation VH z T Mait_ cost aa gs or 100 gt harv
232. rs are regenerated Mosaic tiled DEM data If you have downloaded DEM data for your area that is in multiple adjacent tiles they will need to first be mosaicked together to create a single DEM file In ArcToolbox use Data Management gt Raster gt Mosaic to New Raster entering all of the tiles into the Input Rasters list Look closely at the output raster to make sure that the values are correct along the edges where the tiles were joined If they are not try different values for the Mosaic Method parameter to the Mosaic to New Raster tool Check for missing data After getting and possibly mosaicking the DEM make sure that there is no missing data or holes represented by NoData cells within the area of interest If there are NoData cells they must be assigned values For small holes one way to do this is to use the ArcGIS Focal Mean function within Raster Calculator or Conditional gt CON For example con isnull the DEM focalmean theDEM rectangle 4 4 the DEM Interpolation can also be used and can work better for larger holes Convert the DEM to points using Conversion Tools gt From Raster gt Raster to Point interpolate using Spatial Analyst s Interpolation tools then use CON to assign interpolated values to the original DEM con isnull theDEM interpolated_grid the DEM Another possibility is assigning data from a different DEM if surrounding values are a good match again using CON
233. rties then you should ignore this input or set 1 for all grid cells x To reiterate if we have assigned species group specific habitat suitability scores to each LULC then the threats mitigation weights should be specific to the modeled species group 30 Table 1 Possible degradation sources based on the causes of endangerment for American species classified as threatened or endangered by the US Fish and Wildlife Service Number of species endangered Estimated number of species by threat as indicated by Lowe endangered by threat derived et al 1990 Moseley 1992 by extrapolation of 5 and Beacham 1994 sample from Federal Register Interactions with non native species 305 340 Urbanization HIS 340 Agriculture 224 260 Outdoor recreation and tourism 186 200 development Domestic livestock and ranching 182 140 activities Reservoirs and other running water 161 240 diversions Modified fire regimes and 144 80 silviculture Pollution of water air or soil 144 140 Mineral gas oil and geothermal 140 140 extraction or exploration Industrial institutional and military 131 220 activities Harvest Intentional and incidental 120 220 Logging 109 80 Road presence construction and 94 100 maintenance Loss of genetic variability inbreeding depression or 92 240 hybridization Aquifer depletion wetland draining 77 40 or filling Native species interactions plant 77 160 success
234. rv_cost is the cost ha of removing and delivering Harv_mass to a processing facility or transaction point In general Harv_mass will be given by the aboveground biomass Mg ha content of the forest stand less any portion of the stand that is left as waste e g stems small braches bark etc For example assume a company plans to clear cut 10 of a native forest block in each harvest period Price is expected to be 10 Mg Harv_mass is 800 Mg ha and Harv_cost 5 000 ha The net value created during a harvest period is given by Price x Harv_mass Harv_ cost 1 VH 0 1 x 10 x 800 5000 300 2 A harvest period is a sustained period of harvest followed by a break in extraction Plantation forests tend to have a harvest period every year More natural forests may have more 139 intermittent periods of harvest e g a pulse of harvest activity every 3 years The periodicity of harvest periods in parcel x is given by the variable Freg_harv The variable Freq_harv is used to convert the per hectare value of the parcel VH into a stream of net harvest revenues which is then aggregated and discounted appropriately Specifically the NPV ha of a stream of harvests that engender VH intermittingly from yr_cur or yr_fut to Ty years after yr_cur or yr_fut is given by VH 5 T Mait_ cost h r n harvy xs t 0 a 3 e p 100 100 where ru means any fraction produced by Ty Freg_harv is
235. ryland s Global Land Cover Facility http glcf umiacs umd edu data landcover This data is available in 1 degree 8km and 1km resolutions The simplest categorization of LULCs on the landscape involves delineation by land cover only e g cropland temperate conifer forest prairie Several global and regional land cover classifications are available e g Anderson et al 1976 and often detailed land cover classification has been done for the landscape of interest A slightly more sophisticated LULC classification could involve breaking relevant LULC types into more meaningful types For example agricultural land classes could be broken up into different crop types or forest could be broken up into specific species The categorization of land use types depends on the model and how much data is available for each of the land types The user should only break up a land use type if it will provide more accuracy in modeling For instance for the Water Purification Nutrient Retention model the user should only break up crops into different crop types if they have information on the difference in nutrient loading between crops Along the same lines the user should only break the forest land type into specific species for the water supply model if information is available on the root depth and evapotranspiration coefficients for the different species 5 Nutrient Loading Coefficients Examples of export and loading coefficients can be found i
236. s section is variable among chapters and will improve over time from user input This guide does not include detailed theoretical discussions of the scientific foundation of each model which will be published in an upcoming book from Oxford University Press GETTING STARTED Getting Started with InVEST InVEST tools run as script tools in the ARCGIS ARCTOOLBOX environment To run InVEST you must have ARCGIS 9 2 service pack 2 or 6 or ARCGIS 9 3 service pack 1 ARCINFO level license to run some of the modules Spatial Analyst extension installed amp activated Python 2 4 or higher which is typically installed automatically as part of ARCGIS The pollination model requires additional python libraries Running InVEST does not require Python programming but it does require basic to intermediate skills in ARCGIS A set of sample data is supplied with the models so you can become familiar with the models and how they work To use InVEST for your context however you must compile the data described in the chapter s for the model s you wish to run and format them as indicated Installing the InVEST tool and data on your computer The program InVEST Setup exe contains the InVEST toolbox scripts and training data and is available at the website http invest ecoinformatics org Double click on InVEST Setup exe to install the contents on your computer The Setup wizard will prompt you to download the contents at the default pat
237. s upstream in m 6 Output water_scarcity dbf Table containing values for total water demand and supply for each watershed in m 7 Service hp_yield THIS IS THE MAP OF THIS ECOSYSTEM SERVICE IN BIOPHYSICAL TERMS Grid showing the annual water yield at each pixel that contributes to hydropower production in m 8 Service hp_value THIS IS THE MAP OF THIS ECOSYSTEM SERVICE IN ECONOMIC TERMS This grid shows the value of the landscape according to its ability to yield water for hydropower production over the specified time span in the currency given in the Hydropower table 9 Service hp_energy THIS IS THE MAP OF THIS ECOSYSTEM SERVICE IN ENERGY PRODUCTION TERMS This grid shows the amount of energy produced by the hydropower station over the specified time span that can be attributed to each pixel based on its water yield contribution 10 Service hydropower_value dbf Table containing values for total water supply and demand for each watershed plus the total energy produced and the value of the energy produced per hydropower station Values as specified above The application of these results depends entirely on the objective of the modeling effort Users may be interested in all of these results or a select one or two If costing information is not available or of interest the user may choose to use a unit cost of one so that they can simply compare biophysical results The first several model results provide insight into
238. scenarios InVEST models are spatially explicit using maps as information sources and producing maps as outputs InVEST returns results in either biophysical terms e g tons of carbon sequestered or economic terms e g net present value of that sequestered carbon The spatial resolution of analyses is also flexible allowing users to address questions at the local regional or global scale InVEST results can be shared with the stakeholders and decision makers who created the scenarios to inform upcoming decisions Using InVEST in an iterative process these stakeholders may choose to create new scenarios based on the information revealed by the models until suitable solutions for management action are identified InVEST has a tiered design Tier 1 models are theoretically grounded but simple They are most suitable for areas where few data are available Tier 1 models can identify areas of high or low ecosystem service production and biodiversity across the landscape and the tradeoffs and synergies among services under current or future conditions While some Tier 1 models give outputs in absolute terms others return relative indices of importance More complex Tier 2 and Tier 3 models are under development for biodiversity and each ecosystem service Tier 2 and 3 models will provide increasingly precise estimates of ecosystem services and values which are especially important for establishing contracts for payments for ecosystem services progra
239. se instructions are for Windows XP and may differ for other versions of Windows or other operating systems 1 Install Numpy If you are running ArcGIS 9 3 with Python 2 5 then it is likely that Numpy is already installed To confirm this open Python command line from the Start menu and type import numpy and press enter If no error appears then Numpy is already installed If you need to install Numpy get the appropriate version from this 154 location http sourceforge net projects numpy files and run the install Ensure the version you install matches your python version 2 Download and install GDAL from http download osgeo org gdal win32 1 6 gdalwin32exe160 zip 3 Unzip the GDAL archive into a permanent location e g C gdalwin32 1 6 4 Add your new GDAL bin directory C gdalwin32 1 6 bin if you installed as above to your system Path environment variable To do this right click on My Computer Properties Advanced gt Environment Variables Under system variables select Path system variable edit add a semicolon to separate the existing values then add your GDAL bin directory For example if the existing Path variable was C Program Files soft after editing it should read C Program Files soft C gdalwin32 1 6 bin Do not delete any paths that were there before 5 In the same Environment Variables dialog create a new User Variable named GDAL_DATA with a value of C gdalwin32 1 6 d
240. sensitive to dirt roads than paved roads or agriculture 0 9 versus 0 5 and 0 8 We enter 0 s across all threats for the two developed land covers base soil and cultivation 37 LULC NAME HABITAT L_AG L_ROAD L_DIRT_RD 1 Bare Soil 0 0 0 0 2 Closed Woodland 1 0 5 0 2 0 4 3 Cultivation 0 0 0 0 4 Forest Mosaic 1 0 8 0 8 0 5 8 Half saturation constant required This is the value of the parameter k in equation 4 By default it is set to 30 but can be set equal to any positive integer In general you want to set k to half of the highest grid cell degradation value on the landscape To perform this model calibration you will have to the run the model once to find the highest degradation value and set k for your landscape For example if a preliminary run of the model generates a degradation map where the highest grid cell degradation level is 10 then setting k at 5 will produce habitat quality maps with the greatest variation on the 0 to 1 scale this helps with visual representation of heterogeneity in quality across the landscape It is important to note that the rank order of grid cells on the habitat quality metric is invariant to your choice of k The choice of k only determines the spread and central tendency of habitat quality scores Please make sure to use the same value of k for all runs that involve the same landscape If you want to change your choice of k for any model run then you have to change
241. ser should not make large scale decisions based on a single run of this model The Avoided Reservoir Sedimentation model provides a first cut in prioritization and comparison of landscape management alternatives A more detailed study is required for managers to calculate a specific benefit cost analysis for each reservoir site This model functions best as an indicator of how land use changes may affect the cost of sediment removal and like any model is only as accurate as the available input data Appendix data sources This is a rough compilation of data sources and suggestions about finding compiling and formatting data This section should be used for ideas and suggestions only We will continue to update this section as we learn about new data sources and methods 1 Digital elevation model DEM 129 DEM data is available for any area of the world although at varying resolutions Free raw global DEM data is available on the internet from the World Wildlife Fund http www worldwildlife org freshwater hydrosheds cfm NASA provides free global 30m DEM data at http asterweb jpl nasa gov gdem wist asp Or it may be purchased relatively inexpensively at sites such as MapMart www mapmart com The DEM resolution is a very important parameter depending on the project s goals For example if decision makers need information about impacts of roads on ecosystem services then fine resolution is needed 2 Rainfall erosivity index
242. sion of an urban area or restoring vegetation along a stream bank The model output describes this actual soil loss on an annual basis in tons per hectare summarized in a raster grid over the landscape The user should understand that this USLE method predicts the sediment from sheet wash alone Rill inter rill gullies and or stream bank erosion deposition processes are not included in this model A visit to the watershed and consultation of regional research results need to be used to 128 evaluate the portion of sheet wash in the total sediment loading that is used in testing and verifying this model Total Sediment exported to the outlet of the watershed sediment delivered indicates the volume of soil delivered each year Since this model doesn t simulate the in stream processes where erosion and deposition could have a major impact on this sediment delivered the user should pay great attention to their importance while calibrating or adjusting this model When soil deposition rates are known from observations at interest points the user can aggregate the Sediment Delivered values tons of sediment and compare to observations Remember that USLE only predicts sheet erosion not landslide or roads induces or channel erosion so a sediment budget distribution of observed sediment yield into erosion types must be performed to compare the correct measured sources of sediment with the model output The Value of Sediment Removal is
243. sired defaults for workspace path and any other defaults Important Note do not change the order or data type of parameters in the top window since the program calls these in order Changes to the order or data type will cause the script to fail Carbon Properties Gereia Source Parameters Hep Tia Name a AC pace WORKI E Curentiendcoverm Flaster Laver You of caren law Long Neschtion devine c Long Cabon ook aed bb Tabi Views Curent servest ste Fosie Lauer Fuire lard oore nap Rasla Lever You at hue arde Long lt Clos ary parameter above to see to properties below Palanan Popata apa van Type Recused Diesction Irpur Mulus No Defeul CsHarleaps Carbon Freccurnent Doman Dependercy To add a new osremeter type fe mame imo an smoky row in Ihs meme column cick in ths Date Type column to chooses a dats type then sdk the Parameter Properties OK Cenct Setting parameter properties e Click OK when you have set your desired defaults for workspace path and any other defaults Support Information Authorized users of InVEST i e those who have obtained the software by registering and receiving a password to download it have access to limited online support at http invest ecoinformatics org Users can submit questions formal error reports bug fixes or modified versions of the code to contribute to the next version of the open source product You must register to receive sup
244. soil maps It is defined as the difference between the fraction of volumetric field capacity and permanent wilting point Often plant available water content is available as a volumetric value mm To obtain the fraction divide by soil depth Soil characteristic layers are estimated by performing a weighted average from all horizons within a soil component If PAWC is not available raster grids obtained from polygon shape files of weight average soil texture Y clay sand silt and soil porosity will be needed See Soil Depth above for a description of where to find and how to process soil data http hydrolab arsusda gov SPA W Index htm has software to help you estimate your PAWC when you have soil texture data e Land use land cover A key component for all Tier 1 water models is a spatially continuous landuse land class raster grid That is within a watershed all landuse land class categories should be defined Gaps in data that break up the drainage continuity of the watershed will create errors Unknown data gaps should be approximated Global land use data is available from the University of Maryland s Global Land Cover Facility http glcf umiacs umd edu data landcover This data is available in 1 degree 8km and 1km resolutions The simplest categorization of LULCs on the landscape involves delineation by land cover only e g cropland temperate conifer forest and prairie Several global and regional land cover classi
245. st for pollutant removal when pollutant costs relative to pollutant concentration are non linear The model assumes that non point sources of water pollution result from export that can be mitigated by vegetation serving as intercepting filters It also assumes that water flows downslope along natural flowpaths so it may be less relevant in areas with tile drainage and extensive ditching practices It does not consider the role of ecosystems in affecting point source pollutants It also may be less relevant where there is significant groundwater surface water interaction and in dry eco regions How it Works The model runs on a gridded map of regular cells called raster format in GIS It estimates the quantity and value of pollutants retained for water purification from a landscape in three components The first step calculates annual average runoff from each parcel see Hydropower chapter In the second step we determine the quantity of pollutant retained by each parcel on the landscape First we 98 estimate how much pollutant is exported from each parcel based on export coefficients the user inputs Export coefficients developed by Reckhow et al 1980 are annual averages of pollutant loadings derived from various field studies that measure export from parcels within the United States Since these coefficients were developed in agricultural regions in the US but we want to apply this model in other regions we include a hydrological sens
246. t Tiessen et al 1998 find aboveground carbon stocks for the Brazilian savanna types Caatingas and Cerrados e Africa Zhang and Justice 2001 report aboveground carbon stocks for major forest and shrub LULC types for central African countries Tiessen et al 1998 estimates total aboveground biomass of degraded savanna in Senegal Makundi 2001 reports mean annual incremental growth for three forest plantation types in Tanzania Malimbwi et al 1994 estimates aboveground carbon stocks in the miombo woodlands of Kitungalo Forest Reserve Tanzania Munishi and Shear 2004 report aboveground carbon stocks in the Afromontane rain forests of the Eastern Arc Mountains of Tanzania Glenday 2006 estimates aboveground carbon stocks for 3 forest types in the Kakamega National Forest of western Kenya e North America Smith et al 2006 estimate aboveground carbon stocks for all major forest types in the US e The Carbon On Line Estimator http ncasi uml edu COLE is a tool for calculating carbon characteristics in U S forests based on USDA Forest Service Forest Inventory amp Analysis and Resource Planning Assessment data With this tool carbon characteristics can 64 be examined at the scale of counties Using the variables tab aboveground belowground soil or dead wood carbon pools can be selected e Other Coomes et al 2002 estimate aboveground carbon stocks for native shrubland and forest types in New Zealand One can also c
247. t al 1997 Habitat with high quality is relatively intact and has the structure and function within the range of historic variability Habitat quality depends on a habitat s proximity to human land uses and the intensity of these land uses Generally habitat quality is degraded as the intensity of nearby land use increases Nelleman 2001 McKinney 2002 Forman et al 2003 The model runs using raster data or a gridded map of square cells Each cell in the raster is assigned a LULC type which can be a natural unmanaged cover or a managed cover LULC types can be given at any level of classification detail For example grassland is a broad LULC definition that can be subdivided into pasture restored prairie and residential lawn types to provide much more LULC classification detail While the user can submit up to 3 raster maps of LULC one each for a baseline current and future period at a minimum the current LULC raster map has to be submitted The user defines which LULC types can provide habitat for the conservation objective e g if forest breeding birds are the conservation objective then forests are habitat and non forest covers are not habitat Let H indicate the habitat suitability of LULC type j Which LULC types should be considered habitat If considering biodiversity generally or if data on specific biodiversity habitat relationships are lacking you can take a simple binary approach to assigning habitat to LULC types A c
248. t the point of interest where water supply will be drawn may be more relaxed than these standards if water treatment is in place In situ water quality standards for rivers lakes and streams may also be set at the national state and local level They may be the same across all water bodies of the same type in rivers for example or they may vary depending on the established use of the water body or the presence of endangered species In the U S Total Maximum Daily Loads of various pollutants are typically established by state regulatory agencies in compliance with the Clean Water Act States report information on TMDL s to the U S EPA on specific waterways http www ctic purdue edu kyw tmdl statetmdllists html 9 Marginal pollutant removal costs Cost The cost to remove pollutants may vary greatly for each point of interest If the point of interest is a water supply outtake this value should be obtained from the water treatment entity who uses and treats the water Calculations may need to be performed to transform actual costs to cost per unit volume of pollutant and correlations may need to be run between a proxy pollutant and other pollutants that the treatment process removes If a more general cost of treatment is sought the user may consult engineering texts or literature to obtain average costs The user must be sure to bring these costs into present value and make adjustments as necessary depending on the location and type of
249. t yield increases as pollinator visitation increases but with diminishing returns Greenleaf and Kremen 2006 Crops vary in their dependence on pollinators some crop species are self compatible and yield is less dependent on pollination while other species obligately require pollination to generate any yield Klein et al 2007 We account for both observations and thus calculate the expected yield of a crop c on farm o Yo as Y 1 v tv P K Where v represents the proportion of total crop c s yield attributed only to wild pollination e g ve would be equal to 1 if a crop is an obligately outcrossing species and equal to 0 if the crop species were wind pollinated In the denominator of the third term xo is a half saturation constant and represents the abundance of pollinators required to reach 50 of pollinator dependent yield Once the model has calculated value for each agricultural cell it redistributes this value back to cells that supplied the relevant pollinators creating a map of value at the source First the model assigns fractions of the cell s value to each of the bee species according to their partial contribution to total farm abundance Then each species value is redistributed back to the source cells from which they came using the same distance weighted relationship described above Thus source habitats close by provide greater service value than those farther away Formally we calculate pollinator service
250. table C DWEST Bese Deta Weta Tablas mdb Waer Demad Hydropv er Tabie CIMES Eese Deta Wata Tablas mdb trdropower Ourput resokton Mamm aF Inputs E Roads suff Coptionsl DK Cancel Environments lt lt Hide Help e When the script completes running its results will be saved in the Output folder A description of these results is in the next section Load them into your ArcMap project look at them and check out the attribute table e Finally run the tool Valuation Two outputs from previous tools are required wyield from Water Yield and water_scarcity dbf from Scarcity The interface is below 87 amp 3 Valuation 3 Hels Werkspace k CIEST H Aopovat CEM C INVEST Fase_Dataldem 3 Valuation Estimates the quantity and value of water used for hydropower production as provided by each pixel in the landscape CINFESTIH yd opona Serovas CIES Bese _Detailarduse_s0 CINWEST Bese_Detalwater sheds Scarcty osip table C INWESTIHydroponvariOut putiwaner_scarcity dbt C INVEST Bese_Detal ebar_Tablas ecb tyckopoeer CAES Bese_Detal etar_Tablas mobiwater Demand e im e e e e e Output resoktion Mamm af Inputs E panate 0K Carcel Enviormerts lt lt bce Help e When the script completes running its results will be saved in the Service folder A description of these results is in the next section Load them into your ArcMap project look at them and check out th
251. tely for each pollinator species at each agricultural site is an index of the abundance of bees visiting each farm site i e farm abundance We use the foraging framework described in the previous equation to determine the relative abundance of bees that travel from a single source cell x to forage on a crop in agricultural cell o D a P e X M Dox 2e x 1 where P is the supply of pollinators on cell x Dox is distance between source cell x and agricultural cell o and amp is species average foraging distance The numerator of this equation represents the distance weighted proportion of the pollinators supplied by cell m that forage within P 0 148 cell o and the numerator is a scalar that normalizes this contribution by the total area within foraging distance Winfree et al 2005 The total pollinator abundance on agricultural cell o Po is simply the sum over all M cells This second map represents the relative degree of pollination service at the demand points or points at which this service is delivered agricultural cells The actual economic benefit received from pollination depends on how crops grown in each cell respond to pollinators The model therefore takes two additional optional steps to translate farm abundances of pollinators into indices of expected economic value In lieu of a more detailed agricultural production function we use a simple saturating crop yield function which assumes tha
252. tersheds In order to create a watershed raster for each hydropower station the model user needs to employ the help of a few ArcMap tools in the Spatial Analyst gt Hydrology toolbox The first step is to snap the hydropower location points to the lowest point on the stream within a given distance using the Snap Pour Point tool The user should then select the Watershed tool and use the newly snapped pour points to delineate watersheds for each of the hydropower stations This delineation should be checked to ensure that the watersheds accurately represent reality This reality check may involve talking to a local hydrologist checking the drainage area for a nearby USGS gage or doing a back of the envelope calculation for the annual rainfall multiplied by the watershed area and comparing it to the average annual volume of flow into the hydropower station If the modeled watersheds are too large or too small the user should go back to the Snap Pour Point step and choose a different snapping distance or try an alternate method of delineation use of SWAT utility to delineate watersheds These steps should be repeated until the watersheds make sense See the Working with the DEM section of this manual for more information on generating watersheds and sub watersheds k Hydropower Station Information Detailed information about each hydropower station may only be available from the owner or managing entity of the stations Some inform
253. the landscape Ideally the output grids and summary table will be used together for comparison of land use and management scenarios Appendix Data Sources This is a rough compilation of data sources and suggestions about finding compiling and formatting data This section should be used for ideas and suggestions only We will continue to update this section as we learn about new data sources and methods a Average annual precipitation Average Annual Precipitation may be interpolated from existing rain gages and global data sets from remote sensing models to account for remote areas Precipitation as snow is included If field data are not available you can use coarse data from the freely available global data set developed by the Climatic Research Unit www cru uea ac uk Within the United States the PRISM group at Oregon State University provides free precipitation data at a 30 arcsecond resolution See their website at http www prism oregonstate edu and navigate to 800 m Normals to download data b Average annual reference evapotranspiration ET Reference evapotranspiration ET is the energy expressed as a depth of water e g mm supplied by the sun and occasionally wind to vaporize water Some global products are available on the internet such as FAO Penman Monteith method with limited climatic data as described in FAO Irrigation and Drainage Paper 56 using data from the Climatic Research Unit see http mercury
254. the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence resilience breadth and depth in the area of decline The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis a vis the extent of the same natural land cover types in some baseline period Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area s native biodiversity The model requires basic data that are available virtually everywhere in the world making it useful in areas for which species distribution data are poor or lacking altogether Extensive occurrence presence absence data may be available in many places for current conditions However modeling the change in occurrence persistence or vulnerability of multiple species under future 26 conditions is often impossible or infeasible While a habitat approach leaves out the detailed species occurrence data available for current conditions several of its components represent advances in functionality over many existing biodiversity conservation planning tools The most significant is the ability to characterize the sensitivity of habitats types to
255. threat set the area s threat level equal to 0 If you are analyzing habitat quality for more than one LULC scenario e g a current and future map or a baseline current and future map then you need a set of threat layers for each modeled scenario Add a c at the end of the raster for all current threat layers a P for all future threat layers and a b for all baseline threat layers If you do not use such endings then the model assumes the degradation source layers correspond to the current map If a threat noted in the Threats data table input 4 is inappropriate for the LULC scenario that you are analyzing e g industrial development on a Willamette Valley pre settlement map from 1851 then enter a threat map for that time period that has all 0 values If you do not include threat maps for a submitted LULC scenario then the model will not calculate habitat quality on the scenario LULC map Finally note that we assume that the relative weights of threats and sensitivity of habitat to threats do not change over time we only submit one Threat data table and one Habitat sensitivity data table inputs 4 and 7 If you want to change these over time then you will have to run the model multiple times Name the name of each raster file should exactly match the name of a degradation source in the rows of the Threats data table input 2 above with the added _b _c or _f to indicate the thre
256. tion it is rounded down Also note that equation C5 does not include a harvest that is scheduled to occur in the future year this harvest s carbon is in situ in this accounting Parcels that were harvested on the current landscape but are not expected to be harvested on the future landscape may still have HWP carbon in the future year The remaining HWP carbon in yr_fut on such parcels is given by the first term of equation C5 i e at some point between and yr_fut then the remaining HWP carbon 7 f Decay _cur yr _ fut start _ date t x Freq _cur C5 yr _ fut yr_cur 2 F j Decay _ fut yr _ fut t x Freq _ fut 55 HWP _ fut Cut_cur x yr_ fut yr_ cur y 5 start_ due C6 gt Freg _ cur f Deca y_cur yr_ fut start_ date t x Freq_ cur t In contrast parcels that were not harvested on the current landscape but are expected to be harvested on the future landscape will have the following amount of carbon in the form of HWP in yr_fut HWP_ fut Cut_ fut x 7 fit 2 fut yr_ cur 7 2 yr_ fut yr_cur f C7 j Ane D ecay_ tu to YE _ tut 2 0 ws Freq_ fu j Note that this is the second term of equation C5 If a parcel was harvested on the current landscape and is expected to be harvested on the future landscape the mass of harvested wood that has been removed from a parcel from Start_date to yr_fut is given by yr_ fut yr_cur
257. treatment If the point of interest is an in situ water quality target the marginal pollutant removal cost is much more difficult to obtain The user may be able to estimate the cost of an additional unit volume of pollutant in terms of fish populations lost revenue for recreation or a fine but this 113 may be a complicated calculation not worth the effort at this level of modeling The user may choose to assign a cost of one to save time while still obtaining relative results useful in comparing scenarios References Anderson J R et al 1976 A Land Use And Land Cover Classification System For Use with Remote Sensor Data Geological Survey Professional Paper 964 Edited by NJDEP OIRM BGIA 1998 2000 2001 2002 2005 Ashbolt N J Grabow W O K and Snozzi M 2001 Indicators of microbial water quality in Water Quality Guidelines Standards and Health L Fretwell and J Bartram Editors 2001 World Health Organization WHO IWA Publishing London U K Reckhow K H Beaulac M N amp Simpson J T 1980 Modeling Phosphorus loading and lake response under uncertainty A manual and compilation of export coefficients U S Environmental Protection Agency Washington D C Uusi Kamppa J E Turtola H Hartikainen T Ylaranta 1997 The interactions of buffer zones and phosphorous runoff In Buffer zones Their processes and potential in water protection eds N Haycock T Burt K Goulding and G Pinay 43
258. tx Freq _ cur yx ty Cl yr _cur start _ date Freq _ cur f Decay _cur yr __cur start _ date t x Freq _cur C2 where log 2 Decay_cur measures how much of the carbon was typically removed from a parcel Cut_cur during a harvest period that occurred some number of years ago yr_cur start _date tx Freq _cur still remains trapped in HWP as of the current year yr_cur and given the current decay rate Decay_cury The following are several examples to show how equation C1 works In the first instance assume start_date 1983 yr_cur 2000 and Freq_cur 4 In this case start _ dat ru CE aesha ru e ru 4 25 5 According to the summation term in Freq _cur 4 equation C1 this means we sum over 5 harvest periods t 0 1 2 3 4 Given this series of t we evaluate f at 17 13 9 5 and 1 years since a harvest we use yr _cur start _ date tx Freq_cur to convert the series of t s into years since harvest Alternatively if start_date 1980 yr_cur 2000 and Freg_cur 2 then r_cur start _ date n ru 10 10 Therefore according to equation C1 harvests Freq_cur that contained Cut_cur of carbon ha occurred on the parcel 20 18 16 14 12 10 8 6 4 and 2 years before the year 2000 note that we do not include a harvest that is scheduled to occur in the current year in the HWP carbon pool this carbon is still in_situ
259. uires careful weighting across components and horizons The Soil Data Viewer http soildataviewer nrcs usda gov a free ArcMap extension from the NRCS does this soil data processing for the user and should be used whenever possible The following equation can be used to calculate K Wischmeier and Smith 1978 K 27 66 m 1 14 104 8 12 a 0 0043 b 2 0 0033 c 3 In which K soil erodibility factor t ha MJ mm m silt very fine sand 100 clay a organic matter b structure code 1 very structured or particulate 2 fairly structured 3 slightly structured and 4 solid c profile permeability code 1 rapid 2 moderate to rapid 3 moderate 4 moderate to slow 5 slow and 6 very slow 4 Land use land cover A key component for all water models is a spatially continuous landuse land class raster grid That is within a watershed all landuse land class categories should be defined Gaps in data will create errors Unknown data gaps should be approximated Global land use data is available from the University of Maryland s Global Land Cover Facility http glcf umiacs umd edu data landcover This data is available in 1 degree 8km and Ikm resolutions 131 The simplest categorization of LULCs on the landscape involves delineation by land cover only e g cropland temperate conifer forest prairie Several global and regional land cover classificatio
260. up online their state environmental agency EPA fish and wildlife service or any local universities conducting research on the water body Once data is collected the user may have to convert the values into actual pollutant loads and or correlate a measured pollutant with a proxy modeled pollutant In addition to correlation analysis other calibration methods such as Nash Coefficient ranking analysis and graphical comparison could be used 8 Critical Annual Load Ann_Load Gathering information on water quality standards or targets should be part of the formulation of modeling objectives If the target to be met is a drinking water target standards may be set by the federal state or local level whichever standard is the most stringent The table below provides 112 some general drinking water standards set by global and national agencies Selected Drinking Water Standards by World Health Organization European Union and US EPA Ashbolt et al 2001 Parameter Type WHO EU USEPA Ammonia Social 1 5mgL 1 0 50 mg L 1 No GL pH Social 6 5 8 No guidelines 6 5 8 5 Chloride Social 250mgL 1 250 mg L 1 250 mg L 1 Iron Social 0 3mgL 1 0 2 mg L 1 0 3 mg L 1 Lead Health 0 01 mg L 1 0 01 mg L 1 0 015 mg L 1 Arsenic Health 0 01 mg L 1 0 01 mg L 1 0 01 mg L 1 Copper Health 2 0 mg L 1 2 0 mg L 1 1 3 mg L 1 Te Health 0 counts 100 mL 0 counts 100 mL oo og These standards are set for point of use meaning that the standard a
261. ural succession The problem can be addressed by dividing LULC types into age classes essentially adding more LULC types such as three ages of forest Then parcels can move from one age class to the other in scenarios and change their carbon storage values as a result A second limitation is that because the model relies on carbon storage estimates for each LULC type the results are only as detailed and reliable as the LULC classification used Carbon storage clearly differs among LULC types e g tropical forest vs open woodland but often there can also be significant variation within a LULC type For example carbon storage within a tropical moist forest is affected by temperature elevation rainfall and the number of years since a major disturbance e g clear cut or forest fire The variety of carbon storage values within coarsely defined LULC types can be partly recovered by using a LULC classification system and related carbon pool table which stratifies coarsely defined LULC types with relevant environmental and management variables For example forest LULC types can be stratified by elevation climate bands or time intervals since a major disturbance Of course this more detailed approach requires 48 data describing the amount of carbon stored in each of the carbon pools for each of the finer LULC classes Another limitation of the model is that it does not capture carbon that moves from one pool to another For example i
262. urces on habitat in the grid cell will partly depend on how quickly they decrease or decay over space The user can choose either a linear or exponential distance decay function to describe how a threat decays over space The impact of threat r that originates in grid cell y ry on habitat in grid cell x is given by i xy and is represented by the following equations d i i 3 l if linear or 1 rmax Ly exp 22 if exponential 2 where dy is the linear distance between grid cells x and y and d max is the maximum effective distance of threat r s reach across space Figure 1 illustrates the relationship between the distance decay rate for a threat based on the maximum effective distance of the threat linear and exponential For example if the user selects an exponential decline and the maximum impact distance of a threat is set at 1 km the impact of the threat on a grid cell s habitat will decline by 50 when the grid cell is 200 m from r s source If i gt 0 then grid cell x is in degradation source r s disturbance zone If the expontential funcion is used to describe the impact of degradation source r on the landscape then the model ignores values of i xy that are very close to 0 in order to expedite the modeling process To reiterate if we have assigned species group specific habitat suitability scores 29 to each LULC then threat impact over spece should be specific to the modeled species group
263. use and land cover LULC code for each cell The dataset should be projected in meters and the projection should be defined This coverage must be of fine enough resolution i e sufficiently small cell size to capture the movements of bees on a landscape If bees fly 800 meters on average and cells are 1000 meters across the model will not fully capture the movement of bees from their nesting sites to neighboring farms Name file can be named anything but avoid spaces e g use lulc_samp_cur Format standard GIS raster file e g ESRI GRID or IMG with a column labeled value that designates the LULC class code for each cell e g 1 for forest 3 for grassland etc The LULC value codes must match LULC class codes used in the Land Attributes table described below The table can have additional fields but the only field used in this analysis is one for LULC class code The model also requests three pieces of information about this LULC map which are optional but will be prompted for in the interface a The year depicted by the LULC map optional You can indicate the year of the LULC map if known to designate model runs performed at different time periods i e future scenarios b The resolution at which the model should run optional You can indicate a coarser resolution than that of the native LULC map to prompt the model to resample at this new resolution and to speed up run time For example you could run the mod
264. ust be used for all alternative scenarios on the same landscape Similarly whatever spatial resolution you chose the first time you run the model on a landscape use the same value for all additional model runs on the same landscape If you want to change your choice of k or the spatial resolution for any model run then you have to change the parameters for all model runs if you are comparing multiple scenarios on the same landscape Habitat Rarity While mapping habitat quality can help to identify areas where biodiversity is likely to be most intact or imperiled it is also critical to evaluate the relative rarity of habitats on the landscape regardless of quality In many conservation plans habitats that are rarer are given higher priority simply because options and opportunities for conserving them are limited and if all such habitats are lost so too are the species and processes associated with them 32 The relative rarity of a LULC type on a current or projected landscape is evaluated vis a vis a baseline LULC pattern A rare LULC type on a current or projected map that is also rare on some ideal or reference state on the landscape the baseline is not likely to be in critical danger of disappearance whereas a rare LULC type on a current or projected map that was abundant in the past baseline is at risk In the first step of the rarity calculation we take the ratio between the current or projected and past baseline extents of each LUL
265. ut such as agricultural or residential expansion This does not include forests that are slashed and burned given that the felled and burned wood is not used to create a product In other cases concessions to clear cut certain areas of a natural forest or selectively log a natural forest may be held by entities In these cases an altered version of the natural forest would remain on the landscape into the future Examples of this type of harvest include logging of rainforests in the Amazon or Malaysia for land conversion or in Indonesia to establish palm plantations and selective clear cutting of rainforests in Malaysia The model runs on a vector GIS dataset that maps parcels on the landscape that are or are expected to be used for legal timber harvest over a user defined time period These timber parcels can include a whole forest or just part of a forest In any case a parcel should only include the portion of a forest that is formally designated zoned or managed for harvest Each timber harvest parcel is described by its harvest levels Harv_mass and Perc_Harv in the polygon attribute table see below frequency of harvest Freq_harv and harvest and management or maintenance costs Harv_cost and Maint_cost respectively Fig 1 135 The timber parcel map can either be associated with a current sometimes referred to as base LULC map used in most other InVEST models where the year associated with the current LULC MODEL S
266. various threats Not all habitats are affected by all threats in the same way and the InVEST model accounts for this variability Further the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such For example grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads In addition the distance over which a threat will degrade natural systems can be incorporated into the model Model assessment of the current landscape can be used as an input to a coarse filter assessment of current conservation needs and opportunities Model assessment of potential LULC futures can be used to measure potential changes in habitat extent quality and rarity on a landscape and conservation needs and opportunities in the future How it works Habitat quality We define habitat as the resources and conditions present in an area that produce occupancy including survival and reproduction by a given organism Hall et al 1997 175 Habitat quality refers to the ability of the environment to provide conditions appropriate for individual and population persistence and is considered a continuous variable in the model ranging from low to medium to high based on resources available for survival reproduction and population persistence respectively Hall e
267. ver map if provided e sup_val_cur This is a map of pollinator service value the relative value of the pollinator supply in each agricultural cell to crop production in the surrounding neighborhood It is an index derived by distributing the values in frm_val_cur an intermediate result back to surrounding pollinator sources using information on flight ranges of contributing pollinators This is a map of where pollination services are coming from and their relative values Units are not dollars per se but the index is a relative measure of economic value e sup_val_fut The same as above but for future scenario land cover map if provided Intermediate results found in the folder name intermediate You may also want to examine the intermediate results These files can help determine the reasons for the patterns in the final results e hn_ lt beename gt _cur This is a map of the availability of nesting sites for each pollinator The map depends on the values you provide for the availability of each nesting type in each LULC class and for the nesting habits of each bee species In fact values in this map are simply the product of those two provided numbers e g in the example tables given above species A is entirely a cavity nester and coffee has a 0 2 value for cavity nest availability so the value for species A in a coffee cell will be 1 x 0 2 0 2 Note the lt beename gt portion of each file name wi
268. vice the date and time and the suffix Final Results Final results are found in the Output folder of the workspace for this module degrad_cur suffix Relative level of habitat degradation on the current landscape A high score in a grid cell means habitat degradation in the cell is high relative to other cells Grid cells with non habitat land cover LULC with H 0 get a degradation score of 0 This is a mapping of degradation scores calculated with equation 3 qual_cur suffix Habitat quality on the current landscape Higher numbers indicate better habitat quality vis a vis the distribution of habitat quality across the rest of the landscape Areas on the landscape that are not habitat get a quality score of 0 This quality score is unitless and does not refer to any particular biodiversity measure This is a mapping of habitat qulaity scores calculated with equation 4 rarity_cur suffix Relative habitat rarity on the current landscape vis a vis the baseline map This output is only created if a baseline LULC map is submitted input 3 This map gives each grid cell s value of R see equation 6 The rarer the habitat type in a grid cell is vis a vis its abundance on the baseline landscape the higher the grid cell s rarity_cur value Optional Output Files If you are running a future scenario i e you have provided input 2 and future LULC scenario threat layers you will also see degrad_fut suffix and
269. wground biomass or root to shoot ratios for different habitat types Among those we found e Grace et al 2006 estimate the total average woody and herbaceous root biomass for major savanna ecosystems around the world Table 1 Baer et al 2002 and Tilman et al 2006 estimate the C stored in the roots of plots restored to native C4 grasses in Nebraska and Minnesota U S respectively as a function of years since restoration see Table 2 in Baer et al 2002 and Figure 1D in Tilman et al 2006 e Cairns et al 1997 survey root to shoot ratios for LULC types across the world Munishi and Shear 2004 use a ratio of 0 22 for Afromontane forests in the Eastern Arc forests of Tanzania Malimbwi et al 1994 use 0 20 for miombo woodlands in the same area of 66 Tanzania Coomes et al 2002 use 0 25 for shrublands in New Zealand Gaston et al 1998 report a root to shoot ratio of 1 for African grass shrub savannas 2 3 Carbon stored in soil If local or regional soil C estimates are not available default estimates can be looked up from IPCC 2006 for agricultural pasture and managed grasslands Table 2 3 of IPCC 2006 contains estimates of soil carbon stocks by soil type assuming these stocks are at equilibrium and have no active land management For cropland and grassland LULC types this default estimate can be multiplied by management factors listed in Tables 5 5 and 6 2 of IPCC 2006 For all other LULC types and their
270. xels that have runoff index below the mean will export less than export coefficients and pixels that have higher than mean runoff index will export more than export coefficients The HSS grid provides a relative picture of where runoff is produced across the landscape It is recommended to verify this grid with local knowledge of land use and overland flow to ensure accuracy Adj_Load kg Raster showing critical areas of contaminant loading This raster shows where pollutant loadings are of concern because of high loading rates in high scoring HSS areas It is recommended to verify this grid with local knowledge of major sources of modeled pollutants to ensure accuracy w retain kg Amount of nutrient retained by each pixel from what flows into it from upstream pixels THIS OUTPUT REPRESENTS THE ECOSYSTEM SERVICE OF WATER PURIFICATION IN BIOPHYSICAL TERMS It indicates the pixels that may be most important for keeping waterways free of pollutants w_export kg Raster showing the pollutant loading that reaches the stream from each pixel This raster will be especially useful for managers interested in controlling pollutant inputs w_cum_exp kg Cumulative Nutrient Loading raster This is the cumulative amount of nutrient that it is delivered to the stream from each pixel along flow path It should be used for comparing values with measured data at points along flow path Adjustments of export coefficients and vegetation filtration efficiencies ar
271. xs 1 bk 100 where rd means any fraction produced by Tx Freg_harv is rounded down to the next integer In this case if Freg_harv 3 and T 10 then x experiences a harvest period in years 3 6 and 9 of the time interval 6 ae 140 The selection of T and Freq require some thought First if timber parcel x is expected to only experience one immediate harvest period either in the base year with equation 3 or Freq years into the time interval with equation 6 then set Ty Freq 1 On the other hand if parcel x is in an even aged managed rotation then the value of T can be set very high we assume that harvests can be sustained indefinitely in such systems However we recommend against using large T values for any x for several reasons First in this model timber price harvest cost and management cost are static over time This may only be a reasonable assumption for short periods of time e g 20 years Second in this model timber management is static over time again this may only be a reasonable assumption over short periods of time Third if natural forests are being transformed into plantations a large T would require that we begin accounting for the eventual plantation harvests This complication would make the model less tractable Note that Freq lt T for all x Finally the net present value of timber harvest for the entire area of parcel x from the base year to T years later is given by TNPV
272. y be limited to using an outdated average value from other locations and for a different type of reservoir Second the accuracy of the model is limited by the user s ability to calibrate it with actual sedimentation data The model allows for a calibration constant to be applied and adjusted via the Sediment Delivered output This can greatly improve the model but only if the user has access to reliable sedimentation data for the watershed s of interest Data needs There are nine required data inputs for this model and one optional input required for valuation See the appendix for detailed information on data sources and pre processing For all raster 120 inputs the projection used should be defined and the projection s linear units should be in meters 1 Digital elevation model DEM required A GIS raster dataset with an elevation value for each cell Make sure the DEM is corrected by filling in sinks and if necessary burning hydrographic features into the elevation model recommended when you see unusual streams See the Working with the DEM section of this manual for more information Name File can be named anything but no spaces in the name and less than 13 characters Format standard GIS raster file e g ESRI GRID or IMG with elevation value for each cell given in meters above sea level Sample data set C Invest Base_Data dem 2 Rainfall erosivity index R required R is a GIS raster dataset with an erosivit
273. y index value for each cell This variable depends on the intensity and duration of rainfall in the area of interest The greater the intensity and duration of the rain storm the higher the erosion potential The erosivity index is widely used but in case of its absence there are methods and equations to help generate a grid using climatic data See the Appendix for further details Name File can be named anything but no spaces in the name and less than 13 characters Format standard GIS raster file e g ESRI GRID or IMG with a rainfall erosivity index value for each 1 cell given in MJ mm ha h yr Sample data set C Invest Sedimentation Input erosivity 3 Soil erodibility K required K is a GIS raster dataset with a soil erodibility value for each cell Soil erodibility K is a measure of the susceptibility of soil particles to detachment and transport by rainfall and runoff Name File can be named anything but no spaces in the name and less than 13 characters Format standard GIS raster file e g ESRI GRID or IMG with a soil erodibility value for JEER 1 each cell K is in T ha h ha MJ mm Sample data set C Invest Sedimentation Input erodibility 4 Land use land cover LULC required LULC is a GIS raster dataset with an integer LULC code for each cell Name File can be named anything but no spaces in the name and less than 13 characters Format standard GIS raster file e g ESRI GRID or IMG with L

InVEST 1.004 Beta User's Guide: - High

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