SWIM User Manual - Potsdam Institute for Climate Impact Research
Contents
1. 1 2 2 Processes described in the model 1 2 3 Spatial diSaggregatiON oooocccnnnccnnnnccccccccnncccnannnrrnn cnn nn nc nannnn nn nn 1 2 4 GIS Interface 1 2 5 Modelling procedure 1 3 OVERVIEW OF THE SWIM GRASS INTERFACE 1 3 1 Main MENU oo eee eect cece ana aaa ama aE aa aaa aiaia 1 3 2 Options of the Main MeNl oooocccccnnococoncnnnnncnnnnnnnnnncnnnncnnnnnna non cnnnnos 1 4 OVERVIEW OF THE MODEL COMPONENTS 1 4 1 Hydrological processes 1 4 2 Crop vegetation QrOWth oooonccnnnnncococcccnncccnnnnannnccnnnnnonnnnnnn nn ncnnnos 1 4 3 Nutrient dynamics 1 4 4 Erosion 1 4 5 River routing 2 MATHEMATICAL DESCRIPTION OF THE MODEL COMPONENTS 2 1 HYDROLOGICAL PROCESSES asridan iaai aat 21 1 SNOW A see iaa 2 1 2 Surface runoff 2 1 3 Peak runoff rate 2 1 4 Percolation 2 1 5 Lateral subsurface TUN econnnicccccnnnccccnncaoonccnnnnccnncnnannnn nn cnnnncnnnnnns 2 1 6 Potential evapotranspiration ueussssssnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 2 1 7 Soil evaporation and plant transpiration 2 1 8 Groundwater Tl W 4 eier 2 1 9 Transmission losses 2 2 CROP VEGETATION GROWTH 2 2 1 Crop growth 2 2 2 Growth constraint water stress uuussnssssnnnnnnnnnnnnnnnnnnnnnnnnnnn 2 2 3 Growth constraint temperature stress 2 2 4 Growth2. Fig 2 1 2 2 2 3 2 4 2 5 2 6 2 8 2 9 2 10 2 11 2 12 2 13 2 14 2 15 2 16 2 17 2 18 2 19 2 20 2 21 2 22 2 23 LIST OF FIGURES Chapter 1 Page Model development based on the CREAMS model nnen 13 Flow chart of the SWIM model integrating hydrological processes crop vegetation growth and nutrient DYNAMICS cceeeeeeeeeeeeeeeeeeeeeeeeeees 17 Flow chart of hydrological processes in soil as implemented in SWIM 18 Nitrogen and phosphorus flow charts as implemented in SWIM 19 Three level disaggregation scheme basin sub basins hydrotopes implemented in SWM asisrsicri aian cnn ncnn cnn nana rn nn cnn cnn nn nc nana nr nn 21 Chapter 2 Estimation of surface runoff Q from daily precipitation PRECIP for different values of CN equations 3 and 4 ooooccnnndcninnnccoccccnnncccnnnnannnccnnnncnnnnnnn nn cnn nn cnn 35 Correspondence between CN CN and CN equations 6 7 36 Adjustment of curve number CN to the slope equation 8 for some typical values of CN 67 78 85 and 89 corresponding to straight row crop and four hydrologic groups A B C and D respectiVelY ooooooooocoooocoooccncnnnononnnnnnnons 37 Retention coefficient SMX and curve number CN as functions of soil water content SW equation 9 and 4 assuming CN 60 WP 5 mm mm FC 35 mm mm PO 45 MM MM ee eeeeccesccsenceeesceceeeeeeneece
3. 4 3 4 4 4 5 4 14 4 15 4 16 4 17 4 18 4 19 4 20 4 21 4 22 4 23 4 24 Scheme of operations included in SWIM phosphorus module 85 The LS factor calculated as a function of slope steepness SS for different slope lengths SL equations 166 167 cccccccccccnncnnnononnnnnonononnnonononononnnnnnns 87 Coefficients C C and C as functions of parameter KST as used to calculate flow routing with the Muskingum equations 182 184 assuming that X 0 2 MAS TEE AE ie dee AAA AAA ALI 91 Function SPCON CHV to estimate the sediment delivery ratio DELR equation 191 for different combinations of CHV SPCON 94 Chapter 3 Function tree of SWIM GRASS iNterfaCe ooooococccnnncccnnncoconcccnnnccnnnnannnccannncnnnnnns 107 Scheme of operations in the simulation part of SWIM nn 116 Chapter 4 A set of four maps Digital Elevation Model a land use b soil c and sub basins d that are necessary to run SWIM GRASS interface 163 Comparison of a virtual river networks produced by GRASS blue and a digitazed river network Ted oooooccccnnnccconncccoccccnnncccnnnnaan o cnn nn ncnnnnn nono rra cnn nan ccnnnnns 165 Virtual sub basins obtained by applying r watershed function in GRASS for the Mulde river DasiMoyaua nia reer E eed ARR 175 A set of sub basin and river network maps produced with r watershed using different thresholds 900 a 200 b a
4. 3 2 1 Files and their function Ssa a aea 113 3 2 2 Subroutines and their functions ereere 118 3 2 3 Main administrative subroutines and the parameter read part 122 3 3 INPUT AND OUTPUT FILES oocoonccccccconoccnnnononccnnonnnnnnnonnnncnnnnnnnnn cnn nannnn nana 127 3 4 INPUT PARAMETERS oooccccococccccononnnconnnncnnonnnncnncnnnn cnn nanrn naar rra nrnnnr nn nannnnn 133 3AA Input tiles COO 2 LLL 133 3 4 2 Input file fiJ oo nmonnicinnnndnn nnnncocccnnnnnnnnnnrrarcccnnn no nana n anno rc cnnn nn 136 3 4 3 INPUT SN ilatina 137 AA Input file iii 222er Bone 140 3 4 5 Input file SUD prSt Aat oooooonncnnniniccccccnnncconananannccnnnnconnnnn rn n cn nn cnn 141 3 4 6 Inp t file crop dal ca 141 3 4 7 Input file wgen dat 44440u0nn4nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 148 3 4 8 Inp t file SUD sient antes 149 3 4 9 Input file gw 2u0uunnnsnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 152 3 4 10 Input file rte 224444nnnennnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 153 A niiina aninda nrbe tebe bniah 155 3 4 12 Input file SOIINN Jat siirat a a 156 3 4 13 Block data in the file init f 444400sunnnnnnnnnnnnnnnnnnnnnnnnnnn nn 159 4 HOW TO PREPARE INPUT DATA AND RUN SWIM nennen 161 4 1 SPATIAL DATA PREPARATION 2 cccececeeeeeeneeeeeecaeeeeneeeeesenaeeeeseaeees 162 4 1 1 GIS data overview nns
5. The interface creates a number of input files for the basin and sub basins including the hydrotope structure file indicating sub basin number land use and soil type for every hydrotope and the routing structure file indicating how the sub basins are connected via river network To start the interface the user must have at least four map layers of a basin Three of them are the elevation map the land use map and the soil map The fourth sub basin map should be created in advance either using the r watershed program of GRASS or by subdividing the basin in any other way Step 1 Sub basin attributes This is the first step to be fulfilled The program calculates area resolution and co ordinate boundaries for the basin and each sub basin using a given sub basin map Further the fraction of each sub basin area to the basin area is calculated Step 2 Topographic attributes The program estimates the stream length stream slope and geometrical dimensions using the r stream att tool Srinivasan Arnold 1994 The cross sectional dimensions width and depth of a stream are estimated using a neural network that is embedded in the interface based on the drainage area and average elevation of a sub basin which should be trained on the regional data before use The accumulation area and aspect are computed using the standard methods in GRASS The weighted average method is used to estimate the overland slope and slope length Finally the ch
6. constraints nutri 2 2 5 Crop yield and residue ent SESS 2 2 6 Adjustment of net photosynthesis to altered CO 00 2 2 7 Adjustment of evapotran 2 3 NUTRIENT DYNAMICS 2 3 1 Soil temperature spiration to altered CO eee 2 3 2 Fertilisation and input with precipitation nenn 2 3 3 Nitrogen mineralisation 2 3 4 Phosphorus mineralisation ussnnsssnnnnnnnnnnnnnnnnennnnnnnnn 2 3 5 Phosphorus sorbtion adsorption 4 4ssnnnsnnnnnnnnnnnnnnnn 2 3 6 Benifrificationaiz taste ik 2 3 7 Nutrient uptake by crops 2 3 8 Nitrate loss in surface ru 2 3 9 Soluble phosphorus loss 2 4 EROSION osiinsa 2 4 1 Sediment yield noff and leaching to groundwater IN surface runoffececcer 2 4 2 Organic nitrogen transport by sediment eeen 2 4 3 Phosphorus transport by sediment ussnnsssnnnnnnnnnnnnnnn 2 5 RIVER ROUTING 2 5 1 Flow routing 2 5 2 Sediment rQUtING oooooooccccnnnncononanccnncnnnncnnnnanr cnn cn nn n nn nana rr 2 5 3 Nutrient routing Table 2 1 Abbreviati0NS ooocccoccccnncccnncncnncncnncncnnanonnnnonancnnononcnnna canoa nn nnancnnanas 95 3 SWIM CODE STRUCTURE AND INPUT PARAMETERS 105 3 1 STRUCTURE of the SWIM GRASS INTERFACE 106 3 2 STRUCTURE of the SWIM SIMULATION PART 113
7. runoff and the concentration of labile phosphorus in the top soil layer 30 1 4 4 Erosion Sediment yield is calculated for each sub basin with the Modified Universal Soil Loss Equation MUSLE Williams and Berndt 1977 almost the same as in SWAT The equation for sediment yield includes the runoff factor the soil erodibility factor the crop management factor the erosion control practice factor and the slope length and steepness factor The only difference from SWAT is that the surface runoff the soil erodibility factor and the crop management factor are estimated for every hydrotope and then averaged for the sub basin weighted areal average Estimation of the runoff factor requires the characteristics of rainfall intensity as described in Arnold et al 1990 To estimate the daily rainfall energy in the absence of time distributed rainfall the assumption about exponential distribution of the rainfall rate is made This stochastic element is included to allow more realistic representation of peak runoff rates given only daily rainfall and monthly rainfall intensity This allows a simple substitution of rainfall rates into the equation The fraction of rainfall that occurs during 0 5 hours is simulated stochastically taking into account average monthly rainfall intensity for the area Soil erodibility factor can be estimated from the texture of the upper soil layer The slope length and steepness factor is estimated based on the Digi
8. the mineral nitrogen 80 and active organic nitrogen 20 pools Mineralisation of the active organic nitrogen pool depends on soil temperature and water content Phosphorus mineralization The phosphorus mineralisation model is structurally similar to the nitrogen mineralisation model To maintain phosphorus balance at the end of a day humus mineralisation is subtracted from the organic phosphorus pool and added to the mineral phosphorus pool and residue mineralisation is distributed between the organic phosphorus pool 20 and the labile phosphorus 80 Sorption adsorption of phosphorus Mineral phosphorus is distributed between three pools labile phosphorus active mineral phosphorus and stabile mineral phosphorus Mineral phosphorus flow between the active and stable mineral pools is governed by the equilibrium equation assuming that the stable mineral pool is four times larger Mineral phosphorus flow between the active and labile mineral pools is governed by the equilibrium equation as well assuming equal distribution Denitrification Denitrification as one of the microbial processes is a function of temperature and water content The denitrification occurs only in the conditions of oxygen deficit which usually takes place when soil is wet The denitrification rate is estimated as a function of soil water content soil temperature organic matter a coefficient of soil wetness and mineral nitrogen content The soil water factor i
9. 6 Sensitivity to the alpha factor for groundwater abf0 048 0 1 0 3 217 Sensitivity to initial groundwater contribution to flOW ccocicncnnnnnnnnnnnnnnns 218 Sensitivity to initial water StOraQ oooonnncnncccnnnnnncnnnccnonccnnnncnannnarn nono nnncnnnnnnnnnns 219 Sensitivity to the routing COefficientS ooooncnnnnnnnnnnccnnnnconnncrancccnnn cnn nana nnncn noo 220 Sensitivity to the routing COefficientS ooonncnnnnnicncnccnnnnconnnnnaancccnnncnnnnnarnnnnn no 221 Sensitivity to the crop type winter rye ANd MaiZB oooooccccnnnccccnccoconccnnnncnnnnnnnn nn 222 Sensitivity to the crop type winter rye and winter wheal cccccccnnnnnnnnnnnnnns 223 10 1 Model Description This chapter includes an overview about the model history section 1 1 the general description of the model objectives processes and the spatial disaggregation section 1 2 a short overview of the model components section 1 3 and a detailed mathematical description of the model components section 1 4 11 1 1 Model History The SWIM model is based on two previously developed tools SWAT Arnold et al 1993 8 1994 and MATSALU Krysanova et al 1989a amp b SWAT is a continuous time distributed simulation watershed model It was developed to predict the effects of alternative management decisions on water sediment and chemical yields with reasonable accuracy for ungauged rural basins One of its att
10. SWIM Soil and Water Integrated Model User Manual Valentina Krysanova Frank Wechsung Potsdam Institute for Climate Impact Research Potsdam Germany in collaboration with Jeff Arnold Ragavan Srinivasan and Jimmy Williams USDA ARS Temple TX USA Version SWIM 8 December 2000 Abstract Development of integrated tools for hydrological vegetation water quality modelling at the river basin scale is motivated by water resources management in densely populated agricultural areas water pollution problem arid and semi arid regions water scarcity and mountainous and loess regions erosion problem The other motivation is an ongoing climate change and land use land cover change Development of water resources in the conditions of global change requires an understanding and adequate representation in models of basic hydrologic and related processes at mesoscale and large scale i e in river basins of hundreds thousands or tens of thousands of square kilometers The model SWIM Soil and Water Integrated Model was developed in order to provide a comprehensive GIS based tool for hydrological and water quality modelling in mesoscale and large river basins from 100 to 10 000 km which can be parameterised using regionally available information The model was developed for the use mainly in Europe and temperate zone though its application in other regions is possible as well SWIM is based on two previously developed tools SWAT an
11. a input Besides several modules were excluded from SWAT pesticides ponds reservoirs lake water quality in order to avoid the overparametrization Table 1 1Comparison of advantages and disadvantages of SWAT and MATSALU Advantages Disadvantages SWAT e Coupling with GIS e Two level disaggregation basin and sub basins in SWAT 93 e Hydrological module tested in several meso and e Connection to specific American macroscale watersheds data sets especially soil climate e Vegetation module e SWAT as a long term predictor was simplified EPIC is adopted always tested and validated only for different crops and with monthly time step natural vegetation MATSALU Three level disaggregation Connection to specific Estonian data scheme sets not transferable to other basins e N module was tested in Four coupled models not a coupled mesoscale watershed in watershed model connection with hydrology and river transport In parallel to the model development its modules were sequentially tested in the Elbe basin starting from hydrology In contrast to SWAT the hydrological module of SWIM has been validated with a daily time step During the test some subroutines were modified some parameters were changed and some components have been substituted Currently the model SWIM includes some common or similar modules of both predecessors and some new routines like the flow routing which is based on Muskingum method i
12. als for the duration of an individual runoff event Williams 1980 Thus they are routed by adding contributions from all sub basins to determine the basin load 31 32
13. annel USLE Universal Soil Loss Equation factors K and C are estimated using a standard table Step 3 Hydrotope structure The program defines the basin hydrotope structure by overlaying the sub basin map with land use and soil layers The structure file is created to run the model Each line in the file describes the characteristics of one hydrotope its sub basin number land use and soil Step 4 Weather attributes The program selects the closest weather precipitation station to every sub basin Then either actual weather information can be used or the weather generator in this case the long term monthly statistical parameters must be available for precipitation and temperature for the station This part of the interface has to be modified to provide more flexible input of climate information 20 basin sub basins en hydrotopes river routing aggregation of water N P cycling water N P sediments lateral flows vegetation growth Fig 1 5 Three level disaggregation scheme basin sub basins hydrotopes implemented in SWIM Step 5 Ground water attributes The ground water parameters are estimated for each sub basin using the alpha layer the reaction factor described in 2 2 This parameter defines the time lag needed to the groundwater flow as it leaves the shallow aquifer to reach the stream Step 6 Routing structure The interface creates the routing structure for the basin based on the elevation map The
14. atersheds with varying soils land use and management which resulted in the development of several models like AGNPS Young et al 1989 SWRRB Arnold et al 1990 and MATSALU Krysanova et al 1989a amp b AGNPS AGricultural NonPoint Source is a spatially detailed single event storm model that subdivides complex watersheds into grid cells 12 GLEAMS EPIC ROTO GRASS Interface 7 AGNPS SWRRB SWAT E A MATSALU SWIM CREAMS Fig 1 1Model development based on the CREAMS model SWRRB Simulator for Water Resources in Rural Basins was developed to simulate nonpoint source pollution from watersheds It is a continuous time daily time step model that allows a basin to be subdivided into a maximum of ten sub basins To create SWRBB the CREAMS model was modified for application to large complex rural basins The major changes involved were the following e the model was expanded to allow simultaneous computations on several sub basins e a better method was developed for predicting the peak runoff rate e a lateral subsurface flow component was added e acrop growth model was appended to account for annual variation in growth and its influence on hydrological processes a pond reservoir storage component was adjoined e a weather generator rainfall solar radiation and temperature was added for more representative weather inputs both temporally and spatially e a module accounting for transm
15. before running this step Menu Option 8 Extract Climate Station Input Data Climate Station File number and coordinates UTM of each station Climate Station Raster Map Technical Description This will extract the number of the three nearest climate stations for the basin or each sub basin using the program brb_main_stationno c The step requires a climate station number file name The station numbers are stored in the file name cstn under full_path It also creates a label file to mark the searched stations in the map on the Grass graphics monitor and in map hardcopies The label file name clabel is stored in the necessary path grass databases project_name mapset paint labels 26 To mark one station with its number the input in a label file has to look as follows east 4610296 500000 north 5806264 000000 xoffset yoffset ref lower center font standard color black size 500 background white opaque yes text 46663 After that the function find_subb_stations is called This function prompts for an existing raster map of climate stations It extracts all climate stations in each sub basin using the grass program r mapcalc see description of find_subb_stations Menu Option 9 Extract Precipitation Station Input Data Precipitation Station File number and coordinates of each station Precipitation Station Raster Map Technical Description This will extract the number of the three nearest preci
16. cation fertilisation fertilisation wash off residue mineralization leaching fertilisation erosion mineralization residue mineralization Fig 1 4 Nitrogen and phosphorus flow charts as implemented in SWIM 19 1 2 3 Spatial Disaggregation A three level disaggregation scheme similar to that used in MATSALU is implemented in SWIM for mesoscale basins Fig 1 5 The three level disaggregation scheme in SWIM implies 1 basin 2 sub basins and 3 hydrotopes inside sub basins The idea is that a mesoscale basin from 100 to 10 000 km is first subdivided into sub basins of reasonable average area see explanation in section 3 1 3 This can be done using the r watershed program of GRASS or any other GIS with similar capabilities which is applied to a Digital Elevation Model of the area with a certain threshold for the average size of the sub basin After that the hydrotopes or HRUs are delineated within every sub basin based on land use and soil types Normally a hydrotope is a set of disconnected units in the sub basin which have a unique land use and soil type A hydrotope can be assumed to behave in a hydrologically uniform way within the sub basin 1 2 4 GIS Interface The SWAT GRASS interface Srinivasan Arnold 1993 Srinivasan et al 1993 was adopted and modified for SWIM to extract spatially distributed parameters of elevation land use soil types and groundwater table
17. d MATSALU see more explanations in section 1 1 The model integrates hydrology vegetation erosion and nutrient dynamics at the watershed scale SWIM has a three level disaggregation scheme basin sub basins hydrotopes and is coupled to the Geographic Information System GRASS GRASS 1993 A robust approach is suggested for the nitrogen and phosphorus modelling in mesoscale watersheds SWIM runs under the UNIX environment Model test and validation were performed sequentially for hydrology crop growth nitrogen and erosion in a number of mesoscale watersheds in the German part of the Elbe drainage basin A comprehensive scheme of spatial disaggregation into sub basins and hydrotopes combined with reasonable restriction on a sub basin area allows performing the assessment of water resources and water quality with SWIM in mesoscale river basins The modest data requirements represent an important advantage of the model Direct connection to land use and climate data provides a possibility to use the model for analysis of climate change and land use change impacts on hydrology agricultural production and water quality However the model is quite complicated and it cannot be used as a black box Understanding of the model code is a prerequisite for successful applications CONTENTS 1 MODEL DESCRIPTION 1 1 MODEL HISTORY 1 2 GENERAL DESCRIP TIONG iiinis igsi ass 1 2 1 Model objectives
18. dicating areal distribution of soil PESA IDA EA A een GRASS report about sub basin map bas3 and the map of Thiessen polygons for precipitation StatiOMS oooonniocnccnnnnnonnnnccconccnnnncnannnnnrn cnn nan cran cnn nnnnn rn nn cn nn rca Keystroke guide for using the interface ccccccccccncccncnononononononinininininininininons Format of temperature data cccccccccccccccccnccnnoconnnonnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnss Format of precipitation data ococinnnnnnnncnnnnnnnnnnnonononnnnnnnnnnnnnnnnnnnnnnnnnonnnnnnnonnnos Format of radiation data nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Format of monthly statistics for climate stations nennnnnne nn Format of soil parameters for SWIM ccccccccceceeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeenees Estimation of saturated conductivity based on soil texture class and bulk density based on Bodenkundliche Kartieranleitung 3 Estimation of saturated conductivity based on soil texture class and bulk density based on Bodenkundliche Kartieranleitung 4 en Format of water discharge data for SWIM o ccccccccncncnnnnnnnnnnnnnnnnnnnnnnnnnnos Results of sensitivity analysis for the StepenitZ basin nn 194 195 Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig
19. e drainage area and the average elevation of a sub basin to find its width and depth of channel Description This is one of the tedious operation by automating this operation through the GIS interface the user potentially saves several man hours and days of creating the fig file In addition updating this file is also easy while considering several hypothetical scenarios such as impact of reservoirs or inflow or withdrawal of flows or change in cropping and management information The interface checks the outlet sub basin of the watershed which has to be confirmed by the user Since this is determined by the elevation map there could be errors due to the spatial accuracy and or the resolution of the elevation map On completion of this step the interface creates a file lt project name flow gt which has routing information of each sub basin to the outlet of the basin showing the path of the flow through other sub basins The model allows having multiple outlets for a basin hence if the user accepts more than one outlet then the interface will create several outlets for that basin In the event of an error in the routing structure the interface enters into another menu where in the user can either enter through keyboard or using graphical monitor determines how the flow has to occur Once the user is satisfied with the routing structure the interface prompts for the 2 digit HUA where the basin belongs Hence the user needs to know this info
20. ect basin The elevation map should be true elevations in meters If not the user must apply r mapcalc to convert to meters Programs have been developed to process an elevation surface map and create SWIM slope and aspect map for the basin and for each sub basin The channel length channel slope channel dimensions average overland slope and slope length are the parameters that are required for SWIM are extracted in this step This is one of the time consuming process If this process is not completed due to memory allocation problems the user is advised to either run the interface with a higher memory machine or increase the resolution of the basin map and resample the data and run through all the steps The elevation map can be filtered to remove pits and other potential problems to SWIM modelling with the r fill dir Srinivasan and Arnold 1994 program The extracted topographic attributes are stored in files with sub and rte extensions for each sub basin Menu Option 6 Extract Groundwater Attributes Input Map Alpha Map for Groundwater Technical Description The groundwater parameters are created for each sub basin using the so called alpha layer map This parameter is required to lag the groundwater flow as it leaves the shallow aquifer to return to the stream Arnold et al 1993 Description The interface prompts for the availability of the aloha parameter map If the answer is no the interface assumes a default values fo
21. eeeeeeeeeeeeeeeeeeeeeeeees Structure of the subroutines OPEN READCOD READBAS READCRP READSUB READSOL READWET c cccccccccccsssssseeeeeeeeecsssssseeeeeeeeeeeeeeees PUE sae AD N SAO O Basetlowtactorbiiiii O ttt a aa ea aesa ena tita Crop data base file Crop dat ooooonnnniniccnnnnnnnnnncooccccnnncccnnnanrrnc cnn nn ra nnrannnnnn nn Crop abbreviated names full names and the corresponding land cover Cale TE al e a cate svc eee AN eat Prenat eaten Values of Manning s n for overland TlOW ooooooccccnnnncnnnicccccccnnnccnnnnnrrnncnnnn cnn Water erosion control practice factor P and slope length limits for contouring Values of Manning s n for channels uusssnnnssnnnnnnnnnnnnnnnnnnnnnnnnnn nn Effective hydraulic conductivity of the channel alluvium Mean physical properties of soils ooooooonccnonidiciccconononanacaaanccnnnc cnn anar n corn cnnnnn Curve Numbers for land use categories and four soil groups used in SWIM SCS Curve Numbers for a variety of land use land cover categories Chapter 4 Recommended resolution of DEM for some typical applications List of source ASCII files and the corresponding maps in GRASS Maps created with r watershed using different thresholds Comparison of three sub basin maps created with different thresholds GRASS report about the soil map soil3 in
22. enseseneesentresenseees 38 Hydraulic conductivity as a function of soil water content equation 39 assuming SC 50 8 mm h FC 33 mm mm UL 43 mm mm 44 An example of the annual dynamics of soil albedo equations 58 59 49 Potential soil evaporation ESO as a function of leaf area index LAI equation 61 under assumption that EO 6 MM d nennnn 50 Photosynthetic active radiation PAR as a function of leaf area index LAI for RAD 1000 2000 and 3000 Ly equation 83 c cceeesseeeeeeteeeeeesteeeeeeaes 56 Leaf area index as a function of the heat unit index equation 87 57 Temperature stress factor as a function of average daily air temperature equations 96 and 98 assuming TO 25 C and TB 3 C 60 Nitrogen stress factor as a function of N supply and N demand equations MO fevers rateceece reve tet debsen E ee 61 Harvest index as a function of heat unit index factor HIC equation 103 62 Harvest index as a function of soil water content factor HIC equation 104 63 Scheme of operations included in SWIM crop module n 64 Factor ALFA as a function of CO concentration for wheat and maize estimated using the first method equations 108 109 110 and assuming BE 30 kg m MJ ha d for wheat and BE 40 kg m Mu ha d for maize The CO concentration is changing from 330 to 660 ppm een 66 ALFA fact
23. he fraction of each sub basin within the basin is also estimated Description All raster values in the input sub basin map greater than zero will be used to create reclass rules to set the project MASK to the basin and sub basin areas Each time the project is called the MASK will be automatically set The project resolution is extracted from the sub basin map in meters and used to set the cell size of the all other extraction of data from other GIS layers When the user runs the step 5 to extract topographic attributes and an memory error message appears then either the program has to be run with larger memory or the resolution of the sub basin map has to be increased by running the r resample to set bigger resolution of the sub basin map A part of this step attempts to find the minimal region needed to contain the basin mask at the given resolution A region will be calculated to allow at least a one cell border around the basin area After the project mask region and resolution are set the information is recorded and will be reset automatically each time the project is called If any of the inputs in this step are subsequently reset all other steps that may have been completed will be marked with a status of rerun or run since changing basin resolution or region will require that inputs will have to be resampled and extracted once again Menu Option 4 Extract Hydrotope Structure Map Input Basin Map Land Use Map Soil Map Descrip
24. ission losses was appended e asimple flood routing component was adjoined and e a sediment routing component was added to simulate sediment movement through ponds reservoirs streams and valleys SWRRB was still limited to ten sub basins and had a simplistic routing structure with outputs routed from the sub basin outlets directly to the basin outlet This problem led to the development of a model called ROTO Routing Outputs to Outlet Arnold et al 1990 which took outputs from SWRRB run on multiple sub basins and routed the flows through channels and reservoirs ROTO provided a reach routing approach and overcame the SWRRB sub basin limitation by linking the sub basin outputs Although the combination SWRRB ROTO was quite effective especially in comparison with CREAMS the input and output of multiple files was cumbersome and required considerable computer storage Limitations also occurred due to the fact that all SWRRB runs had to be made independently and then the SWRRB outputs had to be input to ROTO for the channel and reservoir routing Finally the model called SWAT Soil and Water Assessment Tool was developed merging SWRRB and ROTO into one basin scale model SWAT is a continuous time model working with daily time step which allows a basin to be divided into hundreds or thousands of sub watersheds or grid cells One more model MATSALU was developed in Estonia for the agricultural basin of the Matsalu Bay which belongs t
25. ld However the more detailed approach implemented in EPIC for the root growth and nutrient cycling is not included in order to maintain a similar level of complexity of all submodels and to keep control on the model performance A single model is used for simulating all the crops and natural vegetation included in the crop database attached to the model Annual crops grow from planting date to harvest date or until the accumulated heat units reach the potential heat units for the crop Perennial crops maintain their root systems throughout the year although the plants may become dormant after frost Phenological development of the crop is based on daily heat unit accumulation Interception of photosynthetic active radiation is estimated with Beer s law equation Monsi and Saeki 1953 as a function of solar radiation and leaf area index The potential increase in biomass is the product of absorbed PAR and a specific plant parameter for converting energy into biomass The potential biomass is adjusted daily if one of the four plant stress factors water temperature nitrogen and phosphorus is less than 1 0 using the product of a minimum stress factor and the potential biomass The water stress factor is defined as the ratio of actual to potential plant transpiration The temperature stress factor is computed as a function of daily average temperature optimal and base temperatures for plant growth The N and P stress factors are based on the ra
26. lumn subdivided into several layers The simulated hydrological system consists of four control volumes the soil surface the root zone the shallow aquifer and the deep aquifer Fig 1 3 The percolation from the soil profile is assumed to recharge the shallow aquifer Return flow from the shallow aquifer contributes to the streamflow The soil column is subdivided into several layers in accordance with the soil data base The water balance for the soil column includes precipitation evapotranspiration percolation surface runoff and subsurface runoff The water balance for the shallow aquifer includes ground water recharge capillary rise to the soil profile lateral flow and percolation to the deep aquifer The nitrogen module includes the following pools Fig 1 4 nitrate nitrogen NO N active and stable organic nitrogen organic nitrogen in the plant residue and the flows fertilisation input with precipitation mineralisation denitrification plant uptake wash off with surface and subsurface flows leaching to ground water and loss with erosion The phosphorus module includes the pools labile phosphorus active and stable mineral phosphorus organic phosphorus and phosphorus in the plant residue and the flows fertilisation sorption desorption mineralisation plant uptake loss with erosion wash off with lateral flow The wash off to surface water and leaching to groundwater are more important for nitrogen while phosphorus is
27. mainly transported with erosion The module representing crop and natural vegetation is an important interface between hydrology and nutrients The same as in SWAT a simplified EPIC approach Williams et al 1984 is included in SWIM for simulating all arable crops considered wheat barley corn potatoes alfalfa and others using unique parameter values for each crop which were obtained in different field studies Simplification relates mainly to less detailed description of phenological processes and lower requirements on the input information This enables to simulate crop growth in a distributed modelling framework for quite large basins and regions Non arable and natural vegetation is included in the database through some aggregated vegetation types like grass pasture forest etc and can be simulated as well 16 ogical cycle Crop vegetation growth Fig 1 2 Flow chart of the SWIM model integrating hydrological processes crop vegetation growth and nutrient dynamics 5 ee w o E oa a CA O SC o gt z o S al 3 surface runoff 5 5 g subsurface ZN gt lateral N c flow percolation ground water recharge ground water return flow Shallow aquifer percolation to the deep aquifer a2 gt oO as Fig 1 3 Flow chart of hydrological processes in soil as implemented in SWIM 18 erosion plant uptake denitrifi
28. nd 50 Ch oooconnnconnniciccccccnanccanananannccnnncos 178 An overlay of the sub basin map the precipitation station map and the Thiessen polygon map for the Glonn basin a D ccceeeeeeeeeeeteeeeeeeeeetees 181 Estimation of saturated conductivity using three different methods for dominant soils in the Glonn DASIN cccecccceeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeness 193 Sensitivity to the method of estimation of saturated conductivity 206 Sensitivity to saturated conductivity SC TEA ooooooccconnccninicccoccccnnnconannaranc cnn 207 Sensitivity to saturated conductivity sc calculated 208 Sensitivity to soil deptD ooonooocccnnnnninnnicoccccnnncconnnnannccnnnncnnnnnr anar rn ccn nn rana 209 Sensitivity to the soil group assignMeMt ooocccccnnccncccccccccnnnnnonanan nn ncnn nn no nannnn nn 210 Sensitivity to the Curve Number method CN different in comparison to GN 30 Olan tein RA cansas AEA 211 Sensitivity to the Curve Number method CN different in comparison to GN 505 Mina la aa DE ee TE 212 Sensitivity to the Curve Number method CN different in comparison to GNi 50 808 A A A ie A AA A a aid 213 Sensitivity to the baseflow factor bff 1 0 5 O B ooonnnnnnnicnncccnnncconananannccnnnnns 214 Sensitivity to the baseflow factor bff 1 1 5 3 cc eeeeseestteeeeeeeeeeteeeaees 215 Sensitivity to the alpha factor for groundwater abf0 048 024 012 21
29. nder the project name so that they may be copied or recalled for further completion or modification The first menu displayed when running SWIM INPUT includes functions to create a new project to work on existing projects to copy an existing project and to remove existing projects The user has to set the current GRASS mapset where the SWIM GRASS project will be created otherwise an error message or erroneous results will occur The main menu includes steps to be completed to prepare input files for SWIM plus some other miscellaneous functions as following SWIM GRASS Project Data Extraction Menu Project Name Dahme lt example gt Choose desired option 0 Quit 1 Extract data from layers 2 Display Raster Vector and or Site Maps run 3 Extract Basin Attributes run 4 Extract Hydrotope Structure run 5 Extract Topographic Attributes run 6 Extract Groundwater Attributes run 7 Compute Routing Structure and Create fig file run 8 Extract Climate Station run 9 Extract Precipitation Station Option 0__ AFTER COMPLETING ALL ANSWERS HIT lt ESC gt TO CONTINUE OR lt Ctrl C gt TO EXIT Steps 3 9 record and display their status to the left of the step number If a step has not been run run status is displayed as seen above If the step has been successfully completed the status will be listed as done In some cases a change in one step will cause the need to run a
30. nother step again in which case the status will read rerun If a step having the status of done or rerun is run again it will attempt to offer previous inputs as defaults 23 1 3 2 Options of the Main Menu This section describes the options of the main menu All steps are verbose to provide as much immediate information as needed however it is necessary that the user is also familiar with the operations of SWIM itself The sub basin map must be delineated in advance based on the topography The GRASS r watershed command can be used to create the sub basin map from an elevation map layer see e g Section 3 1 6 Step 2 helps to display either a raster vector or site maps over the sub basin map and also to display the sub basin number on the graphical screen Step 2 can be used only in conjunction with the GRASS graphic monitor Steps 3 through 9 collect inputs either maps from the currently available mapsets or other text numerical inputs in order to create or extract the necessary portions of SWIM inputs for that step Step 3 should be the first step step 7 should be completed before moving into step 0 to leave the interface Menu Option 3 Extract Basin Attributes Map Input Sub basin Map Technical Description This is the first step before attempting to extract any other input Using a given sub basin map the program calculates area resolution and geographic co ordinate boundaries for the basin and for each sub basin T
31. nstead of ROTO in SWAT and Sant Venant approach in MATSALU The SWAT GRASS interface was modified for SWIM Further development of the model is planned in the following directions e standardization of climate and crop management input data addition of a module accounting for the carbon cycle in soil addition of the lake and watershed modules improving the description of lateral transport of nutrients and modifying SWIM GRASS interface to include automatic connection of climate precipitation stations to sub basins 15 1 2 General Description 1 2 1 Model Objectives The objectives of the model are two fold e to provide a comprehensive GlS based tool for the coupled hydrological vegetation water quality modelling in mesoscale watersheds from 100 up to 10 000 km which can be parameterised using regionally available data and e to enable the use of the model for analysis of climate change and land use change impacts on hydrological processes agricultural production and water quality at the regional scale 1 2 2 Processes Described in the Model SWIM integrates hydrology erosion vegetation and nitrogen phosphorus dynamics at the river basin scale Fig 1 2 and uses climate input data and agricultural management data as external forcing The hydrological module is based on the water balance equation taking into account precipitation evapotranspiration percolation surface runoff and subsurface runoff for the soil co
32. o the Baltic Sea with the area about 3 500 km and the bay ecosystem in order to evaluate different management scenarios for the eutrophication control of the Bay The model consists of four coupled submodels which simulate 1 watershed hydrology 2 watershed geochemistry 3 river transport of water and nutrients and 4 nutrient dynamics in the Bay ecosystem Similar to SWRRB its watershed components were essentially based on the CREAMS approach Spatial disaggregation in MATSALU is based on the overlay of three map layers a map of elementary watersheds with an average area of 10 km a land use map and a soil map to obtain so called Elementary Areas of Pollution EAP Conceptually the EAPs are similar to Hydrologic Response Units HRU or hydrotopes The three level disaggregation scheme of MATSALU includes the basin elementary sub basins EAPs Since the model was developed for the MATSALU watershed and connected to specific data sets it is not sufficiently transferable 14 Merging the two tools SWAT and MATSALU we tried to keep their best features and maintain their advantages see Tab 1 1 The model code was mostly based on SWAT The more comprehensive three level spatial disaggregation scheme from MATSALU was introduced into SWAT as an initial step The next step was to adjust the model for the use in European conditions where data availability is different This required some efforts in order to modify the dat
33. or for cotton wheat and maize as dependent on temperature in the case of CO doubling a and in the case of 50 increase in CO b assuming initial CO concentration 330 ppm equations 111 115 and 121 68 Comparison of two methods for the estimation of ALFA and BETA factors ALFA and BETA as functions of CO concentration under assumption that temperature is 17 C for the second Method cccccccnnnnnnnnnnnnnnnnnnnnnnnnnnnnninnnnos 70 An example of soil temperature dynamics in five soil layers simulated with SWIM using equation TB ooonnncnccccnnnccocanncconnccnnncnnnnnn ono ncnnnnnn naar nn nn nn cnn nn rca 73 Temperature factor of mineralisation TFM equation 142 76 Soil water factor of denitrification equation 151 u nennen 79 The optimal crop N concentration CNB as a function of growth stage IHUN equation 154 assuming BN1 0 06 g g BN2 0 0231 g g and BN3 0 013209 o oe Robledo oe a 80 Coefficient CW to calculate the amount NO N lost from the layer as a function of water content equation 160 ooooonnnnccocccnnnncccnncanoncnnnncnnnnnann nn ncnnnnos 82 Scheme of operations included in SWIM nitrogen module 84 8 Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig Fig 2 24 2 25 2 26 2 27 3 1 3 2 4 1 4 2
34. pitation stations for the basin or each sub basin using program brb_main_stationno c The step requires a precipitation station number file name The station numbers are stored in the file name pstn under full_path It also creates a label file to mark the searched stations in the map on Grass graphics monitor and in map hardcopies The label file name plabel is stored in the necessary path grass databases project_name mapset paint labels After that function find_subb_stations is called This function prompts for an existing raster map of precipitation stations It extracts all precipitation stations in each sub basin using the grass program r mapcalc see description of find_subb_stations Once the steps are completed from 3 to 9 by choosing the option 0 the user leaves the interface At this time the interface also creates the file cio file which has the entire input file names prepared by interface At this junction the user can run the SWIM model The steps 8 and 9 need further modification Currently they can be also omitted O72 1 4 Overview of the Model Components 1 4 1 Hydrological Processes Snow melt The snow melt component is similar to that of the CREAMS model Knisel 1980 according to a simple degree day equation Melted snow is then treated in the same way as rainfall for further estimation of runoff and percolation Surface runoff The runoff volume is estimated using a modification of the SCS curve numbe
35. plications eneee 224 APPENDIX GRASS commands useful for the spatial data preparation for SWIM 227 REFERENCES iia cui e e a ee eee Ra eee fast 235 ACKNOWLEDGEMENTS 44444444444440004420nB0Rnnnnnnnnnonnnnnnnnnnnnnnnnnnnnnnannnnnnn 239 Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab Tab 3 10 3 11 3 12 3 13 3 14 3 15 3 16 3 17 3 18 3 19 3 20 3 21 LIST OF TABLES Chapter 1 Comparison of advantages and disadvantages of SWAT and MATSALU Chapter 2 Abbreviations to equations 1 194 uuussssssssssnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Chapter 3 Description of files included in SWIM GRASS interface File swimmake used to compile SWIM GRASS interface Files and subroutines included in SWIM COUE cccccccccnnnnnnnnnnnnnnnnnnnnnnnoninnnos File Makefile used to compile SWIM COde cccceeeeseeeeeneeeeeeeeeeeennaaeeeeeees Description of subroutines included in SWIM simulation part Structure of the subroutine MAIN sesssssssssssssseesssseseeeeeeeeeeseeeeeeeeeeeeeeess Structure of the subroutine SUBBASIN cccccccncnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnonnnos Structure of the subroutine HYDROTOP ccccccceeeeeeeeeee
36. r all the groundwater parameters If there is a groundwater parameter alpha map the category values should be in hundreds The other groundwater parameters such as initial groundwater height etc are assumed to be default values A detailed description of the procedure to create the alpha map can be found in Arnold et al 1993 Menu Option 7 Compute Routing Structure and Create fig file Input Data Automatic 25 Technical Description In this step the interface creates a lt project name fig gt file which is the main engine for running the SWIM model This file has the information about when to add flows and route through sub basins and when to route through reservoirs and add inflow or subtract withdrawals from any sub basins and or reaches This step is automated using the elevation map Altering either step numbers 3 or 5 will require running this step again Also this step determines the channel width and depth of flow for routing This is done using neural network that is embedded in the interface which has been trained on the USGS United States Geologic Survey defined 2 digit Hydrologic Unit Areas HUA Several hundreds of width and depth information were collected and used in training the neural network by the 2 digit HUA A detailed description about the neural network method and the datasets used here can be obtained by sending an e mail request to srin brcsunO tamu edu or muttiahObrcsun0 tamu edu The neural network needs th
37. r method Arnold et al 1990 Surface runoff is predicted as a nonlinear function of precipitation and a retention coefficient The latter depends on soil water content land use soil type and management The curve number and the retention coefficient vary non linearly from dry conditions at wilting point to wet conditions at field capacity and approach 100 and 0 respectively at saturation The modification essentially reduced the empirism of the original curve number method The reliability of the method has been proven by multiple validation of SWAT and SWIM in mesoscale basins Nevertheless there is a possibility to exclude the dependence of the retention coefficient on land use and soil leaving the dependence on soil water content only and assuming the same interval for all types of land use and soils Percolation The same storage routing technique as in SWAT is used to simulate water flow through soil layers in the root zone Downward flow occurs when field capacity of the soil layer is exceeded and as long as the layer below is not saturated The flow rate is governed by the saturated hydraulic conductivity of the soil layer Once water percolates below the root zone it becomes groundwater Since the one day time interval is relatively large for soil water routing the inflow is divided into 4 mm slugs in order to take into account the flow rate s dependence on soil water content If the soil temperature in a layer is below 0 C no percola
38. ractive features is that there is a long period of modeling experience behind this model see Fig 1 1 In the mid 1970 s in response to the Clean Water Act the USDA Agricultural Research Service ARS assembled a team of interdisciplinary scientists to develop a process based nonpoint source simulation model From that effort a field scale model called CREAMS Chemicals Runoff and Erosion from Agricultural Management Systems was developed Knisel 1980 to simulate the impact of land management on water sediment and nutrients In the 1980 s several models have been developed with origins from the CREAMS model One of them the GLEAMS model Groundwater Loading Effects on Agricultural Management Systems Leonard ef al 1987 concentrated on pesticide and nutrient load to groundwater Another model called EPIC Erosion Productivity Impact Calculator Williams et al 1984 8 1985 was originally developed to simulate the impact of erosion on crop productivity and has now evolved into a comprehensive agricultural field scale model aimed in the assessment of agricultural management and nonpoint source loads One more model for estimating the effects of different management practices on nonpoint source pollution from field sized areas and also based on CREAMS is the OPUS model Smith 1992 These three models can be applied for the field scale areas or small homogeneous watersheds Other efforts involved modifying CREAMS to simulate complex w
39. routing structure is put in a special file which provides the information about when to add flows and route through sub basins and when to add inflow or subtract withdrawals from any sub basins Steps 1 2 5 6 described above are the same as in the SWAT GRASS interface steps 3 and 4 are new and some other steps from SWAT GRASS such as irrigation and nutrient attributes were excluded 1 2 5 Modelling Procedure First the SWIM GRASS interface runs to produce necessary input files After that the model itself can be run The model operates on a daily time step After the input parameters are read from files the three step modelling procedure is applied First water and nitrogen dynamics and crop vegetation growth are calculated for every hydrotope Then the outputs from the hydrotopes especially the lateral water and nutrient flows are averaged area weighted average to estimate the sub basin output Finally the routing procedure is applied to the sub basin outputs taking transmission losses into account 299 1 3 Overview of the SWIM GRASS Interface 1 3 1 Main Menu A menu driven interface from GRASS to SWIM integrates SWIM with GRASS by preparing a set of input files required to run a SWIM simulation The interface provides a menu of steps to prepare the input files Each simulation is treated as a project by SWIM INPUT which has a name analogously to the GRASS project name The inputs collected for the steps are recorded u
40. s an exponential function of soil moisture with an increasing trend when soil becomes wet Crop uptake of nutrients Crop uptake of nitrogen and phosphorus is estimated using a supply and demand approach Six parameters are specified for every crop in the crop database which describe BN and BP normal fraction of nitrogen and phosphorus in plant biomass excluding seed at emergence BN and BP at 0 5 maturity and BN and BP at maturity Then the optimal crop N and P concentrations are calculated as functions of growth stage The daily crop demand of nutrients is estimated as the product of biomass growth and optimal concentration in the plants Actual nitrogen and phosphorus uptake is the minimum of supply and demand The crop is allowed to take nutrients from any soil layer that has roots Uptake starts at the upper layer and proceeds downward until the daily demand is met or until all nutrient content has been depleted Soluble nutrient loss in surface water and groundwater The amount of NO N and soluble P in surface runoff is estimated considering the top soil layer only Amounts of NO N and soluble P in surface runoff lateral subsurface flow and percolation are estimated as the products of the volume of water and the average concentration Retention factor is taken into account through transmission losses Because phosphorus is mostly associated with the sediment phase the soluble phosphorus loss is estimated as a function of surface
41. soil evaporation is reduced and estimated as a function of the number of days since stage two began Plant transpiration is simulated as a linear function of potential evapotranspiration and leaf area index When soil water is limited plant transpiration is reduced taking into account the root depth 28 Groundwater flow The groundwater model component is the same as in SWAT see Arnold et al 1993 The percolation from the soil profile is assumed to recharge the shallow aquifer Return flow from the shallow aquifer contributes directly to the streamflow The equation for return flow was derived from Smedema and Rycroft 1983 assuming that the variation in return flow is linearly related to the rate of change of the water table height In a finite difference form the return flow is a nonlinear function of ground water recharge and the reaction factor RF the latter being a direct index of the intensity with which the groundwater outflow responds to changes in recharge The reaction factor can be estimated for gaged sub basins using the base flow recession curve 1 4 2 Crop Vegetation Growth The crop model in SWIM and SWAT is a simplification of the EPIC crop model Williams et al 1984 The SWIM model uses a concept of phenological crop development based on daily accumulated heat units Monteith s approach 1977 for potential biomass water temperature and nutrients stress factors and harvest index for partitioning grain yie
42. sssennnnssnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 162 4 1 2 How to choose the spatial resoluti0N oooonccnnnnncnnncccnnnninnnacannnnos 166 4 1 3 How to choose the average sub basin area een 166 4 1 4 GRASS GIS overview uuunesssssnsssnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn nn 167 4 1 5 Useful GRASS programs and functions 168 4 1 6 Map export from ARC INFO to ASCII format eee 170 4 1 7 Watershed analysis program r watershed en 174 4 18 DEMO d t Sbt u nit ei HHHEHHH HHH EN 176 4 2 SWIM GRASS INTERFACE uuunnssssssnsnnnsnnnnnnnnnnennnnnnonnnnnnnnnnnnnenn 183 4 3 RELATIONAL DATA PREPARATION ccccccceeeeeeeeeeeeeneeeeeeneeeeseneeeeees 188 4 3 1 The overview of relational data ccccccceececesceeeeeeeeeeeeestteeeeeees 188 4 32 Climate Gata vii sesesigactyidsdundanginiedi sezcden eden llida 188 43 3 SOM Mata ie o cies cass eesets siii 191 4 3 4 Crop parameters and crop management data u 196 4 3 5 Hydrological and water quality data ooononnnnnnninicnnnnnnnnnccccnccnnnnns 196 44 MODEL RUN vacios A 197 4 4 1 Collect all input data ooooncnnnnnnnnnnccnnnncnanccconccnnnncnnnnnnrn nn cnn nccnnnnos 197 4 4 2 Modification of the code to adjust for specific input data 199 4 4 3 Sensitivity analySiS coooocnnnnnccoccccnnncccnnnnannccnnnnnonnnnnn nn nnnnnncnancnnnnnns 200 4 4 4 Overview of the model ap
43. tal Elevation Model of a watershed by SWIM GRASS interface for every sub basin 1 4 5 River Routing The Muskingum flow routing method Maidment 1993 is used in SWIM The Muskingum equation is derived from the finite difference form of the continuity equation and the variable discharge storage equation The outflow rate for the reach is estimated using a requrrent equation with two parameters They are the storage time constant for the reach KST and a dimensionless weighting factor X In physical terms the parameter KST corresponds to an average reach travel time and X indicates the relative importance of the inflow and outflow in determining the storage in the reach The sediment routing model consists of two components operating simultaneously deposition and degradation in the streams The approach is based on the estimation of the stream velocity in the channel as a function of the peak flow rate the flow depth and the average channel width The sediment delivery ratio is estimated using a power function power 1 to 1 5 of the stream velocity If the sediment delivery ratio is less than 1 the deposition occurs in the stream and degradation is zero Otherwise degradation is estimated as a function of the sediment delivery ratio the channel K factor or the effective hydraulic conductivity of the channel alluvium and the channel C factor Nitrate nitrogen and soluble phosphorus are considered in the model as conservative materi
44. tio of accumulated N and P to the optimal values The fraction of daily biomass growth partitioned to roots is estimated to range linearly between two fractions specified for each vegetation type 0 4 at emergence to 0 2 at maturity Root depth increases as a linear function of heat units and potential root depth Leaf area index is simulated as a nonlinear function of accumulated heat units and crop development stages Crop yield is estimated using the harvest index which increases as a nonlinear function of heat units from zero at planting to the optimal value at maturity The harvest index is affected by water stress in the second half of the growing period 29 1 4 3 Nutrient Dynamics Nitrogen mineralisation The nitrogen mineralisation model is a modification of the PAPRAN mineralisation model Seligman and van Keulen 1981 Organic nitrogen associated with humus is divided into two pools active or readily mineralisable organic nitrogen and stable organic nitrogen The model considers two sources of mineralisation a fresh organic nitrogen pool associated with crop residue and b the active organic nitrogen pool associated with the soil humus Organic N flow between the active and stable organic nitrogen pools is governed by the equilibrium equation Mineralisation of fresh organic nitrogen is a function of the C N ratio C P ratio soil temperature and soil water content The N mineralisation flow from residue is distributed between
45. tion This step prompts for the name of an elevation map land use map and soil map for the project basin The program starts the GRASS command r stats for these three maps and stores the output in the file project_name str except these data where one of the first three numbers is zero 24 Menu Option 5 Extract Topographic Attributes Map Input Elevation Map Technical Description The topographic features required for entire basin and for each sub basin are gathered using an elevation map By masking the entire basin and each sub basin the stream length stream slope and stream dimensions are estimated using the concept of r stream att tool Srinivasan and Arnold 1994 along with proper aggregation methods The accumulation drainage area is computed for each sub basin along with the drainage aspect of which sub basin flows into which sub basin This information is later used to automate the routing structures for the SWIM model The starting and ending nodes of the stream for the basin and each sub basin are estimated Using the r topo att tool Srinivasan and Arnold 1994 overland slope and slope length are estimated and aggregated by the weighted average or mode dominant method The channel USLE K USLE C Manning s n and USLE P factors are estimated using a standard table and the knowledge obtained in the topographic attributes extraction processes Description This step prompts for the name of an elevation map for the proj
46. tion occurs from that layer The soil temperature is estimated for each soil layer using the air temperature as a driver Arnold et al 1990 Lateral subsurface flow Lateral subsurface flow is calculated simultaneously with percolation The kinematic storage model developed by Sloan ef al 1983 is used to estimate the subsurface flow The approach is based on the mass continuity equation in the finite difference form with the entire soil profile as the control volume To account for multiple layers the model is applied to each soil layer independently starting at the upper layer to allow for percolation from one soil layer to the next and percolation from the bottom soil layer past the soil profile as recharge to the shallow aquifer Evapotranspiration Potential evapotranspiration is estimated using the Priestley Taylor method 1972 that requires solar radiation and air temperature as input It is possible to use the Penman Monteith method Monteith 1965 instead if wind speed and relative air humidity data can be provided in addition The actual evapotranspiration is estimated following the Ritchie 1972 concept separately for soil and plants Actual soil evaporation is computed in two stages It is equal to the potential soil evaporation predicted by means of an exponential function of leaf area index Richardson and Ritchie 1973 until the accumulated soil evaporation exceeds the upper limit of 6 mm After that stage two begins The actual
Download Pdf Manuals
Related Search