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User's Guide for Biome

1. summary gt Cchange PLT summary gt Cchange SNSC amp summary gt daily sr amp summary gt daily tr 38 User s Guide for Biome BGC MuSo 3 0 User s Guide for Biome BGC MuSo 3 0 APPENDIX E EXAMPLES FOR ANCILLARY MANAGEMENT FILES Examples are given here for the content of the externally defined ancillary management types that is a new feature of Biome BGC MuSo v3 0 Note that these examples assume that the simulation is performed for 3 years i e the 7 event lines are repeated 3 times In any case the 7 event lines have to be defined separately for each simulation years in each block An example for ancillary file to describe mowing is already presented above so it is not given here Ancillary file for annually varying planting PLANTING day 70 999 9 999 9 999 9 999 9 999 9 999 9 80 999 9 999 9 999 9 999 9 999 9 999 9 90 999 9 999 9 999 9 999 9 999 9 999 9 quantity of seed kg seed ha 10 100 100 100 100 100 100 20 100 100 100 100 100 100 10 100 100 100 100 100 100 C content of seed 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 4 proportion of material of seed which produces leaf 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 Ancillary file for annually varying thinning THINNING day 150 999 9 999 9 999 9 99979 999 9 99959 150 999 9 9 99 9 99 97 9 9999 99 979 999 9 150 999 9 999 9 999 9 999 9 999 9 999 9 THINNING rate 504
2. 3 on 0 29 J9 O 00 o ooo Mo oO O D ct r 3 8 3 ts ooo ooo a Mh Deo anwan aaa Oe aannan a n4naunu yo daa at a a a rt ou oO O E 2 ooou ooo o K oo0ootfooodtooo A E anaona anan O OOM OO Or O70 qa a an ooo Fa Oo on 0 0 0 0 0 0 E 0 0 0 dada aaa oo ooorooon 4ooo ooo ooo al Ancillary file for annually varying grazing FIRST DAY OF GRAZING 120 200 999 9 999 9 999 9 999 9 999 9 130 210 999 9 999 999 9 999 9 9999 140 220 999 9 999 9 999 9 999 9 999 9 LAST DAY OF GRAZING 180 280 999 9 999 9 999 9 999 9 999 9 o 39 190 2 90 999 9 999 9 999 9 999 9 999 9 200 300 999 9 999 9 9999 9997 95 99 99 DAILY INGESTED DRY MATTER Toro 1650 15 00 15 00 L520 To 0 15 50 1540 Lo 0 90 1390001540 190 15 0 ISO ES 050 T50 T5 0 25 00 15 0 ANIMAL STOCKING RATE DTO DET OO OA LOS PO gt Debo Usado O03 00 O Do EROS ODO O On TS DO 0 da DELS 02 15 O OO PROP DM INTAKE FORMED EXCREMENT 29 O 207 252 25 ZO 72530 29 NA 29 A 25 Zoe 25 0 q 295 2s BOP ZS 2D AdAO PROP EXCREMENT RETURNING TO LITTER 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 C CONTENT OF DRY MATTER 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 N CONTENT OF MANURE 5 5 5 5 9 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 C CONTENT OF MANURE 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
3. Development and optimization of an Agro BGC ecosystem model for C4 perennial grasses Ecological Modelling 221 2038 2053 doi 10 1016 j ecolmodel 2010 05 013 Hidy D Barcza Z Haszpra L Churkina G Pint r K Nagy Z 2012 Development of the Biome BGC model for simulation of managed herbaceous ecosystems Ecological Modelling 226 99 119 doi 10 1016 j ecolmodel 2011 11 008 Jarvis N J 1989 A simple empirical model of root water uptake Journal of Hydrology 107 57 72 Jolly W Nemani R R Running S W 2005 A generalized bioclimatic index to predict foliar phenology in response to climate Global Change Biology 11 619 632 doi 10 1111 1365 2486 2005 00930 x Ma S Churkina G Wieland R Gessler A 2011 Optimization and evaluation of the ANTHRO BGC model for winter crops in Europe Ecological Modelling 222 3662 3679 doi 10 1016 j ecolmodel 2011 08 025 Pietsch S A Hasenauer H Kucera J Cermak J 2003 Modeling effects of hydrological changes on the carbon and nitrogen balance of oak in floodplains Tree Physiology 23 735 746 Reichstein M 2001 Drought effects on carbon and water exchange in three Mediterranean ecosystems PhD Thesis Universitat Bayreuth Bayreuth Germany Thornton P E 1998 Regional ecosystem simulation combining surface and satellite based observations to study linkages between terrestrial energy and mass budgets Ph D dissertation School of Forestry Universit
4. User s Guide for Biome BGC MuSo 3 0 User s Guide for Biome BGC MuSo v3 0 by D ra HIDY and Zoltan BARCZA Plant Ecology Research Group of the Hungarian Academy of Sciences Szent Istvan University H 2103 G d ll Pater K u 1 Hungary E mail dori hidy gmail com Department of Meteorology E tv s Lor nd University H 1117 Budapest P zm ny P s 1 A Hungary Institute of Ecology and Botany Centre for Ecological Research Hungarian Academy of Sciences H 2163 V cr t t Alkotm ny u 2 4 Hungary E mail bzoli elte hu w N What s new in Biome BGC MuSo v3 0 in comparison with v2 2 the model runs considerably faster due to optimization of soil moisture calculation algorithm exponential root profile is introduced within the model in this version elevated groundwater effect on soil moisture causes smooth transition towards higher soil moisture values no step wise change of soil moisture is occurring within the soil layers groundwater control is possible during the spinup phase of the simulation not only in normal phase groundwater depth is defined by positive numbers instead of negative ones the effect of grassland mowing and grazing on storage transfer pools is more realistic and management effect on next year s initial growth is less critical unrealistic respiration peak after ploughing is damped fine root transformation into litter is gradual and does not occur within one day
5. FERTILIZING block parameters are described in Appendix A Section 2 5 To illustrate the structure of the management blocks here we present an example for the MOWING block The structure of the other blocks is very similar MOWING 0 flag or filepath do MOWING O no l yes filepath read file 0 flag mowing method 0 on fixday 1 if LAI gt fixed value 6 0 int fixed value of the LAI before MOWING fixvalue method 1 0 int fixed value of the LAI after MOWING fixvalue method 150 234 999 9 999 9 999 9 999 9 999 9 yday MOWING day 1 0 1 0 1 0 1 0 1 0 1 0 1 0 int value of LAI after MOWING fixday method 0 0 0 0 0 0 0 transported part of plant material See Appendix B for a complete example for the new INI file structure OPTION TO USE ANCILLARY MANAGEMENT FILES TO SIMULATE ANNUALLY VARYING HUMAN INTERVENTION Biome BGC MuSo v3 0 has a built in option to define annually varying management settings for all implemented management types This can be achieved by creating ancillary text files with a given structure The names of the ancillary management files can be set by the user but for clarity we suggest to use names that are identical with the name of the management block defined within the INI file e g mowing txt fertilizing txt grazing txt harvesting txt planting txt ploughing txt thinning txt The text files are only utilized if the first line of the given management block in the INI file contains reference t
6. is varying but Ndep is constant both CO and Ndep are varying Note that the first line of the CO2 CONTROL block should not be 2 in any case that was only possible in the original 4 1 1 version of Montana University IMPORTANT NOTE although the CO and Ndep files include the year for the CO2 Ndep data first column within the text files is year then the annual data is given it is important to keep in mind that the model does not take these dates into account during the simulation When working with these files the model uses the first line of the external files for the first simulation year the second for the second year etc irrespective of the year defined by the lines This behavior of the model is not trivial and can be misleading to the users It means that the users have to make sure that the content of the CO and Ndep file is appropriate The usual strategy for CO2 and Ndep control within the INI files is to use constant preindustrial values during the spinup phase CO2_ CONTROL flag is 0 RAMP_NDEP flag is 0 then use annually varying CO and Ndep for the entire normal simulation representative to present day conditions This strategy might be the result of unknown site disturbance history or lack of driving data However this logic can lead to undesired transient behavior of the model as the user may introduce a sharp change within the CO and or Ndep data both are important drivers of plant growth For example in our pr
7. EPC file so that modification of these parameters will be easier Ratio of belowground aboveground management related mortality RRM In order to understand the meaning of this parameter some knowledge about the internal model logic is needed Thornton 1998 Briefly in Biome BGC storage pools are defined that are responsible to control the initial plant growth at the beginning of the consecutive growing season at the end of the actual year storage pools are turned into the transfer pools which are used in next year s initial growth in this sense transfer pool is just another name for the storage pool The physical location aboveground belowground of these storage transfer pools are not defined explicitly within the model In case of forests the storage pool for leaf is clearly an aboveground pool while the storage pool for root is underground In perennial herbaceous vegetation roots are responsible for the survival of the plant during the dormant season Therefore our interpretation for the physical location of the storage transfer pools is the following aboveground material refers to the actual pools of leaf and fruit while belowground material refers to the actual pools of root and storage transfer pools of leaf fruit and root Using this logic the storage transfer pools are supposed to be underground which means that disturbance should have less effect on these important pools as compared to the aboveground biomass In Biome BGC Mu
8. GSI limitl greater that limit gt start of vegper 0 01 GSI limit2 less that limit gt end of vegper intvar GSI txt file of the estimated start and end of the VP OUTPUT CONTROL outputs bugac_2003 2011 MuSo intvar ctrl txt file of the BBGC variables control 1 flag 1 write daily output 0 no daily output 0 flag 1 monthly avg of daily variables 0 no monthly avg 0 flag 1 annual avg of daily variables 0 no annual avg 0 flag 1 write annual output 0 no annual output 1 flag for on screen progress indicator DAILY OUTPUT 5 number of daily output variables 623 GPP 649 TR 34 LHF 509 LAI 638 SOLLe ANNUAL OUTPUT 10 number of annual output variables 545 ann max projLAI 628 cumNPP 630 cumNEE 631 cumGPP 632 cumMR 633 cumGR 634 cumHR 636 vegC 638 soilC 639 totalc MANAGEMENT SECTION PLANTING 0 flag do PLANTING O no l yes filepath reading from file O S999 S99 9 S995 S99 9 B99 9 O yday PLANTING day O10 TOGO OO OO 100 1040 100 0 double quantity of seed kg seed ha 40 40 40 40 40 40 40 C content of seed 90 90 50 50 50 50 50 useful part of seed THINNING 0 flag do THINNING O no l yes filepath reading from file 50 999 9 999 9 999 9 999 9 999 9 999 9 yday THINNING day OLD 0 25 0 25 0 0 10 07 0 0 prop thinning rate 00 100 100 100 100 100 100 transported part of stem 00 100 100 100 100 100 100 transported part of leaf MOWING 0 flag do MOWING O no l yes filepath re
9. and computational cost In accordance with the soil layers we also defined new fluxes within the model The thicknesses of the active layers from the surface to the bottom are 0 1 0 2 0 3 0 4 1 m and 1m again It means that the first layer is located at depth of 0 10 cm the second is at 10 30 cm the third is at 30 60 cm the fourth is at 60 100 cm the fifth is at 100 200 cm and the sixth is at 200 300 cm The thickness of bottom 7th inactive layer is 2 m it is located at depth 300 500 cm The bottom layer is special it is assumed that its temperature is equal to the annual average air temperature of the site and its soil water content is equal to the field capacity constant value It is also assumed that the bottom layer can only be a sink of mineralized N but can NOT be a source to the upper layers The percolation from the bottom layer is a net loss for the soil system Soil texture is assumed to be constant with depth The total soil depth is 5 m with the root zone in the upper part of the soil maximum possible rooting depth is 5 m and its value can be set by a parameter in the INI file but it must be less than 5 meters Soil properties are calculated for each layer separately see below Note that in the Hidy et al 2012 publication only 4 soil layers were used the 7 layer module is an improvement basically aimed to support forest related simulations Water becomes available to the plant through water movement through the
10. and to calculate its diurnal changes The surface layer is special because this is the primary sink of precipitation and the source of bare soil evaporation In the other layers only the processes of diffusion percolation and root water uptake are occurring At the bottom layer the hydraulic diffusivity is assumed to be zero so that the soil water flux is only due to the gravitational deep percolation which is a net water loss from the modeled soil layer Root water uptake can occur from layers where roots are present The transpiration flux of the ecosystem is assumed to be equal to the total root water uptake on a given day The transpiration calculation is based on Penman Monteith equation using stomatal conductance original Biome BGC logic is used The transpiration fluxes are divided between layers according to the soil moisture limitation of the given layer formula is presented below coeffgtoma and the proportion of the total root mass in the given layer definition is described above Hydraulic conductivity and hydraulic diffusivity can change rapidly and significantly with changing soil moisture content Though the main processes of Biome BGC are based on daily 18 User s Guide for Biome BGC MuSo 3 0 time steps the simulation of the soil hydrological parameters required finer grained calculations therefore a nested time step based calculation was introduced into the soil hydrology sub module The time step is dynamically chan
11. content of the given soil layer wp and Osat are the wilting point and the saturation value of the soil moisture content Two different limiting factors determine soil moisture stress Limitation 1 is due to insufficient soil moisture drought stress As presence of elevated groundwater or high soil moisture content close to saturation due to large precipitation events can negatively affect stomatal conductance Bond Lamberty et al 2007 a second limitation is defined to represent 22 User s Guide for Biome BGC MuSo 3 0 to the effect of excessive soil water content similarly to decomposition control described above The start of the water stress due to drought limitation 1 is determined using the newly introduced ecophysiological parameters relative proportion to field capacity soil water content limitation RSWC iti and relative proportion to field capacity soil water potential limitation RSWPorit1 The default relative soil water content threshold limit values are corresponding to field capacity If enough soil water is available no water stress the stress function is equal to 1 If the conductance reduction is complete serious water stress the stress function is equal to 0 at wilting point and below wilting point The other limitation effect of soil moisture content is around the saturation value above RWC it2 close to saturation stomatal conductance is limited again limitation 2 The start of the water
12. field capacity where drought related soil moisture limitation starts relSWCcrit1 If actual RSWC is larger than parameter 46 then soil moisture does not affect stomatal conductance evapotranspiration and root water uptake Linear ramp function is defined between parameter 46 limitation starts and wilting point complete limitation The default value is 1 RSWC 1 means that soil water content is at field capacity it means that there is no water stress above field capacity Note that field capacity wilting point and saturation are estimated from soil texture using empirical relationship If no available data set it to 999 9 moisture limitation starts at RSWC 1 e line 49 parameter 47 critical RSWC where elevated soil moisture content starts to affect stomatal conductance and decomposition thus acts as limitation factor e User s Guide for Biome BGC MuSo 3 0 relSWCcrit2 The idea behind introducing this parameter is that presence of elevated groundwater or a wet rainy period can negatively affect stomatal conductance and decomposition If actual RSWC is smaller than parameter 47 then soil moisture does not affect stomatal conductance at least due to elevated soil moisture evapotranspiration decomposition and root water uptake Linear ramp function is defined between parameter 47 limitation starts and saturation complete limitation The default value is saturation field capacity If no available data set it to 999 9 no w
13. header lines in met file flag flag flag dont read restart write restart 0 dont write restart use restart metyear 0 reset metyear filename name of the input restart file filename name of the output restart file read restart 0 1 1 1 int int int number of meteorological data years number of simulation years first simulation year T spinup run 0 normal run maximum number of spinup years Tmax Tmin for PRCP for VPD for RAD offset for offset for multiplier multiplier multiplier flag ppm filename O constan l vary with file trigger tansient run in spinup constant atmospheric CO2 concentration name of the CO2 file maximum depth of rooting zone sand percentage by volume in silt percentage by volume in clay percentage by volume in m site elevation degrees site latitude DIM site shortwave albedo kgN m2 yr wet dry atmospheric deposition of N kgN m2 yr symbiotic asymbiotic fixation of N Celsius mean annual air temperature unoff parameter Campbell 1988 m3 m3 measured SWC at SAT if no data m3 m3 measured SWC at FC if no data m3 m3 measured SWC at WP if no data rock free soil rock free soil rock free soil for S Hem 99979 9999 999 9 ag O constan l vary with file trigger transient run in spinup t obsolete reference year for industrial N deposition N m2 yr obsolete industrial N deposition value filename name of the N dep file filename E
14. is performed for 3 years so in each block 3 lines are repeated The first line in MOWING day block belongs to the first simulation year then the second is to the second year and finally the third is to the third simulation year Number of lines must be equal with number of simulation years defined within the INI file Additional examples are given in Appendix E separately for each management type MODIFICATIONS MADE IN THE ECOPHYSIOLOGICAL FILE The modifications of the model logic led to changes in the ecophysiological parameterization as well An example EPC file is given in Appendix C New ecophysiological parameters e line 8 parameter 6 year day to start fruit allocation YFL Default value is 150 e line 16 parameter 14 the ratio of fruit and leaf carbon content allocation parameter new fruit C new leaf C FC LC Default value is 0 5 e line 24 parameter 22 the carbon and nitrogen ratio of fruit C N ratio of fruit CN_ fr Default value is equal to the carbon and nitrogen ratio of leaf e line 33 35 parameters 31 33 the fruit litter labile cellulose lignin proportion FLaP FCeP FLiP Default values are equal to the labile cellulose lignin proportions of leaf litter e line 44 parameter 42 fraction of leaf nitrogen in PeP Carboxylase for new C4 photosynthesis routine FLNP Default value is 0 03 e line 48 parameter 46 critical relative soil water content RSWC i e actual soil water content divided by
15. pool from which it gradually enters the litter pool Although mowing is not possible outside of the growing season this temporary pool can contain plant material also in the dormant period depending on the amount of the cut down material and the turnover rate of the cut down non woody biomass This logic was applied because observations indicated that respiration fluxes are unrealistic if we assume that harvested biomass turns immediately into litter pool The turnover rate of mowed harvested biomass to litter TRMB default value is 0 05 can be set in EPC file parameter 55 The plant material returning into litter compartment is divided between the different types of litter pools according to EPC parameterization The water stored in the canopy of the cut down fraction is assumed to be evaporated Eight parameters are defined to simulate mowing in the MOWING section of the INI file e flag for choosing the type of mowing simulation method MOWING on predefined day or MOWING if LAI is greater than a predefined value e predefined value of the LAI before MOWING in case of predefined value method e day of the year of mowing in case of predefined day method e the value of LAI after mowing determines MOWING effect e transported part of plant material Note that the last three parameters have to be repeated seven times in the INI files of MuSo v3 0 If the user only wants to use a subset of the maximum 7 events 999 9 should be written to t
16. rate the proportion of the removed trees the decrease of leaf stem and root pools can be determined After thinning the cut down fraction of the aboveground biomass can be taken away or can be left at the site The rate of transported stem and or leaf can be set by the user The transported plant material is excluded from the further calculations The plant material translocated into CWD or litter compartments are divided between the different types of litter pools according to parameterization coarse root and stem goes into the CWD pool if harvested stem is taken away from the site only coarse root goes to CWD note that storage and transfer pools of woody harvested material are translocated into the litter pool Parameters used to simulate thinning are e day of year of thinning e thinning rate e transported part of stem e transported part of leaf The handling of the cut down non removed pools is different for stem roots and leaves Stem live and deadwood see Thornton 2000 for definition of deadwood in case of Biome BGC is immediately translocated into CWD without any delay However for stump and leaves implementation of an intermediate turnover process was necessary to avoid C and N balance errors caused by sudden changes between specific pools The parameter Turnover rate of cut down non removed non woody biomass to litter TRCN controlled within the EPC file controls the fate of previously living leaves on cut down trees a
17. 0 30 60 60 100 100 200 200 300 and 300 500 cm soil layer thickness is calculated o new init parameters runoff parameter measured volumetric water content at wilting point field capacity saturation if it is available o calculation of the saturation field capacity wilting point and hygroscopic water of hydrological parameters summary c new summary output variables are calculated VEGC LITRC SOILC TOTALC SR soil respiration NBP o NEE is positive if ecosystem is net C source 10 User s Guide for Biome BGC MuSo 3 0 o carbon content change from management and disturbance is summarized e bgc constant h bgc epclist h bgc_struct h firstday c make_zero_flux_struct output init c pointbgc_struct c presim_state init c restart io h state init c summary c state _update c zero _srcsnk c new pools and fluxes for multilayer soil modules fruit simulation GSI calculations management modules calculation of summarized soil stocks e bgc c pointbec c bgc_func h bgc io h pointbgc c pointbgc_func h spinup_bgc c modifications due to new subroutines and new variables e output map _init c modifications due to new variables see Appendix D 2 New subroutines GSI calculation e GSI_calculation c o calculation of phenological state of vegetation onset and offset day from Tmin VPD daylength and cumulative Tavg based on literature o snow cover estimation based on precipitation average temperatur
18. 006 m s cuticular conductance projected area basis 0 04 m s boundary layer conductance projected area basis 999 9 prop relative SWC prop to FC to calc soil moisture limit 1 999 9 field cap 999 9 prop relative SWC prop to FC to calc soil moisture limit 2 999 9 saturation 999 9 prop relative PSI prop to FC to calc soil moisture limit 1 999 9 field cap 999 9 prop relative PSI prop to FC to calc soil moisture limit 2 999 9 saturation 1000 Pa vapor pressure deficit start of conductance reduction 5000 Pa vapor pressure deficit complete conductance reduction 0 01 prop senescence mortality coefficient of aboveground plant material 0 01 prop senescence mortality coefficient of belowground plant material 0 01 prop turnover rate of wilted standing biomass to litter 0 05 prop turnover rate of cut down non woody biomass to litter 0 3 prop growth respiration cost per unit of C grown 0 01 prop N denitrification proportion 0 1 prop N mobile proportion 0 5 prop maturity coefficient 36 APPENDIX D CHANGES IN THE OUTPUT VARIABLES AND THEIR CODE IN BIOME BGC MUSO V3 0 User s Guide for Biome BGC MuSo 3 0 Due to code modifications there are many changes in the output variables Please see output_map_init c for current list of variables Note that in MuSo v3 0 total ecosystem respiration can be retrieved as a single output variable output_map 649 to support Monte Carlo experiments wit
19. 1989 NSWC Oa O im max min where Oact Omin Omax are the actual the minimum and the maximum of the soil moisture content The maximum of the soil moisture content is the saturation value the minimum is the wilting point or the hygroscopic water depending on the type of the simulated process The hygroscopic water the wilting point the field capacity and the saturation values of the soil moisture content are calculated internally by the model based on the soil texture data algorithm of the calculation method is available from the authors in the form of an Excel file or can be defined by the INI file though this can cause inconsistency with the soil water movement calculations In the EPC files RSWC values are defined and alternatively soil relative soil water potential values and the other measures of soil water status are calculated from RSWC internally by the model code 1 2 3 Modified decomposition and root respiration control Within Biome BGC decomposition processes are influenced by soil temperature and soil water status Instead of the averaged soil temperature and soil water status of the whole soil column the average temperature and soil moisture content of the actual root zone avgO is used by the decomposition calculation The depth of the root zone is calculated based on above mentioned rooting depth Below the optimum soil moisture content Oop the decomposition is limited due to drought stress and a
20. 40 40 40 40 40 Ancillary file for annually varying harvesting HARVESTING DAY 100 999 9 999 9 999 9 999 9 999 9 999 9 150 999 9 999 9 999 9 999 9 999 9 999 9 200 999 9 999 9 999 9 999 9 999 9 999 9 AT AFTER HARVESTING SNAG 1 1 1 0 14 0 20 D500 Tb 16 07 1 50 O65 UO Tee 1 0 1 01 10 T 0 OL Lo Tu 0 he Oe 0 110 TRANSPORTED PART OF PLANT MATERIAL 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Ancillary file for annually varying ploughing PLOUGHING DAY 220 999 9 999 9 999 9 999 9 999 9 999 9 999 9 2209999 999 9 999 9 99 9 9 999 9 999 9 999 9 220 999 9 999 9 999 9 999 9 999 9 999 9 999 9 Ancillary file for annually varying fertilization FERTILIZING DAY 60 120 2201 999 29 999 9 999 9 9999 60 120 220 999 9 999 9 999 9 999 9 60 120 220 999 9 999 9 999 9 999 9 40 User s Guide for Biome BGC MuSo 3 0 User s Guide for Biome BGC MuSo 3 0 NITROGEN FROM FERTILIZATION PER DAY 60 0 30 0 30 0 30 0 30 0 30 0 30 0 70 0 30 0 30 0 30 0 30 0 30 0 30 0 80 0 30 0 30 0 30 0 30 0 30 0 30 0 NITRATE CONTENT OF FERTILIZER DP Oe Lots Oe PaO LOL LO LE BO A O LAs Or LO LT 00 deh Od 0 E60 LY 30 17 0 17 0 17 0 17 0 17 0 17 0 17 0 AMMONIUM CONTENT OF FERTILIZER TSO y O Ld OSA E Ole OE 0 LT 50 DOr AO ASO TT Ol LT Oe LTO 10 VAGONES LTO Ae OL O KO
21. 99 9 FERTILIZING 0 flag 60 120720099979 9 9 9 9 99 99 99 9 9 9 30 0 30 0 30 0 30 0 30 0 30 0 30 0 ie EA A a ede eds ET 17 Ay F de 7 7 5 5 5 5 5 5 5 PO OP OA ORO OPO E TO 20 20 20 20 20 20 20 0 0 0 0 0 0 0 10 10 10 10 10 10 10 5 5 5 5 5 5 5 90 90 90 90 90 90 90 END INIT do FERTILIZING O no yday PLOUGHING day l yes yday FERTILIZING day kgN ha day nitrogen from fertilization per day nitrate content of fertilizer ammonium content of fertilizer carbon content of fertilizer labile fraction of fertilizer unshielded cellulose fraction of fertilizer shielded cellulose fraction of fertilizer lignin fraction of fertilizer dissolving coefficient useful part filepath reading from file ee User s Guide for Biome BGC MuSo 3 0 APPENDIX C EXAMPLE EPC FILE FOR BIOME BGC MUSO V3 0 ECOPHYS C3 grass 0 flag 1 WOODY 0 NON WOODY 0 flag 1 EVERGREEN 0 DECIDUOUS flag 1 C3 PSN 0 C4 PSN flag 1 MODEL PHENOLOGY 0 USER SPECIFIED PHENOLOGY 60 yday yearday to start new growth when phenology flag 0 300 yday yearday to end litterfall when phenology flag 0 50 yday yearday to flowering start of fruit allocation 0 prop transfer growth period as fraction of growing season 0 prop litterfall as fraction of growing season 0 1 yr annual leaf and fine root turnover fraction 0 00 1 yr annual live wood turnover fraction 0 05 1 yr annual wh
22. C MuSo 3 0 calculation of the change of soil water content layer by layer taking into account soil hydrological processes precipitation evaporation runoff percolation diffusion using dynamically changing time step the latter is defined based on the amount of precipitation bottom layer 300 500 cm is special the percolated water is net loss for the system while the diffused water is net loss or net surplus for the system depending on the direction of the diffusion note that in Biome BGC MuSo v3 0 upward diffusion from the lower boundary below 3 m is possible as the boundary layer has constant soil moisture we assume that the diffusion from or into the boundary layer is occured by an infinite depth layer with soil moisture content at field capacity theoretical upper limit of water content saturation value amount above saturation is stored on the surface as a pond water theoretical lower limit of water content hygroscopic water content percolation diffusion or evaporation fluxes can be limited due to dry soil pond water simulation in case of saturated top soil layer e multilayer_transpiration c O calculating fractional transpiration fluxes from sum of the transpiration flux determined in canopy_et c based on the soil water status of the soil layers root water uptake is only possible from the layers where root is located if stomata is not closed root water uptake is divided between soil layers when enough soil
23. LFS 0 CARBON CONTENT OF FERTILIZER 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 LABILE FRACTION OF FERTILIZER 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 UNSHIELDED CELLULOSE FRACTION OF FERTILIZER 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 SHIELDED CELLULOSE FRACTION OF FERTILIZER 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 LIGNIN FRACTION OF FERTILIZER 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 DISSOLVING COEFFICIENT 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 USEFUL PART 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 Al User s Guide for Biome BGC MuSo 3 0 REFERENCES Bond Lamberty B Gower S T Ahl D E 2007 Improved simulation of poorly drained forests using Biome BGC Tree Physiology 27 703 715 Campbell G S Diaz R 1988 Simplified soil water balance models to predict crop transpiration in Bidinger F R Johansen C Eds Drought research priorities for the dryland topics International Crops Research Institute for the Semi Arid Tropics Patancheru Andhra Pradesh India pp 15 26 Clapp R B Hornberger G M 1978 Empirical equations for some soil hydraulic properties Water Resources Research 14 601 604 Di Vittorio A V Anderson R S White J D Miller N L Running S W 2010
24. PC file name m2 water stored in snowpack M initial soil water as a proportion of saturation C m2 first year maximum leaf carbon C m2 first year maximum stem carbon C m2 coarse woody debris carbon C m2 litter carbon labile pool C m2 litter carbon unshielded cellulose pool C m2 litter carbon shielded cellulose pool C m2 litter carbon lignin pool C m2 soil carbon fast microbial recycling pool C m2 soil carbon medium microbial recycling pool C m2 soil carbon slow microbial recycling pool 33 User s Guide for Biome BGC MuSo 3 0 0 0 kgC m2 soil carbon recalcitrant SOM slowest N_STATE 0 0 kgN m2 litter nitrogen labile pool 0 0 kgN m2 soil nitrogen mineral pool GROWING SEASON 5 0 kgH20 m2 critical amount of snow limiting photosyn no data 999 9 flag use GSI index to calculate growing season 5 00 Celsius basic temperature to calculate heatsum 00 Celsius limitl under full constrained of HEATSUM index 0 00 Celsius limit2 above unconstrained of HEATSUM index 2 00 Celsius limitl under full constrained of TMIN index 5 00 Celsius limit2 above unconstrained of TMIN index 4000 Pa limitl above full constrained of VPD index 000 Pa limit2 under unconstrained of VPD index 0 s limitl under full constrained of DAYLENGTH index 0 s limit2 above unconstrained of DAYLENGTH index 0 day moving average to avoid the effects of extreme events 0 10
25. So v3 0 specific management types e g grazing mowing and harvest affects decrease the storage transfer pools and also fine roots RRM defines the ratio of the belowground and aboveground pool decline due to grazing mowing and harvest RRM is set to 0 1 in the current model version This means that e g in case of removing 50 of aboveground plant material actual pools of leaf due to cutting causes 5 decrease in both the leaf and root storage transfer pools and also the root pool itself Critical value of soil moisture stress index SMSI defined later below which senescence mortality begins SMSIcrit Currently this parameter is set to 0 5 This 0 5 means that the soil moisture stress index within the entire root zone should fall below 0 5 to trigger plant senescence caused by drought Critical number of drought related water stress days after which water stress is complete CSDcrit it causes total plant death Its value defined within the code is 30 Empirical exponential root distribution parameter Jarvis 1989 Its value is 3 67 User s Guide for Biome BGC MuSo 3 0 Modified ecophysiological parameters In the original model Biome BGC v4 1 1 leaf water potential start of complete conductance reduction LWPS LWPC were used instead of the EPC parameters 46 49 In the current model version if critical relative soil water potential values parameter 48 49 are given by the user while parameters 46 and 47 parameters based
26. actice we use Biome BGC MuSo to simulate the carbon balance of Hungarian grasslands for the time period when eddy covariance measurements were performed For this purpose we use meteorological data for the 1901 2000 time period for the spinup phase then we use measured meteorological data during the normal phase for the time period of e g 2000 2013 Constant CO 280 ppm and Ndep 0 0002 kgN m year data are used in the spinup then we use annually varying CO and Ndep starting with 370 ppm CO and 0 001 kgN m year Ndep in 2000 As the model is sensitive to ambient CO concentration and N deposition discontinuity within these drivers results in undesired model behavior In order to avoid this phenomena and more importantly to take into account site history some of the model users performed one or more transient simulations to enable smooth transition from one simulation phase to the other mainly from the spinup phase to the normal phase However this procedure means that the users have to perform a 3rd model run and 31 User s Guide for Biome BGC MuSo 3 0 sometimes even more using the endpoint file of the spinup phase and create the endpoint file that the normal phase can use In Biome BGC MuSo v3 0 we implemented a novel approach to eliminate the effect of sharp change in the environmental conditions between the spinup and normal phase According to the modifications now it is possible to make an automatic transient simu
27. ading from file 0 flag mowing method 0 on fixday method 1 fixLAI method 6 0 int fixed value of the LAI before MOWING fixLAI method 1 0 int fixed value of the LAI after MOWING fixLAI method 150 234 999 9 999 9 999 9 999 9 999 9 yday MOWING day fixday method LO LO Sk sO 1 0710 sO E0 int value of the LAI after MOWING fixday method A User s Guide for Biome BGC MuSo 3 0 transported part of plant material GRAZING 0 flag do GRAZING O no l yes filepath reading from file 20 200 999 9 999 9 999 9 999 9 999 9 yday first day of GRAZING 200 280 999 9 999 9 999 9 999 9 999 9 yday last day of GRAZING D0 MUDO 15 0 ADO 110 0 9152 0 150 kg dry matter LSU daily ingested dry matter USAS US US US O SS LS O LSU ha animal stocking rate Livestock Units ha 25 A 25 25 25 25 25 prop proportion of DM intake formed excrement 00 100 100 100 100 100 100 prop excrement returning to litter 40 40 40 40 40 40 40 carbon content of dry matter 5 5 5 5 5 5 5 N content of manure 40 40 40 40 40 40 40 C content of manure HARVESTING 0 flag do HARVESTING O no l yes filepath reading from file 200 999 9 999 9 999 9 999 9 999 9 999 9 yday HARVESTING day 0 1 0 22 00 EO 1 0 g 1 0 m2 m2 LAI after HARVESTING snag 100 100 100 100 100 100 100 transported part of plant material PLOUGHING 0 flag do PLOUGHING O no l yes filepath reading from file 200 999 9 999 9 999 9 999 9 999 9 9
28. and bare soil evaporation We have added the simulation of runoff diffusion percolation pond water formation and evaporation processes in order to improve the soil water balance simulation Optional handling of seasonally changing groundwater depth i e possible flooding due to elevated water table is also implemented beginning with MuSo v2 2 e g to support flooding simulation for lowland forests Surface runoff is the water flow which occurs when the rate of rainfall on a surface exceeds the rate at which water can infiltrate into the soil Our runoff simulation method is semi empirical and is based on the assumption that runoff increases as daily precipitation increases Campbell and Diaz 1988 Runoff is subtracted from precipitation and the remainder is available for infiltration An empirical runoff parameter describes the surface storage condition and it defines the critical amount of precipitation over which runoff occurs using an empirical function this can be adjusted within the INI file The downward movement of water within the soil is called percolation The fraction flowing into the soil can be stored in root zone or lost by deep percolation outflow at the bottom In soil a concentration gradient causes net movement of water molecules from high concentration regions to low concentration ones and this gives the movement of water by diffusion The goal was to determine the average soil moisture content of each active soil layer
29. ater stress below saturation so the model will set this parameter as saturation field capacity line 50 parameter 48 critical relative soil water potential proportion to field capacity where drought related soil moisture limitation starts RSWPcrit1 Default value is 1 field capacity The difference between parameter 46 and 48 is only the calculation method in case of parameter 48 the critical soil moisture content value is calculated from critical soil water potential instead of RSWC If no available data set it to 999 9 moisture limitation starts at field capacity line 51 parameter 49 critical relative soil water potential proportion to field capacity value where elevated soil moisture content starts to affect stomatal conductance and decomposition RSWPcrit2 The difference between parameter 46 and 48 is only the calculation method in case of parameter 49 soil water potential is used instead of RSWC If no available data set it to 999 9 no water stress below saturation line 54 parameter 52 drought stress related mortality coefficient causing plant senescence on aboveground plant material SMCA The parameter controls the fraction of aboveground plant material carbon and nitrogen that dies during one day due to long lasting drought The fraction of senescenced biomass is calculated from this parameter but this value is modified to take into account the length of the drought and the severity of the drought The default
30. bove the optimum it is limited due to soil moisture status close to saturation The average temperature of each soil layer in the root zone is used to calculate the respiration of the fine and coarse roots LOW User s Guide for Biome BGC MuSo 3 0 As saturation causes anoxic conditions groundwater can affect decomposition of soil organic matter thus N mineralizaiton Bond Lamberty et al 2007 As soil moisture control is implemented as effect of average soil moisture content within the entire root zone the stress function affecting decomposition has been modified Note that the new stress function is a linear function of relative soil water content which is due to the definition of soil water potential similiar to the original non linear function the higher the relative soil moisture content the lower the value of the stress function is The mininum relative soil mositure content is related to hygroscopic water while the maximum is related to saturation Close to saturation the decomposition becomes limited again If mean relative soil moisture content is greater than a critical value optimum soil moisture content the stress function is calculated based on a linear ramp function to limit decomposition The shape of the modified stress function is similar to the one presented in Bond Lambery et al 2007 their Fig 1 In summary Biome BGC MuSo v3 0 uses the following normalized soil water content DECOMPLIMIT with value betwe
31. cence is occurring due to low soil water content small soil moisture stress index see description below in Chapter 1 3 during a prolonged drought period Number of days since water stress is calculated in waterstress_days c Critical value of soil moisture stress index below which water stress begins is fixed at 0 5 in MuSo v3 0 mortality fluxes enter a temporary senescence pool standing dead biomass from which dead plant material gradually enters the litter pool o turnover rates of aboveground belowground dead plant material can be set in EPC file parameter 52 53 o after a critical number of stress days the water stress is at its maximum so the whole plant material enter into senescence pool in the current model version this is 30 days Management modules planting c auxiliary function planting_init c thinning c auxiliary function thinning_init c mowing c auxiliary function mowing_init c grazing c auxiliary function grazing _init c harvest c auxiliary function harvest_init c ploughing c auxiliary function ploughing _init c s13 User s Guide for Biome BGC MuSo 3 0 e fertilizing c auxiliary function fertilizing _init c Removed obsolete subroutines e nleaching c now included in multilayer_sminn c e soilpsi c now included in multilayer_hydrolparams c e outflow c now included in multilayer_hydrolprocess c percolation at the bottom soil layer ACKNOWLEDGEMENTS BIOME BGC v
32. d in forest related studies where the multilayer soil module can be used to perform more realistic simulations in terms of soil hydrology Since the publication of the Hidy et al 2012 study additional modules were developed to simulate cropland management e g planting harvest ploughing application of fertilizers Forest thinning was also implemented and included as a possible human intervention and dynamic annually varying whole plant mortality was implemented in the model to enable more realistic simulation of forest stand development Annually varying management options were also introduced In the most recent model version separate pools have been defined for fruit following the method of Ma et al 2011 to support cropland related simulations Detailed description of the modifications is given in Appendix A This User s Guide was created to provide practical information for the use of the improved model The Hidy et al 2012 study used Biome BGC v4 1 1 with previous modifications made by Max Planck Institute Germany we refer this version as Biome BGC 4 1 1 MPI version see Trusilova et al 2009 as the starting point for model developments Our improved model is called Biome BGC MuSo v3 0 where the abbreviation refers to Multilayer Soil Module Biome BGC MuSo v3 0 is the updated version of MuSo v1 0 v1 1 v1 2 v1 3 v2 0 v2 1 and v2 2 v3 0 includes developments and bugfixes plus additional control is implemented to avoid impo
33. d into the OUTPUT_CONTROL block The second line now defines a filename where internal model parameters will be written o filename for the Biome BGC internal variables control The rest of the block is unchanged This control file is created for model development purposes only and should be ignored during everyday model runs Note that due to modification of the model logic we defined new internal variables some of which can be written to the output binary files Within Biome BGC source code the index of variables that can be written out are defined in output_map_init c The modified and the new output variables are described in Appendix D In MuSo v3 0 the annual text file was modified the annual net changes of the ecosystem s carbon and nitrogen balance caused by management and senescence are also written out New initialization blocks The following blocks were defined to control growing season estimation and to describe different management activities on the simulated ecosystem Each management type can be activated or deactivated for a given simulation independent of the other management types Implemented management types are documented in Appendix A Growing season definition block is located after the N STATE block The management block starts with the following lines after the annual output block MANAGEMENT SECTION The growing season block and the management blocks must be present even if the user deactiva
34. e BGC MuSo 3 0 simulation However the EPC parameter fraction of leaf N in Rubisco also affects the process of photosynthesis in case of both pathways see Di Vittorio et al 2010 for details 3 5 Optional transient run In the original Biome BGC v4 1 1 released by Montana University Thornton 2000 it was possible to define annually varying CO concentration by an external file However annually varying nitrogen deposition data was only controllable in a restricted way the time trajectory of nitrogen deposition varied according to the CO2 concentration data using two constant values as reference Thornton 2000 page 5 In Biome BGC v4 1 1 MPI version the authors implemented the possibility to define annually varying N deposition file that is completely independent from the CO data file Trusilova et al 2009 This modification possibility to define an external N deposition file by the 4th line of the RAMP NDEP block within the INI file made the 2nd and 3rn line of the RAMP NDEP block unnecessary Those lines remained there as a legacy from the previous versions and they are still there in the latest Biome BGC MuSo version v3 0 The model users should simply neglect these two lines In the current implementation CO concentration and N deposition are handled independently so all four combinations of constant varying CO and N depositions are possible CO and Ndep are both constants CO is constant but Ndep is varying CO
35. e and shortwave radiation growing season does not start until there is snow cover this is a new feature since Biome BGC MuSo v2 1 e auxiliary function GSI init c e further modifications in dayphen c and prephenology c Multilayer soil modules e multilayer_tsoil c o surface soil temperature change caused by air temperature change o shading effect of vegetation is calculated based on an exponential function of LAI if soil temperature is lower than air temperature the effect is zero o deeper soil layer temperature calculations are based on temperature gradient between surface and that below 3m e multilayer_hydrolparams c o calculation of soil moisture content soil water potential hydraulic conductivity and hydraulic diffusivity as a function of volumetric soil water content and constants related to texture o boundary layer below 3 m is special it has infinite depth in the sense that its value does not change with time in reality in the model is defined as a layer with 2 m depth constant temperature equal to the annual air average temperature of the site and soil moisture content field capacity The soil moisture content of this special layer can only change due to elevated water table if the depth of the goundwater is less than 5 meters Details are described below Chapter 1 2 2 o average values are estimated regarding to the root zone e multilayer_hydrolprocess c sa O User s Guide for Biome BG
36. e period plotted in Fig 3 Soil moisture content in the top layer Soilstress index 0 14 0 9 0 12 A 0 6 E 2 E 01 3 E a 0 3 0 08 0 06 0 Fluxes of wilted plant biomass Transpiration flux from the soil layers 3 3 0 10cm 6 E 10 30cm gt plant to senescence temporary pool w 2 30 60cem oa 4 senescence temporary pool to litter O E 21 32 0 0 142 144 146 148 150 152 154 156 158 160 142 144 146 148 150 152 154 156 158 160 day of the year 2003 day of the year 2003 Figure 3 Graphical representation of the drought related plant mortality at the Bugac site Hungary during the summer of 2003 Volumetric soil moisture content of the upper soil layer 0 10 cm is shown on the top left figure and the SMSI index is plotted in the upper right figure Senescence fluxes are shown in the lower left plot and the corresponding transpiration is shown in the lower right plot A2 Implementation of management modules To simulate the effect of management activities on the carbon nitrogen and water pools we defined new fluxes within the pools and between the pools and the environment The parameters of the new modules can be set by the user through the initialization files Since the release of MuSo v2 1 the model contains an important development the User can define 7 different events for
37. each management activity and additionally annually varying management activities can be defined The events can either be defined in the INI file using this method the same events with the same timing will happen each year or in an external ancillary file this case the management events can be defined separately for each year 25 User s Guide for Biome BGC MuSo 3 0 2 1 Mowing Mowing can be simulated based on the one sided LAI before and after mowing due to mowing the carbon nitrogen and water content actual transfer and storage pools as well of the leaf and fruit decrease corresponding to the decrease of the LAI We defined two possible ways of mowing calculation based on before mowing value which means that mowing is occurred when the value of the LAI reaches a fixed value fixLAI method or based on selection of pre defined days regardless from the value of LAI fixday_method Simulation of mowing is not possible out of the growing season because in the dormant period the content of leaf carbon and nitrogen pool is assumed to be zero in Biome BGC for herbaceous ecosystems After mowing the cut down fraction of the aboveground biomass can be taken away or can be left at the site In the first case the cut down plant material is excluded from the further calculations the cut down fraction of plant material carbon and nitrogen is a net loss for the system In the second case cut down plant material first goes into a temporary
38. ecrease In the original model when the stress ends the limitation of the stomatal conductance also ends and the simulated carbon uptake returns to the original value It means that the original model ignores plant wilting and associated senescence where the latter is an irreversible process caused by prolonged drought In order to solve this problem a new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality If the plant available water decreases below a critical value the new module starts to calculate the number of the days under drought stress Soil water status is assumed to be critical if the averaged soil moisture content stress function SMSI of the rootzone is less than 0 5 Due to low soil water content during a prolonged drought period number of days since water stress is calculated in waterstress days c aboveground and belowground plant material senescence is occurring actual transfer and storage carbon and nitrogen pools and the wilted biomass is translocated into the litter pool The senescence mortality coefficient of aboveground and belowground plant material can be set in EPC file parameter 52 53 note that belowground senescence mortality parameter is also used to deplete storage pools due to prolonged drought Mortality fluxes first enter a temporary senescence pool standing dead biomass from which dead plant material gradually enters the litter pool The turnover rate of dead p
39. en 0 and 1 to control decomposition in response to changing soil water content avgO O NSWC aecomp Ea a gt if ave yc O Os z O yg NSWC ponp DEBO a if O lt avgO d ecomp e e ont where avg c is the actual averaged soil moisture content of the rooting zone Ohyg Osat are the hygroscopic water and the field capacity values of the soil moisture content Oop is calculated from the relative soil moisture content for soil moisture limitation2 set in EPC file RSWCerit2 The graphical representation of the soil moisture control is presented in Fig 1 1 0 0 8 g O 061 gt Cc DO a c 3 a a S 0 4 2 D g Lo 5 Biome BGC v4 1 1 D 5 024 2 E 2 J A Biome BGC MuSo v2 2 0 0 IS 0 2 0 4 0 6 0 8 1 0 1 2 relative soil water content Figure 1 Scalar function of soil water content controlling decomposition in the original Biome BGC v4 1 1 and in Biome BGC MuSo v3 0 The scalar function is expressed as function of RSWC In this example RSWCcrit2 parameter 47 was set to 1 1 221s User s Guide for Biome BGC MuSo 3 0 1 2 4 Simulation of the soil mineral nitrogen content in multilayer soil In the original model version uniform distribution of mineral nitrogen was assumed within the soil In Biome BGC MuSo v3 0 we assume that varying amount of mineralized nitrogen is available within the different soil layers that is available for root uptake and other lo
40. ersion 4 1 1 was provided by the Numerical Terradynamic Simulation Group at the University of Montana which assumes no responsibility for the proper use of BIOME BGC by others Model developments were supported by BioVeL Biodiversity Virtual e Laboratory Project FP7 INFRASTRUCTURES 2011 2 project number 283359 by GHG Europe Greenhouse gas management in European land use systems FP7 ENVIRONMENT EU contract number 244122 by the Hungarian Scientific Research Fund OTKA K104816 and by the CarpathCC project ENV D 1 FRA 2011 0006 Testing of different versions of the model was performed in the desktop grid test environment of the MTA SZTAKI PERL within a partnership agreement and EDGeS home desktop grid volunteer computing services are provided by the IDGF We thank Galina Churkina for her continuous support in model development and application We are also grateful to Eszter Lellei Kov cs for valuable suggestions regarding the model logic We are extremely grateful to Shaoxiu Ma for providing us the source code of ANTHRO BGC that led us to the simulation of fruit yield within Biome BGC MuSo We also thank Ryan Anderson for providing us the source code of Biome BGC 4 3 beta which enabled the inclusion of the new enzyme driven C4 photosynthesis routine into the model We are grateful to Laura DOBOR for model beta testing and Attila MAROSI for helping us in the implementation of desktop grid applications of Biome BGC MuSo 14 User s G
41. files used for the transient run If the CO2 CONTROL and RAMP_NDEP flags are set to 0 no transient simulation will be performed CO2 CONTROL can be also set to 1 while RAMP NDEP flag is 0 If this happens then only CO will vary during the transient run Management settings optionally defined within the spinup INI file are only used during the transient run but not during the regular spinup phase Note that in case of transient run the endpoint file created by the regular spinup will be overwritten by the endpoint of the transient run SITE block There are some modifications within the SITE block New lines are also included here o instead of effective soil depth maximum depth of rooting zone is used m mean annual air temperature Celsius has to be defined long term mean temperature e g 1961 1990 mean or 1981 2010 mean It is used by multilayer_tsoil c to calculate temperature of the different soil layers User s Guide for Biome BGC MuSo 3 0 o runoff parameter mm used by multilayer_hydrolprocess c default is 0 1 see Campbell and Diaz 1988 for alternative parameterization of runoff parameter o soil moisture content at wilting point field capacity and saturation at the simulation site m m It is used by multilayer _hydrolprocess c if no data are available it should be set to 999 9 so the model will estimate them based on empirical functions OUTPUT_CONTROL block After the first line a new line was inserte
42. ged no precipitation event 3 hours small precipitation lt 10 mm day 10 minutes big precipitation gt 10 mm day 1 minute The values of water content at wilting point pF 4 2 field capacity pF 2 5 and saturation are calculated using Clapp Hornberger parameter which is estimated based on empirical function of soil composition Clapp and Hornberger 1978 In the developed model version these water content values can also be set by the user if measured data are available in this case soil water potential is calculated based on these water content values instead of empirical functions Experience shows that in some sites the manually defined thresholds may be inconsistent with the soil water processes calculated by the model logic Therefore we suggest to use manually defined thresholds with caution The theoretical upper limit of the volumetric water content is the saturation value In case of very large precipitation event if not all of the precipitation can infiltrate pond water is generated on the surface Water of the pond can infiltrate into soil after water content of top soil layer decreases below saturation level Evaporation of the pond water is assumed based on the soil water evaporation The theoretical lower limit of the volumetric water content is the hygroscopic water 1 e the water content of air dried soil Negative soil water content as well as negative carbon and nitrogen content is not possible Therefore in case of
43. h large number of simulations in order to save disk space New output variables incomplete list see above output ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output_ma output ma output_ma output ma output_ma p p p p p p p p P Pp p p p p p P p p p P Pp p p pP p output_map output_map output_map output_map output_map output_map output_map output_map output_map output_map output_map output_map output map pr11 output_map 11 p 11 p 11 output_map 11 _map 11 output_map 11 p 11 23 27 28 29 30 Sil 32 33 34 43 44 45 46 47 48 49 100 101 102 104 105 106 107 108 109 0 ZO M4 NE 307 315 316 317 318 319 320 32 322 323 324 325 326 amp ws gt soilw SUM amp ws gt soilevap_snk amp ws gt snowsubl_ snk amp ws gt canopyevap_ snk amp ws gt trans_ snk amp ws gt runoff snk amp ws gt deeppercolation_snk amp ws gt deepdiffusion_snk amp wf gt evapotransp wf gt soilw trans SUM wf gt prcp to runoff amp wf gt canopyw_to_ THN amp wf gt canopyw_to_ MOW amp wf gt canopyw_to_GRZ amp ns gt amp wf gt cano
44. he can set it in the spinup INI file by setting the CO2 CONTROL and RAMP_NDEP flag to 1 It means that first a regular spinup will be performed with constant CO and N deposition values set in the INI file then a second run will be performed using the same meteorological data file defined by the spinup INI file The input for the transient run is the endpoint of the regular spinup and the output of the transient simulation is the input for the normal phase As an example spinup INI file might contain the following lines CO2_CONTROL 1 flag O constant l vary with file 280 ppm constant atmospheric CO2 concentration co2 C02 1901 2000 txt filename name of the CO2 file RAMP NDEP 1 flag O constant l vary with file 2000 obsolete line not used in MuSo 0 00200 obsolete line not used in MuSo nitrogen Ndep_ 1901 2000 txt filename name of the N dep file With this settings first a spinup simulation will be performed re using the meteorological data in this example spinup meteorology covers the time period of 1901 2000 not shown here keeping both CO 280 ppm and N deposition constant the latter is defined within the SITE block not shown here Then as the flags are set to 1 transient simulation will be performed using the 100 years long meteorology and the co2_1901 2000 txt CO data file and the 100 years long Ndep_1901 2000 txt files Note that the user has to make sure to construct the proper CO and N deposition
45. he unused places see Appendix B for example This is also true for the other management events described below 2 2 Grazing In case of grazing a given amount of leaf material is consumed by animals every day when grazing occurs An important parameter used to simulate the effect of grazing is the livestock unit LSU LSU is a unit used to compare or aggregate different animal species and 1 LSU is equivalent to 500 kg live weight 1 adult cattle 1 LSU In case of grazing the following parameters determine the decrease of the plant material e first and last day of grazing period animal stocking rate regarding a unit area daily ingested dry matter regarding a unit LSU proportion of ingested dry matter which turns into excrement proportion of excrement returning to litter Ga User s Guide for Biome BGC MuSo 3 0 e carbon content of dry matter e carbon and nitrogen content of manure Besides defoliation effect of grazing i e intake by animals it is also important that a fixed proportion of the above ground biomass flows to the litter compartment as the results of the excretal returns The amount of dead plant material returning to litter is divided among the different type of litter pools according to their labile cellulose and lignin ratios defined in the model 2 3 Harvest As management is not part of the original model logic modifications were necessary for cropland related application of Biome BGC In agriculture harvest i
46. ined calculations therefore a nested 1 minute time step based calculation would be necessary It would mean high increase in model computational time In order to improve model performance within Biome BGC MuSo temperature of the soil layers is estimated based on empirical formula with daily time ee User s Guide for Biome BGC MuSo 3 0 step The method is the following the soil temperature below 3 meters can be approximated with the mean annual air temperature of the given site lower boundary condition set by the user in the INI file The temperature of the surface layer upper boundary condition is estimated based on the method of Zheng et al 1993 it is assumed that the change of the surface soil temperature is calculated by the change of the air temperature considering the insulating effect of the snowcover and the shading effect of the vegetation The temperature of the intermediate soil layers is calculated assuming linear temperature gradient between top soil layer and 3 meter depth The depth of a given soil layer is represented by the middle level of the given layer e g the thicknesses of the top soil is 0 1 m therefore its depth is represented at 0 05 m The average temperature of each soil layer in root zone is used to calculate the respiration of the fine root 1 2 2 Simulation of soil hydrology Among soil hydrological processes the original Biome BGC only takes into consideration canopy interception snowmelt outflow
47. ing the growing season no carbon uptake is possible above the critical snow cover we simply assume that no radiation reaches the surface if snow depth is above a pre defined threshold The snow cover estimation also as the part of 15 User s Guide for Biome BGC MuSo 3 0 the HSGSI calculations that precedes the real simulations is based on precipitation mean temperature and incoming shortwave radiation original model logic is used here The critical amount of snow can be defined in the GROWING_SEASON block of INI file It is important to note that critical amount of snow is also used to limit photosynthesis in case of using original model phenology which means that the flag to use HSGSI is 0 within the GROWING_SEASON block of the INI file The default value of this critical snow cover is 5 kg m which is equivalent circa 5 cm fresh snow but this is of course highly dependent on snow density so the user should be cautious with this default value 1 2 Improvement of the simulation of soil processes in Biome BGC Biome BGC was primarily developed to simulate the carbon and water budget of forests where soil moisture limitation is probably less important due to the larger rooting depth therefore the soil sub model was simple one layer budget model In order to improve the simulation of water fluxes a seven layer soil submodel was implemented into the model Seven layers provide an optimal compromise between the simulation accuracy
48. ion of this turnover process was necessary to avoid C and N balance errors caused by large fluxes between specific pools line 58 parameter 56 growth respiration cost per unit of carbon growth GRC Default value is 0 3 In the original model this parameter was constant fixed within the source code User s Guide for Biome BGC MuSo 3 0 line 59 parameter 57 daily N denitrification proportion NDP amount of N mineralization that is available to volatilization each day Default value is 0 01 In the original model this parameter was constant fixed within the source code line 60 parameter 58 N mobile proportion NMP this is the proportion of mineralized N that is leached each day if there is deep percolation Default value is 0 1 In the original model this parameter was constant fixed within the source code line 61 parameter 59 maturity coefficient for calculating maximum rooting depth MC This parameter defines the time within the growing season when maximum rooting depth set by the INI file is reached Default value is 0 5 which means the middle of the growing season New ecophysiological parameters fixed within the source code There are a few new ecophysiology related parameters that might be important in specific model configurations In Biome BGC MuSo v3 0 these parameters are defined within the source code so modification of these parameters is not simple In forthcoming version of Biome BGC MuSo we plan to extend the
49. l run the transient CO and N deposition files might contain data with a sharp increase in the driving data This sharp change is of course not realistic but it is still useful to eliminate the unrealistic fluxes In any case the best solution is to realistically simulate site history with even 2 3 simulation phases Smooth transition of CO2 and N deposition data between the simulation phases is always important As management might play an important role in site history and consequently in biogeochemical cycles in Biome BGC MuSo 3 0 the new transient simulation can include management The settings of management optionally defined within the spinup INI file are only used during the transient run but not during the regular spinup phase 303 APPENDIX B EXAMPLE INI FILE BBGC_MuSo simulation MET_INPUT metdata bugac_1901 2000 mtc4 4 RESTART 0 Y 0 restart bugac_MuSo endpoint restart bugac_MuSo endpoint TIME DEFINE 100 100 1901 i 6000 CLIM CHANGE 0 0 0 i t 1 1 0020 CO2 CONTROL 1 280 co2 C02 1901 2011 txt SITE 00 85 3 549 8 8 11 4 46 69 0 20 0 00020 0 00050 1 00 0 1 0 51 0 25 0 05 T RAMP_NDEP 0 El 2050 in 0 00200 kg nitrogen Ndep_1901 2011 txt EPC_FILE epc apriori_MuSo c3grass epc DI User s Guide for Biome BGC MuSo 3 0 FOR BIOME BGC MUSO V3 0 3 filename met file name int number of
50. lant material can be set in EPC file parameter 54 The turnover rate is higher multiplier is set to 1 5 in case of high precipitation critical value runoff value or in case of grazing due to trampling This model feature was added to optionally simulate non decomposing intact dead plant material which can strongly modulate e g daily total ecosystem respiration in drought prone semi arid or arid ecosystems Note that this feature is 24 User s Guide for Biome BGC MuSo 3 0 not expected to change the long term carbon balance of the ecosystem but it might cause temporal redistribution of daily fluxes delayed decomposition of wilted leaves Figure 3 shows the effect of drouht stress during the summer of 2003 in the Great Hungarian Plane Bugac site On the upper left figure the soil moisture content in the top soil layer is plotted The upper right figure shows the soil stress index SMSI which decreases below the critical value SMSIerit 0 5 on the 144 day of 2003 On this day plant wilting started which can be seen on the bottom left figure Beside plant wilting the transpiration flux became limited as it is shown on the bottom right figure As the figure shows plant senescence starts blue line in the lower left figure which first goes to the temporary plant senescence pool From this temporary pool the dead biomass is translocated to the litter pool with a delay Note that the temporary pool is not empty at the beginning of th
51. large evaporation or transpiration calculated based on the Penman Monteith equation driven by meteorological data and dried up soil the soil water pools can be depleted approaching hygroscopic water content In this case evaporation and transpiration fluxes are limited This water content is calculated from pF 6 2 value based on the soil properties Hygroscopic water content also becomes the lower limit for decomposition calculation Note that soil C and N pools are not distributed among the soil layers only mineralized N is distributed within the layers Decomposition of litter and soil organic matter is driven by average soil moisture and soil temperature of the entire root zone Poorly drained forests e g in boreal regions or in lowland areas are special ecosystems where groundwater and flooding play an important role in soil hydrology and plant growth Pietsch et al 2003 Bond Lamberty et al 2007 In order to enable groundwater vertically varying soil water saturation effect on the ecosystems in Biome BGC MuSo v3 0 we implemented an option to supply external information about the depth of the water table Groundwater depth is controlled by prescribing the depth of saturated zone groundwater within the soil note that the groundwater implementation of Pietsch et al 2003 and Bond Lamberty et al 2007 is different from our approach We assume that the user has information about groundwater depth from measurements or from another
52. lation after the spinup phase simply using the spinup INI file settings and some ancillary files The idea behind our implementation is that during spinup the CO and N deposition should be kept constant according to preindustrial conditions which means that the CO2_ CONTROL flag is always 0 and the RAMP_NDEP flag is also 0 If the user wants to initiate the transient run he she can set it in the spinup INI file by setting the CO2_ CONTROL or RAMP _NDEP flag to 1 It means that first a regular spinup will be performed with constant CO and N deposition values set in the INI file then a second run will be performed using the same meteorological data file defined by the spinup INI file In this way the length of the transient run is always equal to the length of the meteorological file used for the spinup phase During transient run annually varying CO concentration file is used it should be constructed to provide transition from preindustrial to industrial CO concentration Utilization of annually varying N deposition data is optional but it is preferred The input data of transient run is the output of the spinup phase and the output of the transition run is the input of the normal phase Annually varying CO and N deposition files has to be constructed by the user These files typically differ from those used during the normal phase If the number of meteorological years used for the spinup is small i e same data is used for spinup and norma
53. model e g watershed hydrology model During the spinup phase of the simulation the model can only use daily average data for one typical simulation year defined by a text file groundwater_spinup txt it must contain exactly data for 365 days During the normal phase of the simulation the model can read daily groundwater information defined by another text file groundwater txt These files are supposed to be present next to the model executable If the files are not present no groundwater manipulation is happening in normal phase using this logic groundwater effect can not be represented at all or can be represented only in spinup run only in normal run or in both phases if both txt files are present The structure of groundwater txt and groundwater_spinup txt is simple it is similar to the structure of the annually varying CO concentration file or the annually varying N deposition file day of year then groundwater depth in meters positive value for the given day Note 19 User s Guide for Biome BGC MuSo 3 0 that the current model version does NOT take into account the day of year field in the file for the first day of the simulation the first line is used regardless of the value of the day field for the second day the second line is used etc The User should check whether the length of the groundwater file is in accordance with the length of the normal simulation The handling of the externally supplied groundwater info
54. moisture is available if stomata is closed only cuticular water exchange is possible theoretical lower limit of water content hygroscopic water content transpiration fluxes can be limited due to dry soil e multilayer_rootdepth c O O calculation of changing rooting depth based on empirical function the maximum rooting depth is on maturity day which is calculated using new EPC constant maturity coefficient calculating the number of the soil layers in which root can be found and calculating the relative soil layer thickness calculating the root distribution root length and mass proportion of the layers in the soil based on empirical function calculating the soil mineral N content of rooting zone taking into account changing rooting depth e multilayer_sminn c O O O calculating the change of content of soil mineral nitrogen in multilayer soil decomposition of soil organic matter N mineralization and plant N uptake are calculated separately in daily allocation routine but N loss surplus due soil and plant processes are divided between root zone layers based on the N content of the layers daily atmospheric N deposition is displayed in the first top soil layer but not in the entire root zone o biological N fixation is divided between root zone layers based on the quantity of root in the given layer calculated in multilayer_rootdepth c o nitrogen leaching is calculated in the end of this subroutine in order to a
55. nd it also controls the turnover of dead coarse root stump into coarse woody debris CWD It is important to note that forest related simulations with Biome BGC MuSo v3 0 are in a developing and testing phase Please contact us for update about the process A3 Other modifications 3 1 Implementation of annually varying whole plant mortality dynamic mortality Annual whole plant mortality fraction WPM is part of the ecophysiological parameterization of Biome BGC user supplied value which means that it is assumed to be constant throughout the simulation From the point of view of forest growth constant mortality can be considered as a rough assumption Ecological knowledge suggests that WPM 29 User s Guide for Biome BGC MuSo 3 0 varies dynamically within the lifecycle of forest stands due to competition for resources or due to competition within tree species plus many other causes In order to enable more realistic forest stand development we implemented an option for supplying annually varying WPM to Biome BGC MuSo During the normal phase of the simulation the model can either use constant mortality or it can read annually varying WPM defined by a text file mortality txt which is supposed to be present next to the model executable during spin up only constant mortality is possible The structure of mortality txt is the same as the structure of the annually varying CO concentration file or the annually varying N depositi
56. nning and end of the growing season heatsum minimum air temperature vapor pressure deficit and length of the day HSGSI can be used optionally within the model see settings in the INI file Parameters used to control phenology are the following o snow cover critical amount of snowpack above which growing season start is fully constrained o basic temperature needed to calculate heatsum o threshold limits of heatsum tmin vpd index and daylength index 8 parameters These thresholds are used to define linear ramp functions to constrain or not to constrain the growing season calculation See Jolly et al 2005 for graphical representation of the functions o number of days to calculate moving average from indices to avoid the effect of single extreme events o HSGST limits start end of growing season o filename of the estimated start and end of the vegetation periods output file it is created as control variable during simulation In the original model version snow cover does not affect the start of the vegetation period and the photosynthesis We implemented a new dual snow cover limitation method in MuSo v2 1 and v2 2 First growing season can only start if the snowpack is less than a critical amount given in kg HO m units note that this is not snow depth but rather water content stored in the snowpack it is up to the user to set the threshold if snow depth is known Second the same critical value also limits photosynthesis dur
57. ns into the litter carbon pool Therefore APF decreases day to day after fertilizing until becomes empty which means that the effect of the fertilization ends 2 6 Planting Planting is the process of introducing seeds into the soil sowing In Biome BGC transfer pools are defined to contain plant material as germ in the dormant season from which carbon and nitrogen gets to the normal pools leaf fruit and root in the beginning of the growing season 28 User s Guide for Biome BGC MuSo 3 0 In order to simulate the effect of sowing we assume that the plant material which is in the planted seed goes into the transfer pools thus increasing its content Allocation of leaf fruit and root from seed is calculated based on allocation parameters in EPC file parameter 13 14 We assume that a given part of the seed is destroyed before sprouting Parameters used to simulate planting are e day of year of planting e quantity of seed on a given planting day e the carbon content of seed e the useful part of seed not destroyed before sprouting The nitrogen content of leaf fruit and root can be calculated using ecophysiological parameters carbon and nitrogen ratio of leaf fruit and root EPC parameters 19 22 2 7 Thinning In forestry thinning is the selective removal of trees primarily undertaken to improve the growth rate or health of the remaining trees and to create profit through wood products We assume that based on a thinning
58. nt scalar in multilayer soil using root zone averaged values soil temperature soil water content Water limitation factor is calculated based on soil water content ratio instead of soil water potential epc_init c modifications to handle the new EPC parameters see above maint_resp c o calculates layer specific soil temperature exponents regarding to maintenance respiration of root in the different soil layer o extending leaf and fine root respiration fluxes with fruit respiration flux Root respiration is distributed among soil layers based on their root content metarr_init c modifications for handling the daylight average temperature correctly mortality c o dynamically changing whole plant mortality parameter WPM can be set by the user by creating mortality txt file in the model folder directory next to model executable o additional calculation of fruit mortality fluxes photosythesis c correction of C4 photosynthesis routine based on the work of Di Vittorio et al 2010 using source code of Biome BGC 4 3 beta precision_control c checking precision of gresp_transfer not implemented in earlier model version and check of new variables prephenology c onday and offday are stored in phenology struct in order to use in the new subroutines GSI calculation sitec_init c o instead of soil depth maximum rooting depth parameter is used positive number o soil layer depths positive numbers are fixed 0 10 10 3
59. o the file e g in case of mowing the MOWING section should look like this MOWING management mowing txt flag or filepath do MOWING O no l yes read file 0 flag mowing method 0 on fixday 1 fixLAI In this example the mowing txt file is supposed to be in the management directory relative to the directory of the model executable The ancillary management files only contain rows that define the 7 events for the specific management type In case of mowing the mowing method and the LAI before mowing is always defined in the INI file while the other settings mowing days LAI after mowing transported part are given in the ancillary file if defined Similarly to the 7 management setting within the INI file 999 9 means that the given management event should not be considered applicable if there are less then 7 management events per year note that there can be years when there is no management at all User s Guide for Biome BGC MuSo 3 0 Note that in case of defined ancillary file the 7 event lines given in the INI file are not considered In the present example the ancillary mowing txt should contain these lines MOWING day only fixday method 150 234 999 9 999 9 999 9 999 9 999 9 170 244 999 9 999 9 999 9 999 9 999 9 180 999 9 999 9 999 9 999 9 999 9 999 9 value of the LAI after MOWING 1 0 1 0 1 0 1 a 1 1 transporte erial percent 0 0 0 ooo ss co oO E 0 0 0 In this example normal simulation
60. ole plant mortality fraction 0 0 1 yr annual fire mortality fraction 0 ratio ALLOCATION new fine root C new leaf C Ok ratio ALLOCATION new fruit c leaf c 0 00 ratio ALLOCATION new stem C new leaf C 0 00 ratio ALLOCATION new live wood C new total wood C 0 00 ratio ALLOCATION new croot C new stem C 0 5 prop ALLOCATION current growth proportion 25 0 kgC kgN C N of leaves 45 0 kgC kgN C N of leaf litter after retranslocation 50 0 kgC kgN C N of fine roots 25 0 kgC kgN C N of fruit 0 00 kgC kgN C N of live wood 0 00 kgC kgN C N of dead wood 0 68 DIM leaf litter labile proportion 0 23 DIM leaf litter cellulose proportion 0 09 DIM leaf litter lignin proportion 0 34 DIM fine root labile proportion 0 44 DIM fine root cellulose proportion 0 22 DIM fine root lignin proportion 0 68 DIM fruit litter labile proportion 0 23 DIM fruit litter cellulose proportion 0 09 DIM fruit litter lignin proportion 0 00 DIM dead wood cellulose proportion 1 00 DIM dead wood lignin proportion 0 01 1 LAI d canopy water interception coefficient 0 5 DIM canopy light extinction coefficient 2 0 DIM all sided to projected leaf area ratio 49 0 m2 kgC canopy average specific leaf area projected area basis 2 0 DIM ratio of shaded SLA sunlit SLA 0 2 DIM fraction of leaf N in Rubisco 0 03 DIM fraction of leaf N in PEP Carboxylase 0 006 m s maximum stomatal conductance projected area basis 0 00
61. om the changes of daily air temperature using empirical functions a 10 day running average of daily air temperature is used as initialization value of the soil surface temperature The relation between soil and air temperature also depends on the presence absence of snowcover The rate of the changes in soil temperature the difference between soil surface temperature on actual and previous day under snow cover is less caused by low thermal diffusivity and higher albedo Therefore different equations are used to simulate the rate of the changes in soil temperature if snowpack is present Eq 1 and Eq 2 in Zheng et al 1993 respectively It is hypothesized that vegetation canopies can decrease soil temperature in growing season because less radiation can be absorbed by the soil due to the shading effect of leaves This shading effect is taken into account in the developed model based on the Beer Lambert law using the simulated value of leaf area index LAJ and one of the ecophysiological parameters of Biome BGC the light extinction coefficient defined as the mean projection of the unit foliage area on the plane normal to incident radiation unitless default value is 0 5 Eq 3 in Zheng et al 1993 Theoretically the heat flux between soil layers can be estimated by the diffusion equation for soil temperature based on Fourier diffusion law The simulation of the thermodynamic processes based on Fourier diffusion law would require finer gra
62. on file year then WPM for a given year Note that the current model version does NOT take into account the year field in the mortality txt file this is true for the CO and N deposition file for the first year of the simulation the first line is used regardless of the value of the year field for the second year the second line is used etc 3 2 Correction of bug related to the calculation of daylight average temperature According to the Biome BGC User s Guide 4 2 there was a bug in the source code of the model that is related to the calculation of daylight average temperature Thornton and Running 2002 An incorrect parameter was being used in the calculation of the daylight average air temperature in daymet c The parameter value in version 4 1 1 was 0 212 and the correct value for consistency with the MT CLIM and Daymet code should be 0 45 The daylight average air temperature tday is used in the photosynthesis routine and in the calculation of daytime leaf maintenance respiration As an example of the net result of changing to the correct value the example simulations described later in this guide show an increase in steady state leaf area index of about 10 and an increase in steady state net primary production of about 5 Thanks to Michael Guzy at Oregon State University for finding this bug We corrected this bug but not in terms of the parameter in daymet c but we modified the model in order to use the daylight ave
63. on relative soil water content are undefined then relative soil water potential are used for the calculations If parameters 46 49 are all undefined set to 999 9 then the model calculates water stress limitation based on internally estimated field capacity and saturation If parameters 46 49 are all defined then relative soil water content values parameters 46 and 47 are used and parameters 48 and 49 are ignored Note that in the original model there were only 43 EPC parameters MuSo v3 0 has 61 EPC parameters which means that the number of parameters increased We are fully aware that high number of adjustable parameters might complicate calibration and application of the model However our intention was to extend the EPC file with some of the parameters that were burned in within the source code but might need adjustment Implementation of the new multilayer soil module and fruit yield also involved the definition of new EPC parameters In the simplest case these parameters should be left intact by the user In any case sensitivity analysis is needed to check whether the new parameters have strong influence on the variability of the output We are implementing a so called workflow within the frame of the BioVeL project to enable simple sensitivity analysis of Biome BGC MuSo Please contact us for access to the workflow environment LIST OF MODIFICATIONS IN THE SOURCE CODE C FUNCTIONS Below we describe the changes in subroutines of the s
64. ose used in the growing season length calculation The values of the limits regarding to the different variables can be set by parameters Using the original Biome BGC v4 1 1 with the modifications described by Trusilova et al 2009 we experienced unrealistic peaks in stomatal conductance and consequently in the carbon and water fluxes The reason for this phenomenon is that there are peaks in the stress function of soil water potential and therefore in stomatal conductance Due to the functional form of soil water potential dependence on volumetric soil moisture content the decrease of soil moisture content causes small increase in soil water potential at high soil moisture content This means that close to the saturation high soil moisture content soil water potential is not sensitive to the decrease in the soil moisture content Consequently the stress function of soil water potential and therefore stomatal conductance is not decreasing significantly despite of the decrease in the soil moisture content At lower soil moisture status a small decrease in soil moisture can cause huge decrease in soil water potential To avoid the occurrence of the above described phenomena we have modified the model to use a new stress function instead of soil water potential Note that the new stress function is a linear function of normalized soil water content between specific points Og mk O NSWC O a O w where act is the actual soil moisture
65. ource code 1 Modified subroutines e annual rates c extension of leaf and fine root litterfall rates with fruit litterfall rates e canopy et c calculates only the canopy evaporation and transpiration based on Penman Monteith method The necessary conductance values are calculated separately in a new subroutine conduct_calc c see below It also calculates the limitation of the total transpired water based on the soil moisture limitation index see below e check_balance c new variables are defined and further control is implemented to avoid negative stocks e daily _allocation c and spinup_daily_allocation c o new flag for nitrogen limitation type o nitrogen limitation calculation is based on the averaged mineralized N content of the root zone o warning for negative GPP o completing leaf and fine root allocation rates with fruit allocation e daymet c o initializing soil temperature values in multilayer soil User s Guide for Biome BGC MuSo 3 0 o on the first day original soil temperature calculation method is used o correction for handling the daylight average temperature correctly now daylight average temperature is read from the meteorology file so it is not calculated within the source code see below dayphen c initiation and cessation of growing season onday and offday are stored in phenology structure in order to use in the new subroutines GSI calculation decomposition c calculates the rate consta
66. pyw_to_ HRV amp wf gt canopyw_to_ PLG amp cs gt fruitc amp cs gt fruitc_storage amp cs gt fruitc transfer amp cs gt THNsnk amp cs gt THNsrc amp cs gt MOWsnk amp cs gt MOWsrc amp cs gt GRZsnk amp CS gt GRZsrc amp cs gt HRVsnk amp cs gt HRVsrc amp cs gt PLGsnk amp cs gt PLGsrc amp cs gt PLTsrc amp CS gt FRZsrc C0s gt SNSCsnk amp CS gt SNSCsrc amp ns gt sminn_ RZ amp ns gt THNsnk ns gt MOWsnk amp ns gt GRZsnk amp ns gt GRZsrc amp ns gt HRVsnk amp ns gt H amp ns gt PLGsnk PD P RVsrc PLGsrc amp ns gt PLTsrc amp NS gt FRZsrc amp ns gt SNSCsnk amp ns gt SNSCsrc 373 ou ou ou ou ou ou ou ou ou ou ou ou ou ou ou ou cr cr Cr CT cr set CF och och och oct et ect oct et put put put put pu pu put put put pu put put put put put pu 0 OO 0 0 0 O 00000000050 546 547 548 549 550 551 621 64 64 64 64 64 64 64 64 64 OMDANHDUOBWNE amp epv gt vwc 1 epv gt vwc 2 epv gt vwc 5 sepv gt vwc 0 r r amp epv gt vwc 3 amp epv gt vwc 4 r amp summary gt daily_nbp amp summary gt Cchange MOW amp summary gt Cchange_ HRV amp summary gt Cchange_ PLG amp summary gt Cchange_ GRZ amp summary gt Cchange FRZ
67. rage temperature value that is provided by the meteorological input file MTClim output in many cases We prefer this solution as the user of the model can use his her own calculation method for daylight average temperature e g based on hourly measurements which would be meaningless if the model would re calculate its value based on daily maximum and minimum temperature 3 3 Fruit simulation In order to enable carbon and nitrogen budget simulation of croplands with Biome BGC MuSo v3 0 fruit or grain simulation was implemented After the flowering date defined in EPC file parameter 6 fruit starts to grow therefore growth and maintenance respiration daily allocation mortality phenology and litterfall routines are completed with fruit simulation Besides flowering date there are 5 additional new EPC parameters new fruit carbon ratio to new leaf carbon carbon and nitrogen ratio of fruit and labile cellulose and lignin proportion of fruit Note that fruit simulation can be completely switched off with setting the new fruit carbon ratio to new leaf carbon allocation parameter to zero parameter 16 3 4 New C4 photosynthesis routine Based on the work of Di Vittorio et al 2010 we implemented a new enzyme driven C4 photosynthesis routine into the photosynthesis module In case of C3 photosynthesis pathway the EPC parameter fraction of leaf N in PEP Carboxylase parameter has no effect on the 30 User s Guide for Biom
68. rmation is the following If the depth of the water table reaches the bottom border of the given soil layer the groundwater saturated part of the given layer becomes saturated therefore the average soil moisture content of the given layer increases If the depth of the water table reaches the upper border of the given soil layer the given layer becomes saturated Upward diffusion of water from the saturated layers is possible according to the multilayer soil moisture implementation described above In the original Biome BGC the effect of changing soil water content on photosynthesis and decomposition of soil organic matter is expressed in terms of soil water potential Instead of the soil water potential the volumetric soil moisture content is also widely used to calculate the limitation of stomatal conductance and decomposition A practical advantage of soil moisture content as a stress function is that it is easy to measure in the field The disadvantage is that soil moisture content is not comparable among different soil types A possible solution to calculate a limiting factor from soil moisture content is to take into account the relative soil moisture content RSWC which is the ratio of the actual moisture content and the soil moisture content at field capacity field capacity depends on soil texture Reichstein 2001 Another possible measure to quantify soil moisture effect it the calculation of normalized soil water content NSWC Jarvis
69. roots and root growth to take up water To determine water uptake the correct simulation of both of these processes is necessary In case of herbaceous vegetation fine root growth is simulated based on empirical method in the growing season it increases from the first day ONDAY of the growing season until the maturity date MATUR_DAY note that start and end of growing season is calculated prior to root growt simulation based on a sigmoid function Campbell and Diaz 1988 The actual rooting depth is calculated as the fraction of maximum rooting depth in terms of the fraction of time from onday to maturity The maturity date can be set by the user using a new ecophysiological parameter matur_coeff By default we may assume that it is the middle of the growing season matur_coeff 0 5 set by parameter 59 MATUR _ DAY ONDAY matur _ coeff OFFDAY ONDAY where ONDAY is the start OFFDAY is the end of the growing season days of the year The time step of the rooting depth simulation is one day 2162 User s Guide for Biome BGC MuSo 3 0 In case of forests fine root growth is assumed to occur in the entire root zone represented by coarse roots maximum depth of root zone is set by the INI file see Appendix B Note that in case of forest this depth does not change with time The actual length of the root is simulated based on empirical function Campbell and Diaz 1988 In order to weight the relative importance of the soil laye
70. rs to distribute total transpiration or root respiration among soil layers it is necessary to calculate the distribution of roots in the soil layers The proportion of the total root mass in the given layer Ri is calculated based on empirical function after Jarvis 1989 exponential root profile approximation rr 2 02 Z Z where f is an empirical root distribution parameter 3 67 in the current model version after Jarvis 1989 Azi and zi is the thickness and the midpoint of the given soil layer respectively and z is the actual rooting depth it is set within the INI file see above Since some of the soil properties depend on temperature it is necessary to calculate soil temperature for each active layer so the thermodynamics of the soil submodel is also reconsidered in the developed model We assume that below the lowermost boundary 3 m the soil properties are constant the soil temperature is assumed to be equal to the annual mean air temperature which can be set by a parameter the other soil properties soil moisture content conductivity and diffusivity correspond to field capacity values Chen and Dudhia 2001 1 2 1 Simulation of soil thermodynamics In the developed model the daily soil surface temperature average temperature of the top soil layer with thickness of 10 cm is determined based on the method of Zheng et al 1993 It is assumed that the changes of daily soil surface temperature can be predicted fr
71. s the process of gathering mature crops from the fields It means that the effect of harvest is similar to effect of mowing but the fate of the cut down fraction of aboveground biomass has to be defined We assume that after harvest snags remain on the field as part of the living biomass and part of the plant residue is also left on the field in the form of litter Yield is always transported away from the field while stem and leaves can be transported away and utilized e g as animal bedding or can be left at the site The ratio of harvested biomass that is taken away from the field has to be defined as a parameter It is expected that evolution of soil carbon pool will be highly dependent on the residue management practice which is the result of human decision From the leaf area index of snag model parameter the effect of harvest on the carbon nitrogen and water pools of living biomass can be calculated The fluxes determining the decrease of the pools are calculated from the ratio of LAI before the harvest and the LAI of the snag Similar to mowing the harvested leaf biomass can be taken away or can be left at the site In the first case the cut down plant material is excluded from the further calculations the cut down fraction of plant material carbon and nitrogen is a net loss from the system In the second case cut down plant material first goes into a temporary pool that gradually enters the litter pool The turnover rate of mowed harve
72. s weighted by rootlength proportion Ri of the actual layer The averaged stress function limits not only the stomatal conductance but the transpiration rate as well The actual transpiration rate Tact is assumed to be directly proportional to the potential rate Tpot and the weighted stress index SMSI if the SMSI is less than a critical value SMSlIert default value is 0 5 Ti To SMSI act gt pot According to the modifications if the soil moisture limitation is full SMSI 0 no transpiration can occur A User s Guide for Biome BGC MuSo 3 0 1 0 0 8 0 6 0 4 Biome BGC v4 1 1 saturation 0 2 Biome BGC MuSo v2 2 4q wilting point d field capacity Q g O o O O el O pu D gt XL 0 0 0 2 0 4 0 6 0 8 1 0 1 2 relative soil water content Figure 2 Example for the scalar function of soil water content controlling stomatal conductance in the original Biome BGC v4 1 1 and in Biome BGC MuSo v3 0 In this graph the scalar is plotted as a function of RSWC not NSWC 1 4 Modeling the effect of drought on the biogeochemical processes of the vegetation As we mentioned the stomatal conductance calculation is based on environmental limiting factors The most important limiting factor is the soil water status if the plant available water decreases due to the prolonged low level of precipitation drought the stomatal conductance and so the carbon uptake will d
73. simulation of pond water evaporation transpiration calculated by Penman Monteith function can be limited by dry soil optional transient run between spinup and normal phase is possible as an extension to the spinup phase spinup can consist of two steps INTRODUCTION Biome BGC is a widely used popular biogeochemical model that simulates the storage and flux of water carbon and nitrogen between the ecosystem and the atmosphere and within the components of the terrestrial ecosystems Thornton 2000 Biome BGC was developed by the Numerical Terradynamic Simulation Group NTSG University of Montana http www ntsg umt edu project biome bgc Note that the currently available model version is 4 2 Several researchers used and modified the original Biome BGC model in the past Most recently our research group developed Biome BGC to improve the ability of the model to simulate carbon and water cycle in managed herbaceous ecosystems see Hidy et al 2012 The modifications included structural improvements of the model e g the simple outdated one layer soil module was replaced by a multilayer soil module drought related plant senescence was implemented model phenology was improved and also management modules were developed e g to simulate mowing grazing Although the modifications aimed to support the use of Biome BGC in herbaceous ecosystems the modified model can 1 User s Guide for Biome BGC MuSo 3 0 also be use
74. sses its actual value also depends on the actual rooting depth We calculate the change in content of soil mineral nitrogen layer by layer day by day In the root zone the changes of mineralized N are caused by soil processes decomposition microbial immobilization and plant uptake leaching deposition and biological fixation The produced consumed N calculated by decomposition and daily allocation functions is distributed within the layers of root zone based on their soil mineral N content Atmospheric N deposition goes to the first 0 10 cm soil layer Biological N fixation is divided between rootzone layers based on the quantity of the root in the given layer R calculated in multilayer_rootdepth c In the further soil layers where no roots can be found N content is changed only by leaching Leaching is calculated based on the original empirical function of Biome BGC 1 3 Improvement of stomatal conductance calculation In Biome BGC the stomatal conductance calculation is based on environmental limiting factors Stomatal conductance is calculated as the product of the maximum stomatal conductance and limiting stress functions based on minimum temperature vapor pressure deficit and soil water potential To calculate the limiting stress functions for each variable we set threshold limits within which the relative limiting effect was assumed to vary from fully constrained 0 to unconstrained 1 these are linear ramp functions similar to th
75. ssible values set in the INI files and within the meteorology input data Note that NTSG continues to develop the original model with the inclusion of management with a new disturbance handler module In their implementation disturbance or management is described by a separate disturbance descriptor file Our implementation of disturbance is different from the NTSG approach as we included management settings in the INI file of the model note that in MuSo v3 0 there is an option to use additional management rules provided in separate text files see below Prior to reading this document further the reader should get familiar with the documentation of the original model Biome BGC v4 1 1 see Thornton 2000 and Trusilova et al 2009 The present document provides detailed description about the differences between Biome BGC v4 1 1 MPI version and the Hidy et al 2012 developments and the new modifications including forest thinning with optional clearcut simulation annually varying whole plant mortality cropland management annually varying management options optional groundwater depth control for lowland ecosystems and optional transient simulation control We suggest getting a copy of the Hidy et al 2012 paper as well in order to see simulation results and calibrated parameters based on eddy covariance measurements performed in Hungary Please contact Zoltan BARCZA bzoli elte hu for a reprint of the Hidy et al 2012 paper if you do no
76. sted biomass to litter TRMB can be set in EPC file parameter 55 same parameter is used for grass and crops Although harvest is not possible outside of the growing season this temporary pool can contain plant material also in the dormant period depending on the amount of the cut down material and the turnover rate of the cut down non woody biomass The plant material returning into litter compartment is divided between the different types of litter pool according to parameterization The water stored in the canopy of the cut down fraction is assumed to be evaporated A note on management related plant mortality In case of mowing grazing and harvest the main effect of the management activity is the decrease of the aboveground plant material It is important to note that as a direct result of the management activity due to the mortality of the aboveground plant material the belowground plant material also decreases but at a lower and hardly measurable rate Therefore in the current model version we empirically estimate the decrease of the belowground plant material in case of non woody biomass the belowground plant material is the root and the storage pools The rate of the belowground decrease to the mortality rate of the aboveground plant material is set within the model code it is 0 1 which means that the mortality rate of the belowground plant material is the 10 of the mortality of the aboveground material on a given management day No
77. stress RWC ri due to soil saturation is determined using new ecophysiological parameters relative proportion to field capacity soil water content limitation 2 RSWC rit2 or relative proportion to field capacity soil water potential limitation 2 RSWP rit2 The default soil water content threshold limit values are corresponding to saturation which means no stress due to saturation The summary of the introduced soil moisture stress index SMSI is the following SMSI SSW CaL if NSWC lt NSWC y crit SMSI 1 if NSWC lt NSWC lt NSWC SMSI ee if NSWC dro lt NSWC a crit2 where NSWCact is the actual normlaized soil water content calculated from the actual soil moisture content NSWC i1 and NSWC ori are the critical value of the normalized soil water contents calculated from the RSWCeit1 and RSWCait2 defined in EPC file conversion from relative soil water content to normalized soil water content is made within the source code Note that by definition SMSI is zero below wilting point The graphical representation of the soil moisture control is presented in Fig 2 The shape of the modified stress function is similar to the one presented in Bond Lambery et al 2007 their Fig 1 The model requires only one soil moisture stress function to calculate stomatal conductance but soil water status is calculated layer by layer Therefore averaged stress function is necessary which is the average of the layer factor
78. supplement naturally occurring essential elements in the soil to maintain an optimum supply for plant growth Fertilizers are chemical or organic compounds usually applied through the soil for uptake by plant roots The most important effect of fertilization in Biome BGC is the increase of soil nitrogen Parameters used to simulate fertilization are e day of year of fertilizing quantity of fertilizer put out on a given fertilizing day nitrate ammonium and carbon content of fertilizer labile unshielded shielded and cellulose fraction of fertilizer efficiency of utilization of fertilizer N by plants dissolving coefficient to define the amount of the nitrogen which returns to litter pool on a given day We define an actual pool which contains the whole amount of fertilizer s nitrogen put out onto the ground on a given fertilizing day actual pool of fertilizer APF On the fertilizing day the whole amount of fertilizer QF flows into APF A fixed proportion EC of the fertilizer enters top soil layer on a given day after fertilizing Not all of this fraction gets into the soil pools because a given proportion is leached this is determined by the efficiency of utilization Nitrate content of fertilizer can be taken up by plant directly therefore we assume that it goes into the soil mineral pool Ammonium content of fertilizer has to be nitrified before taken up by plant therefore turns into the litter nitrogen pool Carbon content of fertilizer tur
79. t have access to that publication STRUCTURE OF THE MODIFIED INITIALIZATION FILE Due to the modifications of the model structure and the implementation of the new management modules extension of the INI file was necessary Below we introduce the changes made in the INI file of the model An example INI file is given in Appendix B Modified initializing blocks CO2_CONTROL block and RAMP_NDEP block An important feature of the Biome BGC MuSo model is the possibility to control annually varying CO concentration and N deposition independently driven by separate text files this 2 User s Guide for Biome BGC MuSo 3 0 feature was introduced in Biome BGC v4 1 1 MPI version Trusilova et al 2009 It is important to note that although the text files contain the year of the actual data the model neglects the date and uses data from the text files sequentially first line for the first simulation year second line for the second etc A new feature of Biome BGC MuSo v3 0 is the possibility to trigger a so called transient simulation as an extension of the spinup phase controlled solely by the spinup INI file This feature was introduced to enable smooth transition from constant CO and N deposition used in the spinup phase representing preindustrial conditions up to 1850 to the higher CO and N deposition values representative to present day or past 10 100 years conditions If the user wants to initiate the transient run he s
80. te that in case of non woody biomass aboveground material refers to actual pool of leaf and fruit belowground material refers to actual pool of root and Ta User s Guide for Biome BGC MuSo 3 0 storage transfer pools of leaf fruit and root See above subsection New ecophysiological parameters fixed within the source code for further information about this logic 2 4 Ploughing Ploughing means farming for initial cultivation of soil in preparation for sowing seed or planting The primary purpose of ploughing is to turn over the upper layer of soil bringing fresh nutrients to the surface while allowing the remains of previous crops to break down and return to the soil Since the soil model of Biome BGC is multilayered the effect of layers turnover could be taken into account e g uniform distribution of the soil mineral nitrogen content and or soil water content in the layers affected by ploughing We assume that due to the plough all the plant material of snag that remains after harvest returns a temporary ploughing pool on the ploughing day A fixed proportion parameter is burned into the source code it is set to 0 1 of the temporary ploughing pool enters the litter on a given day after ploughing The plant material returning into litter compartment is divided between the different types of litter pools Parameter used to simulate ploughing is the day s of year of ploughing 2 5 Fertilizing Fertilization is necessary to
81. tes them i e the structure of the INI file is fixed similarly to previous versions of Biome BGC A management type can be activated if the flag in the first line of the block is set to 1 or if there is a filename present in the first line of the block that refers to external management descriptor see below If the flag is 0 it means that the management type is deactivated For each management type maximum 7 events can be defined for each year In case of less than 7 events 999 9 can be used to skip some of the events The new blocks are as follows e GROWING SEASON block the meaning of the parameters is described in Appendix A Section 1 1 see also Appendix B for example INI file If the user wants to use the new growing season estimation method the first line of the block must start with 1 flag to use GSI index to calculate growing season If USER 4 User s Guide for Biome BGC MuSo 3 0 SPECIFIED PHENOLOGY is set to zero in the EPC file then the GSI method will not be used even if the GSI flag is set to 1 e PLANTING block parameters are described in Appendix A Section 2 6 THINNING block parameters are described in Appendix A Section 2 5 MOWING block parameters are described in Appendix A Section 2 1 GRAZING block parameters are described in Appendix A Section 2 2 HARVESTING block parameters are described in Appendix A Section 2 3 PLOUGHING block parameters are described in Appendix A Section 2 4
82. uide for Biome BGC MuSo 3 0 APPENDIX A DETAILED DESCRIPTION OF THE MODIFICATIONS Below we provide theoretical basis for the model developments Note that part of the changes are documented in Hidy et al 2012 in detail though there are some differences due to new developments and model refinements A1 Improvements of model structure 1 1 Improvement of model phenology To determine the start of the growing season the phenological state simulated by the model can be used We experienced that the start of the growing season internally calculated by the original model is unrealistically late at least in case of herbaceous ecosystems in Hungary Hidy et al 2012 Biome BGC has a built in support for using a fixed date for start and end date of the growing season during the simulation However using fixed date large discrepancies are expected between the simulation results and measurement data because the start and end of the growing season exhibit large interannual variability In order to avoid these problems especially in relation with simulations under changing climate when prolongation of the growing season is expected we have modified the phenology module of Biome BGC We developed a special growing season index HSGSI heatsum growing season index which is the extension of the GSI index introduced by Jolly et al 2005 HSGSI similarly to the original GSI combines a set of variables into one variable for the estimation of the begi
83. value is 0 05 which means that 5 of the actual carbon and nitrogen pool is lost during one day due to the drought stress line 55 parameter 53 drought stress related mortality coefficient causing plant senescence on belowground plant material SMCB The default value is 0 05 The parameter is defined similarly to parameter 52 Note that SMCB is also used to deplete the storage pools leaf and fine root storage fruit storage due to prolonged drought Parameters 52 and 53 might be important to simulate the carry over effect of drought stress during the consecutive year as drought also affects the storage transfer pools line 56 parameter 54 turnover rate of wilted standing biomass wilted leaves to litter TRWB Default value is 0 01 This parameter is introduced to enable more realistic simulation of dead leaves behavior which can eventually stay intact for a longer time period before they touch the ground so that decomposition can start line 57 parameter 55 turnover rate of cut down but not transported i e left at the site non woody biomass to litter TRCN The default value is 0 05 This parameter controls harvested plant residues in croplands or clipped grass leaves in case of mown grasslands In case of forests this parameter controls the fate of previously living leaves on cut down trees if thinning option is switched on but it also controls the turnover of dead coarse root stump into coarse woody debris cwd Implementat
84. void negative pools based on the percolation fluxes and the soil mineral N content 21205 User s Guide for Biome BGC MuSo 3 0 groundwater c o calculating the effect of elevated groundwater on soil hydrology Groundwater can be controlled by creating a text file called groundwater txt in the model folder directory next to model executable that contains daily data for the duration of the normal simulation Option is available for using ancillary groundwater information in spinup mode as well see below auxiliary function groundwater _init c Senescence simulation waterstress_days c calculates the number of the days since water stress occurs during the vegetation period based on soil water status water stress if soil moisture stress index is below a critical value details below conduct limit _factors c calculates the limitation factors soil water content ratios for soil moisture limit calculation User can set both volumetric relative soil water content relSWC and relative soil moisture potential relPSI data in EPC file conduct_calc c o calculating the stress functions multipliers for stomatal conductance calculation for photosynthetically active photon flux density vapor pressure minimum temperature and soil properties in order to calculate conductance o soil related stress function is based on the averaged stress function for the root zone senescence c o aboveground and belowground plant material senes
85. y of Montana Missoula MT 280 pp Thornton P E 2000 User s Guide for BIOME BGC Version 4 1 1 Available online at ftp daac ornl gov data model_archive BIOME BGC biome bgc 4 1 1 comp bgc users gui de_411 pdf Thornton P E Running S W 2002 User s Guide for Biome BGC Version 4 1 2 Available online at http www ntsg umt edu sites ntsg umt edu files project biome bgc bgc_users_ guide 412 PDF 42 User s Guide for Biome BGC MuSo 3 0 Trusilova K Trembath J Churkina G 2009 Parameter estimation and validation of the terrestrial ecosystem model Biome BGC using eddy covariance flux measurements Max Planck Institut f r Biogeochemie Technical Reports 16 1 60 Available online at http www bgc jena mpg de mpg websiteBiogeochemie Publikationen Technical_Reports tech _report16 pdf Zheng D Raymond H Running S W 1993 A daily soil temperature model based on air temperature and precipitation for continental applications Climate Research 2 183 191 Ada

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