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User's Guide for Biome

1. ECOPHYS C3 grass 0 flag 1 WOODY 0 NON WOODY 0 flag 1 EVERGREEN 0 DECIDUOUS flag 1 C3 PSN O C4 PSN al 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 0 150 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 10 1 yr annual leaf and fine root turnover fraction 0 00 1 yr annual live wood turnover fraction 0 05 1 yr annual whole plant mortality fraction 0 0 1 yr annual fire mortality fraction 1 0 ratio ALLOCATION new fine root C new leaf C 0 1 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 05 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 cellu
2. MOWING 0 flag do MOWING O no l yes filepath reading from file 0 flag mowing method 0 on fixday 1 if LAI greater than a 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 the 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 User s Guide for Biome BGC MuSo v2 2 OPTION TO USE ANCILLARY MANAGEMENT FILES TO SIMULATE ANNUALLY VARYING HUMAN INTERVENTION Biome BGC MuSo v2 2 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 to the file e g in case of mowing the MOWING section should look like this MOWING management mowing txt flag do MOWING O no l yes filepath file 0 flag mowing meth
3. 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 14 User s Guide for Biome BGC MuSo v2 2 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 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 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 due only to the gravitational deep perco
4. 2 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 v2 2 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 model e g watershed hydrology model During the normal phase of the simulation the model can read daily groundwater information defined by a text file groundwater txt The file is supposed to be present next to the model executable during spin up this option is not possible If the file is not present no groundwater manipulation is happening in normal phase The structure of groundwater txt is simple day of year then groundwater depth in meters negative value for the given day Note that the current model version does NOT take into account the day of year field in the file The User should check whether the length of the groundwater file is in accordance with the length
5. coarse woody debris cwd A3 Other modifications 23 User s Guide for Biome BGC MuSo v2 2 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 suggest that WPM 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 deposition 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
6. 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 fruit or grain simulation was implemented After the flowering date defined in EPC file parameter 6 fruit starts to grow therefore growth and maintenence 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 24 User s Guide for Biome BGC MuSo v2 2 3 4 New C4 photosynthesi routine Based on the work of Di Vittorio et al 2010 we implemented a new enzyme driven C4 photosynthesis routine into he photsynthesis module In case of C3 photosynthesis pathway the EPC parameter fraction of leaf N in PEP Carboxylase parameter has no effect on the 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 23 APPENDIX B User s Guide for Bi
7. is set to 1 or 1f 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 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 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 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 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
8. 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 root in the beginning of the growing season 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 rate the proportion of the removed trees the decrease of leaf
9. 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 managemet type User s Guide for Biome BGC MuSo v2 2 MODIFICATIONS MADE IN THE ECOPHYSIOLOGICAL FILE The modifications of the model logic led to changes in the ecophysiological parameterizatiton as well An example EPC file is given in Appendix C New ecophysiological parameters line 8 parameter 6 yearday to start fruit allocation YFL Default value is 150 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 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 line 33 35 parameter 31 33 the fruit litter labile cellulose lignin proportion FLaP FCeP FLiP Default values are equal to litter labile cellulose lignin proportions of leaf litter line 44 parameter 42 fraction of leaf nitrogen in PeP Carboxylase for new C4 photosysthesis routine FLNP Default value is 0 03 line 48 parameter 46 soil water content ratio start of conductance reduction WCRS If no available data set it to 999 9 no water stress above field capacity where the latter is estimated from soi
10. soil moisture content 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 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 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 soil mineral N content of rooting zone taking into account changing rooting depth e multilayer_sminn c O O calculating the change of content of soil mineral nitrogen in multilayer soil decomposition of soil organic matter N mineralization and
11. v1 1 v1 2 v1 3 v2 0 and v2 1 v2 2 include developments and bugfixes plus additional control is implemented to avoid impossible 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 User s Guide for Biome BGC MuSo v2 2 INI file of the model note that in MuSo v2 2 there is an option to use additional management rules provided in separate text files 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 decription 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 and annually varying management options and optional groundwater depth control for lowland ecosystems We suggest to get 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 perf
12. 22 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 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 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 35 User s Guide for Biome BGC MuSo v2 2 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 36
13. 34 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 reading 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 05 00 LO LAO sO E0 int value of the LAI after MOWING fixday method 27 User s Guide for Biome BGC MuSo v2 2 0 0 0 0 0 0 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 280999 I 9999 99979 9999 999 9 yday last day of GRAZING Sei A 15 0 O 150 T50 o yi kg dry matter LSU daily ingested dry matter USAS US US US O SS LS Os eS LSU
14. 4 User s Guide for Biome BGC MuSo v2 2 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 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 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 j 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 2
15. 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 40 40 40 40 40 Ancillary file for anually 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 anually 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 fil
16. User s Guide for Biome BGC MuSo v2 2 User s Guide for Biome BGC MuSo v2 2 by Dora 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 E mail bzoli elte hu Revison 10 December 2013 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 the currently available model version is 4 2 Seveal 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 gra
17. _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 canopyw_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 30 ou ou ou ou
18. al 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 constant temperature equal to the annual air average temperature of the site and soil moisture content field capacity o average values are estimated regarding to the root zone e multilayer_hydrolprocess c User s Guide for Biome BGC MuSo v2 2 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 200 300 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 v2 2 upward diffusion from the 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 a 100m depth layer with a field capacity
19. 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 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 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 large evaporation or transpiration calculated based on the Penman Montieth equation and driven by meteorological data and dried up soil the soil water pools can be depleted reaching hygroscopic water content In this case evaporation and transpiration fluxes are limited This water content is calculated from pF 6 2 value based on he 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 15 User s Guide for Biome BGC MuSo v2
20. ded 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 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 v2 2 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 11 User s Guide for Biome BGC MuSo v2 2 APPENDIX A DETAILED DESCRIPTION OF THE MODIFICATIONS Below we provide theoretical basis for the model developments Note that the majority of the changes is 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 t
21. ductance 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 e senescence c o abovegroung and belowground plant material senescence is occurring due to low soil water content during a prolonged drought period number of days since water stres is calculated in waterstress_days c o mortality fluxes enter a temporary sensecence pool standing dead biomass from which dead plant material gradually enters the litter pool o turnover rates of abovegrund belowground dead plant material can be set in EPC file parameter 52 53 Management modules e planting c auxiliary function planting _init c e thinning c auxiliary function thinning _init c e mowing c auxiliary function mowing _init c e grazing c auxiliary function grazing _init c e harvest c auxiliary function harvest_init c e ploughing c auxiliary function ploughing _init c 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 10 User s Guide for Biome BGC MuSo v2 2 e outflow c now included in multilayer_hydrolprocess c percolation at the bottom soil layer ACKNOWLEDGEMENTS BIOME BGC version 4 1 1 was provi
22. e for anually 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 9 29 33 User s Guide for Biome BGC MuSo v2 2 User s Guide for Biome BGC MuSo v2 2 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 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 3
23. emperature can be predicted from 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 Eq 3 in Zheng et al 1993 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 grained calculations therefore a nested l minute time step based calculation would be necessary It would mean high inc
24. er above no full constrained of heatsum tmin vpd index and daylength index 8 parameters 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 nor the the start of the vegetation period neither 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 H20 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 during 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 the ASGST 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 12 User s Guide for Biome BGC MuSo v2 2 block of INI file It is important to note that cr
25. erent events for each management activitiy 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 control file this case the management events can be defined separately for each year 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 decrese 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 pool from which it gradually ente
26. esp c o calculates layer specific soil temperature exponents regarding to maintenance respiration of root in the different soil layer o completing leaf and fine root respiration fluxes with fruit respiration flux 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 completing with 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 30 30 100 100 200 200 300 300 500 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 value of hydrological parameters User s Guide for Biome BGC MuSo v2 2 e summary c o
27. essary conductance values are calculated separately in a new subroutine conduct_calc c 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 User s Guide for Biome BGC MuSo v2 2 o completing leaf and fine root allocation rates with fruit allocation daymet c o initializing soil temperature values in multilayer soil 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 constant scalar in multilayer soil using root zone averaged values soil temperature soil metric potential In case of water stress days soil moisture content is below a critical level the water limitation factor is calculated based on relative water content instead of soil water potential epc_init c modifications to handle the new EPC parameters see above maint_r
28. 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 average 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
29. for anually 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 anually varying thining THINNING day 150 999 9 999 9 999 9 99979 09979 99979 150 999 9 9 99 9 99 97 9 9999 99 979 9999 150 9999 999 9 999 9 999 9 999 9 999 9 THINNING rate 504 3 on 0 29 J9 O 00 o ooo Mo oO O D to 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 anually 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 32 190 290 999 9 999 9 999 9 999 9 999 200 300 999 9 999 9 999 9 999 9 999 DAILY INGESTED DRY MATTER
30. 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 EO Es 0 1 0 LOY L 0 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 9 999 9 yday PLOUGHING day FERTILIZING 0 flag do FERTILIZING O no l yes filepath reading from file 60 120 200 999 9 999 9 999 9 999 9 yday FERTILIZING day 30 0 30 0 30 0 30 0 30 0 30 0 30 0 A A LA A E7 17 17 17 17 17 17 17 5 5 5 5 5 5 5 70 70 70 70 70 70 70 20 20 20 20 20 20 20 0 0 0 0 0 0 0 10 HO DO WO WO 10 10 5 5 5 5 5 5 5 90 90 90 90 90 90 90 g 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 END_INIT 28 APPENDIX C EXAMPLE EPC FILE FOR BIOME BGC MUSO V2 2 User s Guide for Biome BGC MuSo v2 2
31. he one presented in Bond Lambery et al 2007 their Fig 1 18 User s Guide for Biome BGC MuSo v2 2 The model requires only one soil moisture stress function to calculate stomatal conductance and soil water status is calculated layer by layer Therefore averaged stress function is necessary which is the average of the layer factors weighted by the thickness of the layers of the root zone where root can be found In summary Biome BGC MuSo v2 2 uses the following scalar coeffstoma with value between 0 and 1 to control stomatal conductance in response to changing soil water content coeff stoma 0 if RSWC lt RSWC RSWC RSWC coeff oma RSWC RSWC coeff oma 1 if RSWC lt RSWC lt RSWC RSWC RSWC coeff oma RSWC RSWC where RSWCact is the actual RSWC RSWC is RSWC at wilting point RSWC x is RSWC at field capacity and RSWCsat is RSWC at saturation RSWCop is RSWC halfway between field capacity and saturation The graphical representation of the soil moisture control is presented in Fig 2 if RSWC lt RSWC lt RSWC if RSWC lt RSWC lt RSWC t 1 0 0 8 0 6 0 4 Biome BGC v4 1 1 saturation 0 2 Biome BGC MuSo v2 2 q wilting point d field capacity g 9 o O O 2 O i D gt XL 0 0 0 2 0 4 0 6 0 8 1 0 1 2 relative soil water content Figure 2 Scalar functio
32. he 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 beginning 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 fully contstrained o basic temperature is needed to calculate heatsum o threshold limits und
33. ial senescence is occurring actual transfer and storage carbon and nitrogen pools and the senescenced 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 Mortality fluxes first enter a temporary sensecence pool standing dead biomass from which dead plant material gradually enters the litter pool The turnover rate of dead plant 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 respiraiton in drought prone semi arid or arid ecosystems Note that this feature is not expected to change the long term carbon balance of the ecosystem but it might cause temporal redistribution of daily fluxes delayed decomposition of senescenced leaves 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 diff
34. in If mean relative soil moisture content is greater than a critical value halfway between field capacity and saturation a linear ramp function is implemented to limit decomposition to increase stress function 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 v2 2 uses the following scalar coeffaecomp with value between 0 and 1 to control decomposition in response to changing soil water content coeff decomp 0 if RSWC lt RSWC act RSWC RSWC co comp sa RSWC RSWC RSWC RSWC coeff iecomp Z sa a RSWC RSWC where RSWChy is relative soil moisture or soil water content RSWC at hygroscopic soil water content RSWC is the actual RSWC RSWCy is RSWC at wilting point and RSWC a is RSWC at saturation RSWCop is RSWC halfway between field capacity and saturation The graphical representation of the soil moisture control is presented in Fig 1 if RSWC lt RSWC lt RSWC act if RSWC lt RSWC lt RSWC 1 0 0 8 0 6 0 4 Biome BGC v4 1 1 saturation 0 2 Biome BGC MuSo v2 2 t g 9 a O O 12 O O 5 O o D 3 A lt field capacity 0 0 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 o
35. itical 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 six layer soil submodel was implemented into the model Six layers provide an optimal compromise between the simulation accuracy 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 7 1 and 1 m 1t means that the first layer is located at depth of 0 10 cm the second is at 10 30 cm the third is at 30 100 cm the fourth is at 100 200 cm and the fifth is 200 300 cm The thickness of bottom 6th inactive layer is 2 m but the properties of this special layer do not change with time It is assumed that its temperature is always equal to the annual average air tem
36. l texture using eprirical relationship line 49 parameter 47 soil water content ratio complete stomatal closure WCRC note that in the original model leaf water potential complete conductance reduction WCRE was used as limiting factor also for parameter 46 If no available data set it to 999 9 below wilting point complete stomatal closure line 54 55 parameter 52 53 senescence drought stress related mortality coefficient effect on aboveground belowground plant material SMCA SMCB These parameters might be important to simulate the carry over effect of drought stress during the consecutive year Default values are 0 01 line 56 parameter 54 turnover rate of wilted standing biomass senescenced leaves to litter TRWB Default value is 0 01 line 57 parameter 55 turnover rate of cut down but not transported i e left at the site non woody biomass to litter TRCN 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 Implementation of this turnover process was needed 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 gr
37. laces 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 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 are needed for cropland related application of Biome BGC In agriculture harvest is the process of gathering mature crops from the fields It means
38. lation 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 accoring to the soil moisture limitation of the given layer formula is presented below on Page 19 coeffgtoma and to the root content ratio of the actual soil layer depth and the total rooting zone 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 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 changed 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
39. lose proportion O22 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 0x5 DIM canopy light extinction coefficient 230 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 00006 m s cuticular conductanc projected area basis 0 04 m s boundary layer conductance projected area basis 999 9 m3 m3 soil water content ratio start of cond red 999 9 field cap 999 9 m3 m3 soil water content ratio complete cond red 999 9 wilting point 999 9 MPa leaf water potential complete cond reduction 999 9 SWCR is used 999 9 MPa leaf water potential complete cond reduction 999 9 SWCR is used 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 p
40. n of soil water content controlling stomatal conductance in the original Biome BGC v4 1 1 and in Biome BGC MuSo v2 2 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 decrease 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 soil drought 19 User s Guide for Biome BGC MuSo v2 2 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 of the rootzone is less than 0 1 Due to low soil water content during a prolonged drought period number of days since water stress is calculated in waterstress_days c abovegroung and belowground plant mater
41. new summary output variables are calculated VEGC LITRC SOILC TOTALC SR soil respiration NBP o NEE is positive if ecosystem is net C source 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 pheonological state of vegetation onset and offset day from Tmin VPD daylength and cumulative Tavg based on literure o snow cover estimation based on precipitation average temperature 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 futher 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 exponenti
42. ng 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 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 wate
43. od 0 on fixday 1 fixLAlI 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 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 T50 234 999 9 99939 999 9 999 9 9999 170 244 999 9 999 9 999 9 999 9 999 9 180 999 9 99979 99979 9 99 9 999 9 9999 value of the LAI after MOWING 1 O10 TO LO TS O10 0 260 Heo 100 LEES O TOs 0 10 2 O 289207450 A 2 0 20 transported part of plant material 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 In this example normal simulation is performed for 3 years so in each block 3 lines are repeated The first line in MOWING day block belongs to the first
44. of the normal simulation The model logic is the following if for a given day groundwater depth is higher than the middle point of a given soil layer e g if groundwater depth is 2 3 m while middle layer of the 5th layer is 2 5 m then it is assumed that the entire layer and all layers below that layer is saturated Upward diffusion of water from the saturated layes 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 decompositon 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 or in other words relative soil water 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 In Biome BGC MuSo v2 2 the scalar functions representing soil moisture limitation on photosynthesys and decomposition is exp
45. ome BGC MuSo v2 2 EXAMPLE INI FILE FOR BIOME BGC MUSO V2 2 BBGC_MuSo simulation MET_INPUT metdata bugac_1901 2000 mtc43 filename met file name 4 int number of header lines in met file RESTART 0 flag 1 read restart 0 dont read restart t flag 1 write restart 0 dont write restart 0 flag 1 use restart metyear 0 reset metyear restart bugac _MuSol3 endpoint restart bugac _MuSol3 endpoint TIME DEFINE 100 100 1901 i 6000 CLIM CHANGE 0 0 0 i t 1 OS cS CO2 CONTROL 1 385 8 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 RAMP_NDEP 0 2050 0 00200 nitrogen Ndep_1901 2011 txt EPC_FILE epc apriori_MuSol3_c3grass epc filename filename name of the input restart file name of the output restart file int number of meteorological data years int number of simulation years first simulation year int flag 1 spinup run 0 normal run int maximum number of spinup years degC offset for Tmax degC offset for Tmin degC multiplier for PRCP degC multiplier for VPD degC multiplier for RAD flag O constan l vary with file 2 based on Ndep ppm constant atmospheric CO2 concentration filename name of the CO2 file m maximum depth of rooting zone sand percentage by volume in rock free soil silt percentage by volume in rock free soil clay percentage b
46. ormed in Hungary Please contact Zoltan BARCZA bzoli elte hu for a reprint of the Hidy et al 2012 paper if you do not 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 SITE block There are some modifications within the SITE block New lines are also used 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 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 inserted into the OUTPUT _ CONTROL block The second line now defines a filename where internal model paramete
47. otosyn 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 GSI limitl greater that limit gt start of vegper 0 01 GSI limit2 less that limit gt end of vegper intvar GSI_bugac_2003 2011 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 6
48. ou ou ou ou ou ou ou ou ou ou ou ou cr cr Cr CT cr sc 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 summary gt Cchange PLT summary gt Cchange SNSC amp summary gt daily sr amp summary gt daily tr 31 User s Guide for Biome BGC MuSo v2 2 User s Guide for Biome BGC MuSo v2 2 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 v2 2 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 As the examples show Ancillary file
49. owth GRC Default value is 0 3 In the original model this parameter was constant fixed within the source code 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 User s Guide for Biome BGC MuSo v2 2 rooting depth set by the INI file is reached Default value is 0 5 which means the middle of the growing season Modified ecophysiological parameters e lines 50 51 parameters 48 49 soil water potential start of complete conductance reduction SWPS SWPC Note that in the original model leaf water potential start of complete conductance reduction LWPS LWPC were used here If soil water potential is given by the user while parameters 46 and 47 soil water content ratio are undefined then soil water potential controls stomatal closure If parameters 46 49 are all undefined 999 9 then the model calculates stomatal closure based on internally es
50. perature 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 Soil texture is assumed to be constant with depth The total active soil depth is 3 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 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 6 layer module is an improvement basically aimed to support forest related simulations where the deepest layer represents the water table that can be accessed by tree roots if rooting zone is deep enough Water becomes available to the plant through water movement through the 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 based on a sigmoidal 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 eco
51. physiological parameter matur_coeff By default we may assume that it is the middle of the growing season matur_coeff 0 5 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 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 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 13 User s Guide for Biome BGC MuSo v2 2 reconsidered in the developed model see below We assume that below the lowermost boundary 3 m to 5 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 t
52. 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 o 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 avoid negative pools based on the percolation fluxes and the soil mineral N content e 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 User s Guide for Biome BGC MuSo v2 2 folder directory next to model executable that contains daily data for the duration of the normal simulation e auxiliary function groundwater_init c Senescence simulation e waterstress_days c calculates the number of the days since water stress occurs during the vegetation period based on soil water status e conduct limit factors c calculates the limitation factors for conductance calculation user can set both volumetric soil water content VWC ratio or PSI data in EPC file but in the calculations VWC ratio values are used e conduct calc c o calculating the stress functions multipliers for stomatal con
53. r potential To avoid the occurrence of this phenomenon we have modified the model to use relative actual values relative to field capacity soil moisture content to define the stress function instead of soil water potential note that the new stress function is a linear function of relative soil water content between specific points see below If enough soil water is available e g near field capacity 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 zero The start and the end of the water stress are determined using new ecophysiological parameters soil water content ratio at the start of the conductance reduction and at the complete conductance reduction The default soil water content threshold limit values are field capacity and wilting point respectively Presence of elevated groundwater can negatively affect stomatal conductance Bond Lamberty et al 2007 As soil moisture control is implemented as effect of average relative soil moisture content within the entire root zone the stress function affecting stomatal conductance has been modified in MuSo v2 2 If mean relative soil moisture content is above a critical soil moisture content halfway between field capacity and saturation linear ramp function is implemented to limit stomatal conductance up to saturation complete closure The shape of the modified stress function is similar to t
54. rease in model computational time Instead soil layer temperatures are estimated based on empirical formula with daily time 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 1 2 2 Simulation of soil hydrology Among soil hydrological processes the original Biome BGC only takes into consideration the canopy interception snowmelt outflow and bare soil evaporation We have added the simulation of runoff diffusion and percolation processes in order to improve the soil water balance simulation Optional handling of seasonally changing groundwater depth i e possible flooding is also implemented beginning with MuSo v2 2 e g flooding simulation for lowland forests
55. ressed by RSWC see below 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 is used by the decomposition calculation The depth of the root zone is calculated based on above mentioned rooting depth The average temperature of each soil layer in the root zone is used to calculate the respiration of the fine and coarse roots As saturation of the soil 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 relative 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 16 User s Guide for Biome BGC MuSo v2 2 definition of soil water potential similiar to the original non linear function the higher the relative soil moisture content is the lower the value of the stress function The mininum reltive soil mositure content is related to hygroscopic water while the maximum is related to saturation Note that close to saturation the decomposition becomes limited aga
56. riginal Biome BGC v4 1 1 and in Biome BGC MuSo v2 2 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 v2 2 we assume that varying amount of mineralized nitrogen is available within the different soil layers that is available for root uptake and other losses its 17 User s Guide for Biome BGC MuSo v2 2 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 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 limiti
57. rop turnover rate of cut down non woody biomass to litter 0 001 prop turnover rate of cut down woody biomass to litter 0 3 prop growth resp per unit of C grown 0 01 prop N denitrification proportion Qi prop N mobilen proportion 0 55 prop maturity coefficient 29 APPENDIX D CHANGES IN THE OUTPUT VARIABLES AND THEIR CODE IN BIOME BGC MUSO V2 2 User s Guide for Biome BGC MuSo v2 2 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 v2 2 total ecosystem respiration can be retrieved as a single output variable output_map 649 to support Monte Carlo experiments with 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 p p p p p p P p p p P p 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
58. rs the litter pool Althoug 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 20 User s Guide for Biome BGC MuSo v2 2 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 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 v2 2 If the user only want to use a subset of the maximum 7 events 999 9 should be written to the unused p
59. rs will be written o filename for the Biome BGC internal variables control The rest of the block is unchanged 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 v2 2 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 User s Guide for Biome BGC MuSo v2 2 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 deactivates 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
60. 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 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 22 User s Guide for Biome BGC MuSo v2 2 before taken up by plant therefore turn into the litter nitrogen pool Carbon content of fertilizer turns into the
61. ss to litter TRMB can be set in EPC file parameter 55 Althoug 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 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 remaining after harvest returns the litter pools 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 supplement naturally occurring essential elements in the
62. 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 neccessary 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 and it also controls the turnover of dead coarse root stump into
63. 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 21 User s Guide for Biome BGC MuSo v2 2 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 from than gradually enters the litter pool The turnover rate of mowed harvested bioma
64. timated field capacity and wilting point If parameters 46 49 are all defined then soil water content ratio parameters 46 and 47 are used parameters 48 and 49 are ignored Note that in the original model there were only 43 EPC parameters MuSo v2 2 has EPC 60 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 has 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 source code 1 Modified subroutines e annual rates c completing leaf and fineroot litterfall rates with fruit litterfall rates e canopy et c calculates only the canopy evaporation and transpiration based on Penman Montieth method The nec
65. y volume in rock free soil m site elevation degrees site latitude for S Hem DIM site shortwave albedo kgN m2 yr wet dry atmospheric deposition of N kgN m2 yr symbiotictasymbiotic fixation of N Celsius mean annual air temperature runoff parameter Campbell 1988 m3 m3 measured SWC at SAT if no data 999 9 m3 m3 measured SWC at FC if no data 999 9 m3 m3 measured SWC at WP if no data 999 9 flag do a ramped N deposition run O no l yes int reference year for industrial N deposition kgN m2 yr industrial N deposition value filename name of the N dep file filename EPC file name kg m2 water stored in snowpack DIM initial soil water as a proportion of saturation kgC m2 first year maximum leaf carbon kgC m2 first year maximum stem carbon kgC m2 coarse woody debris carbon kgC m2 litter carbon labile pool kgC m2 litter carbon unshielded cellulose pool kgC m2 litter carbon shielded cellulose pool kgC m2 litter carbon lignin pool kgC m2 soil carbon fast microbial recycling pool kgC m2 soil carbon medium microbial recycling pool kgC m2 soil carbon slow microbial recycling pool 26 User s Guide for Biome BGC MuSo v2 2 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 ph
66. zing Although the modifications aimed to support the use of Biome BGC in herbaceous ecosystems the modified model can also be used 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 modificaitons are 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 v2 2 where the abbreviation refers to Multilayer Soil Module Biome BGC MuSo 2 2 is the updated version of MuSo v1 0

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