Theoretical basis

Biome-BGC v4.1.1 (with modifications made by Max Planck Institute) was used as a starting point for the developments.
Our research group developed Biome-BGC to improve the ability of the model to simulate carbon and water cycle in managed herbaceous ecosystems (see Hidy et al., 2012). The modifications included structural improvements of the model (e.g., the simple, outdated, one-layer soil module was replaced by a multilayer soil module; drought related plant senescence was implemented; model phenology was improved) and also management modules were developed (e.g. to simulate mowing, grazing). Although the modifications aimed to support the use of Biome-BGC in herbaceous ecosystems, the modified model can 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.
Complete documentation of the code changes were published in Geoscientific Model Development. Without being exhaustive, here we mention a few important improvements. Please read the GMD paper and the User's Guide for more details. Though the current model version is BBGC MuSo v4.0, the User's Guide for Biome-BGCMuSo v3.0 contains theroretical basis documentation that is not part of User's Guide of Biome-BGCMuSo v4.0.

Improvements included in Biome-BGCMuSo:

- a seven-layer soil submodel was implemented (the thicknesses of the active layers (layers 1-6) from the surface to the bottom are 10, 20, 30, 40, 100 and 100 cm again)
- soil texture and soil bulk density can be defined by the User in initialization file layer by layer
- soil properties (saturation, field capacity, wilting point) can optionally estimated based on the soil texture
- the runoff submodel was included (SCS runoff curve method is used)
- two different soil water balance submodules are implemented (one is based on the Richards equation, the second is based on the so-called tipping bucket model)
- two different soil temperature submodules were implemented
- exponential root profile is introduced within the model
- LAI dependent albedo is implemented
- management options have been developed (mowing, grazing, fertilization, ploughing, sowing/planting, harvest, forest thinning, irrigation)
- annually varying management options were implemented
- novel transient run was implemented that connetcts the spinup and normal phase
- elevated groundwater effect on soil moisture was is implemented
- soft stem was added as a possible new pool to support herbaceous vegetation related simulations
- simulation of pond water was included
- drought effect on plant functioning was included in the model (drought related senescence is implemented)
- programmed leaf senescence is implemented as an optional mechanism
- transpiration (calculated by Penman-Monteith function) can be limited during severe drought
- dynamic response and acclimation of autotrophic respiration was implemented in the model
- nitrous oxide and methane soil efflux can be estimated by the model
- dependence of stomatal conductance on ambient carbon dioxide concentration was implemented in the model
- dependence of stomatal conductance on anoxic soil conditions was implemented
- the concept of standing dead biomass was implemented (dead leaves that remain intact for some time period after senescence)
- litter is divided into aboveground and belowground components to simulate residue cover in croplands
- fruit simulation is implemented, where allocation to fruit is controlled by growing degree-day setting
- an option was implemented to support the use of different ecophysiological constant during spinup and normal run