Terrestrial biogeochemical models are essential tools to quantify
climate–carbon cycle feedback and plant–soil relations from local to global
scale. In this study, a theoretical basis is provided for the latest version
of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a
branch of the original Biome-BGC model with a large number of developments
and structural changes. Earlier model versions performed poorly in terms of
soil water content (SWC) dynamics in different environments. Moreover, lack
of detailed nitrogen cycle representation was a major limitation of the
model. Since problems associated with these internal drivers might influence
the final results and parameter estimation, additional structural
improvements were necessary. In this paper the improved soil hydrology as well as the
soil carbon and nitrogen cycle calculation methods are described in detail.
Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case
studies focusing on soil hydrology, soil nitrogen cycle, and soil organic
carbon content estimation. Soil-hydrology-related results are compared to
observation data from an experimental lysimeter station. The results
indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0
(explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to
0.46 for soil evaporation; bias changed from -0.047 to
-0.007 m3m-3 for SWC and from -0.68 to -0.2 mmd-1 for soil evaporation). Simulations related to nitrogen balance and soil CO2 efflux were evaluated based on observations made in a
long-term field experiment under crop rotation. The results indicated that
the model is able to provide realistic nitrate content estimation for the
topsoil. Soil nitrous oxide (N2O) efflux and soil respiration
simulations were also realistic, with overall correspondence with the
observations (for the N2O efflux simulation bias was between -0.13 and
-0.1 mgNm-2d-1, and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for
CO2 efflux simulations bias was 0.04–0.17 gCm-2d-1, while NRMSE was 34.1 %–40.1 %).
Sensitivity analysis and optimization of the decomposition scheme are
presented to support practical application of the model. The improved
version of Biome-BGCMuSo has the ability to provide more realistic soil
hydrology representation as well as nitrification and denitrification process
estimation, which represents a major milestone.
Introduction
The construction and development of biogeochemical models (BGMs) represent the
response of the scientific community to address challenges related to
climate change and human-induced global environmental change. BGMs can be
used to quantify future climate–vegetation interaction including
climate–carbon cycle feedback, and as they simulate plant production, they
can be used to study a variety of ecosystem services that are related to
human nutrition and resource availability (Asseng et al., 2013; Bassu et
al., 2014; Huntzinger et al., 2013). Similarly to models describing
various and complex environmental processes, the structure of biogeochemical
models reflects our current knowledge about a complex system with many
internal processes and interactions.
Processes of the atmosphere–plant–soil system take place on different
temporal (sub-daily to centennial) scales and are driven by markedly
different mechanisms that are quantified by a large diversity of modeling
tools (Schwalm et al., 2019). Plant photosynthesis is an enzyme-driven
biochemical process that has its own mathematical equation set and related
parameters (and a large body of literature; e.g., Farquhar et al., 1980; Medlyn et al., 2002; Smith and Dukes, 2013; Dietze, 2013). Allocation of carbohydrates
in the different plant compartments is studied extensively and also has a
large body of literature and mathematical tool set (Friedlingstein et al., 1999;
Olin et al., 2015; Merganičová et al., 2019). Plant phenology is
quantified by specific algorithms that are rather uncertain components of
the models (Richardson et al., 2013; Hufkens et al., 2018; Peaucelle et al.,
2019). Soil biogeochemistry is driven by microbial and fungal activity and
also has its own methodology and a vast body of literature (Zimmermann et al., 2007;
Kuzyakov, 2011; Koven et al., 2013; Berardi et al., 2020). Emerging
scientific areas like the quantification of the dynamics of non-structural
carbohydrates (NSCs) in plants have a separate methodology that requires
mathematical representation in models (Martínez-Vilalta et al., 2016).
Simulation of land surface hydrology including evapotranspiration is
typically handled by some variant of the Penman–Monteith equation that is
widely studied and thus represents a separate scientific field (McMahon et al.,
2013; Doležal et al., 2018).
Putting it all together, if we are about to construct and further improve a
biogeochemical model to consider novel findings and track global changes, we
need comprehensive knowledge that integrates many, almost disjunct
scientific fields. Clearly, transparent and well-documented development of a
biogeochemical model is of high priority but is challenging from the very
beginning and requires cooperation of researchers from various scientific
fields.
Continuous model development is inevitable, but it has to be supported by
extensive comparison with observations and some kind of implementation of
the model–data fusion approach (Keenan et al., 2011). It is well documented
that structural problems might trigger incorrect parameter estimation that
might be associated with distorted internal processes (Sándor et al.,
2017; Martre et al., 2015). In other words, one major issue with BGMs (and
in fact with all models using many parameters) is the possibility to get
good simulation results for wrong reasons (which means incorrect
parameterization) due to compensation of errors (Martre et al., 2015). In
order to avoid this issue, any model developer team has to make an effort to
also focus on internal ecosystem conditions (e.g., soil volumetric water
content – SWC), nutrient availability, stresses) and other processes
(e.g., decomposition) rather than the main simulated processes (e.g.,
photosynthesis, evapotranspiration).
Historically, biogeochemical models have been developed to simulate the
processes of undisturbed ecosystems with simple representation of the
vegetation (Levis, 2010). As the focus was on the carbon cycle, water and
nitrogen cycles as well as related soil processes were not well represented.
Incorrect representation of SWC dynamics is still an issue with models,
especially in drought-prone ecosystems (Sándor et al., 2017).
Additionally, human intervention representation (management) is still
incomplete in some state-of-the-art BGMs; e.g., thinning, grass mowing,
grazing, tillage, or irrigation is missing in some models (see Table A1 in
Friedlingstein et al., 2020).
In contrast, crop models with different complexity were used for about 50 years or so to simulate the processes of managed vegetation (Jones et al.,
2017; Franke et al., 2020). As the focus of crop models is on final
yield due to economic reasons, the carbon balance or the full greenhouse
gas balance was not addressed or was just partially addressed originally. Crop models
typically have a sophisticated representation of soil water balance with a
multilayer soil module that usually calculates plant response to water
stress as well. Nutrient stress, soil conditions during planting,
consideration of multiple phenological phases, heat stress during anthesis,
vernalization, manure application, fertilization, harvest, and many other
processes have been implemented over the decades (Ewert et al., 2015).
Therefore, it seems to be straightforward to exploit the benefits of crop
models and implement sound and well-tested algorithms into BGMs.
Starting from the well-known Biome-BGC model originally developed to
simulate undisturbed forests and grasslands using a simple single-layer
soil submodel (Running and Hunt, 1993; Thornton and Rosenbloom, 2005), we
developed a complex, more sophisticated model (Hidy et al., 2012, 2016).
Biome-BGCMuSo v4.0 (Biome-BGC with Multilayer Soil module) uses a seven-layer
soil module and is capable of simulating different ecosystems from natural
grassland to cropland including several management options (mowing, grazing,
thinning, planting, and harvest), taking into account many environmental
effects (Hidy et al., 2016). The developments included improvements
regarding both soil and plant processes. In a nutshell, the most important
soil-related developments were the improvement of the soil water balance
module by implementing routines for estimating percolation, diffusion, pond
water formation, and runoff as well as the introduction of multilayer simulation for
belowground processes in a simplified way. The most important plant-related
developments involved the implementation of a routine for estimating the
effect of drought on vegetation growth and senescence, the improvement of
stomatal conductance calculation considering atmospheric CO2
concentration, the integration of selected management modules, the
implementation of new plant compartments (e.g., yield), the implementation of a
C4 photosynthesis routine, the implementation of photosynthesis and
respiration acclimation of plants and temperature-dependent Q10, and
empirical estimation of methane and nitrous oxide soil efflux.
Problems found with the Biome-BGCMuSo v4.0 simulation result (namely the
poor representation of soil water content, the lack of sophisticated
layer-specific soil nitrogen dynamics representation, or model-structure-related problems, such as the lubber parameterization of the model) marked
the path for further developments.
The aim of the present study is to provide detailed documentation of the
current, improved version of Biome-BGCMuSo v6.2, which has many new features
and facilitates various in-depth investigations of ecosystem functioning.
Due to large number of developments, this paper focuses only on the soil-related model improvements. Case studies are also presented to demonstrate
the capabilities of the new model version and to provide guidance for the
model user community.
The original Biome-BGC model
Biome-BGC was developed from the Forest-BGC mechanistic model family in
order to simulate vegetation types other than forests. Biome-BGC was one of
the earliest biogeochemical models that included explicit carbon and
nitrogen cycle modules. Biome-BGC simulates the storage and fluxes of
water, carbon, and nitrogen within and between the vegetation, litter, and
soil components of terrestrial ecosystems. It uses a daily time step and is
driven by daily values of maximum and minimum temperatures, precipitation,
solar radiation, and vapor pressure deficit (Running and Hunt, 1993). The
model calculations apply to a unit ground area that is considered to be
homogeneous.
The three most important components of the model are the phenological,
carbon uptake and release, and soil flux modules. The core logic that is
described below in this section remained intact during the developments. The
phenological module calculates foliage development that affects the
accumulation of C and N in leaf, stem (if present), and root and consequently
the amount of litter. In the carbon flux module gross primary production
(GPP) of the biome is calculated using Farquhar's photosynthesis routine
(Farquhar et al., 1980) and the enzyme kinetics model based on Woodrow and
Berry (2003). Autotrophic respiration is separated into maintenance and
growth respiration. Maintenance respiration is calculated as the function of
the N content of living plant pools, while growth respiration is an
adjustable but fixed proportion of the daily GPP. The single-layer soil
module simulates the decomposition of dead plant material (litter) and soil
organic matter, N mineralization, and N balance in general (Running and Gower, 1991). The soil module uses the so-called converging cascade method
(Thornton and Rosenbloom, 2005) to simulate decomposition, carbon and
nitrogen turnover, and related soil CO2 efflux.
The simulation has two basic steps. During the first (optional) spin-up
simulation the available climate data series is repeated as many times as required to reach a dynamic equilibrium in the soil organic matter
content to estimate the initial values of the carbon and nitrogen pools. The
second, normal simulation uses the results of the spin-up simulation as
initial conditions and runs for a given, predefined time period (Running and
Gower, 1991). The so-called transient simulation option (which is the extension
of the spin-up routine) is a novel feature in Biome-BGCMuSo v6.2 relative to
the previous versions in order to ensure smooth transition between the
spin-up and normal phase (Hidy et al., 2021).
In Biome-BGC, the main parts of the simulated ecosystem are defined as
plant, litter, and soil. The most important pools include leaf (C, N, and
intercepted water), root (C, N), stem (C, N), soil (C, N, and water), and
litter (C, N). Plant C and N pools have sub-pools (actual pools, storage
pools, and transfer pools). The actual sub-pools store C and N for the
current year of growth. The storage sub-pools (essentially the non-structural
carbohydrate pool, the source for the cores or buds) contain the amount of C
and N that will be active during the next growing season. The transfer
sub-pools inherit the entire content of the storage pools at the end of
every simulation year. Soil C also has sub-pools representing various
organic matter forms characterized by considerably different decomposition
rates.
In spite of its popularity and proven applicability, the development of
Biome-BGC was temporarily stopped (the latest official NTSG version is
Biome-BGC 4.2; https://www.ntsg.umt.edu, last access: November 2021). One major drawback of the model
was its relatively poor performance in modeling managed ecosystems and the
simplistic soil water balance submodel using a single soil layer only.
Our team started to develop the Biome-BGC model further in 2006. According
to the logic of the team, the new model branch was planned to be the
continuation of the Biome-BGC model with regard to the original concept of
the developers (keeping the model code open-source, providing detailed
documentation, and providing support for users).
The starting point of our model development was Biome-BGC v4.1.1 that was a
result of the model improvement activities of the Max Planck Institute
(Vetter et al., 2008). Development of the Biome-BGCMuSo model branch has a
long history by now. Previous model developments were documented in Hidy et
al. (2012, 2016). Below, we provide a detailed description of
the new developments that are included in Biome-BGCMuSo v6.2, which is the
latest version released in September 2021. A comprehensive review of the
input data requirement of the model, together with an explanation of the input
data structure, is available in the user guide (Hidy et al., 2021). In this
paper we refer to some input files (e.g., soil file, plant file) that are
described in the user guide in detail.
One of the most important novelties and advantages of the new model version
(Biome-BGCMuSo v6.2) compared to any previous versions due to the
extensive and detailed soil parameter set (the current version has 79, MuSo 4.0
has 39, and the original model version has only 6 adjustable soil-related
parameters) is that the parameterization of the model is much more flexible. But
it might, of course, be a challenging task to define all of the input
parameters. In order to support practical application of the model, the
user guide contains proposed values for most of the new parameters (Hidy
et al., 2021).
Soil-hydrology-related developments
In Biome-BGCMuSo v6.2 a 10-layer soil submodel was implemented. Previous
model versions included a seven-layer submodel, which turned out to be
insufficient to capture hydrological events like drying of the topsoil
layers with sufficient accuracy. The thicknesses of the layers from the
surface to the bottom are 3, 7, 20, 30, 30, 30, 30, 50, 200, and 600 cm. The
center of the given layer represents the depth of each soil layer. Soil
texture can be defined by the percentage of sand and silt for each layer
separately along with the most important physical and chemical parameters
(pH, bulk density, characteristic SWC values, drainage coefficient,
hydraulic conductivity) in the soil input file (Hidy et al., 2021).
The water balance module of Biome-BGCMuSo has five major components to
describe soil-water-related processes at daily resolution (listed here
following the order of calculation): pond water accumulation and runoff,
infiltration and downward gravitational flow (percolation), water movement within the soil (diffusion) driven by water potential
gradient, evaporation and
transpiration (root water uptake), and the downward and upward fluxes to and from
groundwater. In the following subsections these five major components are
described.
Pond water accumulation and runoff
Precipitation can reach the surface as rain or snow (below 0 ∘C
snow accumulation is assumed). Snow water melts from the snowpack as a
function of temperature and radiation and is added to the precipitation input.
The canopy can intercept rain. The intercepted volume goes into the canopy water pool,
which can evaporate. No canopy interception of snow is assumed. The
throughfall (complemented with the amount of melted snow) gives the
potential infiltration.
A new development in Biome-BGCMuSo v6.2 is that maximum infiltration is
calculated based on the saturated hydraulic conductivity and the SWC of the
topsoil layers. If the potential infiltration exceeds the maximum
infiltration, pond water can be formed. If the sum of the precipitation and
the actual pond height minus the maximum infiltration rate is greater than
the maximum pond height, the excess water is added to surface runoff
detailed below (Balsamo et al., 2009). The maximum pond height is an input
parameter. Water from the pond can infiltrate into the soil at a rate at which the
topsoil layer can absorb it. Evaporation of pond water is assumed to be equal
to the potential evaporation.
Surface runoff is the water flow occurring on the surface when a portion of
the precipitation cannot infiltrate into the soil. Two types of surface
runoff processes can be distinguished: Hortonian and Dunne. Hortonian runoff
is unsaturated overland flow that occurs when the rate of precipitation
exceeds the rate at which water can infiltrate. The other type of surface
runoff is the Dunne runoff (also known as the saturation overland flow),
which occurs when the entire soil is saturated but the rain continues to
fall. In this case the rainfall immediately triggers pond water formation
and (above the maximum pond water height) surface runoff. The handling of
these processes is presented in the soil hydrological module of
Biome-BGCMuSo v6.2.
Calculation of Hortonian runoff (kgH2Om-2d-1) is
based on a semi-empirical method and uses the precipitation amount (cmd-1), the unitless runoff curve number (RCN), and the actual moisture
content status of the topsoil (Rawls et al., 1980; this method is known as
the SCS runoff curve number method; SCS: soil conservation service). This type of runoff simulation can be
turned off by setting RCN to zero. A detailed description can
be found in Sect. S1 in the
Supplement. The amount of runoff as a function of the
soil type and the actual SWC is presented in Fig. 1.
Hortonian runoff as a function of rainfall intensity, soil type,
and actual soil water content of the topsoil layer. Sand soil means 92 %
sand, 4 % silt, and 4 % clay; silt soil means 8 % sand, 86 % silt, and
6 % clay; clay soil means 20 % sand, 20 % silt, and 60 % clay. SWC
and SAT denote soil water content and saturation, respectively.
Infiltration, percolation, and diffusion
There are two optional methods in Biome-BGCMuSo v6.2 to calculate soil water
movement between soil layers and actual SWC layer by layer. The first one is
a cascade method (also known as tipping bucket method), and the second is a physical method based on the
Richards equation. The tipping bucket method has a
long history in crop modeling and is considered to be a successful,
well-evaluated algorithm that can accurately simulate downward water flow in
the soil.
The cascade method uses a semi-empirical input parameter (DC:
drainage
coefficient, d-1) to calculate the downward water flow rate. When
the SWC of a soil layer exceeds field capacity (FC), a fraction (equal to
DC) of the water amount above FC goes to the layer next below. If DC is not set
in the soil input file, it is estimated from the saturated hydraulic
conductivity: DC=0.1122⋅KSAT0.339 (KSAT: saturated
hydraulic conductivity, cmd-1; the user can set its value or the
model based on soil texture estimates it internally; see Hidy et al., 2016).
A detailed description of the method can be found in Sect. S2 in the
Supplement. Drainage from the bottom layer is a net loss for the
soil profile.
Water diffusion that is the capillary water flow between the soil layers is
calculated to account for the relatively slow movement of water. The flow
rate is a function of the water content difference of two adjacent layers
and the soil water diffusivity at the boundary of the layers, which is
determined based on the average water content of the two layers. A
detailed mathematical description of the method can be found in Sect. S3 in the
Supplement.
A detailed description of the Richards method can be found in Hidy et al.
(2012). To support efficient and robust calculations of soil water fluxes a
dynamically changing time step was introduced in version 4.0 (Hidy et al.,
2016). The implementation of the more sophisticated Richards method is still
in an experimental phase requiring rigorous testing and validation in the
future.
Evapotranspiration
Biome-BGCMuSo, like its predecessor Biome-BGC, estimates evaporation of
leaf-intercepted water, bare soil evaporation, and transpiration to estimate
the total evapotranspiration at a daily level. The potential rates of all
three processes are calculated based on the Penman–Monteith (PM) method. PM
equation requires net radiation (minus soil heat flux) and conductance
values by definition using different parameterization for the different
processes. The model calculates leaf- and canopy-level conductances of water
vapor and sensible heat fluxes to be used in Penman–Monteith calculations
of canopy evaporation and canopy transpiration. Note that in the Biome-BGC
model family the direct wind effect is ignored but can be considered
indirectly by adjusting boundary layer conductance to site-specific
conditions. A possible future direction might be the extension of the model
logic to consider the wind effect directly.
Canopy evaporation
If there is intercepted water, this portion of evaporation is calculated
using the canopy resistance (reciprocal of conductance) to evaporated water
and the resistance to sensible heat. The time required for the water to
evaporate based on the average daily conditions is calculated and
subtracted from the day length to get the effective day length for
evapotranspiration. Combined resistance to convective and radiative heat
transfer is calculated based on canopy conductance of vapor and leaf
conductance of sensible heat, both of which are assumed to be equal to the
boundary layer conductance. Besides the conductance and resistance parameters
the canopy-absorbed shortwave radiation drives the calculation. Note that
the canopy evaporation routine was not modified significantly in
Biome-BGCMuSo.
Soil evaporation
In order to estimate soil evaporation, first the potential evaporation is
calculated, assuming that the resistance to vapor is equal to the
resistance to sensible heat and assuming no additional resistance component.
Both resistances are assumed to be equal to the actual aerodynamic
resistance. Actual aerodynamic resistance is a function of the actual air
pressure and air temperature as well as the potential aerodynamic resistance
(potRair in
sm-1). potRair was a fixed value in the previous
model versions (107 sm-1). Its value was derived from observations
over bare soil in tiger bush in southwestern Niger (Wallace and Holwill,
1997). In Biome-BGCMuSo v6.2, the potRair is an input parameter that can be
adjusted by the user (Hidy et al., 2021). Another new development in
Biome-BGCMuSo v6.2 is the introduction of an upper limit for daily potential
evaporation (evaplimit) that is determined by the available energy
(incident shortwave flux that reaches the soil surface):
evaplimit=irad⋅daylLHvap,
where irad is the incident shortwave flux density (Wm-2), dayl is the
length of the day in seconds, and LHvap is the latent heat of vaporization
(the amount of energy that must be added to liquid to transform into gas;
Jkg-1). This feature was missing from previous model versions, resulting
in considerable overestimation of evaporation on certain days that was
caused by the missing energy limitation on evaporation.
A new feature in Biome-BGCMuSo v6.2 is the calculation of the actual
evaporation from the potential evaporation and the square root of time
elapsed since the last precipitation (expressed by days; Ritchie, 1998).
This is another method that has been used by the crop modeler community for
many years. A detailed description of the algorithm can be found in Sect. S4 in the
Supplement.
One major new development in Biome-BGCMuSo v6.2 is the simulation of the
reducing effect of surface residue or mulch cover on bare soil evaporation.
Here we use the term “mulch” to quantify surface residue cover in general,
keeping in mind that mulch is typically a human-induced coverage. Surface
residue includes aboveground litter and coarse woody debris as well.
The evaporation reduction effect (evapREDmulch; unitless) is a variable between 0 and 1
(0 means full limitation, and 1 means no limitation) estimated based on a
power function of the surface coverage (mulchCOV in %) and a soil-specific
constant set by the user (pREDmulch; see Hidy et al., 2021). If variable mulchCOV reaches
100 % it means that the surface is completely covered. If mulchCOV is greater than
100 % it means the surface is covered by more than one layer. Surface
coverage is a power function of the amount of mulch (mu, kgCm-2) with
parameters p1mulch, p2mulch, and p3mulch (soil parameters) based on
the method of Rawls et al. (1980).
2mulchCOV=p1mulch⋅(mu/p2mulch)p3mulch3evapREdmulch=pREDmulchmulchCOV100
Another simulated effect of surface residue cover is the homogenization of
soil temperature between 0 and 30 cm depth (layers 1, 2, and 3). The
functional forms of surface coverage and the evaporation reduction factor are
presented in Fig. 2.
Surface coverage as a function of the amount of surface residue or
mulch (a) and the evaporation reduction factor (evapREDmulch) as
a function of mulch coverage (b) using different mulch-specific
soil parameters (pREDmulch). See text for details.
Transpiration
In order to simulate transpiration, first transpiration demand (TD,
kgH2Om-2d-1) is calculated using the Penman–Monteith
equation separately for sunlit and shaded leaves. TD is a function of
leaf-scale conductance to water vapor, which is derived from stomatal,
cuticular, and leaf boundary layer conductances. A novelty in Biome-BGCMuSo
v6.2 is that potential evapotranspiration is also calculated using the
maximal stomatal conductance instead of the actual stomatal conductance,
which means that stomatal aperture is not affected by the soil moisture
status (in contrast to the actual one).
TD is distributed across the soil layers according to the actual root
distribution using an improved method (the logic was changed since
Biome-BGCMuSo v4.0). From the plant-specific root parameters and the actual
root weight Biome-BGCMuSo calculates the number of layers in which roots
can be found together with the root mass distribution across the layers
(Jarvis, 1989; Hidy et al., 2016). If there is not enough water in a given
soil layer to fulfill the transpiration demand, the transpiration flux from
that layer is limited, and below the wilting point (WP) it is set to zero. The
sum of layer-specific transpiration fluxes across the root zone gives the
actual transpiration flux. A detailed description of the algorithm can be
found in Sect. S5 in the
Supplement.
Effect of groundwater
Simulation of the groundwater effect was introduced in Biome-BGCMuSo v4.0 (Hidy
et al., 2016), but the method has been significantly improved, and the new
algorithm it is now available in Biome-BGCMuSo v6.2. In the recent model
version there is an option to provide an additional input file with the
daily values of the groundwater table depth (GWdepth in meters).
Groundwater may affect soil hydrological and plant physiological processes
if the water table is closer to the root zone than the thickness of the
capillary fringe (that is the region saturated from groundwater via
the capillary effect). The thickness of the capillary fringe (CF in meters) is
estimated using literature data and depends on the soil type (Johnson and
Ettinger model; Tillman and Weaver, 2006). Groundwater table distance
(GWdist in m) for a given layer is defined as the difference between GWdepth and
the depth of the midpoint of the layer.
The layers completely below the groundwater table are assumed to be fully
saturated. In the case of layers within the capillary fringe
(GWdist<CF), the calculation of water balance changes: the FC
rises, and thus the difference between saturation (SAT) and FC decreases, and the
layer charges gradually until the increased FC value is reached. The
FC rising effect of groundwater for the layers above the water table is
calculated based on the ratio of the groundwater distance and the capillary
fringe thickness, but only after the water contents of the layers below have
reached their modified FC values. A detailed description of the groundwater
effect can be found in Sect. S6 in the
Supplement.
Soil moisture stress
In the original Biome-BGC model the effect of changing soil water content on
photosynthesis and decomposition of soil organic matter is expressed in
terms of soil water potential (Ψ). Instead of Ψ, the SWC is also
widely used to calculate the limitation of stomatal conductance and
decomposition. A practical advantage of using SWC as a factor in the stress
function is that it is easier to measure in the field and the changes in the
driving function are much smoother than in the case of Ψ. The disadvantage
is that SWC is not comparable among different soil types (in contrast to
Ψ).
The maximum of SWC is the saturation value; the minimum is the wilting point
or the hygroscopic water depending on the type of simulated process.
The novelty of Biome-BGCMuSo v6.2 is that the hygroscopic water, the wilting
point, the field capacity, and the saturation values are calculated
internally by the model based on the soil texture data, or they can be defined in
the input file layer by layer.
In Biome-BGCMuSo v6.2 the so-called soil moisture stress index (SMSI) is
calculated to represent overall soil stress conditions. SMSI is affected by the
length of the drought event (SMSE: extent of soil stress) and the severity of the
drought event (SMSL: length of soil stress), and it is aggravated by extreme
temperature (extremT: effect of extreme heat). SMSI is equal to zero if no soil moisture
limitation occurs and equal to 1 in the case of full soil moisture limitation.
SMSI is used by the model for plant senescence calculations (presentation of
plant-related processes is the subject of a forthcoming publication). The
components of SMSI are explained in detail below.
SMSI=1-SMSE⋅SMSL⋅extremT
The magnitude of soil moisture stress (SMSE) is calculated layer by layer based on
SWC. Regarding soil moisture stress two different processes are
distinguished: drought (i.e., low SWC close to or below WP) and anoxic
conditions (i.e., after large precipitation events or in the presence of a high
groundwater table; Bond-Lamberty et al., 2007). An important novelty of
Biome-BGCMuSo v6.2 is the soil curvature parameter (q), which is introduced
to provide a mechanism for soil-texture-dependent drought stress as it can
affect the shape of the soil stress function (which means a possibility for a
nonlinear ramp function):
SMSEi=0;SWCi<SWCWPiSMSEi=SWCi-SWCWPiSWCdroughti-SWCWPiq;SWCWPi≤SWC<SWCdroughtiSMSEi=1;SWCdroughti≤SWC≤SWCanoxiciSMSEi=SWCSATi-SWCiSWCSATi-SWCanoxici;SWCi>SWCanoxici
where q is the curvature of the soil stress function, and SWCdroughti and
SWCanoxici are critical SWC values for calculating soil stress.
In order to make the SWC values comparable between different soil types,
SWCdroughti and SWCanoxici can be set in normalized form
(such as in Eq. 4) as part of the ecophysiological parameterization of the
model. More details about the adjustment of the critical SWC values can be
found in Hidy et al. (2021).
The layer-specific soil moisture stress extent values are summed across the
root zone using the relative quantity of roots in the layers (RPi) as
weighting factors to obtain the overall soil moisture stress extent
(SMSE):
6SMSE=∑i=0i=nrSMSEi⋅RPi,7RPi=RDziRL⋅e-RD⋅(midi/RL),
where nr is the number of soil layers in which roots can be found, RL is the
actual length of roots, and RD is the rooting distribution parameter (ecophysiological
parameter; see details in the user guide; Hidy et al., 2021). In the
current model version SMSE can also affect the entire photosynthetic
machinery by the introduction of an empirical parameter. This mechanism is
responsible for accounting for the non-stomatal effect of drought on
photosynthesis (details about this algorithm will be published in a separate
paper). Since there is no mechanistic representation behind this empirical
downregulation of photosynthesis, further tests are needed for the correct
setting of this parameter preferentially using eddy covariance data.
The factor (SMSL) related to soil moisture stress length is the ratio of the
critical soil moisture stress length (ecophysiological parameter) and the
sum of the daily (1-SMSE) values. This cumulated value
restarts if SMSE is equal to 1 (no stress). Extreme heat (extremT) is
also considered and is taken into account in the final stress function (see
above) by using a ramp function. Its parameterization thus requires the
setting of two critical temperature limits that define the ramp function
(set by the ecophysiological parameterization; see Hidy et al., 2021). Its
characteristic temperature values can be set by parameterization
(ecophysiological input file).
Soil carbon and nitrogen cyclesSoil–litter module
We made substantial changes in the soil biogeochemistry module of the
Biome-BGC model. Previous model versions already offered solutions for
multilayer simulations (Hidy et al., 2012, 2016), but some pools still
inherited the single-layer logic of the original model. In the new model
version all relevant soil processes are separated layer by layer, which is a
major step forward.
Instead of defining a single litter, soil organic carbon (SOC), and nitrogen
pool, we implemented separate carbon and nitrogen pools for each soil layer
in the form of soil organic matter (SOM) and litter in Biome-BGCMuSo v6.2.
The changes in the mass of the carbon and nitrogen pools are calculated
layer by layer. Mortality fluxes (whole plant mortality, senescence,
litterfall) of aboveground plant material are transferred into the litter
pools of the topsoil layers (0–10 cm, layers 1–2). Mortality fluxes of
belowground plant material are transferred into the corresponding soil
layers based on their location within the root zone. Due to ploughing and
leaching, carbon and nitrogen can also be relocated to deeper layers. The
plant material turning into the litter compartment is divided between the
different types of litter pools (labile, unshielded cellulose, shielded
cellulose, and lignin) according to the parameterization. Litter and soil
decomposition fluxes (carbon and nitrogen fluxes from litter to soil pools)
are calculated layer by layer, depending on the actual temperature and SWC
of the corresponding layers. Vertical mixing of soil organic matter between
the soil layers (e.g., bioturbation) is not implemented in the current model
version.
Figure 3 shows the most important simulated soil and litter processes.
N fixation (Nf) is the N input from the atmosphere to soil layers in the root
zone by microorganisms. The user can set its annual value as an input
parameter. N deposition (Nd) is the N input from the atmosphere to the topsoil
layers (see below). The user can set its annual value as a site-specific
parameter in the initialization input file. Nitrogen deposition can be
provided by annually varying values as well. Plant uptake (PU) is the
absorption of mineral N by plants from the soil layers in the root zone.
Mineralization (MI) is the release of plant-available nitrogen (flux from soil
organic matter to mineralized nitrogen). Immobilization (IM) is the
consumption of inorganic nitrogen by microorganisms (flux from mineralized
nitrogen to soil organic matter). Nitrification (NI) is the biological
oxidation of ammonium to nitrate through nitrifying bacteria.
Denitrification (DN) is a microbial process whereby nitrate (NO3-) is
reduced and converted to nitrogen gas (N2) through intermediate
nitrogen oxide gases. Leaching (L) is the loss of water-soluble mineral
nitrogen from the soil layers. If leaching occurs in the lowermost soil
layer that means loss of N from the simulated system. Litterfall (LI) is the
plant material transfer from plant compartments to litter. Decomposition is
the C and N transfer from litter to soil pools and between soil pools. In
the case of woody vegetation coarse woody debris (CWD) contains the woody plant
material after litterfall before physical fragmentation. Litter has also
four sub-pools based on composition: labile (L1), unshielded and
shielded cellulose (L2, L3), and lignin (L4). Soil organic matter also has
four sub-pools based on turnover rate: labile (S1), medium (S2), slow
(S3), and passive (recalcitrant; S4). The soil-mineralized nitrogen pool
contains the inorganic N forms of the soil: ammonium and nitrate.
Soil- and litter-related simulated carbon and nitrogen fluxes (arrows)
as well as pools (rectangles) in Biome-BGCMuSo v6.2. HR: heterotrophic
respiration, IM: immobilization, MI: mineralization, PU: plant uptake, LI:
litterfall, NI: nitrification, D: decomposition (DL: decomposition of
litter, DS: decomposition of SOM, DC: fragmentation of coarse
woody debris), L: leaching, Nf: nitrogen fixation, Nd: nitrogen deposition,
DN: denitrification. L represents loss of C and N from the simulated system.
Decomposition
In the decomposition module (i.e., converging cascade scheme; Thornton, 1998)
the fluxes between litter and soil pools are calculated layer by layer. The
potential fluxes are modified in the case of N limitation when the potential
gross immobilization is greater than the potential gross mineralization.
To explain the decomposition processes implemented in Biome-BGCMuSo v6.2 the
main carbon and nitrogen pools as well as fluxes between litter and soil organic and
inorganic (mineralized) matter are presented in Fig. 4.
Overview of the converging cascade model of litter and soil
organic matter decomposition that is implemented in Biome-BGCMuSo v6.2. The notation rf
represents the respiration fraction of the different transformation fluxes,
and τ is the residence time (reciprocal of the rate constants, which is the
turnover rate). IM/MI: immobilization and mineralization fluxes, HR:
heterotrophic respiration. Note that both the respiration fraction and the
turnover rate parameters can be adjusted through parameterization.
In the original Biome-BGC and in previous Biome-BGCMuSo versions the C:N
ratio (CN) of the soil pools was fixed in the model code. One of the new
features in Biome-BGCMuSo v6.2 is that the CN of the passive soil pool
(S4 in Fig. 4; recalcitrant soil organic matter) can be set by the user as
a soil parameter. The CN of the other soil pools (labile, medium, and slow;
S1, S2, and S3) is calculated based on the proportion of fixed CN values of
the original Biome-BGC (CNlabile/CNpassive=1.2,
CNmedium/CNpassive=1.2,
CNslow/CNpassive=1). Note that the CN of the donor and acceptor pools
is used in decomposition calculations (see details in Sect. S7 in the
Supplement), and as a result these parameters set the C:N ratio of the soil
pools. The donor and acceptor pools can be seen in Figs. 3 and 4.
For the calculation of nitrogen mineralization, first respiration cost
(respiration fraction) is estimated. Mineralization is then a function of
the remaining part of the pool and its C:N ratio. The nitrogen
mineralization fluxes of the SOM pools are functions of the potential rate
constant (reciprocal of residence time) and the integrated response
function that accounts for the impact of multiple environmental factors. The
integrated response function of decomposition is a product of the response
functions of depth, soil temperature, and SWC (Fr(d)D,
Fr(T)D, Fr(SWC)D; Fig. 5). Its detailed description can be
found in Sect. S7. The dependence of the three
different factors on depth, temperature, and SWC with default parameters is
presented in Fig. 5.
The dependence of the individual factors that form the complex
environmental response function of decomposition on depth
(Fr(d)D),
temperature (Fr(T)D), and SWC in the case of different soil types
(Fr(SWC)D). ED is the e-folding depth, which is one of the adjustable
soil parameters of the model. For the definition of sand, silt, and clay see
Fig. 1.
Soil nitrogen processes
In Biome-BGCMuSo v6.2 separate ammonium (sNH4) and nitrate (sNO3) soil pools are
implemented instead of a general mineralized nitrogen pool. This was a
necessary step for the realistic representation of many internal processes
like plant nitrogen uptake, nitrification, denitrification, consideration of
the effect of different mineral and organic fertilizers, and N2O
emission.
It is important to introduce the availability concept that Biome-BGCMuSo uses and is
associated with the ammonium and nitrate pools. We use the logic proposed by
Thomas et al. (2013), which means that the plant has access only to a part of
the given inorganic nitrogen pool. The unavailable part is buffered as it is
associated with soil aggregates and is unavailable for plant uptake. The
available part of ammonium is calculated based on the NH4mobile proportion (that is a soil
parameter set to 10 % according to Thomas et al., 2013; Hidy et al., 2021)
and the actual pool. The available part of nitrate is assumed to be 100 %.
The amount of ammonium and nitrate is determined layer by layer controlled
by input and output fluxes (F, kgNm-2d-1) listed below:
FsNH4i=INsNH4i-LsNH4i+LsNH4i-1-PUsNH4i-IMsNH4i8+MIsNH4i-NIsNH4i,FsNO3i=INsNO3i-LsNO3i+LsNO3i-1-PUsNO3i-IMsNO3i9+MIsNO3i-DNsNO3i,
where INsNH4i and INsNO3i are the input fluxes to the
ammonium and nitrate pools, respectively; LsNH4i, LsNH4i-1,
LsNO3i, and LsNO3i-1 represent the amount of leached mineralized
ammonium and nitrate from a layer (i) or from the upper layer (i-1),
respectively; PUsNH4i and PUsNO3i are the plant uptake
fluxes of ammonium and nitrate, respectively; IMsNH4i and
IMsNO3i are the immobilization fluxes of ammonium and nitrate,
respectively; MIsNH4i and MIsNO3i are the mineralization
fluxes of ammonium and nitrate, respectively; NIsNH4i is the
nitrification flux of ammonium; and DNsNO3i is the denitrification
flux of nitrate.
In the following subsections the different terms of the equations are
described in detail.
Input to the sNH4 and sNO3 pools (IN in Eqs. 6 and 7)
According to the model logic N fixation occurs in the root zone layers. Its
distribution between sNH4 and sNO3 pools is calculated based on their actual
available proportion in the actual layer (NH4propi):
NH4propi=sNH4availi+sNO3availi,
where sNH4availi and sNO3availi are the available part of
the sNH4 and sNO3 pools in the actual layer.
N-deposition-related nitrogen input is associated with the 0–10 cm soil
layers assuming uniform distribution across layers 1–2 in the model, and the
distribution between sNH4 and sNO3 pools is calculated based on the
proportion of the NH4 flux of the N deposition soil parameter (Hidy et al., 2021).
Organic and inorganic fertilization is also an optional nitrogen input. The
amount and composition (NH4+ and NO3- content) can be
set in the fertilization input file.
Leaching – downward movement of mineralized N (L in Eqs. 6 and 7)
The amount of leached mineralized N (mobile part of the given N pool) from a
layer is directly proportional to the amount of drainage and the available
part of the sNH4 and sNO3 pools. Leaching from the layer above is a net
gain, while leaching from actual layer is a net loss for the actual layer.
Leaching is described in Sect. 4.5.
Plant uptake by roots (PU in Eqs. 6 and 7)
N uptake required for plant growth is estimated in the photosynthesis
calculations, and the amount is distributed across the layers in the root
zone. The partition of the N uptake between sNH4 and sNO3 pools is
calculated based on their actual available proportion in each layer.
Mineralization and immobilization (MI and IM Eqs. 6 and 7)
Mineralization and immobilization calculations are detailed in Sect. 4.2.
The distribution of these N fluxes between sNH4 and sNO3 pools is calculated
based on their actual available proportion in each layer.
Nitrification (NI Eqs. 6 and 7)
Nitrification is a function of the soil ammonium content, the net
mineralization, and the response functions of temperature, soil pH, and SWC
(Fr(pH)NI, Fr(T)NI, and
Fr(SWC)NI, respectively) based on the method of
Parton et al. (2001) and Thomas et al. (2013). Its detailed mathematical
description can be found in Sect. S8 in the
Supplement. The
response functions with proposed parameters are shown in Fig. 6.
The dependence of the individual factors of the environmental
response function of nitrification on soil pH
(Fr(pH)NI), temperature
(Fr(T)NI), and SWC
Fr(SWC)NI in the case of different soil types. The pH and
temperature response functions are independent of the soil texture.
Denitrification (DN Eqs. 6 and 7)
Denitrification flux is estimated with a simple formula (Thomas et al.,
2013):
DNi=DNcoeff⋅SOMrespi⋅sNO3availi⋅WFPSi,
where DN of the actual layer is the product of the available nitrate content
(sNO3avail, kgNm-2), SOMrespi
(gCm-2d-1) is the SOM-decomposition-related respiration
cost, WFPSi is the water-filled pore space, and DNcoeff is the
soil-respiration-related denitrification rate (gC-1), which is an input soil parameter (Hidy et al., 2021). The
unitless water-filled pore space is the ratio of the actual and
saturated SWC. SOM-decomposition-associated respiration is the sum of the
heterotrophic respiration fluxes of the four soil compartments (S1–S4,
Fig. 4).
N2O emission and N emission
During both nitrification and denitrification N2O emission occurs, which
(added to the N2O flux originating from grazing processes if applicable)
contributes to the total N2O emission of the examined ecosystem.
In Biome-BGCMuSo v6.2 a fixed part (set by the coefficient of the N2O emission of the nitrification input soil parameter;
Hidy et al., 2021) of nitrification flux is lost as N2O and not
converted to NO3.
During denitrification, nitrate is transformed into N2 and N2O gas
depending on the environmental conditions: NO3 availability, total
soil respiration (proxy for microbial activity), SWC and pH. The
denitrification-related N2/N2O ratio input soil parameter is used to represent the effect of the soil
type on the N2/N2O ratio (del Grosso et al., 2000; Hidy et al.,
2021). Detailed mathematical description of the algorithm can be found in
Sect. S9 in the Supplement.
Leaching of dissolved matter
Leaching of nitrate, ammonium, and dissolved organic carbon and nitrogen
(DOC and DON) content from the actual layer is calculated as the product of
the concentration of the dissolved component in the soil water and the
amount of water (drainage plus diffusion) leaving the given layer either
downward or upward. The dissolved component (concentration) of organic
carbon is calculated from the SOC pool contents and the corresponding fraction of the dissolved part of SOC soil parameters. The dissolved component of the organic nitrogen content of the
given soil pool is calculated from the carbon content and the corresponding
C:N ratio. The downward leaching is net loss from the actual layer and net
gain for the next layer below; the upward flux is net loss for the actual
layer and net gain for the next layer up. The downward leaching of the
bottom active layer (ninth) is net loss for the system. The upward
movement of dissolved substance from the passive (10th) layer is net
gain for the system.
Case studiesEvaluation of soil hydrological simulation
In order to evaluate the functioning of the new model version (and to
compare simulation results made by the current and previously published
model version), a case study is presented regarding soil water content and
soil evaporation simulations. The results of a bare soil simulation (i.e., no
plant is assumed to be present) are compared to observation data from a
weighing lysimeter station installed at Martonvásár, Hungary
(47∘18′57.6′′ N, 18∘47′25.6′′ E), in 2017. The climate of
the area is continental with a 30-year average temperature of 11.0 ∘C (-1 ∘C in January and 21.2 ∘C in July)
and annual rainfall of 548 mm based on data from the on-site weather station.
The station consists of 12 scientific lysimeter columns 2 m deep
with 1 m diameter (Meter Group Inc., USA) and with soil temperature, SWC, and
soil water potential sensors installed at 5, 10, 30, 50, 70, 100, and 150 cm
depth. Observation data for 2020 from six columns without vegetation cover
(i.e., bare soil) were used to validate the model.
Raw lysimeter observation data were processed using standard methods. Bare
soil evaporation values were derived based on changes in the mass of the
soil columns also considering the mass change in the drainage water.
Additionally, experience has shown that wind speed is related to the high-frequency mass change in the soil column mass. To reduce noise, five-point
(5 min) moving averages were used based on Marek et al. (2014). After
quality control of the data, the corrected and smoothed lysimeter mass
values were used for the calculations. SWC observations were averaged to
daily resolution to match the time step of the model.
Observed local meteorology was used to drive the models for the year 2020. Soil
physical model input parameters (field capacity, wilting point, bulk
density, etc.) were determined in the laboratory using 100 cm3
undisturbed soil samples taken from various depths during the installation
of the lysimeter station. Regarding other soil parameters the proposed
values were used. A detailed description of the input soil parameters and
their proposed values is presented in the user guide (Hidy et al., 2021).
In Fig. 7 the simulated and observed time series of soil evaporation
are presented for Martonvásár for 2020. The figure shows that the
soil evaporation simulation by v6.2 is more realistic than by v4.0.
Biome-BGCMuSo v4.0 provides very low values during summer on some days, which
is not in accordance with the observations. Biome-BGCMuSo v6.2 provides more
realistic values during this time period.
The simulated (blue line: v4.0; red line: v6.2) and
observed (grey dots) daily soil evaporation values at Martonvásár
during 2020. Vertical grey lines associated with the observations represent
the standard deviation of the observations from six lysimeter columns. The
improved model clearly outperforms the earlier version.
In Fig. 8 the simulated and observed SWCs at 10 cm depth are presented
with the daily sum of precipitation representing the bare soil simulation in
Martonvásár for 2020. The soil water balance simulation seems to be
realistic using v6.2, since the annual course captures the low and high end
of the observed values. In contrast, Biome-BGCMuSo v4.0 underestimates the
range of SWC and provides overestimations during the growing season (from
spring to autumn). With a couple of exceptions, the simulated values using
v6.2 fall into the uncertainty range of the measured values defined by the
standard deviation of the six parallel measurements. This is not the case
for the simulations with the 4.0 version.
The simulated (blue line: v4.0; red line: v6.2) and
observed (grey dots) soil water content values at 10 cm depth (right y axis)
with the daily sums of precipitation (left axis; black columns) during 2020
at the Martonvásár lysimeter station. Vertical grey lines associated
with the observations represent ± 1 standard deviation around
the observations. The improved model clearly outperforms the earlier version
in simulating soil water balance.
Model performance was evaluated by quantitative measures such as the coefficient
of determination (R2), mean absolute error (MAE), and mean signed error
(MSE). In Fig. 9 the comparison of the simulated and observed daily
evaporation is presented. Based on the performance indicators it is obvious
that the simulation with the new model version (v6.2) is much closer to
observations than the old version (v4.0). Biome-BGCMuSo v6.2 slightly
underestimated the observations.
Comparison of the simulated (a: v4.0; b: v6.2) and
observed daily soil evaporation representing the means of measured data
obtained from six weighing lysimeter columns with bare soil at
Martonvásár in 2020. R2, MAE, and MSE denote
the coefficient of determination, mean absolute error, and mean signed error
(bias) of the simulated values, respectively.
In Fig. 10 the comparison of the simulated and observed daily SWC from
the lysimeter experiment is presented. Based on the model evaluation it
seems that the simulation with the new model version is much closer to
observations than with old version (4.0). The results obtained from v4.0 are
consistent with earlier findings about the incorrect representation of the
annual SWC cycle (Hidy et al., 2016; Sándor et al., 2017).
Comparison of the simulated (a: v4.0; b: v6.2) and
observed daily SWC representing the means of measured data obtained from six
weighing lysimeter columns with bare soil at Martonvásár in 2020.
R2, MAE, and MSE denote the coefficient of
determination, mean absolute error, and mean signed error (bias) of the
simulated values, respectively.
A thorough validation of the improved model based on observed SWC and ET
datasets from eddy covariance sites is planned to be published in an
upcoming paper about the plant-related improvements.
Evaluation of the soil nitrogen balance module and the simulated soil respiration
Soil-related developments were evaluated with a case study focusing on
topsoil nitrate content, soil N2O efflux, and soil respiration.
Experimental data were collected in a long-term fertilization experiment
that was set up in 1959 at Martonvásár, Hungary (47∘18′41′′ N, 18∘46′50′′ E). According to the FAO-WRB classification
system (IUSS Working Group, 2015), the soil is a Haplic Chernozem, with
51.4 % sand, 34 % silt, and 14.6 % clay content. Bulk density is 1.47 gcm-3, pH(H2O) is 7.3, CaCO3 content is 0 %–1 %, and the mean
soil organic matter content in the topsoil is 3.2 %. The plant-available
macronutrient supply in the soil was poor for phosphorus and medium to good
for potassium based on the ProPlanta plant nutrition advisory system (Fodor
et al., 2011). In the long-term fertilization experiment the treatments were
arranged in a random block design with 6m×8 m plots in four
replicates. Eight different treatments were set up: control (zero artificial
fertilizer applied), only N, only P, NPK – with farmyard manure,
absolute control (zero nutrient supply), only N, only P, and NPK – without
farmyard manure. The crop in the 4-year fertilizer cycles was maize in
the first and second years and winter wheat in the third and
fourth years. Here we used data from the absolute control and from the
farmyard manure (FYM) treatments only. FYM was applied once every 4 years
at a rate of 35 tha-1 in autumn.
Topsoil nitrate content was measured during 2017, 2018, and 2020 on a few
occasions by wet chemical reactions using a stream distillation method after
KCl extraction of soil samples (Hungarian Standards Institution MSZ
20135:1999; Akhtar et al., 2011).
Dynamic-chamber-based soil N2O efflux observations were available from
2020 and 2021. The N2O efflux measurements with a gas incubation time
of 10 min were performed by using a Picarro G2508 (Picarro, USA) cavity
ring-down spectrometer (Christiansen et al., 2015; Zhen et al., 2021). The
cylinder-shaped transparent gas incubation chamber was 16.5 cm in diameter,
and its height was 30 cm. N2O flux measurements were executed in six
replicates per treatment on a biweekly (2020) and precipitation-event-related (2021) basis. Soil respiration was measured with the same
Picarro gas analyzer. Sampling numbers and points were identical to those
of the N2O efflux measurements. CO2 and N2O effluxes were
calculated by a linear equation (Widen and Lindroth, 2003) based on gas
concentration data.
For the simulations we used a maize parameterization from previous studies
(Fodor et al., 2021). A winter wheat parameterization was constructed based on
a country-scale optimization using the AgroMo software package
(https://github.com/hollorol/AgroMo, last access: November 2021) and the NUTS 3 level long-term
(1991–2020) yield database of the Hungarian Central Statistical Office. For
nitrogen-cycle-related parameters we mainly used the values presented in the
user guide (Hidy et al., 2021). Two soil parameters were adjusted
(coefficient of N2O emission for nitrification and N2/N2O
ratio multiplier for denitrification-related N gas flux; Del Grosso et al.,
2000; Parton et al., 2001; Thomas et al., 2013; Hidy et al., 2021) to match
the simulated N2O efflux to the observations.
Figure 11 shows the comparison of the simulated and observed NO3
content of the topsoil for the two selected treatments. The results indicate
that the model underestimates the topsoil NO3 content in the case
of both C and FYM (bias is -2.3 and -2.4 ppm, respectively) treatments, but
the simulation error is in an acceptable range (NRMSE is 45.5 % and
37.6 % for C and FYM, respectively).
Comparison of the simulated and observed
NO3 content of the topsoil for the absolute control
(C; a) and for the farmyard manure (FYM; b) treatment between May
2017 and November 2021 at Martonvásár.
Figure 12 shows the comparison of the observed and simulated N2O efflux
for the 2020–2021 time period. Measurement uncertainties are also indicated. Note that the uncertainty of the observations (e.g., due to
spatial heterogeneity and sample number, soil disturbance, improper chamber
design, methods of sample analysis) is remarkable due to known features of
the chamber technique (Chadwick et al., 2014; Pavelka et al., 2018). The model
captured more of the magnitude of N2O efflux peaks and less of their
timing. Overall the model underestimated the observed values in both cases
(bias is -0.13 and -0.1 mgNm-2d-1 for C and FYM, respectively), with NRMSE of 32.4 % and
37.6 % for C and FYM, respectively.
Comparison of the simulated and observed soil
N2O efflux for two treatments: absolute control (C;
a) and application of farmyard manure (FYM; b) between January 2020
and December 2021 at Martonvásár. Whiskers indicate the uncertainty
(± 1 standard deviation) of the measurements.
Figure 13 presents the comparison of the observed and simulated soil
respiration for the same time period as for the soil N2O efflux.
Observation uncertainty is indicated that represents 1 standard deviation
of the replicates. The model mostly captured the magnitude and variability
of soil respiration flux. The model overestimated the observed values in
both cases with bias of 0.17 and
0.04 gCm-2d-1 for C and FYM, respectively. The NRMSE is
34.1 % and 40.1 % for C and FYM, respectively. It is interesting to note
that the observations and the simulations are particularly different after
harvest time in both years (i.e., beginning of October). The simulated
respiration has peaks corresponding to harvest when the amount of
litter sharply increases due to the by-products left behind (decomposition of
residues left on the site after harvest is accounted for in the model). The
chamber-based CO2 efflux data did not really show similar peaks, likely
because of methodological issues (litter is removed from the soil surface
before placing of the chambers).
Comparison of the simulated and observed soil respiration
flux for two treatments: absolute control (C; a) and application of
farmyard manure (FYM; b) between January 2020 and December 2021 at
Martonvásár. Whiskers indicate the uncertainty (± 1 standard
deviation) of the measurements.
Overall, the model provided nitrate content, N2O emission, and soil
respiration simulation results that are consistent with the observations.
The model was capable of estimating the observed values with comparable
efficiency reported in similar studies (Gabrielle et al., 2002; Andrews et
al., 2020).
Sensitivity analysis and optimization of the soil biogeochemistry scheme
Here we present another case study that provides insight into the
functioning of the converging cascade (decomposition) scheme that is
implemented in Biome-BGCMuSo v6.2. A large-scale in silico experiment is
also presented, the main aim of which was to perform model self-initialization
(i.e., spin-up) at country scale (for the entire area of Hungary) with the
resulting soil organic matter pools expected to be consistent with the
observations.
The observation-based, gridded, multilayer SOC database of Hungary
(DOSoReMI database; Pásztor et al., 2020; see
Figs. S3–S4 in the Supplement) and the FORESEE meteorological database (Kern et al.,
2016) were used for the sensitivity analysis of the soil scheme as well as
for optimizing the most important soil parameters when the model was
calibrated to the observation-based SOC values. As a first step, the area of
the country was divided into 1104 grid cells (regular grid with
0.1∘ by 0.1∘ resolution, corresponding to
approximately 10 km resolution). The 1104 grid cells of the DOSoReMI
database were grouped based on their dominant land use type (cropland,
grassland, or forest based on the CORINE-2018 database; EEA, 2021;
Figs. S1–S2 in the
Supplement) as well as the soil texture class (12 classes
according to the USDA system; USDA, 1987) and SOC content (high and low;
high is greater than the group mean, while low is less than the mean) of the
topsoil (0–30 cm layer). As some of the theoretically possible 72 groups had
no members (e.g., there is no soil in Hungary with sandy–clay texture) soils
of the 1104 grid cells were categorized into 51 groups. For each group one
single cell (so-called representative cell) was selected based on the
topsoil SOC content. The representative cell was the one with the smallest
absolute deviation from the group mean SOC content (land use maps for
Hungary are presented in Sect. S10 in the
Supplement: Figs. S1–S2).
The grassland ecophysiological parameterization without management was used in
the spin-up phase to initialize SOC pools for croplands. For the transient
phase, the cropland parameterization was used with fertilization, ploughing,
planting, and harvest settings. In the case of grasslands, a grassland parameterization was used during both the spin-up
and transient phases, and in the
transient phase mowing was assumed once a year. In the case of forests a generic
deciduous broadleaf forest parameterization was used for both the spin-up and
transient phases with thinning in the latter phase. For our parameterization
presented in the MS the generic, plant-functional-type-specific
ecophysiological parameter sets published by White et al. (2000) served as
starting points. These Biome-BGCMuSo-specific parameter sets are available
at the website of the
model
http://nimbus.elte.hu/bbgc/files/generic_EPC_set_6.2.zip
(last access: November 2021).
.
Soil parameters in Biome-BGCMuSo v6.2 were classified into six groups: (1) 4
generic soil parameters; (2) 24 parameters related to decomposition, nitrification, and denitrification; (3) 14 rate scalars for the converging (decomposition)
cascade scheme; (4) 19 soil-moisture-related parameters; (5) 7 methane-related parameters; and (6) 11 soil composition and characteristic values
(can be set layer by layer). A detailed description and proposed value of each
soil parameters can be found in the user guide (Hidy et al., 2021).
As methane simulation was not the subject of the present case study we
neglected the related parameters. Regarding the soil composition and
characteristic values we used the DOSoReMI database (Pásztor et al.,
2020). From the remaining 61 parameters, soil depth, runoff curve number, and the
three soil-moisture-related parameters (tipping bucket method) were not
included in the analysis. The groundwater module was switched off in this
case (no groundwater is assumed) and the related parameters were not
studied. The remaining 53 parameters were used in the sensitivity analysis
and are listed in Table 1.
Soil parameters of Biome-BGCMuSo v6.2 (referring to SOC
simulation) that were used during the sensitivity analysis. The “Value” column
shows the originally proposed values (Hidy et al., 2021). See Fig. 4 for an
explanation of the compartment names. The parameters that were included in
the second phase of the sensitivity analysis are marked with
italics (see text).
GroupParameter nameAbbreviationValueGeneric soilC:Nratio of stable soil pool (soil4)soil4CN12parametersNH4 mobile proportionamMP0.1aerodynamic resistancepotRair107Decomposition,parameter 1 for temperature response function of decomp.Tp1decomp1.75nitrification,parameter 2 for temperature response function of decomp.Tp2decomp17denitrificationparameter 3 for temperature response function of decomp.Tp3decomp2.6parametersparameter 4 for temperature response function of decomp.Tp4decomp40minimum T for decomposition and nitrificationTp5decomp-5e-folding depth of decomposition rate's depth scalarEFD10net mineralization proportion of nitrificationNITRnetMINER0.2maximum nitrification rateNITRmaxRATE0.1coefficient of N2O emission of nitrificationNITRratioN2O0.02parameter 1 for pH response function of nitrificationpHp1nitrif0.15parameter 2 for pH response function of nitrificationpHp2nitrif1parameter 3 for pH response function of nitrificationpHp3nitrif5.2parameter 4 for pH response function of nitrificationpHp4nitrif0.55parameter 1 for Tsoil response function of nitrificationTp1nitrif1parameter 2 for Tsoil response function of nitrificationTp2nitrif12parameter 3 for Tsoil response function of nitrificationTp3nitrif2.6parameter 4 for Tsoil response function of nitrificationTp4nitrif2.6minimum WFPS for scalar of nitrification calculationminWFPS0.1lower optimum WFPS for scalar of nitrificationopt1WFPS0.45higher optimum WFPS for scalar of nitrificationopt2WFPS0.55minimum value for saturated WFPS scalar of nitrificationminWFPSscalar0.2soil-respiration-related denitrification rateDENITcoeff0.05denitrification-related N2/N2O ratio multiplierDNratioN2O2critical WFPS value for denitrificationcritWFPSdenitr0.50Rate scalarsrespiration fractions for fluxes between compartments (l1s1)RFl1s10.39respiration fractions for fluxes between compartments (l2s2)RFl2s20.55respiration fractions for fluxes between compartments (l4s3)RFl4s30.29respiration fractions for fluxes between compartments (s1s2)RFs1s20.28respiration fractions for fluxes between compartments (s2s3)RFs2s30.46respiration fractions for fluxes between compartments (s3s4)RFs3s40.55potential rate constant of labile litter poolRCS10.7potential rate constant of cellulose litter poolRCS20.07potential rate constant of lignin litter poolRCS30.014potential rate constant of fast microbial recycling poolRCS40.07potential rate constant of medium microbial recycling poolRCS50.014potential rate constant of slow microbial recycling poolRCS60.0014potential rate constant of recalcitrant SOM (humus) poolRCS70.0001potential rate constant of physical fragmentation of woodRCS80.001maximum height of pond waterMP5curvature of soil stress functionq1fraction of dissolved part of S1 organic matterfD10.005fraction of dissolved part of S2 organic matterfD20.004fraction of dissolved part of S3 organic matterfD30.003fraction of dissolved part of S4 organic matterfD40.002mulch parameter: critical amountCAmulch1parameter 1 for mulch functionp1mulch100parameter 2 for mulch functionp2mulch0.75parameter 3 for mulch functionp3mulch0.75mulch parameter: evaporation reductionERmulch0.5
As a first step sensitivity analysis was carried out for the selected 53
soil parameters by running the Biome-BGCMuSo v6.2 model in spin-up mode until
a quasi-equilibrium in the total SOC was reached (that is the usual logic of
the spin-up run). The model was run for each representative cell 2000 times
with varying model parameters using the Monte Carlo method. All model
parameters were varied randomly within the ±10% range of their
initial values that were inherited from the Biome-BGC model or were set
according to the literature. The least square linearization (LSL) method
(Verbeeck et al., 2006) was used for dividing output uncertainty into its
input-parameter-related variability. As a result of the LSL method, the total
variance of the model output and the sensitivity coefficient of each
parameter can be determined. Sensitivity coefficients show the percent of
total variance for which the given parameter is responsible.
The sensitivity coefficients of the soil parameters as the result
of the sensitivity analysis. Black columns refer to the crop, light grey to
the grass, and dark grey to the forest simulations. The sensitivity
coefficients are calculated as the mean pixel-level sensitivity coefficient
for the given land use type. The horizontal line indicates the 5 % threshold
that was used to select the final parameter set for optimization.
In order to simplify the workflow and decrease the degree of freedom another
sensitivity analysis was performed. In this second step, the sensitive
parameters (sensitivity coefficient >1 % for at least one land
use type; a total of 18 parameters) were used in the following sensitivity
analysis with 6000 iteration steps. These 18 parameters are marked
with italics
in Table 1.
Figure 14 shows the summary of the second sensitivity analysis wherein the
overall importance of the parameters is calculated as the mean of all
selected pixels in a given land use category. It can be seen in Fig. 4
that from the 18 parameters (selected during the first phase), the soil carbon
ratio of the recalcitrant pool (soil4CN), the temperature dependence
parameters of the decomposition function (Tp1decomp, Tp2decomp, Tp3decomp,
Tp4_decomp), the respiration fraction of the S2–S3 and S3–S4
decomposition process (RFs2s3 and RFs3s4), the curvature of the soil stress
function (qsoilstress), and the fraction of the dissolved part of S4 organic
matter (fD4) are the most important for all land use types. Among the other
parameters the critical WFPS of denitrification (critWFPSdentir) for
grasslands has a remarkably high sensitivity (greater than 35 %). It means
that in the case of grasslands the nitrogen availability seems to be an
important limitation on primary production, probably because there are
only natural sources of nitrogen (no fertilization is assumed here) and the
rooting zone is shallower than in the case of forest, which involves limited
mineralized N access. Thus, in the case of higher values of critical WFPS of
denitrification, the simulated production of grassland (and therefore
the final SOC) seems to be significantly underestimated.
The 10 selected soil-biogeochemistry-related parameters were optimized for
each of the 51 groups separately using maximum likelihood estimation. For
each group, the parameter set providing the smallest deviation between the
simulated and observed values of the weighted average SOC content
(weight factor of 5 for 0–30 cm and weight factor of 1
for 30–60 cm soil layers) was considered to be the final (optimized)
model parameter set.
The differences of the simulated and observed SOC content for the 0–30 cm
layer (SOC0-30) using the initial (Table 1) and final soil parameters (not
shown here) are presented in Fig. 15. In the upper panel the signed
relative error of SOC0-30 simulation before optimization can be seen, while in the lower
panel the signed relative error of SOC0-30 simulation after optimization
can be seen. It is clearly visible that because of optimization the
overestimation of the SOC0-30 simulation significantly decreased.
Differences (expressed as signed relative error, %)
between the simulated and observed SOC data for the 0–30 layer (SOC0-30)
using the initial (a) and optimized (b) soil parameters.
Visual comparison of the maps reveals the success of the optimization in
terms of capturing the overall SOC for the whole country area.
Comparison of the internal processes simulated in Biome-BGC 4.1.1, Biome-BGCMuSo v4.0, and Biome-BGCMuSo v6.2.
Routineoriginal Biome-BGCBiome-BGCMuSo v4.0Biome-BGCMuSo v6.2Runoffnobased on simple, empirical formulationdistinguishing Hortonian and Dunne runoffPond waternosimple solutiondevelopment of pond water formation (based on infiltration capacity)Soil evaporationBased on Penman–Monteith equation Calculation of the actual evaporation from the potential evaporation and the square root of time elapsed since the last precipitation Based on Penman–Monteith equation Calculation of the actual evaporation from the potential evaporation and the square root of time elapsed since the last precipitation Based on Penman–Monteith equation Parameterization possibility of actual aerodynamic resistance Introduction of an upper limit for daily potential evaporation that is determined by the available energy Calculation of the actual evaporation is based on the method of Ritchie (1981) Simulation of the reducing effect of surface residue or mulch cover on bare soil evaporationTranspirationTranspiration from one-layer bucket soilTranspiration from seven-layer soil based on soil stressTranspiration from 10-layer soil based on available waterGroundwaternoSimple groundwater simulationImprovement of the simulation of groundwater effect (using capillary fringe) Introduction of two different methodsSoil moisture stressnoRelative SWC data are used to calculate soil water stress The hygroscopic water, the wilting point, the field capacity, and the saturation values of the soil layers can be defined in the input file layer by layer The soil moisture stress index is affected by the length and the day since the drought event The hygroscopic water, the wilting point, the field capacity, and the saturation values of the soil layers can be defined in the input file layer by layer The soil moisture stress index is affected by the length and the severity of the drought event, aggravated by the extreme temperature Introduction of the soil curvature parameters to provide mechanism for soil-texture-dependent drought stress since it can affect the shape of the soil stress function Normalized SWC data are used to calculate soil moisture stress indexOrganic carbon and nitrogenOne-layer soil module with one organic carbon and nitrogen poolMultilayered soil module without soil carbon and nitrogen profileInstead of defining a single litter, soil organic carbon, and nitrogen pool, separate carbon and nitrogen pools for each soil layer in the form of soil organic matter and litter were implemented Separation of aboveground and belowground litter pools Litter and soil decomposition fluxes (carbon and nitrogen fluxes from litter to soil pools) are calculated layer by layer, depending on the actual temperature and SWC of the corresponding layers Leaching of dissolved organic carbon and nitrogenInorganic nitrogenOne-layer soil module with one mineralized N poolMultilayer soil module with an empirical inorganic N profile (no layer-by-layer calculations, only estimation of the sub-pools in the different soil layer based on the root length proportion)Separation of ammonium (sNH4) and nitrate (sNO3) soil pools instead of a general mineralized nitrogen pool Nitrification fluxes are calculated layer by layer, depending on the actual pH, temperature, and SWC of the given layers Denitrification fluxes are calculated layer by layer, depending on the depth, actual temperature, and SWC of the given layers
We do not claim, of course, that the optimized parameters have universal
value. Site history is neglected during the spin-up simulations, and we use
many simplifications like disregarding land use change and present-day
ecophysiological parameterization. In this sense, the optimized
parameter set can best be considered a pragmatic solution to provide
initial conditions (equilibrium SOC pools) for the model at country scale
consistent with the observations.
Concluding remarks
In this paper, we presented a detailed description of Biome-BGCMuSo v6.2
terrestrial ecosystem model developments related to soil hydrology as well as the
carbon and nitrogen budget. We mostly focused on changes relative to the
previously published Biome-BGCMuSo v4.0 (Hidy et al., 2016), but our
intention was also to provide a complete, stand-alone reference for the
modeling community with mathematical equations (detailed in the
Supplement). Table 2 summarizes the structural changes that we
made during the developments starting from Biome-BGC v4.1.1 also including
the previously published Biome-BGCMuSo v4.0 (Hidy et al., 2016).
Earlier model versions used a soil hydrology scheme based on the Richards
equation, but the results were not satisfactory. Sándor et al. (2017)
presented results from the first major grassland model intercomparison
project (executed within the framework of FACCE MACSUR) wherein Biome-BGCMuSo v2.2
was used. That study demonstrated the problems associated with proper
representation of soil water content, which was a common shortcoming of all
included models. In the Hidy et al. (2016) paper, wherein the focus was on
Biome-BGCMuSo v4.0, the SWC-related figures clearly indicated problems with
the simulations compared to observations. The SWC amplitude was not captured
well, which clearly influences drought stress, decomposition, and other SWC-driven processes like nitrification and denitrification. For the latter two
processes this is especially critical as they are associated with
contrasting SWC regimes (nitrification is aerobic, while denitrification
is an anaerobic process). This is a good example of an erroneous internal
process representation that may lead to improper results. Note that the
functions currently used for nitrification and denitrification are also subject
to uncertainty that needs to be addressed in the future (Heinen, 2006).
Nevertheless, the presented model developments might contribute to more
realistic soil process simulations and improved results.
The algorithm ensemble approach is already implemented in Biome-BGCMuSo.
Algorithm ensemble means that the user has more than one option for the
representation of some processes. Biome-BGCMuSo v6.2 has alternative
phenology routines (Hidy et al., 2012) and two alternative methods for soil
temperature (Hidy et al., 2016), soil hydrology (described in this study),
photosynthesis, and soil moisture stress calculation. We plan to extend the
algorithm ensemble by providing alternative decomposition schemes to the
model. One possibility is the implementation of a CENTURY-like structure
(Koven et al., 2013) that is a promising direction and might improve the
quality of the equilibrium (spin-up) simulations as well as the simulated N
mineralization related to SOM decomposition. Reported problems related to
the rapid decomposition of litter in the current model structure (Bonan et al., 2013) need to be addressed in future model versions as well.
Plant growth- and allocation-related developments were not addressed in this
study but of course have many inferences with the presented model logic (i.e.,
parameterization and related primary production define the amount and
quality of litter). A forthcoming publication will provide a
comprehensive overview of the plant growth- and senescence-related model
modifications with elements from crop models also included.
Biome-BGCMuSo is still an open-source model that can be freely downloaded
from its website with a detailed user guide and other supplementary files.
We also encourage users to test the so-called RBBGCMuso package (available
at GitHub) that has many advanced features to support model application and
optimization. A graphical environment, called AgroMo (also available at
GitHub: https://github.com/hollorol/AgroMo, last access: November 2021), was also developed around
Biome-BGCMuSo to help users carry out simulations with either site-specific plot-scale data or with gridded databases representing large
regions.
Code and data availability
The current version of Biome-BGCMuSo, together with sample input files and a
detailed user guide, is available from the website of the model at
http://nimbus.elte.hu/bbgc/download.html (last access: November 2021) under the GPL-2 license.
Biome-BGCMuSo v6 is also available at GitHub:
https://github.com/bpbond/Biome-BGC/tree/Biome-BGCMuSo_v6 (last access: November 2021).
The exact version of the model (v6.2 alpha) used to produce the results
in this paper is archived on Zenodo
(10.5281/zenodo.5761202; Hidy and Zoltán, 2021). Experimental data and model
parameterization used in the study are available from the corresponding
author upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-15-2157-2022-supplement.
Author contributions
DH developed Biome-BGCMuSo, maintained the source code, and executed the
sample simulations. The study was conceived and designed by DH, ZB, and
NF, with assistance from TÁ, LD, and RH. It was directed
by DH and ZB. TÁ and LD contributed with model benchmarking.
RH participated with the construction of a modeling framework for
Biome-BGCMuSo. TF, DI, DZ, LP, MD, and ET contributed with experimental data. DH, ZB, NF, and KM prepared the
paper and the Supplement with contributions from all co-authors. All
authors reviewed and approved the present article and the Supplement.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are grateful to Galina Churkina for reviewing the paper.
Financial support
The research was funded by the Széchenyi 2020 program, the European Regional Development Fund, and the Hungarian Government (grant no. GINOP-2.3.2-15-2016-00028). This research was supported by the NRDI Fund FK 20 (grant no. 134547) as well and also supported by the grant “Advanced research supporting the forestry and wood-processing sector's adaptation to global change and the 4th industrial revolution” (grant no. CZ.02.1.01/0.0/0.0/16_019/0000803) financed by OP RDE. Katarína Merganičová was also financed by the project “Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” (grant no. ITMS2014+ 313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF.
Review statement
This paper was edited by Sam Rabin and reviewed by two anonymous referees.
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