Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.
The biogeochemical processes that govern soil organic matter (SOM) formation and persistence impact more than half of the terrestrial carbon (C) cycle and thus play a key role in climate–C feedbacks (Jones and Falloon, 2009; Arora et al., 2013). In order to predict changes to the C cycle, it is imperative that mathematical models describe these processes accurately. However, most ecosystem-scale biogeochemical models represent SOM dynamics with first-order transfers between conceptual pools defined by turnover time, limiting their capacity to incorporate recent advances in scientific understanding of SOM dynamics (Campbell and Paustian, 2015). Due to the use of conceptual pools, empirical data from SOM fractionation cannot be used directly to constrain parameter values that govern fluxes between pools because diverse SOM compounds can have similar turnover times but are differentially influenced by environmental variables (Schmidt et al., 2011; Lehmann and Kleber, 2015). As a result, empirical data are commonly abstracted and transformed before being used to parameterize or evaluate the processes of SOM formation and persistence that the model is intended to simulate (Elliott et al., 1996; Zimmermann et al., 2007). This has resulted in many conventional SOM models (e.g. RothC, Jenkinson and Rayner, 1977, DNDC, Li et al., 1992, EPIC, Williams et al., 1984, and CENTURY, Parton et al., 1987) being structurally similar (i.e. partitioning total SOM into discrete pools based on turnover times determined from radiocarbon experiments; see Stout and O'Brien, 1973, and Jenkinson, 1977) but each taking different approaches to simplify the complex mechanisms that govern SOM dynamics. Consequently, simulations of SOM can vary greatly between models, often predicting contrasting responses to the same driving inputs and environmental change (e.g. Smith et al., 1997).
Structuring SOM models around functionally defined and measurable pools that result from known biogeochemical processes is one way to help minimize these discrepancies. Two recent insights into SOM dynamics present a path towards addressing this issue. There is now strong evidence that (1) low molecular weight, chemically labile molecules, primarily of microbial origin (Liang et al., 2017), persist longer than chemically recalcitrant C structures when protected by organo-mineral complexation (Mikutta et al., 2006; Kögel-Knabner et al., 2008; Kleber et al., 2011); and (2) each soil type has a finite limit to which it can accrue C in mineral-associated fractions (i.e. the C-saturation hypothesis) (Six et al., 2002; Stewart et al., 2007; Gulde et al., 2008; Ahrens et al., 2015). Structuring an SOM model around these known and quantifiable biogeochemical pools and processes has the potential to drastically reduce uncertainty by enhancing opportunities for parameterization and validation of models with empirical data. Furthermore, mechanistic models can have value in process explanation as well their value in predictive capabilities; such models can pinpoint the processes that have the greatest influence on a system even when they are not traditionally determined empirically.
Conventional SOM models readily acknowledge the importance of microbes in plant litter decomposition and SOM dynamics, but model improvement was initially constrained by the concept that stable SOM included “humified” compounds (Paul and van Veen, 1978). This quantified stable SOM using an operational proxy (high pH alkaline extraction) rather than relating stabilization to the mechanisms that are now widely recognized, such as organo-mineral interactions and aggregate formation (Lehmann and Kleber, 2015). As our contemporary understanding of stable SOM moves away from humification theory, so too must the way we represent SOM stabilization pathways in biogeochemical models. Similarly, many SOM models partition plant residues into labile and recalcitrant pools with turnover times that reflect the assumption of selective preservation (i.e. chemically recalcitrant litter-C is only used by microorganisms when labile compounds are scarce). While many existing models do include a flux from labile residues into stable SOM, this is typically a much smaller absolute amount than the flux from recalcitrant residues. Evidence indicates that biochemically recalcitrant structural litter C compounds may not be as important in the formation of long-term persistent SOM as originally thought (Marschner et al., 2008; Dungait et al., 2012; Kallenbach et al., 2016). Instead, they form light particulate organic matter (POM) (Haddix et al., 2015), a relatively vulnerable fraction of SOM with a turnover time of years to decades (von Lützow et al., 2006, 2007). Consequently, there have been several calls to represent this new understanding and re-examine how microbial activity is simulated in SOM models (Schmidt et al., 2011; Moorhead et al., 2014; Campbell and Paustian, 2015; Wieder et al., 2015).
Current conceptual frameworks more clearly link the role of microbes to SOM dynamics (e.g. Cotrufo et al., 2013; Liang et al., 2017) and generally isolate two discrete litter decomposition pathways for SOM formation (Cotrufo et al., 2015): a “physical” path through perturbation and cryomixing that moves fragmented litter particles into the mineral soil forming coarse POM and a “dissolved” path, through which soluble and suspended C compounds are transported vertically through water flow and, when mineral surfaces are available, form mineral associated organic matter (MAOM). Microbial products and very small litter particles can be transported by both pathways, forming a heavy POM fraction with “biofilms” and aggregated litter fragments around larger mineral particles (i.e. sand; Heckman et al., 2013; Ludwig et al., 2015; Buks and Kaupenjohann, 2016). Attempts to formulate these empirical observations of litter decomposition into mathematical frameworks recently culminated with the development of the LIDEL model (Campbell et al., 2016), which in turn built upon the relationships of litter decomposition described by Moorhead et al. (2013) and Sinsabaugh et al. (2013). While the LIDEL model was evaluated against a detailed lab experiment of litter decomposition (Soong et al., 2015), it does not simulate SOM pools and dynamics. In nature, litter decomposition processes and SOM formation processes are necessarily coupled but are often studied and modelled separately. However, models that link litter decomposition to SOM formation are required to represent SOM dynamics in ecosystem models.
Beside the processes of leaching and fragmentation that control the two pathways mentioned above, litter decomposition processes that form SOM are governed by the balance between microbial anabolism and catabolism (Swift et al., 1979; Liang et al., 2017). A recent paradigm has emerged that emphasizes the role of microbial life strategies (e.g. K vs. r, referring to copiotrophic and oligotrophic microbial functional groups) and carbon use efficiency (CUE) in the formation of SOM from plant inputs (Dorodnikov et al., 2009; Cotrufo et al., 2013; Lehmann and Kleber, 2015; Kallenbach et al., 2016). As a result, scientists have explored several approaches to represent microbes in SOM models. Research has indicated that explicitly representing microbes in an SOM model can provide very different predictions of SOM dynamics and include important feedbacks such as acclimation, priming and pulse responses to wet–dry cycles (Bradford et al., 2010; Kuzyakov et al., 2010; Lawrence et al., 2009; Schmidt et al., 2011). This research has shown that, compared to conventional models, microbially explicit SOM models have drastically different simulated responses to environmental change (Allison et al., 2010; Wieder et al., 2015; Manzoni et al., 2016). However, these responses are generally validated against data on microsite spatial scales and are not necessarily generalizable over larger spatial scales (Luo et al., 2016).
Microbes have been explicitly represented in SOM models in many ways and for
many years, from relatively simple approaches using a single microbial
biomass pool or fungal
While microbial efficiency largely controls SOM formation rates, and
microbial products are major components of the MAOM and the coarse, heavy POM
fractions of SOM (Christensen, 1992; Heckman et al., 2013) the long-term
persistence of SOM is determined by mineral associations that are subject to
saturation. Saturation limits for SOM were proposed more than a decade ago
(Six et al., 2002) and have been supported by several empirical studies
(e.g. Gulde et al., 2008; Stewart et al., 2008; Feng et al., 2012; Beare et
al., 2014). Briefly, the concept of C saturation suggests that each soil has
an upper limit to the capacity to store C in mineral-associated (i.e. silt
Attempts to consolidate the concepts of microbial control on litter decomposition and mineral control on SOM stabilization resulted in the MEMS framework (Cotrufo et al., 2013). To date, we are aware of only one attempt to represent MEMS within a mathematical model, the Millennial model (Abramoff et al., 2017). However, this model does not simulate litter decomposition explicitly and as a result does not include the impact of litter input chemistry, which is a fundamental component of the MEMS framework and needed to improve ecosystem modelling, as discussed previously.
In this study we describe and demonstrate the application of a new mathematical model (MEMS v1.0) that applies three major concepts of SOM dynamics: (1) litter input chemistry-dependent microbial CUE informing SOM formation (Cotrufo et al., 2013), (2) separate dissolved and physical pathways to SOM formation (Cotrufo et al., 2015), and (3) soil C saturation related to litter input chemistry (Castellano et al., 2015). The scope of this inaugural model description is limited to representing these three concepts and is not intended to include every mechanism relevant to SOM cycling. Our objective is to demonstrate the benefits of structuring an SOM model around key biogeochemical processes rather than turnover times. Using measured SOM physical fractions from 154 forest and grassland sites across Europe, key parameters were optimized to improve model performance when simulating POM-C (consisting of both light and heavy POM) and MAOM-C under equilibrium conditions. The resulting model was then used to test whether the behaviour of simulated SOM dynamics concur with the expected theoretical relationships. Finally, the model performance in predicting soil C stocks at equilibrium was evaluated by simulating 8192 forest and grassland sites across Europe, representing a diverse set of driving variables (i.e. climate, soil type and vegetation type).
Conceptual model diagram of MEMS v1.0 (see Table 1 for detailed
information regarding each pool). Litter pools of MEMS v1.0 are defined as
The MEMS model (herein MEMS v1.0) is designed to be as parsimonious as possible while simulating the spatial and temporal scales relevant to management and policy decision-making. The model is structured (Fig. 1) to simulate plant litter decomposition explicitly, with decomposition products defining C inputs to discrete soil pools that can be isolated with common SOM fractionation techniques (Table 1). Each state variable in MEMS v1.0 can be quantified directly using common measurement protocols and therefore calibration and evaluation data can be generated with a single fractionation scheme (Table S1). Detailed information about the model structure, the mathematical representation (i.e. differential equations) and how each mechanism is described mathematically can be found in the Supplement. All model parameters can be found in Table 2.
State variables of MEMS v1.0 and fractionation definitions
(measurement proxy and protocol) for isolating each pool. C1 to C4, and C6,
refer to the organic layer (above ground,
Description and default values of all parameters used with MEMS v1.0. Where possible, notation has been used to remain consistent with further details in the supplement. Driving variables are reported in Table 3. Ranges are indicative of those observed in the literature. Refer to Sect. 2 and Table S2 for details of the optimized parameter ranges.
Continued.
MEMS v1.0 is an SOM model that operates at the ecosystem scale on a daily time step. Carbon inputs to the model are resolved for each source (in the case of multiple input streams, e.g. manure, crop residue, compost) discretely, partitioning daily C inputs between solid-phase (C1, C2, C3) and dissolved (C6) litter pools as a function of litter chemistry (nitrogen, N, content and the acid-insoluble fraction, i.e. lignin) that influences microbial decomposition processes. This structure is similar to the LIDEL model (Campbell et al., 2016) and follows the hypothesis that both N availability and lignin content influence decomposition by affecting microbial activity (Aber et al., 1990; Manzoni et al., 2008; Sinsabaugh et al., 2013; Moorhead et al., 2013). Similar approaches have been used in many of the updated traditional SOM models (e.g. lignin : N ratios in CENTURY; Kirschbaum and Paul, 2002). These input partitioning coefficients can be determined experimentally for each C input source (Tables 1 and S1). Upon reaching the soil, C compounds are subject to biotic and abiotic processes that transform and transport organic matter through an organic horizon and subsequent mineral soil layers. As described here, MEMS v1.0 currently only simulates a surface organic horizon and a single mineral soil layer and does not yet differentiate between above- and belowground litter input chemistry to avoid requiring additional input parameters on root litter chemistry. However, the model architecture is sufficiently generalizable to apply to multiple soil layers and/or multiple discrete sources of C input. Where possible we use the parameter names and abbreviations from the LIDEL model (Campbell et al., 2016).
Many of the biogeochemical processes represented by MEMS v1.0 are assumed to
be microbially mediated (and therefore result in exoenzyme breakdown and
Even though not all pools explicitly produce microbial biomass, all pools do produce DOM. Recent studies have shown that DOM and small suspended particulates result from the decomposition and fragmentation of all forms of inputs including those characterized as inert, such as pyrolized material (Soong et al., 2015). Consequently, the model assumes that all microbially mediated decomposition produces some C in DOM with rates specific to the pool from which the C originates. Since DOM generation is strongly influenced by the elemental composition of the input material (Soong et al., 2015), it is intrinsically linked to microbial CUE, employing the same formulation as LIDEL, which accounts for input N content and LCI of the litter layer (Campbell et al., 2016). At present, root exudation is not explicitly represented, but the presence of a soil DOM pool (C8) will allow for incorporation of root exudation processes in later versions. More detail regarding the microbially transformed organic matter inputs compared to those directly incorporated into the soil can be found in the Supplement.
While microbial activity directly influences DOM production and therefore its
transport with water flow (pool C8), the physical pathway to SOM formation
(i.e. forming pools C5 and C10; POM) results from perturbation and
fragmentation processes (Cotrufo et al., 2015). The exact mechanisms of
perturbation are hard to generalize over the globally diverse conditions that
an ecosystem-scale model such as MEMS v1.0 is designed to operate.
Consequently, the litter fragmentation and perturbation rate
(LIT
Vertical transport of DOM can be simulated as a function of water flow in a
process-based soil hydrology model. However, in this first, stand-alone
version, MEMS v1.0 assumes that DOM is transported rapidly downward through
percolation and advection according to a constant water flux. As with the
LIT
The organo-mineral complexes that define a large portion of MAOM-C in MEMS
v1.0 operate under the principles of Langmuir isotherms, which have also been
used in the COMISSION and Millennial models (Ahrens et al., 2015, and
Abramoff et al., 2017, respectively). These isotherms represent a net C
transfer between soil DOM (pool C8) and MAOM (pool C9) that encapsulates all
sorption mechanisms (e.g. cation bridging, surface complexation).
While MEMS v1.0 uses the same general Langmuir saturation function as the
Millennial model, it estimates maximum sorption capacity (parameter
In addition to the
Aside from the litter layer DOM (pool C6), each of the state variables in
MEMS v1.0 decay with unique specific maximum rates, with the resultant C flux
being partitioned into
Temperature is used as the main environmental control on maximum specific
decay rates of each pool. The rate-modifying function used by MEMS v1.0 is
adapted from that of the StandCarb model (Harmon and Domingo, 2001). This
function is consistent with empirical data and enzyme kinetics, implying that
microbial decomposition rates peak at an optimum temperature with reduced
rates above and below. Coefficients that define the function also include the
MEMS v1.0 is a series of ordinary differential equations solved for discrete time steps by numerical integration using finite differencing techniques from the Runge–Kutta family of solvers. Implementation is performed through the deSolve package (Soetart et al., 2010) written for R (all equations and associated details can be found in Supplement). Parameters used to solve MEMS v1.0 are described along with their default values and associated references in Table 2.
List of required driving variables for the MEMS v1.0 model. Baseline values represent mean values as reported in the LUCAS database (Toth et al., 2013) of 8192 forest and grassland sites across Europe and were used for all qualitative testing and sensitivity analyses.
Initializing MEMS v1.0 requires external inputs of basic site characteristics (climatic and edaphic conditions as well as land management information) and ideally measurements of daily C input. However, C inputs are rarely available on daily timescales. Consequently, for this inaugural version of the MEMS model we employ a simple function to interpolate daily C inputs from annual net primary productivity (NPP), partitioning above and below ground and to the simulated soil layer using land-use specific root : shoot ratios and a simple root distribution function (Poeplau, 2016). These driving variables are external inputs of the initial model version but may be obtained from coupled climate and plant growth submodels in future versions when incorporated into a full ecosystem model. Details of these approaches are given in the Supplement and all required driving variables are shown in Table 3. Since the major C pools can each be quantified using common analytical methods (Table 1), the best way of initializing the size of these pools in MEMS v1.0 is to use measured data. However, when measured data are not available, a typical site simulation employs a spin-up that runs the model to steady-state conditions based on average climatic and edaphic conditions, as well as average C inputs.
The default parameter values (i.e. those governing C turnover and fluxes
between pools) used by MEMS v1.0 are informed by data from relevant
literature (Table 2). However, different studies may suggest different values
based on discrete site conditions, meaning a priori estimates may not
necessarily be generalizable across all sites that the model could simulate.
A variance-based global sensitivity analysis was performed to determine each
parameter's relative contribution to the change in each state variable (i.e.
determining which parameters have the largest influence on the size of each
model pool). The sensitivity analysis was repeated for different simulation
lengths (1–1000 years) as different fluxes operate on different temporal
scales, thereby meaning that the relative importance of each parameter
changes through time. Initial pool sizes were set to 0 and the model was
initialized to simulate a steady-state scenario based on average site
conditions derived from
To determine the model's steady-state response to changes in each individual
driving variable, a local one-at-a-time (OAT) sensitivity analysis was
performed by sequentially simulating different equilibrium conditions for
1000 years. The baseline estimates for edaphic inputs, temperature and C
input quantity were informed by the LUCAS data set (Toth et al., 2013; see
Table 3 and below for more details), with mean values defining the mid-points
and ranges defined as the minima and maxima. Litter chemistry driving
variables were adapted from the ranges described by Campbell et al. (2016).
Note that, while typically described as a sensitivity analysis, an OAT
approach is not as robust as variance-based techniques because it cannot
determine interactions between input variables. However, OAT results are
easier to interpret as there are no confounding impacts and observed
relationships are solely a result of changing one variable. Additionally, we
assess the model's qualitative relationships between driving variables by
comparison to a study by Castellano et al. (2015); combinations of high and
low sand content and high and low soil pH were used to examine whether model
projections agree with the hypothesized relationships between input litter
chemistry and MAOM-C stocks at steady state. In these scenarios, alfalfa
(
Parameter optimization for MEMS v1.0 used data from the LUCAS data set (Toth et al., 2013). This data set contains basic soil properties including C data for almost 20 000 sites across Europe, sampled in 2009, representing a wide spatial range over 25 countries with diverse gradients of soil types, climates and land uses (Fig. S1). Complimented with geo-referenced estimates of annual NPP from MODIS satellite data (ORNL DAAC, 2009) and daily temperature data from the Climate Prediction Center's Global Temperature (CPC-GT) database (NOAA, 2018), this provided all driving variables required to run MEMS v1.0. The use of modelled and interpolated NPP as well as climate data is not recommended over measurement data directly collected from the site(s) being simulated, but for the analysis herein these measured data were unavailable.
A representative subsample (Fig. S2) of forest and grassland sites from LUCAS
was selected for fractionation to generate data for POM and MAOM pools (see
data set online available at the European Soil Data Centre). Specifically,
topsoil (0–20 cm) samples from 78 grassland sites and 76 forested sites
were fractionated by size (53
Informed by the global sensitivity analysis, four parameters accounted for
To determine the optimized parameter values, a single fold was chosen at random from those that reported the lowest RMSE for each subset of training sites (i.e. each fold). Optimized values differ depending on which measured fraction is compared to model predictions (whether comparing pool C9 to measured MAOM-C, the sum of pools C5 and C10 to measured total POM-C or the sum of pools C5, C8, C9 and C10 to measured bulk SOC). The new, optimized parameter values (Table S2) were derived from a randomly chosen fold that minimized the RMSE when compared to the MAOM fraction. This was chosen (instead of those optimized for POM or bulk SOC) since the MAOM fraction is typically the largest single soil C pool and using this approach led to the biggest overall decrease in RMSE when compared to all available data (Table S2). In future analyses, a more rigorous approach may be to apply a cost function regarding all available measured pool data (e.g. including litter pool data when it is also measured), but for our initial model evaluation we deemed this random choice sufficient.
Having optimized key parameter values, the new global parameter set for MEMS
v1.0 was used to simulate the remaining forest and grassland sites of the
LUCAS data set for independent evaluation. Driving variables of edaphic
conditions and land-use type were extracted for each site from LUCAS and
combined with daily estimates of C inputs and temperature (derived from
simple interpolations assuming a normal distribution of MODIS annual NPP data
(see Supplement for details) and CPC-GT daily maximum and minimum air
temperature data, respectively). Where these data were unavailable, the site
was removed from further evaluation. Three forest land-use classes (as
described in LUCAS) were included, along with the pure grassland land-use
class. This resulted in a final data set of 8192 sites (3487 grasslands, 1713
coniferous forests, 1590 broadleaved forests and 1402 mixed forests).
Mixed forests are defined to contain coniferous and broadleaved species that
each contribute
Global sensitivity analysis results showing the relative
contribution of each parameter to a change in carbon stock of each pool in
MEMS v1.0 (leached carbon to deeper soil layers [pool C11] is omitted for
clarity) after simulation to steady state. The two top-left panels represent
the sum of soil pools (C5, C8, C9 and C10) and organic layer pools (C1, C2,
C3, C4 and C6). Details of each parameter and the abbreviations
used can be found in Table 2. The sensitivity analysis was repeated annually
for simulation times between 1 and 100 years, every 10 years after that to
400-year simulations and every 100 years after that up to a 1000-year
simulation. Results are presented on a log scale in years. The four
parameters that were optimized in our analysis (Table S2) are coloured to
highlight their importance in the different pools (mid-point of logistic
curve where nitrogen content of input influences microbial carbon use
efficiency,
Each of the 8192 sites was initialized with zero pool sizes and simulated for
1000 years to achieve steady-state conditions. This assumed the same
intra-annual distribution of daily temperature and C input for each year.
Organic carbon content reported in LUCAS was converted to SOC stock using the
estimated bulk density reported with the database and reduced according to
the measured rock/gravel content (Eq. 1), i.e.
Bulk SOC stocks were sensitive to different sets of parameters depending on
the duration of the simulation (Figs. 2 and S5). Parameters that define
litter fragmentation and perturbation rates (LIT
The ratio between mineral-associated organic matter and total
particulate organic matter (MAOM : POM) under steady-state input conditions
in MEMS v1.0 as a response to the full, realistic range of driving variables.
Note that total POM refers to the sum of pools C5 and C10. Each input was varied
individually, while all others remained fixed at baseline values (indicated by
dashed lines) – mean, maximum and minimum values for litter chemistry
driving variables (
Alone, each driving variable (edaphic conditions, temperature, and input
litter quantity and quality) in MEMS v1.0 has a discrete and non-linear
relationship to the proportion of soil C stored in the MAOM and POM pools
under steady-state conditions (Fig. 3). This analysis alters only one driving
variable at time while holding others constant at an average value. Bulk C
stocks are predicted to be mostly MAOM in all cases except when C inputs
(
Mineral-associated organic matter (MAOM) stock response to different levels of input litter quality and quantity compared for edaphic conditions which equate to different MAOM sorption relationships in MEMS v1.0. Formatting adopted from Castellano et al. (2015) to aid comparison between the hypothetical relationship postulated and the actual response simulated by MEMS v1.0 here.
MAOM-C saturation in the model is largely dependent on an interaction between the quantity of C inputs, the soil texture (i.e. sand content) and mineralogy (i.e. for which soil pH is used as a proxy). Figure 4 shows that our mathematical formulation of sorption to mineral surfaces generated a very similar relationship to that proposed by Castellano et al. (2015). When C inputs are low, litter input chemistry has the greatest influence on the MAOM-C stock under steady-state conditions. This is particularly true in soils with the strongest mineral bonding (i.e. low pH) and high sorption capacity (i.e. low sand %; Fig. 4 top-right panel).
Initial parameter values derived from relevant literature provided good
estimates judging from model performance with measured fractionation data
(Table S2). Prior to optimization, the difference between measured and
modelled bulk soil C stocks of fractionated LUCAS sites was insignificant for
all four land uses (one-way ANOVA,
Measured and modelled soil C stocks (split into mineral-associated
organic matter, MAOM; total particulate organic matter, POM; and total soil
organic carbon, SOC) for the forest and grassland land-use classes of the
fractionated sites from the LUCAS data set (
Comparisons between average (
Measured fractionation data from the four major land-use classes showed a
wide range of soil C stocks and a significantly different MAOM : POM ratio
between grassland and forests (Figs. 5 and S4). This was predominantly due to
grassland topsoil (0–20 cm) having more MAOM and less total POM compared
to coniferous soils (Fig. S3). On average, simulations of the fractionated
sites agreed well with measured data, demonstrating no significant
differences (
Evaluation results of comparisons between measured and modelled
topsoil (0–20 cm) C stock for 8192 grassland and forest sites across Europe
(see Fig. 7 for geographic distribution of residuals). Mean absolute error
(MAE) and mean bias error (MBE) describe the overall difference and
directional difference between measured and modelled values, respectively.
The model is deemed to describe the trend of the measured data better than
the mean of the measurements when the modelling efficiency (EF) is positive,
or when the coefficient of determination (CofD) is above 1. Each is a
discrete evaluation metric. Divisions of high and low site conditions (mean
annual temperature, mean annual precipitation, annual C inputs, sand content)
were used to derive statistical significance (root mean square error, RMSE,
and
Despite only including a few of the many factors that influence SOM dynamics,
MEMS v1.0 was able to capture the expected relationships between site
conditions and total mineral soil C stocks based on an evaluation of the
optimized model with independent data (Fig. 6). Mean absolute error over all
sites (
Model residuals of topsoil (0–20 cm) C stocks (Mg C ha
In general, discrepancies between measured and modelled values were largest
for the broadleaved forest land-use class (Fig. S6). Results from analysis of
the fractionated sites suggest that the model cannot achieve the very high
POM-C stocks measured at some sites. Optimized parameter values aim to
produce a good overall model fit but are unlikely to be able to capture the
full range of measured values (for example, the lowest bulk topsoil C stock
for a broadleaved site was 7 Mg C ha
MEMS v1.0 was designed to consolidate recent advances in our understanding of SOM formation and persistence into a parsimonious mathematical model that uses a generalizable structure which, after further development, can be implemented in Ecosystem and Earth System model applications. In this study we aimed to provide proof-of-concept that a model structure built around known biogeochemical mechanisms (Fig. 1) and measurable pools could be advantageous for application over varied site conditions. Another advantage of using this novel structure is that each aspect is empirically quantifiable, allowing for straightforward model evaluation of both total and fractionated SOM, addressing a common concern among conventional SOM models (Campbell and Paustian, 2015).
The relationships between model driving variables and soil C stocks at
steady state highlight the importance of litter chemistry on relative
proportions of MAOM and total POM in MEMS v1.0 (Fig. 3). This is generally
because both POM pools accumulate C when input litter has a high
acid-insoluble fraction and a low N content, resulting from reduced microbial
accessibility and reduced DOM production (Scheibe and Gleixner, 2014). This
trend is also common in empirical studies and often associated with land-use
change from herbaceous to woody vegetation (Filley et al., 2008). Many of the
parameters that influence the processes of POM formation and persistence
(e.g. LIT
One main objective of structuring MEMS v1.0 around empirically defined biogeochemical processes is so that it can accurately represent the timescales on which different processes operate, rather than being solely dependent on turnover times of conceptual pools. This is particularly relevant given our new understanding that the MAOM fraction has short-term dynamics (Jilling et al., 2018). Consequently, it is reassuring to see that this knowledge, which is incorporated into the MEMS v1.0 design, can be seen in Fig. 2 (and Fig. S5), where the parameters that operate on short timescales also have an immediate impact on the MAOM pool given the complexity of controls in the model structure. The model's agreement with the hypothesized relationship from Castellano et al. (2015) is also reassuring, and represents an important proof of concept that associates litter chemistry and C saturation capacity with MAOM-C stocks at steady state (Fig. 4).
While average agreement between measured and modelled soil C stocks was very good for MEMS v1.0, the model failed to capture the wide range in total POM-C stocks that were observed at the fractionated LUCAS sites (Fig. 5). This may be because this first version of the model does not include several of the key controls on POM dynamics, such as water/oxygen limitations (Keiluweit et al., 2016), aggregation (Gentile et al., 2011), activity of soil fauna (Frouz, 2018) and nutrient availability (Bu et al., 2015; Averill and Waring, 2018). There are also limitations of our approach given that very few of the sites will likely be under true steady-state conditions, leading to further discrepancies between model predictions and measured values. Furthermore, the variability in driving variables of litter chemistry, N content and root:shoot ratios are underestimated when using our approach of grouping many different land uses into broad classes.
When examining the comparison between measured and modelled bulk soil C stocks for the 8192 forest and grassland sites, residuals were particularly large for high-latitude forestry sites in southern Sweden and the UK (Fig. 7). We hypothesize that this is primarily due to the fact that MEMS v1.0 does not simulate soil moisture controls on decomposition, and temperature effects are applied through a simple function. In reality, these sorts of forest soils are known to have very high total POM-C stocks, resulting from decades of consistent inputs and cold, wet climates, resulting in low decomposition rates (Berg, 2000). Differences between measured and modelled soil C stocks are also likely due to uncertainties with driving variables and specifically the MODIS estimates of NPP. The 2009 NPP data from MODIS were used to estimate the C inputs to soils in our simulations, and these data may not be representative of the average historical C inputs for those sites, which would impact the observed amounts of soil C.
The current iteration of the MEMS model is not intended to be able to
simulate all scenarios and environmental conditions, but this study indicates
it can be reasonably accurate in simulating forest and grassland sites in
Europe under steady-state conditions (Fig. 6; Table 4). That said, several of
the parameters in MEMS v1.0 are either poorly constrained or loosely defined
in the current model. The LIT
As with vertical distribution of physical SOM, the transport of DOM vertically between layers lacks a mechanistic foundation in MEMS v1.0. A noteworthy approach that attempts to simulate this transport while also representing bioturbation through diffusion and sorption-desorption processes is presented in the COMISSION model (Ahrens et al., 2015). While these models apply more mechanistic functions to represent these key processes, one can debate whether the increased complexity and computational demands are necessary. This, of course, depends on the model objectives, and in MEMS v1.0 we have prioritized parsimony and deliberately minimized the number of algorithms and parameters. While the model cannot yet address hypotheses about litter fragmentation or DOM leaching, the generic structure of MEMS v1.0 can incorporate these processes in a more explicit manner in future versions.
Additional parameters of MEMS v1.0 that are poorly constrained include those
associated with the LIDEL model. These parameters (specifically those related
to DOM generation and microbial assimilation; see Table 2) were estimated
using Bayesian analysis that employed empirical data (Soong et al., 2015) but
resulted in large posterior distributions with high uncertainty as noted by
Campbell et al. (2016). Consequently, more data are required from different
litter types to help constrain these parameter values. In particular, the
amount of DOM leached from decaying microbial biomass (parameter la
In its current capacity, MEMS v1.0 is far from being able to simulate full ecosystems and is limited in scope regarding the land-use scenarios it can simulate accurately. Specifically, the initial model does not simulate the hydrological or nitrogen cycles and currently operates on a single soil layer. However, MEMS v1.0 has been built to have a modular architecture, with careful consideration given to how additional processes can be addressed through future model development.
The relationship between C and N in soils is fundamental to SOM dynamics (McGill and Cole, 1981), and therefore simulating the N cycle is at the forefront of plans to develop in the MEMS model. Since the MEMS model structure is based on soil fractions that can be physically isolated, each current soil C pool in MEMS v1.0 (i.e. pools C5, C8, C9 and C10) can also have a direct equivalent for N, and be consistent with the fractionation scheme for the C dynamics (Table S1). However, additional pools of nitrate and ammonium (and associated mechanisms to describe N fixation, nitrification and denitrification) are needed to accurately describe plant–soil nutrient feedbacks. This highlights a major objective of future MEMS model development, i.e. to ensure the model can be easily coupled with existing modules that describe other aspects of the ecosystem (e.g. plant growth routines).
Another key feature of MEMS v1.0 is its ability to test specific hypotheses directly against empirical data, such as the effects of soil priming on soil C stocks, effects of microbial feedbacks on OM sorption to mineral surfaces, or the effects of soil fauna on SOM formation. Because each of the existing model pools can be isolated physically and quantified, the rates of flux between these pools can also be quantified with isotopic tracer studies. Not only does this mean parameterization and evaluation data can be generated easily, but also that experiments can be designed with this mathematical framework in mind, specifically generating the data required to develop, evaluate and improve the model. While the current scope of MEMS v1.0 does not address all climate-C feedbacks, it does provide the basis for a more mechanistic model that can simulate SOM dynamics on the ecosystem scale.
As a carbon model designed around the processes that govern SOM formation,
MEMS v1.0 provides an analytically tractable framework that can be used to
test specific hypotheses by pairing empirical experiments with model
simulations. While the inaugural version of this new model has limitations
for direct evaluation with real-world measurements, on average, its
performance with simulating steady-state conditions equates well with topsoil
C stocks measured for
The next steps for MEMS model development will require detailed routines of N and hydrological cycling, as well as additional external drivers of SOM dynamics (e.g. land management practices). To reliably incorporate these aspects in the MEMS model will require effective collaboration between modellers and experimentalists to design studies that can both (i) elucidate the underlying mechanisms that MEMS is built upon and (ii) generate the parameterization and validation data required to reduce model uncertainty. Successful execution of this strategy will help to develop an ecosystem-scale model that can improve assessments of management and policy action on sustainability of soils and associated ecosystem services.
The LUCAS data set is available online (European Soil Data Centre, 2013) along with details of the larger project. The additional MAOM and POM fractionation data for the 154 sites used in this analysis can also be found in the European Soil Data Centre (ESDAC) repository online. Access to model code is currently restricted to those directly collaborating with the MEMS development team. This is to ensure all bugs are caught and treated before release to the public. Detailed information and code relevant to specific questions can be provided upon request.
The supplement related to this article is available online at:
All authors contributed to the conceptualization of the MEMS model framework with MFC, KP and MDW formalizing the original foundational science. The in-practice model structure was then formalized by ADR, MFC, KP, SO and MWD. All model building, coding, statistical analyses and data analysis on the measured fractionation data and all model–measure comparisons were performed by ADR. Guidance on the optimization procedures was provided by SO. The LUCAS database was provided by EL and all initial analysis and preparation of the data (e.g. refining bulk density estimates and NPP values for each site) were performed by EL. The project was overseen by all authors but primarily led by MFC. Funding was initially provided by MDW and later through grants awarded to MFC and KP. The development, testing and evaluation of the model was performed solely by ADR, as was all data presentation apart from the final conceptual diagram (Fig. 1), which was outsourced (see acknowledgments). The manuscript was written and edited by ADR with comments and feedback from all co-authors.
The authors declare that they have no conflict of interest.
This research was supported by a National Science Foundation CAREER grant (number 255228) awarded to MDW, the US DOE Advanced Research Projects Agency-Energy programme (ROOTS project; DE-FOA-00001565), the NSF-DEB award no. 1743237 and the JRC (purchase order D.B720517). The authors like to thank Michelle Haddix for the soil organic matter fractionation work and Yao Zhang for help with regard to various parts of data generation (e.g. climate inputs) and model development. The conceptual figure diagram was redrawn and stylized by Katie Burnet.
This paper was edited by Sébastien Fontaine and reviewed by Thomas Wutzler and one anonymous referee.