There is increasing recognition that lateral soil organic
carbon (SOC) fluxes due to erosion have imposed an important impact on the
global C cycling. Field and experimental studies have been conducted to
investigate this topic. It is useful to have a modeling tool that takes
into account various soil properties and has flexible resolution and scale
options so that it can be widely used to study relevant processes and
evaluate the effect of soil erosion on SOC cycling. This study presents a
model that is capable of simulating SOC cycling in landscapes that are
subjected to erosion. It considers all three C isotopes (
Soil organic carbon (SOC) is the largest organic C pool on land, with approximately 1550 Pg C in
the upper meter of soil (Lal, 2008). This is about 2 times
the C in the atmosphere (ca. 760 Pg C). The annual C flux between soil and
the atmosphere is ca. 60 Pg C, which makes atmospheric
Recent studies have shown that lateral soil redistribution by erosion could
also impose an important impact on SOC stock and soil–atmosphere C exchange
(Chappell et al., 2016; Doetterl et al., 2016). During erosion events,
soil aggregates are broken by raindrops and overland flow, which can enhance
SOC decomposition (Van Hemelryck et al., 2011). In the eroding
region, SOC in the topsoil is removed by erosion, resulting in depletion of
SOC. SOC is added to soil minerals that are moved upwards from below due to soil erosion
by inputs from plants (Harden et al., 1999).
SOC deposited in depositional settings is buried to depth, and the buried
SOC is well preserved (Van Oost et al., 2012). This lateral
redistribution of SOC and the consequent disturbance of SOC cycling of both
eroding and depositional regions result in spatial variability in SOC stocks
and properties. It has been found that eroding sites are depleted of SOC
compared to stable sites, while depositional sites are enriched in SOC
compared to stable sites in agricultural fields (Li et al., 2007; Van
Oost et al., 2005; Yoo et al., 2005). Soil redistribution could lead to
differences in SOC stability between eroding and depositional areas.
Berhe et al. (2008) found that SOC decomposes at faster rates
in eroding areas compared to depositional areas using signatures of
Apart from the empirical studies mentioned above, various models have been
developed to simulate soil erosion and SOC cycling. At the event scale,
there are models simulating processes such as rainfall detachment, sediment
entrainment and sediment transport (e.g., Hairsine and Rose, 1992a, b).
Some models separate sediments into different grain sizes, and these models
are suitable for simulating size selectivity in erosion and deposition
(Nearing, 1989; Van Oost et al., 2004). These models are further modified
to simulate the selectivity of SOC in erosion and deposition
(Wilken et al., 2017). Models based on USLE (Universal Soil
Loss Equation) utilize annual mean precipitation as model input to simulate
long-term soil erosion (Renard et al., 1997). Given
that atmospheric fallouts of
C turnover models have been developed under the condition of stable landscapes (i.e., free of erosion and deposition) to explore the effects of climate, land use and soil environment on SOC cycling. The decomposition of C is often represented by a first-order kinetic rate. Because SOC is a complex of different components, it is often represented by various pools with respect to C input and decomposition rates in models such as CENTURY (Parton et al., 1987), ICBM (Andren and Katterer, 1997) and RothC (Coleman and Jenkinson, 1995). C fractions obtained in laboratories have been related to C pools in models and have been used to calibrate model parameters to investigate the turnover of various C pools (Skjemstad et al., 2004; Wang et al., 2015a; Zimmermann et al., 2007).
These multiple-pool C models were further integrated with soil erosion models to make them applicable in eroding landscapes. For instance, a study adding erosion processes to the CENTURY model has been used to investigate the balance between the lateral SOC loss by erosion and in situ replacement of lost SOC by photosynthesis in eroding areas, and it has been found that proper management is important to maintain the dynamic replacement of lost C (Harden et al., 1999). At the depositional areas, Wang et al. (2015b) calibrated a profile-scale model integrating erosion and SOC cycling processes using observed SOC content and long-term depositional rate, and it was found that sedimentation rate plays an important role in determining burial efficiency of SOC in colluvial settings. Models have also been developed to investigate the relationships between erosion, crop productivity and SOC cycling (Bouchoms et al., 2019). At the field scale, models that combine SOC redistribution by erosion and SOC dynamics are now able to reproduce the spatial heterogeneity of SOC stock in fields under land uses with eroding areas depleted of SOC and depositional areas enriched in SOC (Liu et al., 2003; Rosenbloom et al., 2001, 2006; Van Oost et al., 2005; Yoo et al., 2005).
Carbon isotopes have also been included in SOC cycling models to
constrain model parameters or explore controlling factors.
Baisden et al. (2002) used C and N isotopes to simulate
the turnover and transport of SOC along soil depth and showed that
hydrological conditions had an important role in controlling the vertical
transport of SOC. Also, an SOC cycling model integrating C isotope
discrimination was utilized to explore the effects of SOC decomposition and
physical mixing on the formation of the vertical increase in
Here, we integrate SOC and soil erosion models and present a model tool that is capable of simulating SOC dynamics in an eroding landscape. The objectives of this modeling tool are the following: (i) it should be a multiple C pool model that is able to represent the complexity of SOC and to be related to the measurable SOC fractions; (ii) it should include various C isotopes so that it could not only represent these C metrics but also use them to constrain the model; and (iii) it should be flexible in terms of spatial and temporal scales so that it would be applicable in various cases regarding spatial and temporal settings.
The first study site is located in the Belgian loam belt. The study area has
a temperate climate with average annual precipitation of 750–800 mm and a
mean annual temperature of approximately 9.5
This study also used published SOC and
Here we present WATEM_C that simulates the
redistribution of eroded soil and associated C within the catchment and its
effects on the dynamics of SOC. The soil redistribution by water erosion is
based on WATEM (Water And Tillage Erosion Model; Van Oost
et al., 2000), while the simulation of C dynamics is based on a three-pool C
model (Wang et al., 2015a). All three C isotopes (
The RUSLE (Revised Universal Soil Loss Equation) (Renard et
al., 1997) is used to simulate the long-term potential water erosion
(
The local erosion rate is considered to be equal to the potential erosion
rate if the potential erosion rate does not exceed the local transport
capacity. The local transport capacity (
A routing algorithm was applied to transfer the mobilized sediments towards the catchment outlet. First, the grids of the study area were sorted in descending order based on the digital elevation model (DEM). Then, after comparing the local transport capacity of a grid cell with the incoming sediment and the locally produced sediment (Van Oost et al., 2000), sediments were routed downslope. Prediction of the flow direction was based on Takken et al. (2001).
The mobilization of SOC or
The deposited SOC or
The enrichment ratios of SOC or
In our model, the three C isotopes (
Similarly, the decomposition rate of a
The
The input of the C isotopes from plant roots decreases exponentially with
depth (Gerwitz and Page, 1974; Van Oost et al., 2005):
The
Thus, the
We use the atmospheric
The
In the model, soil profiles are represented as a series of soil layers with equal depths. Given that C input and SOC decomposition rate are related to soil depth, SOC cycling is simulated in each layer independently. Because erosion and deposition change the depth of soil profiles, the model updates the depth of soil profiles and the carbon content of each soil layer every time step. At the eroding locations, soils are removed from the top layer and the soil profile is truncated by the amount of eroded soil. In the meantime, SOC is also lost with the local C enrichment ratio. To keep the soil layer with fixed thickness, soils and associated SOC from soil layers below are incorporated into the upper soil layer at the erosion rate. At the depositional locations, because the top layer is buried by the deposited sediments at the deposition rate, soils and associated SOC are moved downward. For all the soil profiles, the component pools of each C isotope of every layer are updated by homogeneously mixing the component materials every time step.
The vertical transport of mineral and organic components of soil is a
complex phenomenon driven by a number of distinct mechanisms such as
bioturbation (Jagercikova et al., 2017; Johnson et al., 2014) and
chemical mobilization (Taylor et al., 2012). We use the
advection–diffusion equation to model vertical transport:
In order to make the model applicable at various temporal and spatial
resolutions, the time step of model iteration and the vertical resolution of the
soil profile were not fixed but modifiable as parts of the model input
parameters. Given the long-term temporal iteration in SOC cycling processes
and the possible large spatial regions where the model may apply, the model
was developed using a computation-efficient language (Pascal). The compiled
executable file can then be called in other environments such as R (R
Development Core Team, 2011), for which the preparation of the input maps is
easier. In our model, the default values of the input parameters were given,
and at the same time users are allowed to assign custom values to the
input parameters in the R environment when calling the executable file. The
description and relevant parameters regarding SOC cycling used in this study
are listed in Table 1. For the initialization of the
Values of parameters for SOC cycling used this study.
Five model scenarios was tested in this study (Table 2). A set of three
scenarios was assumed in order to investigate the effect of advection and
diffusion as well as lateral soil redistribution by erosion on the spatial and
vertical distribution of SOC and
The model was also evaluated using observed C,
Observed and simulated C content and
In order to quantify the effects of C decomposition, vertical soil advection
and diffusion, and lateral soil redistribution on the C,
The Fourier amplitude sensitivity test (FAST) (Cukier et al., 1973, 1975) was applied using simulations obtained in a Monte Carlo
approach to assess the contribution associated with relevant parameters. The
FAST method is based on the analysis of variance (ANOVA) decomposition,
which quantifies the relative contribution of only one given parameter to
the total variance of the model output. Eight parameters of the model
relevant to C decomposition (
Model scenarios implemented in this study.
The optimal parameter values obtained after model calibration are reported
in Tables 3 and 4. The model simultaneously simulated both the observed C content and
C isotopic composition profiles well, with the MRMSE being
2.26 and 4.46 for the Belgian and the USA study sites, respectively (Figs. 1 and 2). The model not only reproduced the horizontal difference of
the C,
Observed and simulated C content and
Calibrated optimal parameter values for the Belgian study
site.
Calibrated optimal parameter values for the USA study
site.
Our model is able to reproduce the general pattern of the SOC profile for decreasing SOC content with depth in all scenarios despite rates of advection, diffusion, erosion or deposition (Fig. 3). In Scenario 2, higher rates of soil advection and diffusion result in more SOC transferred to depth, and therefore the difference in SOC content between top layers and bottom layers is smaller under the condition of higher soil advection and diffusion rates compared to SOC profiles with lower advection and diffusion rates (Fig. 3b). In Scenario 3, eroding soil profiles contain less SOC compared to the stable soil profiles free of erosion and deposition, while soil profiles at the depositional area are enriched in SOC compared to the stable soil profile (Fig. 3c).
The simulated C content profiles in
In Scenario 1, the
The simulated
Effects of plant type change and the Suess effect on the
Our model is able to reproduce the general pattern of decreasing
The simulated
C decomposition played the primary role in controlling the C,
The matrix of the proportion of variance of the difference between reference profiles and Monte Carlo scenario profiles caused by model parameters as indicated by the FAST coefficients. SRR indicates the soil redistribution rate, and ET indicates erosion time.
The model is able to generate a reasonable pattern of soil redistribution,
with erosion occurring in upland areas and deposition occurring in footslope
areas or valleys (Fig. 8b). Soil redistribution results in higher
Model simulations of erosion and erosion-induced spatial
variability of SOC stock and isotopic compositions.
In Scenario 1, the shape of the SOC profile is determined by the vertical
patterns of SOC input and decomposition rates, both of which decrease with
depth. The fact that the basic shape of the SOC profile can be well
represented in Scenario 1 shows that the pattern of C input and
decomposition rates is the primary controlling factor on the SOC profile,
while other factors such as advection, diffusion, erosion or deposition
are relatively secondary (Fig. 3a). It is natural that higher rates of
advection and diffusion would result in more SOC transferred to deep
layers (Fig. 3b). Given that it is less favorable for SOC to be
mineralized in deep layers, the transferred SOC by advection and diffusion
to depth would be better preserved. Simulations in Scenario 2 show that
SOC stock in the top 1 m under the condition of high advection and diffusion
rates (
Our model is able to reproduce the widely observed decrease in
WATEM_C focuses on the catchment scale, which allows it
to account for processes of both erosion and deposition. It is a spatially
distributed model with parcel maps denoting various land use types. Also, it
allows accounting for soil conservation measurements, which enables the
model to investigate anthropogenic effects (such as land use and management)
on erosion and SOC cycling. Compared to previous models, the model presented
here is more comprehensive. It includes SOC cycling processes and the
redistribution of soil and associated SOC by erosion. It is a three-pool C
model that discriminates C isotopes (
This paper presents a model (WATEM_C) that is capable of
simulating SOC dynamics on an eroding landscape. It allows tracking the
redistribution of soils and associated
The source codes are provided through a GitHub repository at
All the authors were involved in the design of the model. ZW further developed WATEM_C based on WATEM by KVO. ZW, JQ and KVO wrote the paper together.
The authors declare that they have no conflict of interest.
This research has been supported by BELSPO (IUAP, contract P7-24) and the Natural Science Foundation of China (grant nos. 41871014, 41771216, and 41971031).
This paper was edited by Sandra Arndt and reviewed by two anonymous referees.