Modelling biogeochemical fluxes and soil organic carbon dynamics in soil systems

At present large uncertainty exists in predicting carbon climate feedbacks, as current models do not agree whether the land surface will remain as a sink or become a source of atmospheric carbon under changing climate and land use. Current ecosystem and earth system models need appropriate representation of soil carbon dynamics and their environmental controllers in order to reduce the existing uncertainty in predicting carbon climate feedbacks. In this session, we invite contributions from an ecosystem to earth system scales that incorporate field observations, remote sensing, and laboratory experiments into geospatial and process-based models to represent unique soil carbon processes that operate at large spatial scales. We encourage submissions that demonstrate: 1) emergent environmental controllers of soil carbon storage and dynamics, and 2) data-model integration to address critical uncertainties that exists in the carbon dynamics of mineral and organic soils.

Convener: Umakant Mishra | Co-Conveners: Yiqi Luo, Jianyang Xia, Katherine Todd-Brown, Zhifeng Yan
| Fri, 21 May, 03:00–04:30 (CEST)
| Fri, 21 May, 04:30–06:00 (CEST)

Oral: Fri, 21 May

Feng Tao, Yuanyuan Huang, Bruce A. Hungate, Xingjie Lu, Toby D. Hocking, Umakant Mishra, Gustaf Hugelius, Xiaomeng Huang, and Yiqi Luo

Soil carbon storage is a vital ecosystem service. Yet mechanisms that regulate global soil organic carbon (SOC) dynamics remain elusive. Here we explicitly retrieve the spatial patterns of key biogeochemical mechanisms and their regulation pathways on SOC storage using the novel PROcess-guided deep learning and Data-driven modelling (PRODA) approach. PRODA integrates data assimilation, deep learning, big data with 54,073 globally distributed vertical SOC profiles, and the Community Land Model version 5 (CLM5) to best represent and understand global soil carbon cycle. The PRODA-optimised CLM5 can represent 56±2% spatial variation of SOC across the world. Among all the mechanisms we explored in this study, microbial carbon use efficiency (CUE) emerges as the most critical regulator of global SOC storage. Increasing CUE, where more carbon flow is channelled into stabilisation, coincides with decreasing temperature and favours SOC accrual. Global sensitivity analysis further confirms the CUE, surpassing carbon input and decomposition, as the primary driver to SOC storage and its spatial variation. An increase of CUE by 1% from its standing value will lead to an additional 76±3 petagrams global SOC accumulation. We conclude that how efficiently soil microorganisms utilise organic carbon in metabolism is central to global SOC stabilisation. Understanding detailed processes underlying CUE and its environmental dependence will be critical in accurately describing soil carbon dynamics and its feedbacks to climate change.

How to cite: Tao, F., Huang, Y., Hungate, B. A., Lu, X., Hocking, T. D., Mishra, U., Hugelius, G., Huang, X., and Luo, Y.: PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-10,, 2021.

Jinyun Tang and William Riley

In ecosystem biogeochemistry, Liebig’s law of the minimum (LLM) is one of the most widely used rules to model and interpret biological growth. Although it is intuitively accepted as being true, its mechanistic foundation has never been clearly presented. We here first show that LLM can be derived from the law of mass action, the state of art theory for modeling biogeochemical reactions. We further show that there are (at least) another two approximations (the synthesizing unit (SU) model and additive model) that are more accurate than LLM in approximating the law of mass action. We then evaluated the LLM, SU, and additive models against growth data of algae and plants. For algae growth, we found all three models are equally accurate, albeit with different parameter values. For plants, LLM failed to accurately model one dataset, and achieved equally good results for other datasets with very different parameters. We also find that LLM does not allow flexible elemental stoichiometry, which is an oft-observed characteristic of plants, when an organism’s growth is modeled as a function of substrate uptake flux. In summary, we caution the use of LLM for modeling biological growth if one is interested in representing the organisms’ capability in adapting to different nutrient conditions.   

How to cite: Tang, J. and Riley, W.: On the mechanistic foundation and limit of Liebig’s law of the minimum, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-16,, 2021.

Yuanhe Yang, Leiyi Chen, Kai Fang, Bin Wei, Shuqi Qin, Xuehui Feng, Tianyu Hu, and Chengjun Ji

Elucidating the processes underlying the persistence of soil organic matter (SOM) is a prerequisite for projecting soil carbon feedback to climate change. However, the potential role of plant carbon input in regulating the multi-layer SOM preservation over broad geographic scales remains unclear. Based on large-scale soil radiocarbon (Δ14C) measurements on the Tibetan Plateau, we found that plant carbon input was the major contributor to topsoil carbon destabilisation despite the significant associations of topsoil Δ14C with climatic and mineral variables as well as SOM chemical composition. By contrast, mineral protection by iron–aluminium oxides and cations became more important in preserving SOM in deep soils. These regional observations were confirmed by a global synthesis derived from the International Soil Radiocarbon Database (ISRaD). Our findings illustrate different effects of plant carbon input on SOM persistence across soil layers, providing new insights for models to better predict multi-layer soil carbon dynamics under changing environments.

How to cite: Yang, Y., Chen, L., Fang, K., Wei, B., Qin, S., Feng, X., Hu, T., and Ji, C.: Soil carbon persistence governed by plant input and mineral protection at the regional and global scales, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-17,, 2021.

Elisa Bruni

Soils represent the largest terrestrial reservoir of organic carbon on land and have the ability to sequester carbon dioxide from the atmosphere. Increasing soil organic carbon (SOC) stocks also improves soil fertility, water holding capacity and prevents erosion. Maintaining SOC stocks is particularly relevant in agricultural soils, where they have been depleted through historical land use. Simulation models representing the dynamics of carbon in the soil are used for predicting the impact of future climate change on SOC dynamics. It is necessary to reduce the uncertainties related to SOC predictions and increase confidence on long-term model simulations. Multi-modeling simulations allow predicting the evolution of SOC stocks, while estimating the uncertainty related to different modeling approaches.

In this study, we used a multi-modeling ensemble (ICBM, AMG, RothC and Century) to estimate the amount of carbon inputs required to maintain and increase SOC stocks in 17 agricultural experiments around Europe. Models were run once without calibration and once fitting SOC stocks to long-term observations though parameters’ optimization. Outputs were significantly different among the models and, although no effect of the optimization was found, we observed a significant interaction effect between models and parameters’ optimization. We found that maintaining and increasing SOC stocks is realistic for some experimental conditions, but might be hard to implement at a larger scale.

How to cite: Bruni, E.: Increasing soil organic carbon stocks in croplands: a multi-modelling analysis evaluating the carbon inputs required to maintain and increase soil organic carbon stocks in Europe, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-18,, 2021.

Rose Abramoff, Bertrand Guenet, Haicheng Zhang, Katerina Georgiou, Xiaofeng Xu, Raphael Viscarra-Rossel, Wenping Yuan, and Philippe Ciais

Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, soil C models that represent these measurable quantities can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and dissolved organic C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three soil fractionation data sets spanning a range of climate and geochemistry in Australia (N=495), Europe (N=176), and across the globe (N=730). Millennial V2 (RMSE = 1.98 – 4.76 kg, AIC = 597 – 1755) generally predicts SOC content better than the widely-used Century model (RMSE = 2.23 – 4.8 kg, AIC = 584 – 2271), despite an increase in process complexity and number of parameters. Millennial V2 reproduces between-site variation in SOC across a gradient of plant productivity, and predicts SOC turnover times similar to those of a global meta-analysis. Millennial V2 updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.

How to cite: Abramoff, R., Guenet, B., Zhang, H., Georgiou, K., Xu, X., Viscarra-Rossel, R., Yuan, W., and Ciais, P.: Site-level simulations of measurable soil fractions with Millennial Version 2, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-19,, 2021.

Debjani Sihi and Stefan Gerber

Models of soil organic matter (SOM) decomposition are critical for predicting the fate of soil carbon (and nutrient) under changing climate. Traditionally, models have used a simple set-up where the substrate is divided into conceptual pools to represent their resistance to microbial degradation, and decomposition rates are often proportional to the amount of substrate in each pool. Emerging models now consider explicit microbial dynamics and show that SOM loss under warming may be fundamentally different from the classical models. Microbial explicit models use reaction kinetics, represented on a concentration basis. However, when the substrate makes up most of the volume of soils (e.g., the organic horizon in forest soils or peat), an increase or decrease in SOM does not, or only very little, affect concentrations of microbes and substrate. Consequently, reduction in SOM does not reduce the amount of substrate the microbial biomass encounters. This problem does not occur in classical models like CENTURY. We incorporated the effect of organic matter on soil volume in several microbial models. If microbes are solely limited by enzymes, organic soils or peats are decomposed very quickly as there is no mechanism that stops the positive feedback between microbial growth and SOM concentration until the substrate is gone. Alternative formulations that account for carbon limitation or microbial ‘cannibalism’ display a sweet spot of soil carbon concentration. Interestingly, a response to warming will depend on the amount of organic vs. mineral materials. Apparent Q10 was higher in fully organic soil than in mineral soils, which was pronounced when small to moderate amounts of the mineral matter was present that diluted the substrate for microbes. We suggest that model formulations need to be clear about the assumption in key processes, as each of the steps in the cascade of biogeochemical reaction can produce surprising results.

How to cite: Sihi, D. and Gerber, S.: Challenges of using microbial explicit models for evaluating organic matter decomposition in predominantly organic soils , 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-57,, 2021.

Interactive: Fri, 21 May, 04:30–06:00<

Francis Durnin-Vermette, Paul Voroney, and Adam Gillespie

Carbon sequestration reduces GHG emissions while improving soil fertility. In order for carbon sequestration through agriculture to be viable, however, accurate estimations of sequestration values are crucial in order to guide policy-making. Currently, Ontario’s provincial Ministry of Agriculture, Food and Rural Affairs (OMAFRA) uses sequestration values from the federal government’s farm-level greenhouse gas emission model (Holos), however these estimates fall short in one respect: a 2018 analysis demonstrated that manure application is not completely considered in the government’s estimates, which is a critical gap.

The main purposes of our study were 1) to assess the accuracy of soil organic carbon estimations of process-based soil carbon models (Century and RothC) which were calibrated with data from long-term manure addition experiments in Ontario, and 2) to modify these models such that they were able to fully take manure application into account when estimating carbon sequestration in Ontario’s croplands, and determine whether this substantially increases model accuracy.

The models’ estimations for soil organic carbon sequestration were respectively calibrated and validated using data from two long-term manure addition experiments in Ottawa and Harrow. By calibrating multiple models using multiple datasets, model-specific and site-specific biases were minimized. The statistical analyses consisted of a suite of tests that assess the modelling accuracy compared to baseline measured data: the coefficient of determination (R2), root mean square error (RMSE), average relative error (ARE), and the Nash-Sutcliffe efficiency statistic (NSE).

As a result of these improved provincial estimates, Canadians will be better-informed about the greenhouse gas mitigation potential of long-term manure addition to croplands, which will help guide decisions made by policymakers as well as farmers. These improved provincial estimates will also be reported to Canada’s national greenhouse gas inventory, and will be ultimately disclosed to the UN’s Intergovernmental Panel on Climate Change (IPCC) in their global GHG summary report.

How to cite: Durnin-Vermette, F., Voroney, P., and Gillespie, A.: Improving modelling estimations of soil organic carbon sequestration in manure-amended croplands, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-20,, 2021.

Mingming Wang and Zhongkui Luo

Vertical carbon transport along the soil profile redistributes soil carbon fractions in soil layers, which may have significant consequences on whole-soil profile organic carbon (SOC) dynamics. We developed three varieties of vertically resolved SOC models to simulate SOC dynamics (down to 2 m). The three models took into account mechanisms underpinning the increased persistence of SOC in deeper soil layer depths by explicitly simulating microbial processes and the interactions between old and new carbon pools. Model sensitivity analyses indicated that vertical carbon transport must to be considered; otherwise the profile distribution of SOC stock cannot be captured by the models. The models were further constrained by global data sets of whole-soil profile observations of vertical distribution of SOC stocks and carbon inputs, and then were used to predict the spatial pattern of the depth-specific amount of vertically transported organic carbon (V, g C m-2 yr-1) across the globe. The V showed great variability across the globe as well as across different depths. Precipitation was the most important for influencing the global pattern of V; and soil texture and organic carbon content for the profile pattern. Applying the models across the global, we assessed the response of SOC to 2℃ global warming at the resolution of 1 km. The results suggested that without considering the vertical carbon transport, SOC loss under warming would be underestimated by 10%, particularly in the deeper layers. In wetter areas or areas with stronger soil profile disturbance such as bioturbation and cryoturbation, SOC was more sensitive (i.e., more SOC loss) to climatic warming due to the stronger vertical carbon transport and/or carbon-mixing. Our modelling demonstrates the vital role of vertical carbon transport in controlling whole-soil carbon dynamics, which is a key determinant of whole-soil profile SOC persistence under warming.

How to cite: Wang, M. and Luo, Z.: Whole-soil profile carbon dynamic in response to climate change modulated by vertical carbon transport, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-36,, 2021.

Yuehong Shi, Xiaolu Tang, Xinrui Luo, Zhihan Yang, Yunsen Lai, and Peng Yu

Soil is the largest carbon pool in terrestrial ecosystems, storing up to 2 or 3 times the amount of carbon present in the atmosphere, and a small change in soil carbon stock could have profound effects on atmospheric CO2 and climate change. However, an accurate estimate of soil organic carbon (SOC) stock is still challenging. Previous studies on SOC stock prediction across China were mainly from biogeochemical models and national soil inventories, and large uncertainties still remained. In this study, we predicted SOC stock at 0 – 20 cm and 0 – 100 cm with 3419 and 2479field observations using artificial neural network (ANN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting regression trees (GBRT) across China with the linkage of climate, vegetation and soil variables. Results showed that RF performed best among the four machine learning approaches with model efficiency of 0.61 for 0 – 20 cm and 0.52 for 0 – 100 cm. The trained RF model was used to predicted the temporal and spatial patterns of SOC stock at a spatial resolution of 1 km from 2000 to 2014 across China. Temporally, SOC stock at 0 – 20 cm (p = 0.07) and 0 – 100 cm (p = 0.3) did not change significantly. However, SOC density showed strong spatial patterns, the mean value of SOC density at 0-20 cm and 0-100 cm increased firstly, then decreased and then increased with the increase of latitude, and the minimum density was 39.83° and 41.59°, respectively. The total SOC stocks across China were 33.68 and 95.01 Pg C for 0 – 20 cm and 0 – 100 cm, respectively. The developed SOC stock could serve as an independent dataset that could be used for decision-making and help with baseline assessments for inventory and monitoring SOC stocks for global biogeochemical models in China.

How to cite: Shi, Y., Tang, X., Luo, X., Yang, Z., Lai, Y., and Yu, P.: Estimation and spatio-temporal analysis of soil organic carbon stock in China using machine learning algorithms from 2000 to 2014 , 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-41,, 2021.

Jie Hu, Jingyi Huang, Alfred Hartemink, and Ankur Desai

Previous studies of long-term soil change have been focusing on the impacts of climate and land-use change, while neglecting the impacts of soil taxonomy on soil’s response to vegetational and human disturbance. In this study, a spatial-temporal framework was used to study the change in soil organic carbon (SOC) across National Ecological Observatory Network (NEON), USA over 30 years. We hypothesize that: 1) on the continental scale, the hot-spots and cold-spots of SOC change vary with soil orders across different eco-climatic domains, controlled by all soil forming factors that affect carbon input and output; 2) within the same eco-climatic regime, the effects of disturbance on SOC change are controlled by physical and biogeochemical processes, represented by varying soil properties including clay, bulk density, pH, and CEC. To separate the effects of disturbance under different land-use scenarios on SOC change, space-for-time substitution was used in combination with the Continuous Change Detection and Classification algorithm and structural equation models. Results suggested that 1) under natural vegetation, Ultisols, Spodosols, and Inceptisols showed a large SOC accumulation especially in the eastern coast, while Inceptisols, Andisols, and Aridisols in the western US showed a large SOC loss; 2) compared with the same reference soils under natural vegetation, Mollisols and Alfisols showed a large SOC decrease due to human disturbance (e.g., farming and grazing); 3) Inceptisols (+6.2 g/kg) and Gelisols (+27.5 g/kg) in Alaska presented the largest SOC increase among all the soil orders within the subsoil (B horizon); 4) clay content and pH were the most dominant factors that affected SOC content across the NEON sites. This empirical analysis of the 30-years SOC change across eco-climatic regimes could be used for ecosystem modelers to benchmark the models across biomes and study the physical and biogeochemical controls on SOC change under different land management scenarios.

How to cite: Hu, J., Huang, J., Hartemink, A., and Desai, A.: A climate-soil-vegetation-human interaction analysis for SOC change monitoring over 30 years across the National Ecological Observatory Network, USA, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-58,, 2021.

Christian Dold, Herbst Michael, Weihermüller Lutz, and Vereecken Harry

The limitation of global warming to +1.5°C compared to preindustrial levels requires net-zero CO2 emissions globally by mid-century and substantial removal of CO2 thereafter. Carbon sequestration in agricultural soils has been proposed as a potential mitigation strategy. Aim of this study is to quantify current carbon storage and emission reduction potential in agricultural soils, and assess the impact of mitigation measures in a prognostic modeling approach. The land surface model Community Land Model 5.0 (CLM) is used to assess soil carbon changes in agricultural soils in Germany. The simulation domain was set up with an 8 x 8 km grid across Germany using recent land use and soil texture maps, and parameters for major field crops. The model was spun up for ~1500 years with a 30-year climate dataset. Preliminary results show that spinup-derived organic carbon density (OCD, 0-188 cm) was significantly related to Soil Grid v2 OCD (R2 = 0.82), but only weakly related to field-measured OCD (R2 = 0.21). The simulated OCD values in the upper 32 cm soil layer were lower in Northwestern Germany compared to Soil Grids. This is probably due to the intensive use of organic amendment application in the region, and CLM5 lacks a subroutine for simulating organic carbon application. In a next step, carbon storage for different climate projections (regional EUR11 RCP2.6 and RCP8.5 scenarios) and management systems from 2020 - 2100 will be investigated. We will present preliminary results and discuss improvements of CLM5 to better represent agricultural soils.

How to cite: Dold, C., Michael, H., Lutz, W., and Harry, V.: Carbon sequestration in agricultural soils as potential climate change mitigation strategy for Germany, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-86,, 2021.

Stefan Gerber and E.N.Jack Brookshire

Anaerobic microsites in soils are critical features in the Earth system as they are prime locations for generating powerful greenhouse gases. These processes occur in hot spots and hot moments and are therefore difficult to capture in mean-field approaches. Typically, they are captured as empirical functions of soil moisture.

We present a mechanistic upscaling of microsites from single soil particles to the soil column, by considering existing formulations that link the processes of solute diffusion, pore sizes and particle size distributions, and water retention. The upscaling allows to predict probability density functions of volume and surface area of anaerobic microsites, which can then be integrated to the scale of a laboratory soil sample or a field site. Our goal was to make these predictions based on variables typically measured in soils and are routine diagnostic or prognostic variables in Earth system model. While the detailed expressions can only be solved numerically, we found closed-form solutions with little loss of accuracy.  Our result have the necessary hooks for direct implementation of anaerobic microbial carbon processing, methane production and nitrification-denitrification processes in Earth System models. A first application yields two soil moisture-CO2 efflux hypotheses that could potentially be tested and which set this upscaling apart from empirical formulations 1) the degree of temperature sensitivity and dependence of carbon concentration in anaerobicity and 2) different CO2 response to soil moisture if measured in laboratory jars vs. measured in the field.


How to cite: Gerber, S. and Brookshire, E. N. J.: Predicting Anaerobic Microsites in Soils, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-88,, 2021.

Mingxi Zhang and Raphael Viscarra Rossel

Rangelands in Australia are vast and occupy more than 80% of the continental land area. They extend across arid, semi-arid, and the tropical regions with seasonal, variable rainfall in the north. They include diverse, relatively undisturbed grasslands, shrublands, woodlands and tropical savanna ecosystems. They represent Australia’s largest terrestrial carbon sink as they account for almost 70% of Australia's total soil organic carbon stock (Viscarra Rossel et al., 2014), more than all above-ground sources of carbon (native grasses, trees and shrubs) in these regions (Gifford et al., 1992). Here we have developed a novel space-time approach for projecting the long-term C dynamics of rangelands soils using Long Short-Term Memory (LSTM) deep learning neural networks. We further demonstrate how the networks might be interpreted and quantified the influence of explanatory variables on the spatiotemporal dynamics of soil C in these regions. Our results provide an improved ability to accurately model long-term carbon dynamics, which is needed to confidently predict changes in soil C from change in climate or anthropogenic disturbance. The information is critical for improving our understanding of soil C in these regions and for understanding the potential for sequestering C in the rangelands.

How to cite: Zhang, M. and Viscarra Rossel, R.: Projecting long-term soil organic dynamics in Australia's rangelands, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-92,, 2021.

Simulation of soil carbon dynamics in Australia with {\sc Roth C}
Raphael Viscarra Rossel, Juhwan Lee, Mingxi Zhang, Zhongkui Luo, and YingPing Wang