ISMC2021-9

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, https://doi.org/10.5194/ismc2021-20, 2021.

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, https://doi.org/10.5194/ismc2021-36, 2021.

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, https://doi.org/10.5194/ismc2021-41, 2021.

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, https://doi.org/10.5194/ismc2021-58, 2021.

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, https://doi.org/10.5194/ismc2021-86, 2021.

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, https://doi.org/10.5194/ismc2021-88, 2021.

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, https://doi.org/10.5194/ismc2021-92, 2021.