Modeling of Spatiotemporal Variability of Carbon Stocks (Review)

Considerable attention is now paid to the development of effective land management methods to mitigate the effects of climate change and preserve and increase soil fertility. Reliable forecasts of soil responses to future climate change and anthropogenic impacts are essential for selecting optimal management strategies. Mathematical modeling is a leading method for analyzing and predicting the spatiotemporal variability of soil organic carbon stocks. It is currently being developed in two main directions. The first one uses empirical models obtained by digital soil mapping methods, and the second one represents process-based models of the biogeochemical carbon cycle. Each direction has strengths and weaknesses. Digital soil mapping models are empirical, based only on the analysis of data on soil properties and environmental characteristics, and therefore are limited in explaining the spatial variability of soil carbon stocks. The uncertainty of forecasts based on these models depends on the volume and quality of training data. The prediction of the spatial distribution of soil organic carbon stocks made by them is more correct as compared to process-based models in cases, when large high-quality data sets are used. The advantage of biogeochemical models consists in the fact that they are based on accumulated soil science knowledge of the processes that determine the carbon cycle, and this makes them effective in studying the mechanisms of soil carbon stock dynamics. However, forecasts by these models, at regional and global levels in particular, are characterized by high uncertainty. The ensemble modeling (the integration of various algorithms of artificial intelligence used in digital soil mapping with process-based models) is now proposed to reduce the forecast uncertainty. Effective development of this strategy requires a thorough understanding of the role of key biogeochemical processes in soil organic carbon stock dynamics to improve the conceptual foundations of the models and increase confidence in the predictions. This article discusses the factors of uncertainty in biogeochemical models and the potential use of minimal models to substantiate the selection of the required set of key processes and mathematical formalisms for their explicit description in models of different spatiotemporal scales. This may reduce the structural uncertainty of nonlinear biogeochemical models.