MODELING SPATIOTEMPORAL VARIABILITY OF CARBON STOCKS (REVIEW)

Abstract

To mitigate the effects of climate change and preserve and enhance soil fertility, considerable attention is being paid to the development of effective land management methods. 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 uses empirical models obtained using digital soil mapping methods. The second direction represents process-based models of the biogeochemical carbon cycle. Each 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 the training data. They demonstrate the superiority of process-based models in predicting the spatial distribution of soil organic carbon stocks in cases where large, high-quality data sets were used. The advantage of biogeochemical models is that they are based on accumulated soil science knowledge of the processes that determine the carbon cycle, making them effective in studying the mechanisms of soil carbon stock dynamics. However, forecasts using these models, especially at regional and global scales, are characterized by high uncertainty. Currently, ensemble modeling (the integration of various artificial intelligence algorithms used in digital soil mapping with process-based models) is proposed to reduce 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 sources of uncertainty in biogeochemical models and the potential use of minimal models to inform the selection of the required set of key processes and mathematical formalisms for their explicit description in models of different spatiotemporal scales. This can reduce the structural uncertainty of nonlinear biogeochemical models.

References

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