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Soil organic carbon modeling : estimating carbon input changes required to reach policy objectives aimed at increasing soil organic carbon stocks

Abstract : Anthropogenic greenhouse gases (GHGs) emissions are causing irreversible climate change. To address this issue, the European Union (EU) committed to strong decreases in GHGs emissions. However, to reach carbon (C) neutrality by 2050 it will also be necessary to implement atmospheric C removals by natural sinks, such as soils. To partially compensate for CO2 emissions, the 4 per 1000 initiative of 2015 proposed an annual 4‰ soil organic carbon (SOC) stock increase in the first 30-40 cm depth of the soil. Yet, the feasibility of such an ambitious target is still under debate because it may require substantial and rapid changes in agricultural practices that would be hard to achieve. The most efficient way to increase SOC stocks is to increase the C input to the soil. Pro-cess-based biogeochemical models can simulate the dynamics of SOC and are increasingly used to support decision-makers on SOC mitigation policies. Despite the numerous models available to describe the SOC dynamics, simulations are still somewhat unreliable. This is because uncertainties not only derive from the mechanistic structure of the models and the processes included, but also from the input data and the parameter values used.The objective of this thesis is to estimate the C input required to yearly increase SOC stocks by 4‰ in European croplands. To solve this problem, we build an inverse modeling approach and apply it to a multi-model ensemble to assess the uncertainties of the estimations according to different representations of the SOC dynamics. Then, to improve the simulation of SOC stocks, we test a new, statistically derived, parametrization technique.As a first attempt to provide insights for policymakers, we generate maps of the C input required to reach the 4‰ target in the whole European cropland area, under two scenarios of climate change. Our study demonstrates that there are substantial uncertainties around the C input required to reach a 4‰ target. However, a general pattern emerges at the European cropland scale, where the 4‰ target seems feasible under future scenarios of climate change, only assuming drastic increases of C input to the soil. In particular, higher C input is required in Northern Europe, while higher uncertainties are associated with the European South. The high variability of the simulated C input requirements highlights the advantage of using multi-model ensembles, in order to consider the range of uncertainty linked to their different mechanistic structures. Yet, multi-model ensembles still tend to underestimate the C input required to increase SOC stocks. Major efforts should be made to improve model simulations, especially to capture the effect of additional C input on the accumulation of SOC. At a local scale, the calibration of model parameters was necessary to fit observed SOC stock variations. When long-term SOC stock monitoring is not available, the necessity for improved parametrization techniques emerges. The calibration that we proposed at the European scale improved the simulation of first-year SOC stocks. However, it increased the divergence of predicted SOC stocks across models. Future work should focus on the reduction of model uncertainties in order to provide reliable predictions of future SOC stock variations and their related processes.
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  • HAL Id : tel-03650802, version 1

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Elisa Bruni. Soil organic carbon modeling : estimating carbon input changes required to reach policy objectives aimed at increasing soil organic carbon stocks. Ecology, environment. Université Paris-Saclay, 2022. English. ⟨NNT : 2022UPASB012⟩. ⟨tel-03650802⟩

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