Improving drought forecasting to support decision‐making

Abstract

This work integrates new satellite observations into the Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model via data assimilation. This improves our understanding of the impact of droughts on forests and agricultural land at global and regional scales. First, innovative artificial intelligence methods are employed to generate observation operators, enabling ISBA to simulate satellite microwave radiances ranging from the L to X bands, as well as sun‐induced fluorescence (SIF) observations. Second, these observations are assimilated into ISBA sequentially. Examples of sequential data assimilation are presented for SIF and C‐band backscatter observations derived from the tropospheric monitoring instrument and the advanced scatterometer on board the Sentinel‐5P and MetOp satellites respectively. We also demonstrate how machine learning can be used to derive new variables from those calculated by the land surface model in order to monitor natural disasters related to drought, as well as to characterise the regional exposure to drought‐related hazards. We present a prototype regional application for monitoring and forecasting clay shrink–swell and wildfire danger in France.