Detecting the resilience of soil moisture dynamics to drought periods as a function of soil type and climatic region

Le résumé fourni par la source

Abrupt changes in climatic conditions and land management can cause permanent shifts in the soil hydraulic response to climatic inputs, impacting soil functions and established soil–climate interactions. To quantify the resilience of soil water content dynamics after abrupt changes in environmental conditions, we present a modelling framework that combines a neural network with seasonal trend analysis (STL). Using data from a series of lysimeters within the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network, we identified changes in the response of soil water content after an extremely hot and dry summer in Germany in 2018. The model incorporates meteorological variables decomposed into seasonal and long-term components, together with a categorical indicator of current moisture conditions. It was trained on data from a reference site with a stable soil water content response and applied to lysimeters from multiple origins exposed to contrasting climates. By analysing annual residual patterns – particularly mean bias over time – the soil water content dynamics are classified as being in a “stable”, “resilient”, or “changed” state, reflecting whether the system maintains, recovers, or diverges from its original state. We found that soils preserved their response function to environmental forcing under typical conditions but exhibited changes in hydraulic behaviour when relocated to new environments, even when soil texture remained constant. The proposed method offers a scalable and non-invasive tool for tracking changes in soil water content response to climate change and provides early indicators of changes in essential soil functions and soil health status.

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