Impact of compound soil heat and drought on terrestrial vegetation productivity

Compound climate extremes are projected to increase in both intensity and frequency under future climate scenarios. While atmospheric heatwaves are well-documented, recent evidence suggests that soil temperature extremes can be more persistent and prominent than air temperature, leading to devastating compound soil heat and drought (CSHD) events. Despite their potential severity, our understanding of how these soil compound extremes impact terrestrial vegetation productivity remains limited.

This study utilizes high-frequency datasets from a global network of eddy covariance towers—including AmeriFlux, FLUXNET-2015, ICOS, and JapanFlux—to quantify the impact of CSHD on Net Ecosystem Productivity (NEP). We employ a data-driven machine learning framework to isolate and estimate the productivity losses specifically attributable to the compound soil heat and drought stress. We move beyond and use an explainable machine learning approach (XAI) to identify the primary drivers and reveal the non-linear sensitivities of various ecosystems to these extremes. Our findings provide critical insights into the resilience of the terrestrial biosphere and improve our ability to predict ecosystem responses to increasingly complex climate stressors.