Global vegetation responses to wet and dry soil moisture extremes

Abstract

Hydrological extremes are continuing to intensify under climate change. However, the responses of vegetation to dry and wet soil moisture extremes, and the dominant mechanisms of these responses, have not yet been analysed consistently. In this study, we utilized long-term observations of Normalized Difference Vegetation Index (NDVI) as a proxy of vegetation responses to soil moisture extremes. We then analysed related predictors with a machine-learning attribution approach to assess the role of pre-extreme vegetation conditions, characteristics of extremes, and of the environmental background. Vegetation generally loses greenness during dry extremes, indicated by widespread and consistent negative NDVI anomalies. This is mainly modulated by the characteristics of the extreme (especially seasonal timing) and pre-extreme vegetation conditions, which reflect varying vegetation vulnerability. In contrast, wet extremes lead to more heterogeneous responses, including both positive and negative NDVI anomalies. Negative vegetation responses during wet extremes are most consistently associated with pre-extreme vegetation conditions, while environmental background variables such as climate (e.g., long-term mean air temperature, aridity) and topographic variability show comparatively stronger relevance than for dry extremes. Extreme characteristics also contribute substantially, though with greater regional variability than for dry extremes. This illustrates that vegetation response to wet extremes is complex and potentially influenced by different processes. Further, vegetation stress can occur even under relatively less severe extremes when responses are strongly modulated by environmental background conditions, indicating localized vulnerability arising from adverse climatic, soil, or topographic conditions. These results highlight the roles of seasonal timing and of environmental background conditions for impacts of soil moisture extremes on vegetation. This clarifies the predictability of ecosystem responses to hydrological extremes and serves as a basis for related management planning.