Soil moisture regulates energy, water, and carbon exchanges across the land-vegetation-atmosphere interface. Extremes in soil moisture, either dry or wet, can amplify natural hazards and drive compound events, such as drought-heatwave-wildfire chains, clustered storms, flooding, landslides, and vegetation die-offs. This Review synthesizes current understanding of the physical mechanisms linking soil moisture to compound hazards across climatic, meteorological, ecological, and hydrogeomorphic domains. Despite its predictive potential, soil moisture remains underrepresented in operational early warning systems due to data limitations, complex nonlinear interactions, and disciplinary fragmentation. Advancing observations, data assimilation, and physics-informed modeling is essential for improving hazard prediction and preparedness.
Soil moisture acts as a central mediator in Earth system processes. Under dry conditions, reduced evaporation increases sensible heat flux and air temperature, intensifying drought and heatwaves while elevating wildfire risk through lower fuel moisture. Conversely, wet soils can enhance atmospheric instability, promoting mesoscale convective systems and re-intensifying landfalling cyclones by increasing latent heat flux and convective available potential energy. In ecological systems, soil moisture extremes trigger vegetation mortality via hydraulic failure under hot-dry conditions or root anoxia under prolonged flooding. Hydrogeomorphic hazards, landslides and floods are strongly modulated by antecedent soil moisture, which controls soil storage, pore pressure, and slope stability. These processes often interact across categories, forming cascading or spatially compounding events.
Despite clear mechanistic understanding, integrating soil moisture into operational early-warning systems remains limited. Key barriers include: (1) lack of high-resolution, hazard-relevant soil moisture data, especially for root-zone depth and rapid-onset events; (2) incomplete and biased hazard records, particularly in the Global South and remote regions; (3) nonlinear and threshold-dependent soil moisture–hazard relationships that vary with climate, vegetation, and soil properties; (4) model divergence and scale mismatches; and (5) disciplinary fragmentation, where definitions and target variables (volumetric moisture vs. plant-available water) differ across fields. These limitations constrain the accuracy, transferability, and actionability of forecasts.
Advancing compound hazard prediction requires a coordinated roadmap: expanding in situ and remote sensing observations across multiple depths and domains; clarifying threshold and feedback processes through integrated field, laboratory, and modeling studies; developing adaptive, high-resolution, physics-informed modeling frameworks that assimilate real-time soil moisture data; and fostering interdisciplinary collaboration to build interoperable multi-hazard early-warning systems. Emerging approaches like physics-informed machine learning and convergent modeling linking hydrology, atmosphere, ecology, and human activities hold particular promise for capturing complex interactions and reducing vulnerabilities in a changing climate.
Sources:
Nature Geoscience
https://www.nature.com/articles/s41561-026-01936-z .
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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