Spatiotemporal dynamics of active root zone storage revealed from hybrid machine learning

Water within the root zone regulates water, energy, and carbon exchanges at the land-atmosphere interface, yet the dynamically active portion of subsurface water that ecosystems mobilize for evapotranspiration (ET) remains unclear, because it represents only part of the total subsurface storage. To address this gap, we develop a catchment-scale, ecosystem-oriented hybrid machine learning model that integrates a parsimonious ecohydrological structure with data-driven flexibility to infer time-varying active root zone storage (a S rz ) as a latent state variable. Without prescribing rooting depth or soil layers, the model learns adaptive water partitioning from multi-source observations of ET, runoff, snow, and terrestrial water storage, enabling diagnosis of both dynamic a S rz and its realized capacity. Applied across hundreds of U.S. catchments over the period 2001-2020, the framework reveals coherent spatial and hydroclimatic patterns, with a S rz peaking in transitional regions where atmospheric demand and water supply are balanced. The realized capacity reflects how belowground water access varies with long-term hydroclimatic and substrate constraints. We further show that defining storage as a realized quantity through system dynamics improves stability and interpretability under varying observational and structural conditions. Overall, this study demonstrates how hybrid modeling can be used to diagnose ecosystem-accessible water storage as a latent state, providing a physically consistent and observation-informed pathway to quantify belowground processes that remain inaccessible to direct measurement and control key land-atmosphere interactions.