Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1029/2025WR042803 |
Licence  |
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| Title (Primary) | Spatiotemporal dynamics of active root zone storage revealed from hybrid machine learning |
| Author | Blougouras, G.; Jiang, S.; Brenning, A.; Migliavacca, M.; Slater, L.; Zhou, J.; Kumar, R.
; Tian, L.; Wang, C.; Reichstein, M. |
| Source Titel | Water Resources Research |
| Year | 2026 |
| Department | CHS |
| Volume | 62 |
| Issue | 10 |
| Page From | e2025WR042803 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.18160/5NZG-JMJE https://doi.org/10.5067/TEMSC-3MJ634 https://doi.org/10.5067/0GGPB220EX6A https://doi.org/10.5281/ZENODO.14056079 https://doi.org/10.5281/ZENODO.15529786 https://doi.org/10.5281/zenodo.23047044 https://doi.org/10.5281/zenodo.8281930 |
| Supplements | Supplement 1 |
| Keywords | hybrid modeling; soil moisture; root zone soil moisture; interpretability; ecohydrology; land surface modeling |
| Abstract | 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 (aSrz) 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 aSrz 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 aSrz 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. |
| Blougouras, G., Jiang, S., Brenning, A., Migliavacca, M., Slater, L., Zhou, J., Kumar, R., Tian, L., Wang, C., Reichstein, M. (2026): Spatiotemporal dynamics of active root zone storage revealed from hybrid machine learning Water Resour. Res. 62 (10), e2025WR042803 10.1029/2025WR042803 |
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