,,,,- University of Turin, Interuniversity Department of Regional and Urban Studies and Planning (DIST), Torino, Italy (aurora.olivero@unito.it)
Effective drought monitoring in agricultural systems requires accurate estimation of root zone soil moisture to assess crop water stress and optimize irrigation decisions, yet translating continuous satellite-derived surface soil moisture into root zone dynamics remains a significant challenge.
This study presents muSEC (multilayer Surface Evaporative Capacitor), a physically based model developed from the recently proposed Surface Evaporative Capacitor (SEC) framework. muSEC links surface observations to deeper soil layers during drydown periods through a two-stage evaporation formulation and simplified vertical redistribution scheme, maintaining physical parameters across different soil types.
Spatial variability was assessed by evaluating the model across sites with contrasting soil textures and land uses, combining Time Domain Reflectometry and Cosmic Rays in situ measurements with NASA SMAP satellite retrievals. The latter provide high temporal resolution and show strong correlation with ground observations. When compared against models of varying complexity, muSEC demonstrated robust performance in reproducing soil moisture dynamics at multiple depths, thereby confirming its potential to predict agricultural water availability and drought conditions from satellite-derived surface observations.
This model framework enables deeper root-zone drought forecasting from readily available satellite surface observations, thus supporting the development of effective early warning systems and improved irrigation management in water-scarce agricultural regions.
This work is part of the NODES project, which has received funding from the Italian Ministry of University and Research (MUR) under the PNRR – M4C2, Investment 1.5 (grant no. ECS00000036). Additional support was provided by the PRIN 2022 Project SUNSET (grant no. 202295PFKP) and by the 2021 Funding Programme of Fondazione CRT (grants no. 2022.0998, 2023.0369, and 2025.0780).
How to cite: Olivero, A., Martini, T., Gentile, A., Gisolo, D., Canone, D., and Ferraris, S.: From Near-Surface to Root-Zone Soil Water Losses: A Physically Based Model for Drought Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7941, https://doi.org/10.5194/egusphere-egu26-7941, 2026.
