Earth observation constrained calibration improves soil moisture drought representation: a multi-model analysis in the Rhine River basin

作者：Ehsan Modiri, Oldřich Rakovec, Pallav Kumar Shrestha, Almudena García-García, Leandro Avila, Katie Blackford, Elizabeth Cooper, Bram Droppers, Paolo Filippucci, Milan Fischer, Matěj Orság, Pietro Stradiotti, Luca Brocca, Douglas B. Clark, Wouter Dorigo, Stefan Kollet, Jian Peng, Niko Wanders, Luis Samaniego · 年份：2026 · DOI：10.5194/egusphere-2026-1012 · 研究领域：Soil Moisture and Remote Sensing、Hydrology and Drought Analysis、Hydrology and Watershed Management Studies

Abstract. Accurate characterisation of soil moisture drought is essential for operational water management and early warning systems. Yet, hydrological model simulations of drought often diverge substantially, even when forced with identical meteorological inputs. This study assesses the extent to which Earth Observation (EO) data can constrain model calibration and influence multi-model drought representation in the Rhine basin. Four hydrological and land-surface models (CLM, JULES, mHM, PCR-GLOBWB), simulated at ~1 km resolution, were calibrated using three strategies: (1 – baseline) discharge-only, (2 – EO-only) using satellite soil moisture (SM), evapotranspiration (ET), or both, and (3 – hybrid) calibration integrating discharge and EO constraints. Simulations were evaluated against the ESA CCI Soil Moisture satellite product (v9.1 COMBINED) as a quasi-independent large-scale benchmark and against in-situ observations from the International Soil Moisture Network (ISMN) as a site-scale temporal reference. To ensure comparability, all datasets were transformed into quantile-based Soil Moisture Index (SMI), and model outputs were aggregated to the 0.25° ESA CCI grid for spatial comparison. Across three major drought events (2015, 2018, 2019), EO-only calibration increased inter-model spatial agreement, quantified using the Inter-Model Agreement Index (IMAI), from 0.648±0.087 (baseline) to 0.663±0.039, while reducing event-to-event variability in agreement. Hybrid calibratio...