1,1,2,1,3,4,6,7,8,9,8,6,10,3,4,7,1,11- 11. Helmholtz Centre for Environmental Research – UFZ, Department of Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
- 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
- 3Helmholtz Centre for Environmental Research – UFZ, Department of Remote Sensing, Permoserstrasse 15, 04318 Leipzig, Germany
- 4Institute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany
- 5Institute of Bio- and Geosciences Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
- 6UK Centre for Ecology and Hydrology, Wallingford, United Kingdom
- 7Department of Physical Geography, Utrecht University, P.O. Box 80.115, 3508 TC, Utrecht, The Netherlands
- 8National Research Council of Italy, Research Institute for Geo-Hydrological Protection, Perugia, Italy
- 9Global Change Research Institute CAS, Brno, Czech Republic
- 10Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
- 11Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany
Reliable characterisation of soil-moisture drought is critical for water management, yet hydrological models can diverge substantially because of parametric uncertainty [1] even when forced with identical meteorology. This work is conducted within the ESA 4DHydro initiative (https://4dhydro.eu/) and builds on our EO-constrained parameter estimation framework [2]. We assess whether Earth Observation (EO) data reduce this divergence using a four-model ensemble (CLM, JULES, mHM, PCR-GLOBWB) over the Rhine Basin. We compare three parameter estimation strategies: (i) a non-EO baseline using default model configurations, (ii) EO-only calibration using satellite soil moisture (SM) and evapotranspiration (ET), and (iii) a hybrid EO+Q calibration combining EO constraints with streamflow (Q).
The latter ensures both spatial pattern matching of EO-derived SM, ET, and water balance closure. For the major droughts of 2015, 2018, and 2019, EO-only calibration notably reduces inter-model spread and strengthens the detection of extreme dry conditions, improving ensemble agreement by up to ~0.09 in extreme-event cases. Joint SM+ET calibration provides the best trade-off between sensitivity to extremes and ensemble stability across models.
The EO+Q strategy yields the highest temporal skill, including station-scale improvements (e.g., RMSE reductions of ~0.02 and correlation gains of ~0.06 in independent validation), but also exposes larger between-model differences, especially in Alpine headwaters where snow and glacier processes remain challenging. Overall, EO constraints can meaningfully tighten multi-model drought estimates, while also highlighting persistent structural uncertainties that should be communicated in operational drought early-warning systems.
References:
[1] Samaniego, L., Kumar, R. and Attinger, S., 2013. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Journal of Hydrology, 476, pp.253–265.
[2] Modiri, E. et al., 2026. Toward improved soil moisture drought representation through Earth Observation constrained parameter estimation: A multi-model ensemble analysis over the Rhine River basin. In submission to HESSD.
How to cite: Modiri, E., Rakovec, O., Shrestha, P. K., García-García, A., Avila, L., Blackford, K., Cooper, E., Droppers, B., Filippucci, P., Fischer, M., Orság, M., Stradiotti, P., Brocca, L., Clark, D., Dorigo, W., Kollet, S., Peng, J., Wanders, N., and Samaniego, L.: Added Value of Earth Observation Constraints for Multi-Model Drought Detection in the Rhine Basin , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9592, https://doi.org/10.5194/egusphere-egu26-9592, 2026.
