High-resolution soil moisture forecasts for drought early warning in Germany
5 affiliations
- 1Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
- 2Helmholtz-Centre for Environmental Research - UFZ
- 3Helmholtz-Zentrum fur Umweltforschung UFZ
- 4Czech University of Life Sciences Prague Faculty of Environmental Sciences
- 5UFZ Umweltforschungszentrum Leipzig-Halle
Abstract
Drought early warning increasingly relies on high-resolution monitoring, but the skill of daily soil moisture forecasts at lead times of 1–2 weeks driven by real-time meteorological ensembles has rarely been evaluated. The Hydrological Sub-seasonal Forecasting System (HS2S) extends an established national drought-monitoring framework into high-resolution probabilistic soil moisture forecasts up to two weeks ahead for Germany at 0.015625° resolution. We evaluated a daily-initialized HS2S configuration using the 51-member real-time forecast ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) during the exceptionally dry spring and summer of 2025 against the operational, model-based German Drought Monitor (GDM), generated within the same mHM framework using observation-based meteorological forcing. The assessment combined drought onset/recovery transition diagnostics with probabilistic (Brier skill score) and categorical verification against an initialized two-state Markov persistence benchmark, and continuous verification against an mHM soil-moisture climatology. HS2S reproduced the timing and mean magnitude of GDM-indicated drought onset and recovery. It showed positive probabilistic skill relative to the initialized Markov benchmark and positive continuous skill relative to the mHM climatology at both lead times, while improving the critical success index for moderate drought (SMI < 0.20) by 15.0 and 15.5 percentage points at lead weeks 1 and 2, respectively. Given HS2S and the GDM share the same mHM–SMI framework and initial states, the verification is model-consistent and evaluates skill in anticipating the GDM-indicated drought state rather than validating absolute soil-moisture accuracy. The added value of real-time meteorological forecasts relative to historical meteorological sequences was not assessed. Within this operationally relevant framework, HS2S showed forecast skill up to two weeks ahead during the 2025 drought, highlighting the potential of high-resolution probabilistic soil-moisture forecasts to complement national drought monitoring with forward-looking information.
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BibTeX
@article{najafi2026highresolution,
title = {{High-resolution soil moisture forecasts for drought early warning in Germany}},
author = {Husain Najafi and Friedrich Boeing and Stephan Thober and Oldrich Rakovec and Pallav K Shrestha and Matthias Kelbling and Ute Weber and Toni Harzendorf and Luis Samaniego},
journal = {Environmental Research Letters},
year = {2026},
month = oct,
doi = {10.1088/1748-9326/aeb369},
url = {https://iopscience.iop.org/article/10.1088/1748-9326/aeb369},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - High-resolution soil moisture forecasts for drought early warning in Germany
AU - Husain Najafi
AU - Friedrich Boeing
AU - Stephan Thober
AU - Oldrich Rakovec
AU - Pallav K Shrestha
AU - Matthias Kelbling
AU - Ute Weber
AU - Toni Harzendorf
AU - Luis Samaniego
PY - 2026
DA - 2026/10/13
JO - Environmental Research Letters
DO - 10.1088/1748-9326/aeb369
UR - https://iopscience.iop.org/article/10.1088/1748-9326/aeb369
AB - Drought early warning increasingly relies on high-resolution monitoring, but the skill of daily soil moisture forecasts at lead times of 1–2 weeks driven by real-time meteorological ensembles has rarely been evaluated. The Hydrological Sub-seasonal Forecasting System (HS2S) extends an established national drought-monitoring framework into high-resolution probabilistic soil moisture forecasts up to two weeks ahead for Germany at 0.015625° resolution. We evaluated a daily-initialized HS2S configuration using the 51-member real-time forecast ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) during the exceptionally dry spring and summer of 2025 against the operational, model-based German Drought Monitor (GDM), generated within the same mHM framework using observation-based meteorological forcing. The assessment combined drought onset/recovery transition diagnostics with probabilistic (Brier skill score) and categorical verification against an initialized two-state Markov persistence benchmark, and continuous verification against an mHM soil-moisture climatology. HS2S reproduced the timing and mean magnitude of GDM-indicated drought onset and recovery. It showed positive probabilistic skill relative to the initialized Markov benchmark and positive continuous skill relative to the mHM climatology at both lead times, while improving the critical success index for moderate drought (SMI < 0.20) by 15.0 and 15.5 percentage points at lead weeks 1 and 2, respectively. Given HS2S and the GDM share the same mHM–SMI framework and initial states, the verification is model-consistent and evaluates skill in anticipating the GDM-indicated drought state rather than validating absolute soil-moisture accuracy. The added value of real-time meteorological forecasts relative to historical meteorological sequences was not assessed. Within this operationally relevant framework, HS2S showed forecast skill up to two weeks ahead during the 2025 drought, highlighting the potential of high-resolution probabilistic soil-moisture forecasts to complement national drought monitoring with forward-looking information.
SN - 1748-9326
ER -