Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1088/1748-9326/aeb369 |
Licence  |
|
| Title (Primary) | High-resolution soil moisture forecasts for drought early warning in Germany |
| Author | Najafi, H.; Boeing, F.
; Thober, S.
; Rakovec, O.; Shrestha, P.K.; Kelbling, M.; Weber, U.; Harzendorf, T.; Samaniego, L.
|
| Source Titel | Environmental Research Letters |
| Year | 2026 |
| Department | CHS; WKDV |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.48758/ufz.17383 https://doi.org/10.48758/ufz.17404 |
| Keywords | Drought early warning; soil moisture forecasting; probabilistic forecasting; ensemble hydrological forecasting; subseasonal forecasting; drought monitoring; forecast verification |
| 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. |
| Najafi, H., Boeing, F., Thober, S., Rakovec, O., Shrestha, P.K., Kelbling, M., Weber, U., Harzendorf, T., Samaniego, L. (2026): High-resolution soil moisture forecasts for drought early warning in Germany Environ. Res. Lett. 10.1088/1748-9326/aeb369 |
|