Najafi et al. (2026) High-resolution soil moisture forecasts for drought early warning in Germany

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Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-10-13
• Authors: Husain Najafi, Friedrich Boeing, Stephan Thober, Oldřich Rakovec, Pallav Kumar Shrestha, Matthias Kelbling, Ute Weber, Toni Harzendorf, Luis Samaniego
• DOI: 10.1088/1748-9326/aeb369

Research Groups

• Hydrological Sub-seasonal Forecasting System (HS2S) team
• European Centre for Medium-Range Weather Forecasts (ECMWF)
• German Drought Monitor (GDM) team

Short Summary

This study evaluates the skill of daily soil moisture forecasts at lead times of 1–2 weeks driven by real-time meteorological ensembles using the Hydrological Sub-seasonal Forecasting System (HS2S). The results show that HS2S reproduces drought onset and recovery timing, with positive probabilistic and continuous skill relative to benchmarks.

Objective

• Evaluate the skill of daily soil moisture forecasts at lead times of 1–2 weeks driven by real-time meteorological ensembles using the Hydrological Sub-seasonal Forecasting System (HS2S)

Study Configuration

• Spatial Scale: Germany at 0.015625° resolution
• Temporal Scale: Daily-initialized HS2S configuration evaluated during the exceptionally dry spring and summer of 2025

Methodology and Data

• Models used: Hydrological Sub-seasonal Forecasting System (HS2S) and the mHM framework
• Data sources: Real-time forecast ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF), observation-based meteorological forcing, and initialized two-state Markov persistence benchmark

Main Results

• HS2S reproduced the timing and mean magnitude of GDM-indicated drought onset and recovery
• Positive probabilistic skill relative to the initialized Markov benchmark at both lead times
• Positive continuous skill relative to the mHM climatology at both lead times
• Improved critical success index for moderate drought (SMI < 0.20) by 15.0 and 15.5 percentage points at lead weeks 1 and 2, respectively

Contributions

The study highlights the potential of high-resolution probabilistic soil-moisture forecasts to complement national drought monitoring with forward-looking information.

Funding

• This research was funded by [project/program name] (reference code: [code])

Citation

@article{Najafi2026Highresolution,
  author = {Najafi, Husain and Boeing, Friedrich and Thober, Stephan and Rakovec, Oldřich and Shrestha, Pallav Kumar and Kelbling, Matthias and Weber, Ute and Harzendorf, Toni and Samaniego, Luis},
  title = {High-resolution soil moisture forecasts for drought early warning in Germany},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/aeb369},
  url = {https://doi.org/10.1088/1748-9326/aeb369}
}