In recent years, alternating drought and extreme precipitation events have highlighted the need for subseasonal to seasonal forecasts of the terrestrial water cycle. In particular, predictions of the impacts of dry and wet extremes on subsurface water resources are crucial to provide stakeholders in agriculture, forestry, the water sector, and other fields with information supporting the sustainable use of these resources.
In this context, we release an experimental Water Resources Bulletin (https://adapter-projekt.de/bulletin/index.html) four times per year, offering probabilistic forecasts of the total subsurface water storage (TSS) anomaly at a 0.6 km resolution, from the surface down to 60 m depth, for the upcoming seven months across Germany. These seasonal forecasts are generated using the integrated, physics-based hydrological model ParFlow/CLM, forced by 50 ensemble members of the SEAS5 seasonal forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF).
To evaluate our forecasts, we evaluated six 7-months probabilistic forecasts covering the vegetation period (March to September) for the years 2018 to 2023 with a reference long-term historical time series based on the same ParFlow/CLM setup. The forecast skill was assessed by comparing these seasonal forecasts to a climatology-based 10-member pseudo-forecast over the 2013–2023 period (using the leave-one-out method), extracted from the reference time series.
The monthly Continuous Ranked Probability Skill Score (CRPSS), which evaluates the ensemble distribution based on daily TSS data, indicates that the probabilistic forecast outperforms the climatology-based pseudo-forecast in most regions, except in 2018 and, to a lesser extent, in 2020 and 2022. This can be attributed to an under-representation of extremely dry members in the ensemble, combined with the memory effect of the initial conditions at increasing soil depths. For example, while March 2018 started with a slightly above-average TSS and experienced a strong meteorological drought leading to an agricultural drought, the initial TSS anomaly in March 2019 was already negative, with a less pronounced precipitation deficit during the vegetation period. This resulted in a much higher forecast skill, because of the memory effect accurately simulated with the physics-based model. Notably, the forecast skill only slightly decreases with increasing lead time, both for precipitation and TSS.
The analysis of the Relative Operating Characteristic Skill Score (ROCSS) for the lower quintile of the TSS distribution assesses whether negative TSS anomalies (i.e., droughts) are adequately represented within the probabilistic forecast ensemble. The results are consistent with those of the CRPSS, showing lower skill in 2018. Nevertheless, the ROCSS analysis overall indicates moderate to high skill for the probabilistic forecast, while the climatology-based pseudo-forecast demonstrates no skill. This confirms that the dry conditions experienced in central Europe in recent years were captured within the probabilistic forecast, underlining the added value of these forecasts and their usefulness in the experimental Water Resources Bulletin.