An ensemble groundwater prediction (EGP) system to forecast groundwater levels in alluvial aquifers in Switzerland
Bibliographic record
Abstract
Abstract. Groundwater is a key source of freshwater for drinking water supply and agricultural irrigation on a global scale. Groundwater in Switzerland (and beyond) is traditionally regarded as a reliable source of freshwater. Recent extreme drought events (i.e., in 2018, 2020, and 2022) have shown, however, that groundwater does respond to these events and can cause problems in water supply and groundwater availability. With hydrological extremes becoming more frequent, there is a growing need for early warning systems and improved forecasting. This study develops and tests a scalable ensemble groundwater prediction (EGP) system with a 32-day lead time. The system combines extended-range precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with the lumped-parameter groundwater model Pastas. Forecasts were evaluated at six monitoring wells across Switzerland, representing diverse hydrogeological settings, and compared against naive persistence and climatology benchmarks. Results indicate that the EGP system produces skillful forecasts up to one month ahead, with Spearman correlations exceeding 0.77 for most wells. However, the required model–data complexity varies: in long-memory aquifers, forecasts driven by recent meteorology and climatology are sufficient, while in short-memory systems, meteorological forecast data adds clear value. Forecast skill in mountainous regions (e.g., Davos) remains limited due to difficulties in predicting local meteorology. These findings highlight both the potential and the limitations of short-term groundwater forecasting. Future work should explore larger lead times, particularly in slow-responding aquifers, and investigate methods to improve forecasts in alpine environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".