The Spatial Dynamics of Droughts and Water Scarcity in England and Wales
Bibliographic record
Abstract
Abstract Water scarcity occurs when water demand exceeds natural water availability over a range of spatial and temporal scales. Though meteorological and hydrological droughts have been analyzed over large spatial scales, the impacts of water scarcity have typically been addressed at a catchment scale. Here we explore how droughts and water scarcity interact over a larger and more complex spatial domain, by combining climate, hydrological, and water resource system models at a national scale across England and Wales. This approach is essential in a highly connected and heterogeneous region like England and Wales, where we represent 80 different catchments; 70 different water resource zones; 16 water utility companies; and the water supply for over 50 million people. We find that if a reservoir's storage is in its first percentile (i.e., the volume that is exceeded 99% of the time), then there is, on average, a 40% chance that reservoirs in neighboring catchments will also be at or below their first percentile storage volume. The coincidence of low reservoir storage decays relatively quickly, stabilizing after about 100–150 km, implying that if interbasin transfers are to be provided to enhance drought resilience, they will need to be at least this length. Based on a large ensemble of future climate simulations, we show that extreme droughts in precipitation, streamflow, and reservoir storage volume are projected to worsen in every catchment. The probability of a year with water use restrictions doubles by 2050 and is four times worse by 2100.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".