Evaporation Loss From Small Agricultural Reservoirs in a Warming Climate: An Overlooked Component of Water Accounting
Bibliographic record
Abstract
Abstract Small agricultural reservoirs support water demands during dry spells. However, evaporative losses that are often overlooked in water accounting and management diminish the storage efficiency of these popular but un‐inventoried resources. We developed a predictive framework to identify the spatio‐temporal extent of small reservoirs (900–100,000 m 2 ) and quantify their evaporative losses using a physically‐based model. Focusing on water‐stressed regions of Europe (Italy, Spain, and Portugal), our results indicate that the total number and cumulative area of small reservoirs in drier areas of Europe almost doubled in two decades from about 6,200 reservoirs with the cumulative area of 46 km 2 in 2,000 to 11,800 reservoirs with the cumulative area of 93.5 km 2 in 2020. We observed climate‐driven trends in the expansion of agricultural reservoirs and their evaporative losses which exceeded 72 million cubic meters during warm months (April to September) accounting for 38% of their total storage capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".