Watershed‐Scale Runoff Efficiency Response to Climate Variability
Bibliographic record
Abstract
ABSTRACT The fraction of precipitation converted to stream discharge within a watershed, termed as runoff efficiency, may shift as climate changes. Runoff efficiency is known to be temperature‐sensitive in some watersheds, but temperature sensitivity is unquantified in many other watersheds. We identify regions where runoff efficiency is temperature‐sensitive using 942 watersheds, minimally influenced by anthropogenic activity, across the continental United States and Canada. Stepwise regression using historical discharge and climate records shows that runoff efficiency in 10 of 16 hydrologically similar hydro‐regions is sensitive to temperature, expanding the number of locations expected to experience temperature‐driven water stress, particularly in the North American continental interior. Runoff efficiency in all hydro‐regions demonstrates sensitivity to precipitation, but during wet years, runoff efficiency temporarily decreases, likely reflecting increasing groundwater storage. The temporary decrease in runoff efficiency is followed by an increase in the following year, likely due to the release of stored groundwater. This effect suggests changes in runoff efficiency help to stabilise watersheds, making it more difficult to both enter and leave drought as climate changes. The latter effect may partially explain observations of hydrologic drought persistence after meteorological drought ends. Understanding regional temperature sensitivity and the multiple‐year effect of precipitation will improve the ability to forecast runoff efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".