Agriculture’s impact on water–energy balance varies across climates
Bibliographic record
Abstract
Agriculture is a cornerstone of global food production, accounting for a substantial portion of water withdrawals worldwide. As the world's population grows, so does the demand for water in agriculture, leading to alterations in regional water-energy balances. We present an approach to identify the influence of agriculture on the water-energy balance using empirical data. We explore the departure from the Budyko curve for catchments with agricultural expansion and their associations with changes in the water-energy balance using a causal discovery algorithm. Analyzing data from 1,342 catchments across three Köppen-Geiger climate classes-temperate, snowy, and others-from 1980 to 2014, we show that temperate and snowy catchments, which account for over 90% of stations, exhibit distinct patterns. Cropland percentage (CL%) emerges as the dominant factor, explaining 47 and 37% of the variance in deviations from the Budyko curve in temperate and snowy catchments, respectively. In temperate catchments, CL% shows a strong negative correlation with precipitation-streamflow (P-Q) causal strength (Spearman [Formula: see text]), suggesting that cropland exacerbates precipitation-driven deviations. A moderate negative correlation with aridity-streamflow (AR-Q) causal strength ([Formula: see text]) indicates additional influences of cropland through aridity-driven interactions. In snowy catchments, CL% is similarly influential, with a positive correlation with P-Q causal strength ([Formula: see text]). However, the negative correlation with AR-Q causal strength ([Formula: see text]) underscores the role of aridity as a secondary driver. While vegetation and precipitation seasonality also contribute to the deviations, their impacts are comparatively lower. These findings underscore the need for inclusion of agricultural activities in changing water-energy balance to secure future water supplies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".