Terrestrial contribution to the heterogeneity in hydrological changes under global warming
Bibliographic record
Abstract
Abstract This study investigates a physical basis for heterogeneity in hydrological changes, which suggests a greater detectability in wet than dry regions. Wet regions are those where atmospheric demand is less than precipitation (energy limited), and dry regions are those where atmospheric demand is greater than precipitation (water limited). Long‐term streamflow trends in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at global scales show geographically heterogeneous detectability of hydrological changes. We apply the Budyko framework and state‐of‐the‐art climate model data from CMIP5 to quantify the sensitivity and detectability of terrestrial hydrological changes. The Budyko framework quantifies the partitioning of precipitation into evapotranspiration and runoff components. We find that the terrestrial hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited rather than water limited. This additional source (the terrestrial part) contributes to 30–40% greater detectability in energy‐limited regions. We also quantified the contribution of changes in the catchment efficiency parameter that oppose the effects of increasing evaporative demand in global warming scenarios. Incorporating changes to the catchment efficiency parameter in the Budyko framework reduces dry biases in global runoff change projections by 88% in the 21st century.
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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".