Global Analysis of the Hydrologic Sensitivity to Climate Variability
Bibliographic record
Abstract
Identifying the regions with greatest changes in their hydrologic behavior under extreme weather events in the 21st century, constitutes a study priority of global impact. Here, we present a global assessment assessing the sensitivity of the world’s water landscapes to climate variability during 2001-2016, using a new metric called the Hydrologic Sensitivity Index (HSi). This equation is based on the well-known Budyko curve that uses annual values of Potential and Actual Evapotranspiration (PET and AET), and Precipitation (P), to assess the hydrologic behavior of a location under a given climatic condition by plotting the Evaporative Index (AET/P) against the Dryness Index (PET/P). For values ����i ≥1: Sensitive and ����i<1: Resilient. Also, since elevation, slope and aspect are the three of the defining factors in temperature and humidity regimes, we evaluate their influence on HSi. Overall, majority of the world’s biomes display tendency toward drier state. Particularly, we identify the regions with hydrologic sensitivity to climate variability in tropical rainforests accompanied with decreasing water yields and warmer/drier conditions evident along southernmost part of Amazon and central part of the Congo basin. High sensitivity is also seen along easternmost Canadian and Eurasian arctic tundra and boreal forests with increasing water yield trends and dominant warmer/drier climate conditions. The hydrologic sensitivity is amplified at high elevations and steep-sloped terrain outlining the importance of the topography in modulating these effects. We direct the attention towards climate warming resulting in decreased forest cover as potential mechanism driving the decreasing water yield patterns in tropical zones, while snow melt and increasing precipitation in the tundra and boreal forests resulting in surplus water yields. Our global study highlights the particular locations with greatest hydrologic changes to climate variability while outlining the main water yield and climate directions—a study that indicates where water resources have been changing the greatest and in what ways.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 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".