Assessing the changing risks of hydroclimatic transitions across north America
Bibliographic record
Abstract
<!--!introduction!--> Hydroclimatic extremes, including floods and droughts, have become increasingly frequent and intense worldwide, leading to severe environmental and socio-economic consequences. However, assessing these events in isolation without considering their interactions can underestimate their compounding risks. This study aims to provide a thorough understanding of the changing risks of dry and wet transitions in North America, by using multiple hydroclimate variables to identify these extremes and their transitions. To achieve this, the study merges dry-wet spell indices, estimated by precipitation, soil moisture, and runoff simulations, into an integrated indicator, and applies an ensemble pooling approach to enhance the sample size for index estimation, which enables projecting the characteristics more robustly. The research also investigates nonstationary hydrological swings between flood and drought based on streamflow data. The analyses are conducted using a suite of downscaled CMIP5 GCM simulations, that are used to drive the Variable Infiltration Capacity hydrologic model, and multiple large ensembles for global warming levels of 1.5°C-4°C. Results indicate that hydroclimatic whiplash in North America is expected to become more frequent and intensified in a warmer climate. The study highlights the urgent need for effective adaptation strategies that address the compounded risks associated with hydroclimatic whiplash events, which are projected to increase in frequency, intensity, and duration in a warming climate. Specifically, the importance of developing and implementing adaptive water management strategies, such as constructing resilient infrastructure and adopting effective water conservation practices, is underscored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".