Characterization of Lagged Compound Floods and Droughts Under Climate Change
Bibliographic record
Abstract
Increases in the frequency and intensity of hydroclimatic extremes (floods and droughts) and their temporal swings have led to severe consequences in many regions around the world. Traditionally, these contrasting extremes have been assessed in isolation without considering their spatial and temporal interactions, implications for infrastructure design and management and the overall compounding risks. Nonetheless, understanding the changing characteristics of such lagged compound events is critical to developing effective mitigation and adaptation strategies. In this study, we propose a novel framework to identify and characterize the hydroclimatic whiplash events and investigate their spatiotemporal projections under climate change. Multiple hydroclimate variables such as precipitation, evapotranspiration, soil moisture, runoff, and streamflow are used to identify dry and wet extremes and their transitions. Different scenarios for nonstationary hydrological swings between flood and drought are investigated based on streamflow data. Meteorological wet and dry conditions are investigated using standardized drought indices calculated based on the downscaled and statistically bias-adjusted simulations of CMIP5 for 1.5°C-4 °C global warming levels over three major river basins in northwest North America. Further, three dry-wet spell indices estimated by precipitation, soil moisture, and runoff simulations are merged into an integrated indicator to provide a thorough perspective on the changing risks of such transitions across North America using the Canadian Regional Climate Model version 4 Large Ensemble. We apply an ensemble pooling approach to enhance the sample size for index estimation, which enables projecting the characteristics more robustly. Frequency, intensity, transition time, spatial fraction, aggregation index, and seasonality are quantified for each warming period and compared with those of the baseline period to investigate their projected changes. In addition, we assess the contribution of external forcing and internal variability to the historical and projected changes of the lagged compound events. The results of this study suggest that hydroclimatic whiplash across North America is expected to become more frequent and intensified in a warmer climate. Projections show overall increases in the frequency of hydroclimatic whiplash and a decrease in the corresponding transition times as the climate gets warmer. In addition, the magnitude, intensity, and duration of wet and dry components of such lagged compound events are projected to increase based on the analyses with streamflow. Increasing trends of spatial fraction and spatial aggregation during both transitions between dry and wet spells also imply higher risks and future challenges for water resources management. The findings of this study support the necessity of developing appropriate mitigation measures targeting lagged compound floods and droughts that can lead to severe environmental and socio-economic disasters in North America.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".