Atmospheric Rivers and Compound Inland Flooding under Climate Change
Bibliographic record
Abstract
Compound Inland Flooding (CIF) arises from the interactions between multiple hydrometeorological drivers, often magnified by landfalling Atmospheric Rivers (ARs) along the Pacific Northwest coast and interior basins of North America. This study investigates the mechanisms behind two primary CIF types, Rain-on-Snow (ROS) and Saturation Excess Flooding (SEF), using the CanRCM4 large ensemble under global warming levels of +1.5°C, +2°C, and +4°C. By examining the joint occurrence of ARs with ROS and SEF across key sub-regions, including the Cascade Range, Sierra Nevada, and the Great Lakes Basin, we assess the probabilities, seasonal shifts, and hydrological impacts of CIFs in the 21st century. Results show distinct regional patterns, with ROS events projected to decrease in frequency across the Pacific Northwest and Great Lakes Basin but remain significant in high-elevation regions prone to seasonal snowmelt, such as the Canadian Rockies. Conversely, SEF events are projected to increase substantially, particularly in the eastern U.S. and southern Great Lakes, driven by intensified precipitation and persistently saturated soils. The findings indicate that under higher warming levels, the contribution of ROS to extreme runoff can decrease, while SEF-driven flood events become dominant. Signal-to-noise ratio analysis shows that internal climate variability contributes considerable uncertainty to CIF projections in transitional climate zones but is overshadowed by external climate forcing at higher warming levels, particularly in coastal regions. By capturing the compounded effects of precipitation extremes, snowmelt dynamics, and soil moisture conditions, this study underscores the necessity of integrating AR-driven compound events into regional flood risk management strategies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".