Climate Change Drives Shift in Cold Season Flood Generation Mechanisms in a Seasonally Frozen Region
Bibliographic record
Abstract
ABSTRACT Climate warming is reshaping flood‐generating mechanisms in seasonally frozen regions worldwide. Antecedent conditions such as soil ice content, soil liquid content, and snowpack are known to influence flooding responses to meteorological forcing. However, the combined contributions of these factors to flooding, specifically in the context of climate change, are largely unknown. To address this gap, this study applies the one‐dimensional Simultaneous Heat and Water (SHAW) hydrological model to simulate winter dynamics associated with flooding conditions for a typical soil system in Nova Scotia, Canada under an evolving climate. The model is forced by a 32‐member climate ensemble spanning four Shared Socio‐economic Pathways (SSP1‐2.6 to SSP5‐8.5). Extreme events are defined from daily hydrological partitioning using a peak‐over‐threshold approach that considers rain and snowmelt combinations. The study examines temporal changes in the state variables as well as the relative likelihoods and probabilities of the joint densities of these variables prior to flooding events. The analysis indicates several representative scenarios with contrasting hydrological responses may capture the dominant flood‐generating mechanisms during the climate transition. Rising radiative forcing systematically reduces the maximum relative likelihood for snow depth and the overall frequency of cryogenic dynamics, yet cryogenic‐related floods, such as rain‐on‐frozen‐ground and rain‐on‐snow scenarios, remain a relatively probable generator of extreme floods. Results provide a tractable approach for modifying cold‐region flood hazard mapping studies and guidelines to proactively account for climate change.
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 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 | 0.000 | 0.000 |
| 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".