Multi-Scale Flood Simulations Under Climate Change Scenarios
Bibliographic record
Abstract
The present study focuses on quantifying the impact of the choice of spatio-temporal resolution and hydrology models on the projection of extreme flow and their link to the catchment size. We use two process-based distributed hydrology models forced with a large-ensemble regional climate model (50-member ClimEx dataset) over the 1990-2100 period at different spatio-temporal scales. The extreme summer-fall flow corresponding with each spatio-temporal resolution was extracted by pooling the members together and computing the empirical cumulative distribution function. The results show that by refining the time-step from daily to sub-daily, the summer-fall extreme flow projected over the future period exceeds that of the reference period for the small but not large catchments. By increasing the catchment size, the hydrology model’s contribution to the variability of extreme flow increases. Moreover, the choice of spatial resolution affects the extreme flow’s trend in terms of magnitude, significance, and direction. But no pattern regarding the catchment size and spatial discretization variations exists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".