Can We Adequately Quantify the Increase/Decrease of Flooding Due to Climate Change?
Bibliographic record
Abstract
Changes in global climate may have significant impacts on local and regional hydrological regimes. Consequences of these changes may in turn result into potentially significant impacts on flooding occurrence and severity, as well as more or less prolonged droughts. Adaptation strategies, including revisiting engineering design standards and practice, as well as developing innovative water management approaches, will need to be devised to cope with potentially deleterious impacts consequential to shifts in climate. It is therefore imperative to capitalize on the latest and finest tools to quantify impacts of climate change on river flows. A number of studies investigated the potential impacts of climate change on river flooding. Most are directly or indirectly based on linking General Circulation Models (GCM) outputs to hydrological models to generate current and anticipated river flows. The approach suffers from the low spatial and temporal resolution of GCMs which is not suitable for carrying hydrological studies on all but a few watersheds. The number of GCMs and climate scenarios adds to the uncertainty in quantifying watershed hydrological response to climate change. Downscaling techniques, either dynamical or statistical, offer new perspectives for conducting climate change impact studies, as they are used to bridge the spatial and temporal resolution gaps between climate models and what impact assessors need. However, most downscaling methods have problems dealing with the uncertainty linked to models and emission scenarios. This paper presents results from one possible approach and discuss the implications for flood estimation in the snowmelt period and summer/fall season.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".