Estimation of intensity duration frequency curves for current and future climate
Bibliographic record
Abstract
Climate variability and change are expected to have important impacts on the hydrologic cycle at different temporal and spatial scales In order to build long-lasting drainage systems, civil engineers and urban planners should take into account these potential impacts in their hydrological simulations. However, even if Global Climate Models (GCM) are able to describe the large-scale features of the climate reasonably well, their coarse spatial and temporal resolutions prevent their outputs from being used directly in impact assessment models at regional or local scales. This study proposes a statistical downscaling approach, based on the scale invariance concept, to incorporate GCM outputs in the derivation of Intensity-Duration-Frequency (IDF) curves and the estimation of urban design storms for current and future climates under different climate change scenarios. The estimated design storms were then used in the estimations of runoff peaks and volumes for urban watersheds of different shapes and different levels of surface imperviousness using the popular Storm Water Management Model (SWMM). Finally, a regional analysis was performed to estimate the scaling parameters of extreme rainfall processes for locations with limited or without data. In summary, results of an illustrative application of the proposed statistical downscaling approach using rainfall data available in Quebec (Canada) have indicated that it is feasible to estimate the IDF relations and the resulting design storms and runoff characteristics for current and future climates in consideration of GCM-based climate change scenarios. Furthermore, based on the proposed regional analysis of the scaling properties of extreme rainfalls in Singapore, it has been demonstrated that it is feasible to estimate the IDF curves for partially-gaged or ungaged sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".