Assessment of the added value of using statiscally downscaled precipitation fields for hydrological forecasting
Bibliographic record
Abstract
When an extreme precipitation event is imminent, meteorological forecasts may be used as input to a physically-based, distributed, hydrological models to estimate the resulting peak flow. However, meteorological forecasts are generally available on mesoscale grids (102 - 103 km2), which might not be accurate enough to simulate local-scale stream flows. Statistical disaggregation models can rapidly provide several series of high-resolution precipitation data while preserving the total amount of precipitation at the mesoscale grid. The aim of the present work is to evaluate the potential of using precipitation series from a recently developed disaggregation model in a distributed hydrological model to predict local stream flows within a watershed. As a case study, we analyze the June 2002 flood on the Des Anglais watershed (730 km2), located in the Saint Lawrence Lowlands, Quebec, Canada, using HYDROTEL. Results show that disaggregation of mesoscale precipitation from 52.8 to 4.4-km fields produces a large spectrum of runoff estimations, especially on the smaller hydrological units, and reduces rain and runoff biases. For this extreme-event case study, runoff estimation also strongly depends on soil types and the set of estimated parameter values of HYDROTEL.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".