Sensitivity of hydrological ensemble forecasts to different sources and temporal resolutions of probabilistic quantitative precipitation forecasts: flash flood case studies in the Cévennes‐Vivarais region (Southern France)
Bibliographic record
Abstract
Abstract This article analyses the performance of an integrated hydrological ensemble prediction system adapted to small to mid‐sized catchments (100–600 km2) situated in the Cévennes‐Vivarais region (Southern France) and characterized by short lag times (3–12 h). In this framework, flood forecasts need hourly Probabilistic Quantitative Precipitation Forecasts (PQPF) so as to provide early warning with 24–72 h of anticipation. Here, two sources of PQPF at daily and subdaily (6 h) meteorological time steps are considered: Ensemble Prediction Systems from the European Centre for Medium‐range Weather Forecast (ECMWF) and analogy‐based PQPF provided in real‐time at the Laboratoire d'étude des Transferts en Hydrologie et Environnement. The two PQPF are firstly disaggregated to respect the required hydrological hourly time step, through either the use of a stochastic rainfall generator or the application of a multimodel approach. Then, disaggregated PQPF are used as input to a hydrological model, which is called TOPSIMPL, to provide hourly ensemble discharge forecasts up to 48 h ahead. Illustration and evaluation of ensemble discharge forecasts issued in near real‐time conditions are given for some recently observed flash flood events. It is shown that hourly discharge forecasts are first‐order conditioned by the accuracy of PQPF at daily or subdaily time step. Six‐hour ensemble prediction systems correctly reproduce the rainfall temporal dynamics, whereas daily analogy‐based PQPF are less underdispersive in terms of rainfall amounts. As a result, the merging of the two sources of PQPF substantially increases the performance of discharge forecasts, the contribution of a more sophisticated hourly rainfall generator becoming marginal. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".