Fully integrating probabilistic flood forecasts into the decision-making process across southern Quebec, Canada: some factors to consider
Bibliographic record
Abstract
Flood forecasts can enable decision-makers to plan for mitigation measures, ensure the safety of people and reduce damages to private and public property. However, while flood forecasting systems are becoming increasingly sophisticated, those technical improvements do not always translate into a reduction of damages. In particular, ensemble and probabilistic streamflow forecasts provide more information regarding the uncertainty, but such forecasts are also more difficult to interpret. The connection between streamflow, water depth and flood extent is also not straightforward, as this connection requires extensive previous experience with a given river reach. Streamflow forecasts can be transformed in predictive flood maps through a hydraulic model, and those maps might have a more intuitive interpretation. However, if the system is designed to also represent the uncertainty of both water depth and extent, the interpretation becomes considerably more complex. Within this context of continuous improvement of flood forecasting systems, a broad consultation (90 participants) of forecast users was conducted across southern Quebec, Canada, with the aim of understanding their perception of flood forecasts and how they use those forecasts for decision-making. Participants responses emphasize the importance of contextualizing the forecasts according to potential consequences, providing an accurate representation of the time evolution of flood events, and facilitating the communication between forecasters and decision-makers. Following this, our main recommendation is to organize workshops to provide training for decision-makers on how to interpret hydrological forecasts correctly. Another recommendation is to implement additional communication channels between forecasters and end-users.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".