Visualizing and communicating probabilistic flood forecasts maps for decision-making
Bibliographic record
Abstract
Probabilistic flood forecasts often concentrate on streamflow, but water depth and extent might convey more tangible flood information for some people. Water depths and extent can also be used more directly than streamflow as part of an impact-based forecasting set-up. However, within a probabilistic or ensemble approach, the uncertainty inherent to water extent and depth applies to all three spatial dimensions: the depth itself is uncertain, and so is the extent in terms of latitude and longitude. The notion of forecast uncertainty is generally well accepted by users, and on the one hand, the addition of new information (flood extent, depth, velocity, etc.) has the potential to be useful for decision makers. On the other hand, it also has the potential to be overwhelming and confusing. Therefore, visualising probabilistic flood forecast maps and communicating the information to the general public and to decision-makers poses multiple challenges. In this presentation we will synthesise the results from a large-scale survey of forecast users, including 28 government representatives, 52 municipalities, 9 organisations, as well as 37 citizens and farmers. Those different groups have different roles, realities, and perspectives. They also have different needs and preferences in terms of hydrological forecasts. The survey consisted of individual and group interviews. The participants were asked a variety of open questions regarding their needs and preferences for hydrological forecasts and also for the visualisation and the communication of those forecasts. One key element of the interviews was the presentation of four alternative visualisation prototypes for probabilistic forecasts of flood depth and extent. The participants were asked to compare those prototypes, to express their preferences in terms of colour maps, wording and the representation of uncertainty. They also provided useful comments on potential modifications to those prototypes and sometimes suggested ideas for entirely new prototypes. Our results highlight that most participants, regardless of their role or background, had the same overall preference in terms of the proposed prototypes, with prototype number 2 the overall favorite (all prototypes will be shown and explained during the presentation). Nevertheless, we also found several specificities among the respective preferences of different user groups. Our results also highlight specific issues related to the understanding of probabilities in the context of flood forecast maps. The results of this research are currently being used to inform the design of the new forecast communication and visualisation platform in the province of Quebec, Canada.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".