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This simple app is the first step of the project : help people get more acquainted with flood probabilities on a given year and on N years.
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The javascript version is not yet as complete as the Python code, but it has the huge advantage to be easy to use for anyone : just download the file and drag-and-drop it in your browser's navigation bar !
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Other stages are : understanding Gumbel distributions (for students and professionnals) and understanding probabilistic floodmaps with several flood extent maps overlayed one atop the other (more tricky than it seems if the legend is deceivlingly simple ! )
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This project ":moneybag: bag of floods" is a spinoff of its French counterpart ":flag_fr: Sac de Crue".
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It started in jan. 2023 and will gradually catch up with the french projet.
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### First step : draw marbles from a bag to help understand flood probabilities... and use an app to mimick series of draws
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So far, we are working the first step of the project : help to convey messages about **flood probability on a given year** and **on N years**, because you don't buy a house or design a flood mitigation project for one year.
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This can be achieved by drawing from a bag marbles colour-coded with respect to their probability, that can be understood as this year's flood return period. After this, we can move on to series of N years, and for this an app is very useful to automatically generate series of "floods".
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The HTML version is not yet as complete as the Python code (in French only so far), but it does provide the results of N draws visually (one token per drawn marble) and as stats (number and empirical frequency for each colour). HTML has the huge advantage to be easier for anyone to use : just download the file and drag-and-drop it in your browser's navigation bar !
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### Other parts of the project not yet available in English :disappointed:
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The french "Sac de Crues" project also offers small apps and texts to discuss:
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- interpretation of probabilistic floodmaps with several flood extent maps overlayed one atop the other (more tricky than it seems if the legend is deceivlingly simple ! ) ;
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- understanding Gumbel distributions (for students and professionnals) and the confidence intervals.
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