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The cities of for which tweets are being monitored in GFM are based on the populated places (cities) database from the Natural Earth Data. We classified these cities according to their exposure to different types of flooding in: riverine flooding, coastal flooding, or riverine and coastal flooding, based on their location. For those cities exposed to riverine flooding (about 40% of the populated places according to our analysis), we paired a flow measuring station from the Global Runoff Data Centre (GRDC) in the same floodplain, with the city. For these measuring stations we have real-time and forecasted flow from GLOFFIS, and measured runoff from the GRDC dataset. The runoff data is normalized per station to make the data more informative. The normalized flow conditions are expressed as the percentage of time over the last 30 years during which the modeled runoff was lower than current modeled runoff. In this way, for each city susceptible to riverine flooding (based on the paired station) we can get real-time and forecasted normalized flow conditions. This information can be presented in the global flood monitor together with ground truth flood observations.
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After this proof of concept we will now automate the link between social media flood observations and modeled runoff. The results will be presneted together in a webviewer. A first mockup of how this could look like is shown in Figure 5. Recently a similar system to GLOFFIS has been developed at Deltares to forecast storm surge levels on sea called GLOSSIS. When furhter developed, this system could be added to the GFM to provide similar flood information for coastal cities.
Figure 5. Mockup improved Global Flood Monitor with linked ground-thruth and simulated hydrological data
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