As part of the December flooding theme, we thought it would be interesting to publish this case study from Previsico. Maps and more importantly real-time flood event maps, are vital in tracking events and ultimately settling claims. You can even prevent claims by advising those in the path of the storm to take immediate action. Here’s the data;
Previsico provides real-time property level flood forecasts and warnings with a 48-hour lead time. Uniquely, these are continuously modelled every three hours using Previsico’s world leading flood nowcasting technology for surface water in partnership with IBM’s Weather Company.
Our forecasts include the depth and time at which flood is predicted at a 25-metre resolution. Previsico’s surface water flood outputs provide actionable forecasts which enable people and organisations, including the UK government, to proactively mitigate surface flood impacts.
On 13th of August 2020 persistent hot weather created ideal conditions for intense localised storms across southern England. In particular, South London experienced heavy rain, lightning and hail which led to severe flooding events later in the afternoon, causing considerable disruption. This document showcases Previsico’s surface water flood forecasting for the event.
Our forecasts are in the form of geospatial files showing hourly water depth from the issue date. For the August 2020 South London flooding events the forecast was issued at 12:00:00 PM (GMT+01:00) on the 13th of August. To identify sites of flooding from the event we use crowdsourcing as a method to source proxies using social media outlets (Twitter, Facebook etc.) and news stories (BBC, TheGuardian etc.). This allows us to validate our surface water flood forecasts and identify how accurate our forecasting abilities are for a particular event.
As well as getting an overall view of flooding in a particular area, using crowdsourced photos of flood water on one of the M25 junctions also proved useful. Having data like this allows insurers and claims handlers to rate localised incidents and speed up the response.
By cross-referencing the geo-location of the photos being uploaded via social media, news outlets or emergency services, a constantly updated overall view of the flood event can be provided. That in turn offers clear decision making on offering policyholder flood alerts, handling claims and getting help to policyholders.
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