First Street’s Post

🗓 Upcoming Webinar | Tuesday, May 14th Understanding First Street Flood Modeling with Dr. Edward Kearns First Street’s Chief Science Officer, Dr. Ed Kearns, explains how we created our climate-driven flood risk model and how it provides the most accurate view of flood risk. Register to learn: - Why our compound (pluvial and fluvial flooding), physics-based flood model (FS-FM) is superior to other models. - How we have implemented our Flood Model (FS-FM) Model and the key data inputs we used. - Understanding how our model delivers accurate flood risk and damage estimates for every property in the US, now and 30 years into the future https://lnkd.in/gY72eWWf

We have developed new science that increases the speed of flood modeling democratizing access and opening up flood modeling to emergency operations and management applications. As flood events become more frequent and severe due to #ClimateChange, new approaches are needed to protect communities from catastrophic damage. Learn more about Stantec’s #FloodPredictor: https://stantec.co/49XbQTM

#FMApprovals presented at the 2023 #RIFMA Conference. Insights related to implementing suitable flood mitigation solutions were shared with attendees in support of improved effectiveness and efficiency of all aspects of flood: http://ms.spr.ly/6040gi60i

Floodwaters from recent flooding receded in #Germany and insurers are counting losses in the hundreds of millions EUR. Modelling experts of Aon’s Impact Forecasting were able to showcase the new flood model and provide a scenario for the event. Rainfall-runoff #modelling was conducted using precipitation data as input, and return periods were calculated using a frequency analysis used in the German Flood Model. Flood extents were created in 3 versions: fluvial, pluvial and off-floodplain. For more information, contact IF’s Flood Model Development team! Thanks to Monika Vaľková, Vít Kovačka, Michal Lörinc, Tomáš Čejka, Adam Podlaha and Marek Goldbach #flood #hochwasser #Niedersachsen

Flood Predictor was developed for the Tennessee Department of Economic and Community Development (TNECD) and utilized the state's own flood data. The engineering, data, and physics complexity built within Flood Predictor is immense, but the output is easily digestible. In West Tennessee, that means flood resilience decisions are made faster and smarter: #flood #floodmitigation #floodprotection #machinelearning

Stanford develops rapid method for flood severity analysis New Stanford research has developed the Floodplain Adaptation Strategies Testbed, a rapid method to analyze flood severity, exposure, and impact. #FloodAnalysis #Stanford #Floodplain

As severe weather increases in frequency, increasing flood resilience will be essential for communities. We’re doing that with Flood Predictor. It’s a digital solution built with machine learning and informed by over 30,000 hours of engineered flood modeling. It can produce accurate flood predictions in hours, whereas typical flood modeling efforts can take days or weeks. Here's a video I wrote and produced on Flood Predictor. The engineering, data, and physics complexity built within Flood Predictor is immense, but the output is easily digestible. In West Tennessee, that means flood resilience decisions are made faster and smarter. To help get Flood Predictor available to all residents of Tennessee, reach out to [email protected]. For more on Flood Predictor, check out the product page: https://lnkd.in/gjvtU7SK

Get started with Flood Modeller with the new 14-day free trial!!

Powerful tools like Stantec’s #FloodPredictor allow communities to forecast when and where a flood event will happen. Here’s how: https://stantec.co/49XbQTM

A new approach is needed to make flood modelling more accessible and less expensive. At the MIT Concrete Sustainability Hub, we developed a novel flood modelling approach: The 3-Dimensional Lattice Approach (3DLA). It estimates the flooding risk of urban areas based on their neighbourhood textures. https://bityl.co/OCHP