COASTAL FLOODING & SOLUTIONS, Workshop Case Studies
Building-Level Damage Estimation for the Combined Impacts of Storm Surge, Rain, and Tides including projections of Sea Level Rise and Land Subsidence Using SCHISM
Jon Derek Loftis
Center for Coastal Resources Management, Virginia Institute of Marine Science, William and Mary, Research Assistant Professor
Fine-scale spatial resolution and timeliness of flood predictions and building impacts are vital to estimating risk, accurately calculating damage, with significant implications for disaster response, and future flood mitigation and planning. Traditionally, flood forecast models are limited in spatial resolution, which combined with the lack of first-floor building elevation data, has limited the accuracy of pre-disaster flood damage estimates. This session presents innovative approaches to these inter-related problems, including development of a resolute, adaptable polymorphic forecast grid, a flood impact visualization viewer, and spatial analysis of estimated outcomes developed from an unique perspective on depth/damage curves derived from surveyed first-floor elevations. Working in five watersheds in the Cities of Virginia Beach, Newport News, and Portsmouth, the Virginia Institute of Marine Science (VIMS) partnered with the Virginia Department of Emergency Management and these municipal partners through separate projects to develop a prototype approach to flood forecasting, building-level flood damage assessment, and web-based flood impact visualization. Leveraging VIMS’ SCHISM hydrodynamic model, a streamlined system of geospatial outputs, and geostatistics was applied to thousands of homes in the Hampton Roads region through hydrodynamic simulations of 2016 Hurricane Matthew, 2011 Hurricane Irene, and 2003 Hurricane Isabel in the context of spatially-varying modeled parameters for sea level rise and land subsidence. SCHISM is driven by atmospheric forecast inputs, tides and currents at the open boundary, and capable of being initially parameterized with real-time rainfall, water levels, and stream flow sensor data connected to the National Water Model.
Being able to depict inundation scenarios from pre-cached model simulations for scenario planning while also using these model GIS visualization data to coincide with real time sensor data to depict anticipated impacts of inundation from near-term 36-hr automated storm tide time series forecasts from VIMS' Tidewatch Charts
More funding and more expedient access to permitting resources for sensor installation.