Integrating Prediction & Performance Models into Scenario-based Resilient Community Design

Globally, more than 600 million people live in coastal regions lower than 10 meters above sea level and almost 2.4 billion people live within 100 km of a coastline. In the U.S., 39% of the population reside in coastal counties. Sea level rise due to climate change makes coastal populations more susceptible to flood risks. The use of land change prediction modelling to inform scenario-based planning has been shown to help increase capabilities when dealing with uncertainties in urbanization such as urban growth and flood risk. This research uses the Land Transformation Model (LTM) to predict three different urban growth scenarios for Tampa, FL to determine how effective the current comprehensive plan is in adapting urban growth to decreasing flood risk and pollutant load. The LTM, a GIS-based neural network, is used to predict the future urban growth of Tampa, FL according to three different scenarios: 1) Business as usual – predicted urban growth based on current growth patterns; 2) Growth as planned – predicted urban growth based on the current land use plan; and 3) Resilient growth – predicted urban growth based on all future development occurring outside of the floodplain. Then, we develop master plans according to each scenario and assess their probable impacts using the Long-Term Hydrologic Impact Analysis model. Findings show that the current future land use plan for Tampa appears has higher flood exposure, stormwater runoff, and pollutant discharge than current conditions but more than a purely resilient approach to future growth. -------------------------------------------------------------------------------------------------------------------------- Follow us on Social Media! Twitter: https://twitter.com/Esri Facebook: https://facebook.com/EsriGIS LinkedIn: https://www.linkedin.com/company/esri Instagram: https://www.instagram.com/esrigram The Science of Where: http://www.esri.com