Deterministic and probabilistic flood modeling for contemporary and future coastal and inland precipitation inundation

Abstract

While hazard mitigation lowers hazard impacts, communities cannot mitigate all possible risks. Targeting mitigation allows agencies with limited resources to mitigate areas within a community where hazard impacts are highest. To target these areas effectively, better hazard modeling is needed to provide more accurate hazard extents to pinpoint mitigation and conduct more complete vulnerability assessments. Deterministic models are useful for developing mitigation policies based on their hazard identification and exposure outputs in vulnerability analyses, but are limited because they do not calculate risk. Probabilistic models provide more information about the range of risk allowing decision makers to target mitigation and land-use management focuses toward areas of higher risk. Deterministic models used in conjunction with probabilistic models can also be used to perform all three levels of vulnerability assessment and produce more complete hazard modeling extents, which is something not traditionally done. To explore the need for the use of probabilistic models in conjunction with deterministic modeling, research presented here creates a theoretical framework for a stochastic storm surge model using deterministic hazard extents that depict coastal hazard inundation, using Sarasota County, Florida as a case study. The deterministic hazard extents are created for use in multiscalar vulnerability assessments that simulate storm surge, inland precipitation and sea-level rise (SLR) to depict holistic coastal hazard inundation. The deterministic extents also provide higher-resolution hazard identification that can aid local decision makers targeting high-risk areas, not all exposed areas.