Category Archives: ADCIRC
Conference: ASBPA Coastal Conference 2019
Posters: ASBPA Coastal Conference 2019
CA Rucker, N Tull, JC Dietrich, R Luettich, R Cyriac. “Improving the accuracy of a real-time ADCIRC storm surge downscaling model.” ASBPA 2019 National Coastal Conference, Myrtle Beach SC, 23 October 2019.
Improving the accuracy of a real-time ADCIRC storm surge downscaling model.
JL Woodruff, JC Dietrich, AB Kennedy, D Wirasaet, D Bolster, Z Silver, RL Kolar. “Improving predictions of coastal flooding via sub-mesh corrections.” ASBPA 2019 National Coastal Conference, Myrtle Beach SC, 23 October 2019.
Improving predictions of coastal flooding via sub-mesh corrections.
Sustainability of Barrier Island Protection Policies under Changing Climates
This project will address methods to adapt beach and dune nourishment to improve resilience in a changing climate. As storms become more powerful and seas continue to rise, major erosion events will occur more frequently. However, coastal communities do not yet understand how to evaluate their increasing vulnerabilities and adapt their long-term planning. In this project, we will identify the climate patterns that most often trigger the need to nourish, the variability of the time interval between such nourishments, and the economic costs and sediment volumes necessary to maintain this coastal protection policy into the 21st century.
A stochastic climate emulator will first be developed to simulate 1000s of realizations of chronological climate patterns (forced by satellite and GCM products) to create future storm events coupled with sea level rise scenarios. A library of high fidelity, open source, hydrodynamic and morphodynamic simulations (ADCIRC+SWAN and XBeach) will then be used to develop a surrogate model to predict erosion and flooding for each future realization. Triggers like beach width, dune height, and community preferences will be used to identify how often communities will need to re-nourish, contingent on future climate and sea level rise scenario.
JC Dietrich, DL Anderson. “Sustainability of Barrier Island Protection Policies under Changing Climates.” U.S. Coastal Research Program, 2019 Academic Research Opportunities, 2019/10/18 to 2021/10/17, $226,624 (Dietrich: $226,624).
Webinar: USCRP Progress Review
Research Image #2: Breaching of Barrier Island
This animation shows the storm-driven dune erosion and breaching on a barrier system. This is part of Alireza’s erosion and breaching modeling research.
Presentation: ASCE NC Fall Conference
Predictive Multi-Hazard Hurricane Data-Based Fragility Model for Residential Homes
Multi-hazard hurricane data-based fragility models are able to represent multiple predictor variables, be validated based on observed data, and consider variability in building characteristics and hazard variables. This paper develops predictive hurricane, multi-hazard, single-family building fragility models for ordered categorical damage states (DS) and binary complete failure/non-complete failure using proportional odds cumulative logit and logistic regression models, respectively. In addition to their simplicity, these models are able to represent multiple hurricane hazard variables and include variable interactions, thus improving model fitting and damage prediction. Surveys of physical damage in coastal Mississippi following Hurricane Katrina (2005) and high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN + ADCIRC) models are used as model input. Prediction accuracy is expressed in terms of cross-validation (CV) and evaluated using leave-one-out cross-validation (LOOCV).
Thirty-nine combinations of global damage response variables were investigated. Of these models, six DS and one complete failure model met the evaluation criteria. Maximum significant wave height was the only significant hazard variable for the DS models, while maximum 3-s gust wind speed, maximum surge depth, and maximum water speed were found to be significant predictors for the complete failure model. Model prediction external accuracy ranged from 81% to 87%.