@article{gharagozlou_anderson_gorski_dietrich_2022, title={Emulator For Eroded Beach And Dune Profiles Due To Storms}, volume={127}, ISSN={["2169-9011"]}, url={http://dx.doi.org/10.1029/2022jf006620}, DOI={10.1029/2022JF006620}, abstractNote={Abstract}, number={8}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE}, publisher={American Geophysical Union (AGU)}, author={Gharagozlou, A. and Anderson, D. L. and Gorski, J. F. and Dietrich, J. C.}, year={2022}, month={Aug} }
 @article{roberts_dietrich_wirasaet_pringle_westerink_2021, title={Dynamic load balancing for predictions of storm surge and coastal flooding}, volume={140}, ISSN={["1873-6726"]}, url={http://dx.doi.org/10.1016/j.envsoft.2021.105045}, DOI={10.1016/j.envsoft.2021.105045}, abstractNote={As coastal circulation models have evolved to predict storm-induced flooding, they must include progressively more overland regions that are normally dry, to where now it is possible for more than half of the domain to be needed in none or only some of the computations. While this evolution has improved real-time forecasting and long-term mitigation of coastal flooding, it poses a problem for parallelization in an HPC environment, especially for static paradigms in which the workload is balanced only at the start of the simulation. In this study, a dynamic rebalancing of computational work is developed for a finite-element-based, shallow-water, ocean circulation model of extensive overland flooding. The implementation has a low overhead cost, and we demonstrate a realistic hurricane-forced coastal flooding simulation can achieve peak speed-ups near 45% over the static case, thus operating now at 80−90% efficiency.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, publisher={Elsevier BV}, author={Roberts, Keith J. and Dietrich, J. Casey and Wirasaet, Damrongsak and Pringle, William J. and Westerink, Joannes J.}, year={2021}, month={Jun} }
 @article{thomas_dietrich_asher_bell_blanton_copeland_cox_dawson_fleming_luettich_et al._2019, title={Influence of storm timing and forward speed on tides and storm surge during Hurricane Matthew}, volume={137}, ISSN={1463-5003}, url={http://dx.doi.org/10.1016/j.ocemod.2019.03.004}, DOI={10.1016/j.ocemod.2019.03.004}, abstractNote={The amount and extent of coastal flooding caused by hurricanes can be sensitive to the timing or speed of the storm. For storms moving parallel to the coast, the hazards can be stretched over a larger area. Hurricane Matthew was a powerful storm that impacted the southeastern U.S. during October 2016, moving mostly parallel to the coastline from Florida through North Carolina. In this study, three sources for atmospheric forcing are considered for a simulation of Matthew's water levels, which are validated against extensive observations, and then the storm's effects are explored on this long coastline. It is hypothesized that the spatial variability of Matthew's effects on total water levels is partly due to the surge interacting nonlinearly with tides. By changing the time of occurrence of the storm, differences in storm surge are observed in different regions due to the storm coinciding with other periods in the tidal cycles. These differences are found to be as large as 1 m and comparable to the tidal amplitude. A change in forward speed of the storm also should alter its associated flooding due to differences in the duration over which the storm impacts the coastal waters. With respect to the forward speed, the present study contributes to established results by considering the scenario of a shore-parallel hurricane. A faster storm caused an increase in peak water levels along the coast but a decrease in the overall volume of inundation. On the other hand, a slower storm pushed more water into the estuaries and bays and flooded a larger section of the coast. Implications for short-term forecasting and long-term design studies for storms moving parallel to long coastlines are discussed herein.}, journal={Ocean Modelling}, publisher={Elsevier BV}, author={Thomas, Ajimon and Dietrich, JC and Asher, TG and Bell, M and Blanton, BO and Copeland, JH and Cox, AT and Dawson, CN and Fleming, JG and Luettich, RA and et al.}, year={2019}, month={May}, pages={1–19} }
 @article{massarra_friedland_marx_dietrich_2019, title={Predictive multi-hazard hurricane data-based fragility model for residential homes}, volume={151}, ISSN={0378-3839}, url={http://dx.doi.org/10.1016/j.coastaleng.2019.04.008}, DOI={10.1016/j.coastaleng.2019.04.008}, abstractNote={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%.}, journal={Coastal Engineering}, publisher={Elsevier BV}, author={Massarra, Carol C. and Friedland, Carol J. and Marx, Brian D. and Dietrich, J. Casey}, year={2019}, month={Sep}, pages={10–21} }