High winds and storm surges associated with torrential rain from tropical cyclones (TCs) cause massive destruction to property and cost the lives of many people. The coastline of the Bay of Bengal (BoB) ranks as one of the most susceptible to storm surges in the world due to low‐lying elevation and a high frequency of TC occurrence. This study uses data from 1885 to 2011 and a bivariate statistical copula to describe the relationship and dependency between empirical TC storm surge and reported wind speed before landfall at the BoB. Among the copulas and their families, an Archimedean, Gumbel copula with margins defined by the empirical distributions is specified as the most appropriate choice for the BoB. The model provides return periods for pairs of TC storm surge and 12‐hour pre‐landfall wind along the BoB coastline. On the shortest time scale, the BoB can expect a TC with 12‐hour pre‐landfall winds of at least 24 m s⁻¹ and surge heights of at least 4.0 m, on average, once every 3.9 years. On the other hand, the long‐term, worst case scenario suggests the BoB can expect 12‐hour pre‐landfall winds of 62 m s⁻¹ and surge heights of at least 8.0 m, on average, once every 311.8 years. Using a copula to model the combined frequency of cyclone wind speeds along with storm surges along the BoB coastline increases the understanding of the dangerous TC characteristics in this region, which can reduce fatalities and monetary losses.

In recent years, shoreline determination has become an issue of increasing importance and concern, especially at the local level, as sea level continues to rise. This study identifies the rates of absolute and net erosion, accretion, and shoreline stabilization along the coast of Kuakata, a vulnerable coastal region in south-central Bangladesh. Shoreline change was detected by applying remote sensing and geographic information system (RS-GIS)-based techniques by using Landsat Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) satellite images at 30-m resolution from 1989, 2003, 2010, and 2020. The band combination (BC) method was used to extract the shoreline (i.e., land-water boundary) due to its improved accuracy over other methods for matching with the existing shoreline position. This study also used participatory rural appraisal (PRA) tools which revealed the societal impacts caused by the shoreline changes. Coupling RS-GIS and PRA techniques provides an enhanced understanding of shoreline change and its impacts because PRA enriches the RS-GIS outcomes by contextualizing the findings. Results show that from 1989 to 2020, a total of 13.59 km² of coastal land was eroded, and 3.27 km² of land was accreted, suggesting that land is retreating at about 0.32 km² yr^–1. Results from the PRA tools support this finding and demonstrate that fisheries and tourism are affected by the shoreline change. These results are important in Kuakata, a major tourist spot in Bangladesh, because of the impacts on fisheries, recreation, resource extraction, land use planning, and coastal risk management.

Drought is a hazard that inflicts costly damage to agricultural, hydrologic, and ecological systems and affects human health and prosperity. A comprehensive assessment of resilience to the drought hazard in various communities and an identification of the main variables that affect resilience is crucial to coping with the hazard and promoting resilience. This study assessed the community resilience to drought hazards of all 503 counties of Arkansas, Louisiana, New Mexico, Oklahoma, and Texas using the resilience inference measurement (RIM) model for the period of 2000 to 2015. Through k-means cluster analysis, stepwise discriminant analysis (74.7 percent accuracy, 72.8 percent leave-one-out cross-validation accuracy), and regression analysis (adjusted R² = 0.69), four variables (significant at p < 0.05) representing the social, economic, agriculture, and health sectors were identified as the main resilience indicators. Higher resilience counties were found in central Oklahoma and eastern Texas, with the few highest counties located near large metropolitan areas such as Dallas, Houston, Austin, and Albuquerque. Lower resilience counties were concentrated in western Texas. The study provides useful insights into the relationship between drought incidence, inflicted damage, and community resilience.

Flood depth grids from U.S. Federal Emergency Management Agency (FEMA) provide model-output estimates of the depth of water that can, on average, be expected to occur at various return periods for localized areas. However, use of these depth grids can be limited by spurious data and an insufficient number of return periods for certain planning applications. This research proposes a new method for estimating flood depth grids to address these shortcomings. The Gumbel distribution is used to characterize the flood depth-return period relationship for grid cells for which the data are plausible. Then the Gumbel parameters of slope (α) and intercept (u) are used to project flood elevations for extreme return periods for which an entire area can be assumed to be submerged. Spatial interpolation methods are then used to impute the flood elevations for spurious or missing grid cells. Then, the flood depth is recomputed from the flood elevations, once they are re-calculated at the shorter return periods. Validation of this technique for a Metairie, Louisiana, U.S.A. study area suggests that the cokriging spatial interpolation technique provides the most suitable estimates of flood depth, provided that the FEMA-generated model output is assumed to provide the “correct” results. These methods may assist engineers, developers, planners, and others in mitigating the world’s most widespread and expensive natural hazard.

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.