This paper uses a new rainfall algorithm to simulate the long‐term tropical cyclone precipitation (TCP) climatology in Texas based on synthetic tropical cyclones generated from National Center for Atmospheric Research/National Centers for Environmental Prediction reanalysis data from 1980 to 2010. The synthetic TCP climatology shows good agreement with the available observations with respect to TCP return periods, especially for daily and event TCP. Areas within 200 km of the coast have higher TCP risk with two hot spots located near Houston and Corpus Christi. Based on this technique, there are locations in Texas where a TCP event > 1000 mm has a return period of 500 years and a TCP event > 1400 mm has a return period of 1000 years. There is a high degree of spatial heterogeneity in TCP risk in central Texas due to the topography.

Tropical cyclones pose a significant flood risk to vast land regions in their path because of extreme precipitation. Thus it is imperative to quantitatively assess this risk. This study compares exceedance frequencies of tropical cyclone precipitation derived from two independent observational datasets with those estimated using a tropical cyclone rainfall algorithm applied to large sets of synthetic tropical cyclones. The modeled rainfall compares reasonably well to observed rainfall across much of the southern United States but does less well in the mid-Atlantic states. Possible causes of this disparity are discussed.

Hurricanes can severely damage the electric power system, and therefore, predicting the potential impact of an approaching hurricane is of importance for facilitating planning and storm-response activities. A data mining approach, classification and regression trees (CART), was employed to evaluate whether the inclusion of soil and topographic variables improved the predictive accuracy of the power outage models. A total of 37 soil variables and 20 topographic variables were evaluated in addition to hurricane, power system, and environmental variables. Hurricane variables, specifically the maximum wind gust and duration of strong winds, were the most important variables for predicting power outages in all models. Although the inclusion of soil and topographic variables did not significantly improve the overall accuracy of outage predictions, soil type and soil texture are useful predictors of hurricane-related power outages. Both of these variables provide information about the soil stability which, in turn, influences the likelihood of poles remaining upright and trees being uprooted. CART was also used to evaluate whether environmental variables can be used instead of power system variables. Our results demonstrated that certain land cover variables (e.g., LC21, LC22, and LC23) are reasonable proxies for the power system and can be used in a CART model, with only a minor decrease in predictive accuracy, when detailed information about the power system is not available. Therefore, CART-based power outage models can be developed in regions where detailed information on the power system is not available.

Spatial and temporal variations of tropical cyclone precipitation (TCP) in Texas are examined using 60 years of precipitation data from Cooperative Observing Network gages (1950 to 2009). An automated extraction method is used to identify TCP. Texas receives an average of 123.5 mm of TCP/year, which is ~13% of the state's mean annual precipitation. September is the month with the most TCP with an average of 18.5 mm. As expected, TCP generally deceases as you move inland. Long‐term trends (>50 years) in TCP are evident at some locations, but there are no statistically significant long‐term trends in aggregated annual TCP metrics for Texas. Despite the lack of long‐term trends, TCP metrics show some spectral power at periodicities of ~2‐3 years, ~5‐8 years, and >10 years. Areas within 400 km of the coast have higher risk of extreme daily TCP (>100 mm), but inland Texas can also occasionally experience extreme TCP. In some areas in southeastern Texas the probability of receiving >100 mm of daily TCP in any given year is ~0.30 (i.e., daily TCP exceeds 100 mm, on average, 1 out every 3 years).

[1] Previous research has focused on predicting tropical cyclone (TC) size in near real time for individual storms. The purpose of this study is to develop models to explain interannual variations in mean Atlantic TC size, as measured by radius of maximum winds (RMAX) and radial extent of 34 knot winds (17 m s −1 ; R34), and to identify the nature of the relationship between various environmental and storm-related characteristics and TC size. Our analysis demonstrates that mean annual TC size varies systematically among the subbasins in the Atlantic and therefore it is inappropriate to develop a single model for TC size for the entire Atlantic basin. The most important variable for explaining variations in mean annual TC size is the maximum tangential wind (VMAX). VMAX is negatively related to RMAX in all subbasins and positively related to R34 in all subbasins except the Gulf of Mexico, suggesting that years with more intense TCs tend to have smaller (larger) than average RMAX (R34). Other factors, such as the relationships between sea surface temperature, sea level pressure, and Niño 3.4 suggest that environmental factors may play a secondary role in modulating mean annual TC size. Although there are some similarities with the models developed for predicting short-term changes in TC size, our results indicate that it is not appropriate to apply these models to explain variations in TC size at larger spatial scales and longer temporal scales.

Pluvial floods generated by tropical cyclones (TCs) are one of the major concerns for coastal communities. Choosing Houston as an example, we demonstrate that there will be significantly elevated risk of TC rainfall and flood in the future warming world by coupling downscaled TCs from Model Intercomparison Project Phase 6 models with physical hydrological models. We find that slower TC translation speed, more frequent stalling, greater TC frequency, and increased rain rate are major contributors to increased TC rainfall risk and flood risk. The TC flood risk increases more than the rainfall. Smaller watersheds with a high degree of urbanization are particularly vulnerable to future changes in TC floods in a warming world.

Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges.

The decisions that individuals make when recovering from and adapting to repeated hazards affect a region’s vulnerability in future hazards. As such, community vulnerability is not a static property but rather a dynamic property dependent on behavioral responses to repeated hazards and damage. This paper is the first of its kind to build a framework that addresses the complex interactions between repeated hazards, regional damage, mitigation decisions, and community vulnerability. The framework enables researchers and regional planners to visualize and quantify how a community could evolve over time in response to repeated hazards under various behavioral scenarios. An illustrative example using parcel-level data from Anne Arundel County, Maryland—a county that experiences fairly frequent hurricanes—is presented to illustrate the methodology and to demonstrate how the interplay between individual choices and regional vulnerability is affected by the region’s hurricane experience.