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A significant number of crashes on roads are caused by adverse weather conditions. Among the most serious consequences of rainfall and flooding in regard to road safety are the motor vehicle-related flash flood fatalities. These fatalities are of particular concern in Texas. Information on motor vehicle fatalities caused by flash floods was extracted from the National Climatic Data Center Storm Data reports. Review of reports on flash flood fatalities in general, where the death circumstances are provided, reveals that most fatalities are motor vehicle-related (80%). Moreover, data from the reports indicate that from 1959 to 2009 the number of motor vehicle-related flash flood fatalities in Texas exceeds by far the total number of flood fatalities in any other state. Demographic analysis of the flash flood fatalities indicates that, in Texas, all ages are at risk and that males are much more affected than females. Spatial analysis indicates that the highest numbers of fatalities occur in counties having major urban areas. The increase in the frequency and intensity of storms and floods that is projected to result from climate changes and variability, and the rapid urbanization in the region may considerably increase the impacts of weather on road safety in Texas.

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The need for accurate hydrologic analysis and rainfall–runoff modelling tools has been rapidly increasing because of the growing complexity of operational hydrologic and hydraulic problems associated with population growth, rapid urbanization and expansion of agricultural activities. Given the recent advances in remote sensing of physiographic features and the availability of near real‐time precipitation products, rainfall–runoff models are expected to predict runoff more accurately. In this study, we compare the performance and implementation requirements of two rainfall–runoff models for a semi‐urbanized watershed. One is a semi‐distributed conceptual model, the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS). The other is a physically based, distributed‐parameter hydrologic model, the Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Four flood events that took place on the Leon Creek watershed, a sub‐watershed of the San Antonio River basin in Texas, were used in this study. The two models were driven by the Multisensor Precipitation Estimator radar products. One event (in 2007) was used for HEC‐HMS and GSSHA calibrations. Two events (in 2004 and 2007) were used for further calibration of HEC‐HMS. Three events (in 2002, 2004 and 2010) were used for model validation. In general, the physically based, distributed‐parameter model performed better than the conceptual model and required less calibration. The two models were prepared with the same minimum required input data, and the effort required to build the two models did not differ substantially. Copyright © 2012 John Wiley & Sons, Ltd.

This study uses fatal crash data and geospatial analysis to examine the temporal and spatial distribution of rain-related fatal crashes in Texas from 1982 to 2011. The data were obtained from the Fatality Analysis Reporting System maintained by the National Highway Traffic Safety Administration. The GetisÀOrd G i à (d i ) statistic was used to identify spatial clustering patterns of rain-related fatal crashes and their correlation with rainfall and compare them to spatial patterns of other crashes. The spatial statistical analysis reveals spatial patterns for rainrelated crashes that are clustered in different areas at different levels of confidence. The temporal variability of raw and normalized fatal crashes and rain-related crashes was also investigated at the state level. Although the population of Texas increased by more than 67% over the study period, with a corresponding increase in the number of vehicles, the fatal crashes and rainrelated crashes in Texas did not increase but decreased instead. Results suggest that rain is a contributor to crashes in few counties but at less than 95% confidence in some of the wetter counties. These counties should be the focus of further research and detailed analysis to identify underlying crash contributing factors such that safety countermeasures can be developed.