Editorial: Coastal Flooding: Modeling, Monitoring, and Protection Systems

Coastal flooding has received significant attention in recent years due to future sea-level rise (SLR) projections and intensification of precipitation, which will exacerbate frequent flooding, coastal erosion, and eventually create permanently inundated low-elevation land. Coastal governments will be forced to implement measures to manage risk on the population and infrastructure and build protection systems to mitigate or adapt to the negative impacts of flooding. Research in this area is required to establish holistic frameworks for timely and accurate flooding forecast and design of protection systems

Land use/land cover change along the Eastern Coast of the UAE and its impact on flooding risk

This study was conducted to investigate the spatiotemporal changes of land use/land cover (LULC) along the eastern coast of the United Arab Emirates (UAE) over a 20-year period using an integration of remote sensing and Geographic Information Systems techniques. The impact of land use change on flooding potential was also investigated through hydrologic model simulations. Landsat images of the years 1996, 2006 and 2016 were processed and analyzed. Change detection was carried out to assess changes in the built-up areas. Furthermore, the impact of urbanization on flooding was assessed using a hydrologic model in two major watersheds of Fujairah Emirate. It was observed that for the period 1996–2006 the vegetation and the built-up areas had increased at a rate of 11.23% and 24.56%, respectively. For the period 2006–2016, this expansion more than doubled in terms of the vegetation class (27.51%) and slightly increased for the built-up class (28.98%). The change detection analysis revealed that urbanization has mostly occurred along the coastal boundary. Hydrologic model simulations quantified the role of urbanization in increasing the flooding potential. The increase depends on watershed characteristics and the rate of change in urbanization and the magnitude of the rainfall event

Investigation of the Relationship between Rainfall and Fatal Crashes in Texas, 1994–2018

Understanding how crash factors are impacted by rain is critical to road safety planning and management. This study assesses the impact of rain on traffic safety by conducting an analysis of the fatal crashes related to rain in Texas from 1994 to 2018. The fatal crash data was gathered from the Fatality Analysis Reporting System (FARS) database maintained by the National Highway Traffic Safety Administration (NHTSA). Environmental variables used in the analysis include month of the year, time of the day, temperature, and weather condition. The roadway-related factors identified include the posted speed limit, the number of lanes, route sign, and Vehicle Miles Traveled (VMT). The driver-related factors identified include age, gender, and the number of licensed drivers in total. Relative risk analysis was performed to statistically quantify the impact of rainy conditions at the hourly and monthly time scales. On average, rain-related fatal crashes represented about 6.8% of the total fatal crashes. However, the proportion shows higher variability at the annual, monthly, and hourly time scales and seems to be influenced by other factors such as the age and gender of the driver, type of the road, and posted roadway speed limit. Total and rain-related crashes show statistically significant decreasing trends when normalized by the total number of licensed drivers or vehicle miles travelled. The relative risk of a fatal crash during rainy conditions was always greater than 1.0 at hourly, monthly, and annual time scales. However, it shows significant variability at the monthly (1.07 to 2.78) and hourly scales (1.35 to 2.57). The relative risk is higher in less urbanized and drier counties, in general. Gender and age analysis reveals that male and young drivers are more likely to be involved in a fatal crash but less likely to be killed in the crash

Vehicle-Related Flood Fatalities in Texas, 1959–2019

Texas has the highest number of flood fatalities and vehicle-related flood fatalities in the United States. This study provides a detailed analysis of vehicle-related flood fatalities in Texas from 1959 to 2019. The data was compiled from the Storm Data publication maintained by the National Weather Service and includes demographics of the victims, dates, flood types, roadway types, and fatality location. There were 570 vehicle-related flood fatalities during the study period, with almost all fatal accidents resulting in one fatality. These fatalities represent 58% of total flood fatalities. The spatial analysis reveals that most counties with high vehicle-related flood fatalities are clustered in Flash Flood Alley. These counties accounted for over 80% of the fatalities. The annual distribution of these fatalities follows a statistically significant decreasing trend. Monthly distribution of vehicle-related fatalities follows that of rainfall in the Flash Flood Alley, with flash floods causing 61% of all vehicle-related flood fatalities. Night was the time of the day when the most vehicle-related deaths occurred. Males accounted for 63% of the fatalities and the age group of 20–29 was the most affected. The study discusses how the results can be used to increase awareness of flood hazards, used as input into state and regional disaster mitigation plans, and help tailor education and outreach programs

Analysis of Flood Fatalities in the United States, 1959–2019

Flooding is one of the main weather-related disasters that cause numerous fatalities every year across the globe. This study examines flood fatalities reported in the contiguous United States (US) from 1959 to 2019. The last two decades witnessed major flood events, changing the ranking of the top states compared to previous studies, with the exception of Texas, which had significantly higher flood-related fatalities than any other state. The rankings of counties within some states changed as well. The study aims to improve understanding of the situational conditions, demographics, and spatial and temporal characteristics associated with flood fatalities. The analysis reveals that flash flooding is associated with more fatalities than other flood types. In general, males are much more likely to be killed in floods than females. The analysis also suggests that people in the age groups of 10–19, 20–29, and 0–9 are the most vulnerable to flood hazard. Purposely driving or walking into floodwaters accounts for more than 86% of total flood fatalities. Thus, the vast majority of flood fatalities are preventable. The results will help identify the risk factors associated with different types of flooding and the vulnerability of the exposed communities

Spatio-Temporal Analysis of Precipitation Frequency in Texas Using High-Resolution Radar Products

Understanding the frequency and intensity of precipitation is needed for many vital applications including water supply for agricultural, municipal, industrial, and power generation uses, design of hydraulic structures, and analysis and forecasting of hazards such as flood, drought, and landslide. This study examines, in detail, the spatial and temporal variability of precipitation frequency over the State of Texas and its trends from 2002 to 2019. The results indicate that Texas receives around 325 wet hours on average annually (3.7% of the time). The northern part of the Gulf Coast region witnesses the highest average precipitation frequency reaching 876 wet hours annually. The year 2015 was found to have the highest precipitation frequency across the state with an average frequency of 6% (525 wet hours) and 2011 was the driest, with an average frequency of 1.9% (170 wet hours). In terms of seasonality, the highest precipitation frequency was observed in the summer with a frequency of 4.1%. The areal average time-series of the precipitation frequency indicates that the 2011–2012 drought to be a change point. The Mann–Kendall trend analysis shows that 16.2% of the state experienced a significant positive trend in precipitation frequency including the dry western region and major cities. The results can provide useful information about storm characteristics and recent change and variability of precipitation at high spatial resolutions and can be used in a multitude of practical applications

Rainfall impacts on traffic safety: rain-related fatal crashes in Texas

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 Getis–Ord Gi*(di) 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 rain-related 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 rain-related 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

Analysis of Damage Caused by Hydrometeorological Disasters in Texas, 1960–2016

Property damages caused by hydrometeorological disasters in Texas during the period 1960–2016 totaled $54.2 billion with hurricanes, tropical storms, and hail accounting for 56%, followed by flooding and severe thunderstorms responsible for 24% of the total damages. The current study provides normalized trends to support the assertion that the increase in property damage is a combined contribution of stronger disasters as predicted by climate change models and increases in urban development in risk prone regions such as the Texas Gulf Coast. A comparison of the temporal distribution of damages normalized by population and GDP resulted in a less statistically significant increasing trend per capita. Seasonal distribution highlights spring as the costliest season (March, April and May) while the hurricane season (June through November) is well aligned with the months of highest property damage. Normalization of property damage by GDP during 2001–2016 showed Dallas as the only metropolitan statistical area (MSA) with a significant increasing trend of the 25 MSAs in Texas. Spatial analysis of property damage per capita highlighted the regions that are at greater risk during and after a major disaster given their limited economic resources compared to more urbanized regions. Variation in the causes of damage (wind or water) and types of damage that a “Hurricane” can produce was investigated using Hazus model simulation. A comparison of published damage estimates at time of occurrence with simulation outputs for Hurricanes Carla, 1961; Alicia, 1983; and Ike, 2008 based on 2010 building exposure highlighted the impact of economic growth, susceptibility of wood building types, and the predominant cause of damage. Carla and Ike simulation models captured less than 50% of their respective estimates reported by other sources suggesting a broad geographical zone of damage with flood damage making a significant contribution. Conversely, the model damage estimates for Alicia are 50% higher than total damage estimates that were reported at the time of occurrence suggesting a substantial increase in building exposure susceptible to wind damage in the modeled region from 1983 – 2010

Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values

Modeling the Projected Changes of River Flow in Central Vietnam under Different Climate Change Scenarios

Recent studies by the United Nations Environment Programme (UNEP) and the Intergovernmental Panel on Climate Change (IPCC) indicate that Vietnam is one of the countries most affected by climate change. The variability of climate in this region, characterized by large fluctuations in precipitation and temperature, has caused significant changes in surface water resources. This study aims to project the impact of climate change on the seasonal availability of surface water of the Huong River in Central Vietnam in the twenty-first century through hydrologic simulations driven by climate model projections. To calibrate and validate the hydrologic model, the model was forced by the rain gage-based gridded Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) V1003R1 Monsoon Asia precipitation data along with observed temperature, humidity, wind speed, and solar radiation data from local weather stations. The simulated discharge was compared to observations for the period from 1951 until present. Three Global Climate Models (GCMs) ECHAM5-OM, HadCM3 and GFDL-CM2.1 integrated into Long Ashton Research Station-Weather Generator (LARS-WG) stochastic weather generator were run for three IPCC–Special Report on Emissions Scenarios (IPCC-SRES) emissions scenarios A1B, A2, and B1 to simulate future climate conditions. The hydrologic model simulated the Huong River discharge for each IPCC-SRES scenario. Simulation results under the three GCMs generally indicate an increase in summer and fall river discharge during the twenty-first century in A2 and B1 scenarios. For A1B scenario, HadCM3 and GFDL-CM2.1 models project a decrease in river discharge from present to the 2051–2080 period and then increase until the 2071–2100 period while ECHAM5-OM model produces opposite projection that discharge will increase until the 2051–2080 period and then decrease for the rest of the century. Water management impacts, such as irrigation or dam regulation, were not considered in this study. However, the results provide local policy makers with quantitative data to consider possible adjustment of future dam capacities for development of flood control policies