Environmental, climatic, and situational factors influencing the probability of fatality or injury occurrence in flash flooding: a rare event logistic regression predictive model.

Flash flooding is considered one of the most lethal natural hazards in the USA as measured by the ratio of fatalities to people affected. However, the occurrence of injuries and fatali- ties during flash flooding was found to be rare (about 2% occurrence rate) based on our analysis of 6,065 flash flood events that occurred in Texas over a 15-year period (2005 to 2019). This article identifies climatic, environmental, and situational factors that affect the occurrence of fatalities and injuries in flash flood events and provides a predictive model to estimate the likelihood of these occurrences. Due to the highly imbalanced dataset, three forms of logit models were investigated to achieve unbiased estimations of the model coef- ficients. The rare event logistic regression (Relogit) model was found to be the most suit- able model. The model considers ten independent situational, climatic, and environmental variables that could affect human safety in flash flood events. Vehicle-related activities dur- ing flash flooding exhibited the greatest effect on the probability of human harm occur- rence, followed by the event's time (daytime vs. nighttime), precipitation amount, location with respect to the flash flood alley, median age of structures in the community, low water crossing density, and event duration. The application of the developed model as a simula- tion tool for informing flash flood mitigation planning was demonstrated in two study cases in Texas.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.

Potential of Citizen Science for Enhancing Infrastructure Monitoring Data and Decision-Support Models for Local Communities.

Citizen science is a process by which volunteer members of the public, who commonly lack advanced training in science, engage in scientific activities (e.g., data collection) that might otherwise be beyond the reach of professional researchers or practitioners. The purpose of this article is to discuss how citizen-science projects coordinated by interdisciplinary teams of engineers and social scientists can potentially enhance infrastructure monitoring data and decision-support models for local communities. The article provides an interdisciplinary definition of infrastructure data quality that extends beyond accuracy to include currency, timeliness, completeness, and equitability. We argue that with this expanded definition of data quality, citizen science can be a viable method for enhancing the quality of infrastructure monitoring data, and ultimately the credibility of risk analysis and decision-support models that use these data. The article concludes with a set of questions to aid in producing high-quality infrastructure monitoring data by volunteer citizen scientists.

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems.

Climate change and increasing natural disasters coupled with years of deferred maintenance have added pressure to infrastructure in urban areas. Thus, monitoring for failure of these systems is crucial to prevent future impacts to life and property. Participatory assessment technique for infrastructure provides a community-based approach to assess the capacity and physical condition of infrastructure. Furthermore, a participatory assessment technique for infrastructure can encourage grassroots activism that engages residents, researchers, and planners in the identification of sustainable development concerns and solutions. As climate change impacts disproportionately affect historically disenfranchised communities, assessment data can further inform planning, aiming to balance the distribution of public resources towards sustainability and justice. This paper explains the development of the participatory assessment technique for infrastructure that can provide empirical data about the condition of infrastructure at the neighborhood-level, using stormwater systems in a vulnerable neighborhood in Houston, Texas as a case study. This paper argues for the opportunity of participatory methods to address needs in infrastructure assessment and describes the ongoing project testing the best use of these methods.