Integrated flood risk assessment of properties and associated population at county scale for Nebraska, USA.

Risk assessment of properties and associated population was conducted for the state of Nebraska, leveraging only open-source datasets. The flood risk framework consisted of interactions among drivers, i.e. hazard, exposure, vulnerability, and response, to assess the risks related to properties and associated populations. To quantify hazard on a county scale, we considered properties at risk of flooding based on a flood score (a higher score represents a greater chance of flooding). Exposure was quantified by considering population density at the county level. We quantified vulnerability under four categories: social, ecological, economic, and health. Response, a relatively newer component in flood risk assessment, was also quantified under three distinct categories: structural, non-structural, and emergency. Overall, we found that counties in eastern Nebraska (Sarpy, Dakota, Wayne, and Adams) have a higher risk of flooding consequences due to more exposure to vulnerable assets such as population and property. The assessment also observed that counties in eastern Nebraska are in the process of improving their flood control measures with dams, levees, and higher insurance coverage that can subdue the risks associated with flooding. The results from this study are anticipated to guide water managers and policymakers in making more effective and locally relevant policies and measures to mitigate flood risks and consequences.

An autoencoder-based snow drought index.

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents.