16 Jun 2025

Enhancing Resilience: Integrating Future Flood Modeling and Socio-Economic Analysis in the Face of Climate Change Impacts

Abstract: As climate change intensifies, floods will become more severe in some areas with geographic variation, necessitating governments implementing systems providing information for climate adaptation. We aimed to develop a methodology identifying areas at an increased risk. In this study, 100-year recurrence interval flood extents and depths were estimated using an ensemble of six independent Coupled Model Intercomparison Project Phase 6 climate models for a past and future period under the highest-emissions climate scenario. The flood inundation results were related to social vulnerability for two study areas in the Mississippi River Basin. To identify at-risk areas, the relationship between the spatial distribution of flood depths and vulnerability was assessed. Finally, an analysis of current and future damages on infrastructure from flooding on residential housing to determine whether damages correlated with higher vulnerability areas. Results show flood extents and depths are increasing in the future, ranging from an increase of 6 to 76 km2 in extent. A statistically significant relationship between spatial clusters of flooding and of vulnerability was found. Overall, a framework was established to holistically understand the hydrologic and socioeconomic impacts of climate change, and a methodology was developed for allocating resources at the local scale.

10 Jun 2024

Post-wildfire Curve Number Estimates for the Southern Rocky Mountains in Colorado, USA

Abstract: The curve number method first developed by the USDA Soil Conservation Service (now the Natural Resources Conservation Service) is often used for post-wildfire runoff assessments. These assessments are critical for land and emergency managers making decisions on life and property risks following a wildfire event. Three approaches (i.e., historical event observations, linear regression model, and regression tree model) were used to help estimate a post-wildfire curve number from watershed and wildfire parameters. For the first method, we used runoff events from 102 burned watersheds in Colorado, southern Wyoming, northern New Mexico, and eastern Utah to quantify changes in curve number values from pre- to post-wildfire conditions. The curve number changes from the measured runoff events vary substantially between positive and negative values. The measured curve number changes were then associated with watershed characteristics (e.g., slope, elevation, northness, and eastness) and land cover type to develop prediction models that provide estimates of post-wildfire curve number changes. Finally, we used a regression tree method to demonstrate that accurate predications can be developed using the measured curve number changes from our study domain. These models can be used for future post-wildfire assessments within the region.