Abstract
California endures the most diverse geography and weather in the United States, with precipitation that is both variable and extreme. Innovative approaches to statewide emergency preparedness and response are crucial for defending regional areas and prioritizing regional needs. The USDA Curve Number method is broadly used to estimate flood runoff in small watersheds. Design storms are also broadly used by engineers and hydrologists to predict future storm conditions based on historical precedents; however, climate change is impacting these predictive storms. In this research, I built a GIS model which applied the Curve Number method and five design storm parameters to the state’s smallest watershed basins. Their analysis reveals patterns of flood vulnerability.
Many models predict rainfall estimates for climate change. However, the model produced in this project specifically analyzes both the increase in runoff to California watersheds with respect to increasing rainfall due to climate change, The Absolute Runoff Increase Index (∂Q/∂P), as well as the vulnerability that this increased runoff with respect to rainfall would impose as compared to the runoff expected per design storm, The Runoff Increase Vulnerability Index ((∂Q/∂P)/Q)). Most published research applying the Curve Number method are only able to assess a small number of small watersheds. Here, with the advantage of GIS software and Python, a total of 4,472 watershed basins were analyzed statewide. These two indices identify the regions statewide that are the most vulnerable to absolute and relative increases in flood runoff due to climate change; their detection allows regional stakeholders to plan and prioritize accordingly.