Rain-induced natural hazards can lead to devastating and potentially life-threatening impacts. Understanding areas susceptible to flash flooding and characterizing the intensity of flash flood events is critical in improving the mitigation and emergency preparedness of vulnerable communities. Flash floods occur on small spatial scales and for short durations making it challenging to classify flash flood susceptibility and forecast events. Modeling flash flooding becomes even more difficult when focusing on data-poor regions. This study is based in California, El Salvador, an agricultural community located in the Central American Dry Corridor (CADC), a region experiencing the impacts of climate change and associated natural hazards, including flash flood events. The research objective is to improve knowledge of rain-induced hazards in remote, low-resource communities using methods from hazard mapping and modeling. ArcGIS Pro is used to create a flash flood susceptibility map of the Usulután Department, El Salvador to gain a spatial understanding of the hazard. The Water Evaluation and Planning system (WEAP) is then applied to model sub-daily flash flooding events in a California drainage well-known for flash flooding. This study can provide insights into how an area with little surface water can still experience flash flooding, an initial step in understanding groundwater hydrology in a data-poor region. The flash flood susceptibility map created in ArcGIS Pro provides valuable information for determining potential locations of interest for flood monitoring and more in-depth analysis. The WEAP model applied field work, climate data, topography, soil infiltration rates, and other estimated variables to model flash flood events by simulating time-varying streamflow rates for various scenarios. This research seeks to promote further monitoring of rainfall and land use change, encourage increased incorporation of local knowledge to improve future flash flood research, and inspire future flash flood mapping and modeling in data-poor regions
