Abstract
According to the United Nations (UN), an additional 1.35 billion people will live in cities by 2030. Well-planned measures are essential for reducing the risk of flash floods. Flash floods typically inflict more damage in densely populated areas. The province of Hail encompasses 120,000 square kilometers, or approximately 6% of the total land area of the Kingdom of Saudi Arabia. Due to its innate physiographic and geologic character, Hail city is susceptible to a wide variety of geo-environmental risks such as sand drifts, flash floods, and rock falls. The aim of this work is to evaluate the rate of urban sprawl in the Hail region using remote sensing data and to identify urban areas that would be affected by simulated worst-case flash floods. From 1984 to 2022, the global urbanization rate increased from 467 to 713% in the Hail region. This is a very high rate of expansion, which means that the number of urban areas exposed to the highest level of flood risk is rising every year. With Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a wide range of hydrologic scenarios can be simulated. The data sources for the soil type, infiltration, and initial moisture were utilized to create the coverage and index maps. To generate virtual floods, we ran the GSSHA model within the Watershed Modeling System (WMS) program to create the hazard map for flash flooding. This model provides a suitable method based on open access data and remote data that can help planners in developing countries to create the risk analysis for flash flooding.