Flash flood hazard mapping: A pilot case study in Xiapu River Basin, China

Flash flood hazard mapping is a supporting component of non-structural measures for flash flood prevention. Pilot case studies are necessary to develop more practicable methods for the technical support systems of flash flood hazard mapping. In this study, the headwater catchment of the Xiapu River Basin in central China was selected as a pilot study area for flash flood hazard mapping. A conceptual distributed hydrological model was developed for flood calculation based on the framework of the Xinanjiang model, which is widely used in humid and semi-humid regions in China. The developed model employs the geomorphological unit hydrograph method, which is extremely valuable when simulating the overland flow process in ungauged catchments, as compared with the original Xinanjiang model. The model was tested in the pilot study area, and the results agree with the measured data on the whole. After calibration and validation, the model is shown to be a useful tool for flash flood calculation. A practicable method for flash flood hazard mapping using the calculated peak discharge and digital elevation model data was presented, and three levels of flood hazards were classified. The resulting flash flood hazard maps indicate that the method successfully predicts the spatial distribution of flash flood hazards, and it can meet the current requirements in China.

Implementation of Remote Sensing and GIS Techniques to Study the Flash Flood Risk at NEOM Mega-City, Saudi Arabia

Southern Red Sea flooding is common. Assessing flood-prone development risks helps decrease life and property threats. It tries to improve flood awareness and advocate property owner steps to lessen risk. DEMs and topography data were analyzed by RS and GIS. Fifth-through seventh-order rivers were studied. Morphometric analysis assessed the area’s flash flood danger. NEOM has 14 catchments. We determined each catchment’s area, perimeter, maximum length, total stream length, minimum and maximum elevations. It also uses remote sensing. It classifies Landsat 8 photos for land use and cover maps. Image categorization involves high-quality Landsat satellite images and secondary data, plus user experience and knowledge. This study used the wetness index, elevation, slope, stream power index, topographic roughness index, normalized difference vegetation index, sediment transport index, stream order, flow accumulation, and geological formation. Analytic hierarchy considered all earlier criteria (AHP). The geometric consistency index GCI (0.15) and the consistency ratio CR (4.3%) are calculated. The study showed five degrees of flooding risk for Wadi Zawhi and four for Wadi Surr, from very high to very low. 9.16% of Wadi Surr is vulnerable to very high flooding, 50% to high flooding, 40% to low flooding, and 0.3% to very low flooding. Wadi Zawhi’s flood risk is 0.23% high, moderate, low, or extremely low. They’re in Wadi Surr and Wadi Zawhi. Flood mapping helps prepare for emergencies. Flood-prone areas should prioritize resilience.