GIS-Based dynamic flood modelling along Jukskei River, Johannesburg, South Africa

Abstract

This research focuses on utilizing Geographic Information Systems (GIS) for dynamic flood modelling along the Jukskei River in Johannesburg, South Africa. The study aims to enhance flood risk assessment by integrating GIS technology with hydraulic model. Through spatial data analysis and flood scenario simulations, the research seeks to provide insights into water surface elevation (WSE), vulnerability, and flood impact in the catchment area. The model utilized several parameters: the DEM was STRM data obtained from USGS, precipitation data was acquired from CHIRPS, LULC data was obtained from South Africa's Department of Forestry, Fisheries, and the Environment (DFFE), and soil types were acquired from the international soil reference and information centre. The spatial data analysis included examining trends in precipitation spanning a period of 20 years (2000 - 2020), reclassifying land use land cover (LULC), and outlining river channels using digital elevation models (DEM) and reprojection to common coordinate reference system (CRS). Furthermore, the flood scenario simulations were conducted by combining different factors (DEM, LULC, soil types, and precipitation) and assigning and adjusting Manning's n values for different LULC classes to calibrate the model. From the analysis of the precipitation data spanning two decades (2000 - 2020) with the aid of RStudio, it was noted that certain months exhibited sporadic instances of elevated precipitation throughout the study period, showing a rising trend. The examination indicated that spring, summer, and autumn experienced the highest levels, with the peak observed in autumn at 142.03mm, while the lowest maximum average was 35.23mm. This notable precipitation contributed significantly to flooding within the catchment area, with an R-squared value of 0.0638. The flood depth map, WSE maps, and flood inundation maps indicated that with 28mm of precipitation, 1473 buildings were affected within an area of 324,268 square meters, with a flood depth of 2.64 meters. Similarly, with a precipitation level of 49.43mm, 2803 buildings faced vulnerability within an area of 526,217 square meters, with a flood depth of 5.76 meters. Furthermore, at the highest observed precipitation of 142.03mm, 7242 buildings were at risk within an area of 1,137,908 square meters, with a flood depth measuring 8.41 meters.