HYDROLOGICAL MODELLING FOR FLOOD RISK ANALYSIS IN SELECTED SUB COUNTIES, NAIROBI, KENYA

The study is on flood risk analysis in Mathare, Kamukunji and Makadara Sub-Counties. The objective of this study is to utilize Geographic Information System (GIS)-based hydrological model with remote sensing to analyze the risk of flood in the residential areas of the three sub-counties. Synthetic Aperture Radar (SAR) data was obtained from Sentinel Mission 1 via Copernicus Open Access Hub. Corrected data SAR data was used to create a 10m resolution Digital Elevation Model (DEM). Multispectral Satellite Imagery that was used to perform Land-use/Land-cover analysis using object-based classification method. Ten classes were created. Several factors affecting flood risk were analyzed and among them were LULC and channel flow length, which mainly affected floods in the Area of Study (AOS). The area of study was delineated into four catchments using Arc Hydro Tools. The Hydrological Soil Groups of the AOS were used to sub-divide the catchments into 17 sub-catchments in order to get accurate CN values. Hydrological Engineering Center’s Geospatial Hydrological Model System (HEC-GEOHMS) helped in obtaining channel slope and flow length that were used in calculating the time of concentration for peak discharge and runoff. U.S Soil Conservation Service Technique Release 55 (SCS TR-55) model helped in predicting rainfall-induced flood. Runoff was determined using equations in the model and the peak discharge was computed by the model’s graphical method. The overall map was produced using ArcGIS software by summing up the values obtained from runoff. The results indicated that rainfall-induced flood is a serious problem with flood depth of 13-19.5cm. Decrease in catchment’s flow length and increase in the number of impervious areas due to growth of urbanization increased flood risk in the area. The results of this study will be useful in coming up with solutions for flood risk control which include drainage systems that will improve the infiltration capacity of runoff, appropriate infrastructure e.g., green infrastructure and early warning systems such as sensors on rivers, drainage systems etc.