Application of GIS-based SCS-CN Method for Runoff Estimation in the Kubanni Drainage Basin, Zaria, Nigeria

The problem of soil erosion and sedimentation of watercourses is becoming widespread due to increasing unwholesome land-use practices and population pressure on the limited landscapes. To ascertain the response of the Kubanni drainage basin to natural and anthropogenic forcing, this study adopted SCS-CN and geospatial technology to estimate the runoff of the Kubanni drainage basin. Some of the unique contributions of this study include the determination of the HSG and SCS-CN for the Kubanni drainage basin as a precondition to estimating runoff in a GIS environment. Satellite images of Landsat OLI for December 2014 and 2018, rainfall data from 2014 to 2018, soil data and DEM of 30-meter resolution were utilized for the study. A maximum likelihood supervised classification method was adopted in processing the satellite images to determine the LULC classes for the Kubanni drainage basin landscape. The LULC classes for the study area include built up area, water, vegetation, farmland and bare land. The SCS-CN values for the sub basins of Goruba, Maigamo, Tukurwa and Malmo were discovered to be 79.724, 76.506, 71.470 and 66.004 respectively, while the average SCS-CN value was found to be 73.426. The volume of runoff for the year 2014, 2015, 2016, 2017 and 2018 were discovered to be 1,435,722.7m 3 , 1,651,498.6m 3 , 1,281,367.5m 3 , 1,051,406.9m 3 and 1,592,346.9m 3 respectively, while the average runoff was found to be 1,402,406.52m 3 year -1 . The model of this study is empirically viable and demonstrates its applicability in investigating watershed runoff due to anthropogenic and climatic forcing.