A SCS-CN TECHNIQUE FOR GEOSPATIAL ESTIMATION OF RUNOFF PEAK DISCHARGE IN THE KUBANNI DRAINAGE BASIN, ZARIA, NIGERIA

Abstract

The problem of soil loss is becoming widespread due to increasing unwholesome land use practices and population pressure on limited landscape. This study employed the integration of satellite imageries, rainfall and soil data and modern GIS technology to estimate runoff peak discharge in the Kubanni drainage basin. Some of the contributions of this study include the determination of the Hydrologic Soil Group (HSG) and Soil Conservation Service Curve Number (SCS CN) for the Kubanni drainage basin with a view to investigating runoff peak discharge using geospatial technology. Satellite images of Landsat OLI for February, July and November 2019, rainfall data from 2014 to 2018, soil data and SRTM 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 Land Use and Land Cover (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 Goruba, Maigamo, Tukurwa and Malmo sub basins were discovered to be 79.72, 76.51, 71.47 and 66.00 respectively. The runoff peak discharges for the Kubanni drainage basin was found to be , , and for the years 2014, 2015, 2016, 2017 and 2018 respectively. The study has demonstrated the viability of adopting the SCS CN technique, satellite images, rainfall data and geospatial tools for the estimation of runoff peak discharge in the Kubanni drainage basin.