Rainfall runoff modeling in Yala River basin of Western Kenya
Volume 5:1
Kiluva V.M.*, Mutua F.M **, Makhanu S.K.*** and Ong’or B.T.***
*Centre for Disaster Management and Humanitarian Assistance, MMUST
**Department of Meteorology, University of Nairobi
***Department of Civil and Structural Engineering, Masinde Muliro University of Science and Technology
CORRESPONDING AUTHOR
Kiluva Veronica Mwikali
Centre for Disaster Management and Humanitarian Assistance,
Masinde Muliro University of Science and Technology
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When rainfall is received on a watershed, depending on the initial soil moisture content some of the water seeps underground while the excess forms surface water response. The nature of the runoff and its effects in the watershed can be represented by the application of hydrologic models to predict streamflow. In this study, the Geological Streamflow Model (GeoSFM) and the Muskingum Cunge (M-C) model were used to model the hydrologic processes of the Yala river network. The objective of the study was to develop a flood early warning system to mitigate potential flood hazard risk exposed to the downstream inhabitants. Historical hydro-metric datasets of 1975-2005 were used for calibration, verification and streamflow routing based
on a split record analysis. For the runoff generation, rainfall and evaporation on datasets were provided by the Kenya Meteorological Department (KMD) while for model calibration and verification, streamflow was obtained from Water Resources Management Authority (WRMA). To determine the hydrologic connectivity, the 30 meters by 30 meters Digital Elevation Model was obtained from the International Centre for Research in Agro-Forestry (ICRAF). The Digital Soil Map of the World developed by Food and Agricultural Organization (FAO) and the Global Land Cover data of the United States Geological Survey (USGS) were used for model parameterization. The soil moisture accounting and routing method transferred water through the subsurface,
overland and river phases. The percentage of the square of the correlation coefficient (R2% value) was used to determine model performance. The GeoSFM modeled streamflow at the Bondo streamflow gauging station, coded 1FG02 where during the calibration and verification phases, streamflow was modeled at R2 value of 80.6% and 87.3% respectively. The M-C model routed streamflow from 1FG02 to the Kadenge streamflow gauging station, coded 1FG03 at R2 value of 90.8%, Muskingum K value of 2.76 hours and Muskingum X value of 0.4609. The error in predicted peak streamflow was 2.3% with a posi ve 1.5% error in predicted speed. This ensured a forecast of the time of peak streamflow on the safe side before the actual flood peak arrival at 1FG03 station. It was concluded that the GeoSFM and M-C models were hence useful tools for flood mitigation by issuing flood early warning messages defined by peak streamflow and flood wave travel time.
Key words: Flood, Yala River, Geological Streamflow Model, Muskingum Cunge Model.
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