Abstract:
The nature of surface runoff and its effects in the watershed can be represented by
the application of hydrologic and hydraulic models. 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 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 datasets
were provided by the Kenya Meteorological Department while for model
calibration and verification, streamflow was obtained from Water Resources
Management Authority. 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. The Digital Soil Map of the World developed by Food
and Agricultural Organisation and the Global Land Cover data of the United States
Geological Survey were used for model parameterisation. The soil moisture
accounting and routing method transferred water through the subsurface,
overland and river phases. The percentage 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 extreme
value analysis done on the modeled streamflow portrayed a unique behaviour of
the system when compared to the ideal system model that should mimic the real
world. 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.
