Simulating the Influence of Rainfall Variability on Discharge in the Upper River Yala Basin, Kenya

Abstract

Climate variability is significantly altering river flows globally, increasing the risk of floods and droughts. Projections indicate both rising and declining flows across various regions, influenced by the impacts of climate variability and land use changes. Research has shown that climate change, land use, and pollution exacerbate water scarcity for half the global population, impacting ecosystems, especially in vulnerable regions. This study focuses on the Upper Yala River in Kenya, exploring climate variability's influence on discharge in various Land Use contexts using the SWAT model. Existing research highlights the significance of land use, hydrological indicators, and climate data, establishing a framework to analyze stream flow trends. The study analyzed climate and stream flow data from 1990-2020 using the SWAT model for hydrological assessment and predictions for the years 2024 to 2040 was done. The research was guided by Water Balance Theory and employed a descriptive and analytical design. Data collection included meteorological data from weather stations, hydrological data from gauging stations, and land use and land cover (LULC) data from remote sensing and satellite imagery. The Soil and Water Assessment Tool (SWAT) was used to simulate river discharge and assess the impacts of climate variability, integrating climate, land use, soil type, and topographic data. Data analysis involved descriptive statistics to summarize discharge data, correlation analysis to link rainfall variability and discharge patterns, and performance metrics like the Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) to validate the model. Statistical techniques identified long-term trends in climate and streamflow, focusing on inter-seasonal and inter-annual variations. The Upper Yala River Basin experiences significant inter-seasonal and inter-annual streamflow variations, primarily influenced by rainfall fluctuations. A strong correlation between simulated and observed discharge data for the Upper Yala River Basin was demonstrated. The mean observed discharge was 48.69 m³/s, with maximum and minimum values of 163.09 m³/s and 0.328 m³/s, and a standard deviation of 34.28 m³/s. In contrast, the simulated discharge had a mean of 53.56 m³/s, with maximum and minimum values of 174.41 m³/s and 0.360 m³/s, and a standard deviation of 37.87 m³/s. The minimal differences between the observed and simulated values underscore the model's effectiveness in accurately reflecting the impacts of rainfall variability on river flow dynamics. The study concluded that in the Upper River Yala watershed, rainfall variability accounted for 94.2% of the variations in river discharge quantity. The study recommends enhancing climate monitoring by adding weather stations and stream gauges in the basin and utilizing remote sensing for tracking land use and vegetation changes. Improved data availability from these measures will enable better discharge predictions and inform water management decisions to mitigate climate impacts on the river basin and surrounding communities.