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Egerton University (2021)

Modelling and Forecasting of Climate Variability Impact on Spatio-Temporal Distribution of Stream Flow In Njoro River Catchment, Kenya

Otieno, Adwin Amisi

Titre : Modelling and Forecasting of Climate Variability Impact on Spatio-Temporal Distribution of Stream Flow In Njoro River Catchment, Kenya

Auteur : Otieno, Adwin Amisi

Université de soutenance : Egerton University

Grade : Master of Science Degree in Agricultural Engineering 2021

Résumé
Climate variability continues to alter hydrological regimes and response of many catchments globally thus threatening water security. Njoro River catchment has not been an exception, there is a steady recognition of climate variability adverse influences like deterioration of ecosystems, surface and groundwater sources. Therefore, the study focused on modelling and forecasting the impact of climate variability on spatial and temporal distribution of stream flow in Njoro River catchment, Kenya. Trends in climate variables from Egerton University weather station (ID : 09035092) were first analysed by the use of Mann-Kendall test for the period (1978 -2017). Then, modelling of stream flow response to climate variability using SWAT was carried out based on USGS/NASA downloaded Digital Elevation Model, FAO soil data, Landsat (MSS 1-5) LULC of 1978, and meteorological data for the period (1978 - 2017). Simulation of spatial and temporal impacts of climate variability then followed, and finally, a hybrid modelling technique of coupling SWAT and ANN models were then applied to forecast climate variability impact on stream flow for the period (2018-2037). Trend results for the period (1978-2017) showed that annual precipitation had a positive trend that was not significant at p < 0.05. Solar radiation, maximum and minimum temperatures had significant positive trends. Relative humidity had a negative trend that was not significant. Wind speed had a significant decreasing trend. Based on SWAT modelling, the most sensitive parameter was CN2 while the least was CANMX. Overall uncertainty analysis results indicated a good model performance value of P-factor (0.72) and R-factor (0.38). The values of R2, NSE, and PBIAS for calibration and validation of monthly stream flow using observed data from Water Resources Authority were 0.88 and 0.77, 0.86 and 0.74, and 5.51 % and -15.42 % respectively. Spatio-temporal impacts of climate variability on stream flow revealed that on average, stream flow reduced by 30.91 % in the 2nd and increased by 5.47 % and 63.63 % in the 3rd and 4th decades respectively. The annual average temperatures and precipitation were forecasted to increase from the baseline values by about 0.6 0C and 18.99 %, and 0.85 0C and 28.26 %, respectively for the period (2018 - 2027), and (2028 - 2037). From the SWAT and ANN coupling approach, the forecasting stream flow model indicated an overall better performance of (R2 = 0.92) and (NSE = 0.89). The average annual stream flow was forecasted to increase from the baseline values by about 83 % and 130 % for the periods (2018-2027) and (2028-2037) respectively. These findings provide pertinent insights, which may perhaps enlighten decision-making in designing adaptable mitigation measures, catchment rehabilitation, and strategic initiatives for the integrated management of water resources.

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