Volume 5, Issue 5, October 2020, Page: 126-137
Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya
Edwin Otieno Amisi, Department of Agricultural Engineering, Egerton University, Nakuru, Kenya
Peter Musula Kundu, Department of Agricultural Engineering, Egerton University, Nakuru, Kenya
Raphael Muli Wambua, Department of Agricultural Engineering, Egerton University, Nakuru, Kenya
Received: Sep. 16, 2020;       Accepted: Oct. 5, 2020;       Published: Oct. 13, 2020
DOI: 10.11648/j.jccee.20200505.14      View  17      Downloads  23
Abstract
To establish the effect of climate variability on annual discharge in Upper Njoro Catchment, hybrid models were developed by coupling Soil and Water Assessment Tool and Artificial Neural Networks. Daily surface runoff, lateral flow, and groundwater flow were first simulated with SWAT for the period (1978-1987) using climate variables from Egerton University weather station and LULC of 1978. The daily hydrologic variables simulated without calibration and validation of SWAT and observed discharge data were then used for ANN training, which led to the creation of discharge generation hybrid models for the dry, wet and wetter seasons. SWAT_ANN models generated discharges were compared with observed data and the performance rating were achieved at R2 (0.94, 0.91, 0.92) and NSE (0.89, 0.87, 0.87) for DJFM, AMJJ, and ASON seasons respectively. SUFI-2 algorithm in SWAT-CUP was run separately to compare the performance of SWAT with that of SWAT_ANN. SWAT-CUP sensitivity analysis revealed satisfactory values of both the p-factor (0.61) and the r-factor (0.69). Calibration and validation of monthly streamflow were realized at R2 (0.86 and 0.78) and NSE (0.83 and 0.74). The results showed that coupling SWAT and ANN improved flow prediction. Further, the potential of the SWAT_ANN modeling approach to separate the influence of climate variability on river regime from the effect of LULC was evaluated by comparing trends in the differences between observed and SWAT_ANN simulated monthly streamflow with trends of the quantified LULC changes. The findings provided sufficient evidence that the SWAT_ANN modeling approach was reliable and could also be applied to detect changes in LULC.
Keywords
Climate Variability, Land Use, River Regime, SWAT-CUP, SWAT, SWAT_ANN
To cite this article
Edwin Otieno Amisi, Peter Musula Kundu, Raphael Muli Wambua, Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya, Journal of Civil, Construction and Environmental Engineering. Vol. 5, No. 5, 2020, pp. 126-137. doi: 10.11648/j.jccee.20200505.14
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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