Journal of Civil, Construction and Environmental Engineering

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Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya

Received: 16 September 2020    Accepted: 05 October 2020    Published: 13 October 2020
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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.

DOI 10.11648/j.jccee.20200505.14
Published in Journal of Civil, Construction and Environmental Engineering (Volume 5, Issue 5, October 2020)
Page(s) 126-137
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Climate Variability, Land Use, River Regime, SWAT-CUP, SWAT, SWAT_ANN

References
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Author Information
  • Department of Agricultural Engineering, Egerton University, Nakuru, Kenya

  • Department of Agricultural Engineering, Egerton University, Nakuru, Kenya

  • Department of Agricultural Engineering, Egerton University, Nakuru, Kenya

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    Edwin Otieno Amisi, Peter Musula Kundu, Raphael Muli Wambua. (2020). Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya. Journal of Civil, Construction and Environmental Engineering, 5(5), 126-137. https://doi.org/10.11648/j.jccee.20200505.14

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    ACS Style

    Edwin Otieno Amisi; Peter Musula Kundu; Raphael Muli Wambua. Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya. J. Civ. Constr. Environ. Eng. 2020, 5(5), 126-137. doi: 10.11648/j.jccee.20200505.14

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    AMA Style

    Edwin Otieno Amisi, Peter Musula Kundu, Raphael Muli Wambua. Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya. J Civ Constr Environ Eng. 2020;5(5):126-137. doi: 10.11648/j.jccee.20200505.14

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  • @article{10.11648/j.jccee.20200505.14,
      author = {Edwin Otieno Amisi and Peter Musula Kundu and Raphael Muli Wambua},
      title = {Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya},
      journal = {Journal of Civil, Construction and Environmental Engineering},
      volume = {5},
      number = {5},
      pages = {126-137},
      doi = {10.11648/j.jccee.20200505.14},
      url = {https://doi.org/10.11648/j.jccee.20200505.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.jccee.20200505.14},
      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.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Modeling Climate Variability Influence on River Regime in Upper Njoro Catchment, Kenya
    AU  - Edwin Otieno Amisi
    AU  - Peter Musula Kundu
    AU  - Raphael Muli Wambua
    Y1  - 2020/10/13
    PY  - 2020
    N1  - https://doi.org/10.11648/j.jccee.20200505.14
    DO  - 10.11648/j.jccee.20200505.14
    T2  - Journal of Civil, Construction and Environmental Engineering
    JF  - Journal of Civil, Construction and Environmental Engineering
    JO  - Journal of Civil, Construction and Environmental Engineering
    SP  - 126
    EP  - 137
    PB  - Science Publishing Group
    SN  - 2637-3890
    UR  - https://doi.org/10.11648/j.jccee.20200505.14
    AB  - 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.
    VL  - 5
    IS  - 5
    ER  - 

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