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

Edwin Otieno Amisi; Peter Musula Kundu; Raphael Muli Wambua

doi:doi:10.11648/j.jccee.20200505.14

| Peer-Reviewed

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

Edwin Otieno Amisi, Peter Musula Kundu, Raphael Muli Wambua

Published in Journal of Civil, Construction and Environmental Engineering (Volume 5, Issue 5)

Received: 16 September 2020 Accepted: 5 October 2020 Published: 13 October 2020

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Journal of Civil, Construction and Environmental Engineering (Volume 5, Issue 5)
DOI	10.11648/j.jccee.20200505.14
Page(s)	126-137
Creative Commons	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), 2020. Published by Science Publishing Group

Keywords

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

References

[1]	Amisi, L. (2018). Analysis of event-based soil hydrologic response for different land-use types in Upper Njoro river catchment (Masters Dissertation, Egerton University).
[2]	Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R.,... & Kannan, N. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55 (4), 1491-1508.
[3]	Assouline, S., & Mualem, Y. (2000). Modeling the dynamics of soil seal formation: Analysis of the effect of soil and rainfall properties. Water Resources Research, 36 (8), 2341-2349.
[4]	Baldyga, T. J. (2005) Assessing Land Cover Change Impacts in Kenya’s River Njoro Watershed Using Remote Sensing and Hydrologic Modelling. MSc Thesis, University of Wyoming, Laramie.
[5]	Bao, Z., Zhang, J., Wang, G., Fu, G., He, R., Yan, X.,... & Zhang, A. (2012). Attribution for decreasing streamflow of the Haihe River basin, northern China: climate variability or human activities? Journal of Hydrology, 460, 117-129.
[6]	Benson, R. D., Tarlock, A. D., James, N., Getches, D. H., & Bates, S. F. (2014). Water resource management. Foundation Press.
[7]	Devia, G. K., Ganasri, B. P., & Dwarakish, G. S. (2015). A review on hydrological models. Aquatic Procedia, 4, 1001-1007.
[8]	Dias, L. C. P., Macedo, M. N., Costa, M. H., Coe, M. T., Neill, C., 2015. Effects of land cover change on evapotranspiration and strm, eamflow of small catchments in the Upper Xingu River Basin, Brazil. J. Hydrol.: Reg. Stud. 4B, 108–122.
[9]	Fan, M., & Shibata, H. (2015). Simulation of watershed hydrology and stream water quality under land use and climate change scenarios in Teshio River watershed, northern Japan. Ecological Indicators, 50, 79-89.
[10]	Farley, K. A., Jobbagy, E. G., Jackson, R. B., 2005. Effects of afforestation on water yield: a global synthesis with implications for policy. Glob. Change Biol. 11 (10), 1565–1576.
[11]	Haghighi, F., Gorji, M., & Shorafa, M. (2010). A study of the effects of land use changes on soil physical properties and organic matter. Land Degradation & Development, 21 (5), 496-502.
[12]	Hamidreza, J., Ahmadian, A., Golkar, M. A., Elkamel, A., & Almansoori, A. (2018). Solar Irradiance Forecasting Based on the Combination of Radial Basis Function Artificial Neural Network and Genetic Algorithm.
[13]	Hayati, M., & Shirvany, Y. (2007). ANN approach for short term load forecasting for Illam region‖.
[14]	Hayhoe, S., Neill, C., Porder, S., McHorney, R., Lefebvre, P., Coe, M., Elsenbeer, H., Krusche, A., 2011. Conversion to soy on the Amazonian agricultural frontier increases streamflow without affecting stormflow dynamics. Glob. Change Biol. 17 (5), 1821–1833. http://dx.doi.org/10.1111/j.1365-2486.2011.02392.x.
[15]	Githui, F., Mutua, F., Bauwens, W., 2009. Estimating the impacts of land-cover change on runoff using the soil and water assessment tool (SWAT): case study of Nzoia catchment, Kenya. Hydrol. Sci. J 54, 899–990. http://dx.doi.org/10.1623/hysj.54.5.899.
[16]	Goyal, M. K. & Ojha, C. S. P. (2010). Analysis of mean monthly rainfall runoff data of Indian catchments using dimensionless variables by neural network. Journal of Environmental Protection, 1 (02), 155.
[17]	Isik, S., Kalin, L., Schoonover, J. E., Srivastava, P., & Lockaby, B. G. (2013). Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. Journal of Hydrology, 485, 103-112.
[18]	Jacobs, J. H., Angerer, J., Vitale, J., Srinivasan, R., & Kaitho, R. (2007). Mitigating economic damage in Kenya’s upper Tana River basin: an application of Arc-View SWAT. Journal of Spatial Hydrology, 7 (1).
[19]	Jajarmizadeh, M., Lafdani, E. K., Harun, S., & Ahmadi, A. (2015). Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran. KSCE Journal of Civil Engineering, 19 (1), 345-357.
[20]	Jayakrishnan, R. S. R. S., Srinivasan, R., Santhi, C., & Arnold, J. G. (2005). Advances in the application of the SWAT model for water resources management. Hydrological Processes: An International Journal, 19 (3), 749-762.
[21]	Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A Comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of Peninsular Spain. Water, 10 (2), 192.
[22]	Kalin, L., Isik, S., Schoonover, J. E., Lockaby, B. G., 2010. Predicting water quality in unmonitored watersheds using artificial neural networks. JEQ 39 (4), 1429–1440.
[23]	Khalid, K. (2017). SWAT and ANN model hydrological assessment using Malaysia soil data (Doctoral dissertation, Universiti Teknologi MARA).
[24]	Khashei, M., &Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37 (1), 479-489.
[25]	Khoi, D. N., & Thom, V. T. (2015). Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam. Global ecology and conservation, 4, 538-548.
[26]	Kişi, Ö. (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrology Research, 39 (1), 27-40.
[27]	Kundu, P. M. (2007) Application of Remote Sensing and GIS Techniques to Evaluate the Impact of Land Use and Land Cover Change on Stream Flows: The Case for River Njoro Catchment in Eastern Mau-Kenya. PhD Thesis, Egerton University, Njoro.
[28]	Mango, L. M., Melesse, A. M., McClain, M. E., Gann, D., & Setegen, S. G. (2010). Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management.
[29]	Marhaento, H., Booij, M. J., & Hoekstra, A. Y. (2016). Attribution of changes in stream flow to land use change and climate change in a mesoscale tropical catchment in Java, Indonesia. Hydrology Research, 48 (4), 1143-1155.
[30]	Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50 (3), 885-900.
[31]	Mwetu, K. K. (2010). Modelling Responses of Hydrology to Land Use/ Land Cover Change and Climate Variability: A case of River Njoro Catchment of Kenya: Doctoral Thesis. University of Natural Resources and Applied Sciences, Vienna.
[32]	Mwetu, K. K. (2019). Influence of Land Cover Changes and Climatic Variability on Discharge Regime of Njoro River Catchment in Kenya. Open Access Library Journal, 6 (7), 1-22.
[33]	Noori, N. & Kalin, L. (2016). Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology, 533, 141-151.
[34]	Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute.
[35]	Okelo, M. O., Onyando, J. O., Shivoga, W. A., & Miller, S. N. (2015). Assessment of infiltration using a mini rainfall simulator in the river njoro watershed. In Proceedings of Taal 2007: The 12th World Lake Conference. 777, 786-796.
[36]	Liu, J. (2013). Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation. Springer Science & Business Media.
[37]	Renner, M., Brust, K., Schwärzel, K., Volk, M., & Bernhofer, C. (2014). Separating the effects of changes in land cover and climate: a hydro-meteorological analysis of the past 60 yr in Saxony, Germany. Hydrology and Earth System Sciences, 18, 389-405.
[38]	Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25 (1), 25-37.
[39]	Rodriguez, D. A., Tomasella, J., Linhares, C., 2010. Is the forest conversion to pasture affecting the hydrological response of Amazonian catchments? Signals in the Ji-Paraná Basin. Hydrol. Process. 24, 1254–1269. http://dx.doi.org/10.1002/hyp.7586.
[40]	Rouholahnejad, E., Abbaspour, K. C., Vejdani, M., Srinivasan, R., Schulin, R., & Lehmann, A. (2012). A parallelization framework for calibration of hydrological models. Environmental Modelling & Software, 31, 28-36.
[41]	Rwigi, S. K., 2014. Assessment of Potential Impacts of Climate Change and Deforestation on Surface Water Yields from the Mau Forest Complex Catchments in Kenya. University of Nairobi, Kenya PhD thesis, (July), 204p.
[42]	Schipper, T. C. (2017). The attribution of changes in streamflow to climate and land use change for 472 catchments in the United States and Australia (Master's thesis, University of Twente).
[43]	Senent-Aparicio, J., Jimeno-Sáez, P., Bueno-Crespo, A., Pérez-Sánchez, J., & Pulido-Velázquez, D. (2019). Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction. Biosystems engineering, 177, 67-77.
[44]	SP Ojha, C., Kumar Goyal, M., & J Adeloye, A. (2010). Downscaling of precipitation for lake catchment in arid region in India using linear multiple regression and neural networks. The open hydrology Journal, 4 (1).
[45]	Wambua, R. M. (2008). Determination of spatial distribution of runoff and sediment yield within Njoro catchment, Kenya. Masters degree Thesis, Faculty of Engineering and technology, Egerton University, Kenya.
[46]	Webb, A., Jarrett, B., Turner, L., 2007. Effects of plantation forest harvesting on water quality and quantity: Canobolas State Forest, NSW. In: Wilson, A., Deehan, R., Watts, R., Page, K., Bownan, K., Curtis, A. (Eds.), Proceedings of the 5th Australian Stream Management Conference, Australian Rivers: Making a Difference. Charles Sturt University: Thurgoona, Australia.

Cite This Article

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download