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Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya

Published in Hydrology (Volume 6, Issue 2)
Received: 3 May 2018     Accepted: 22 May 2018     Published: 12 June 2018
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Abstract

Drought and wet-event patterns in the Upper Tana River basin have significantly been changing due to variation of climatic and human-induced factors. This paper presents the analysis of drought and wet-events using Severity-Duration-Frequency (SDF) curves for the Upper Tana River basin, Kenya based on Surface Water Supply Index (SWSI). The extreme value EV1 (Gumbel) frequency distribution function was used to formulate SDF curves. The developed SDF curves were used to develop isoseverity maps for the basin. From the results, the event-probability show that likelihood of drought events increased linearly with increase in magnitude of SWSI while the return period of drought events increased exponentially with decrease in magnitude of SWSI. The findings show that the probability and magnitude, the return period and magnitude of drought have linear and exponential regression coefficients of 0.984 and 0.980 respectively. On the other hand the probability of wet-period events decreased linearly with increase in magnitude of SWSI while the return period of the events increased exponentially with increase in magnitude of SWSI with regression coefficients of the linear and exponential functions of 0.804 and 0.881 respectively. This indicates that both the drought and wet-events probability and magnitude, and the return period and magnitude have a strong correlation. Spatially, it was found that generally the river basin exhibit an increasing pattern in cumulative SWSI in south-eastern areas than the north-eastern and generally a more increase in extreme wet-events than droughts in the basin. The developed (SDF) curves are critical for design of hydrologic, hydraulic and water resources supply systems while the spatial event-patterns can be incorporated in prioritized mitigation of extreme events.

Published in Hydrology (Volume 6, Issue 2)
DOI 10.11648/j.hyd.20180602.11
Page(s) 43-52
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), 2018. Published by Science Publishing Group

Keywords

SDF Curves, Drought, Wet-Event, SWSI, Isoseverity, Return Period, Event-Probability, Upper Tana River Basin

References
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[2] Barua, S. (2010). Drought assessment and forecasting using a non-linear aggregated drought index, PhD thesis, Victoria University, Australia.
[3] Belayneh, A. and Adamowski, J. (2013). Drought forecasting using new machine learning methods, Journal of Water and Land Development, 18 (I-IV): 3-12.
[4] Castano, A. (2012). Monitoring drought at river basin and regional scale: application in Sicily, PhD Dessertation in Hydraulic Engineering, University of Catania, Italy.
[5] Dalezios, N., Loukas, A., Vasiliades, L. and Liakopoulos, E. (2000). Severity-duration-frequency analysis of droughts and wet periods in Greece, Hydrological sciences journal, 45(5): 751-769.
[6] Dracup, J. A., Lee, K. S. and Paulson, E. G. (1980a). On statistical characteristics of drought events, Journal of Water Resources Research, 16(2): 289-296.
[7] El-Jabi, N., Turkkan, N. and Caissie, D. (2013). Regional climate index for floods and droughts using Candian climate model, American journal of climate variability, 2: 106-115.
[8] GoK. (2012). Upper Tana natural resources management project; A strategic environmental assessment draft report.
[9] Hosking, J. R. M. and Wallis, J. R. (1997). Regional Frequency Analysis, Cambridge University Press, Cambridge.
[10] IFAD. (2012). Upper Tana catchment natural resource management project report, east and southern Africa division, project management department.
[11] Jacobs, J. Angerer, J., Vitale, J., Srinivasan, R., Kaitho, J. and Stuth, J. (2004). Exploring the Potential Impact of Restoration on Hydrology of the Upper Tana River Catchment and Masinga Dam, Kenya, a Draft Report, Texas A & M University.
[12] Karamouz, M. Rasouli, K. and Nazi, S. (2009). Development of a hybrid index for drought prediction: case study, Journal of Hydrologic Engineering, 14(6): 617-627.
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[15] Mishra, A. K. and Singh, V. P. (2011). Drought modelling-A Review, Journal of Hydrology, 403(2011): 157-175.
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[17] Shafer, B. A. and Desman, L. E. (1982). Development of a Surface Water Supply Index (SWSI) to assess drought conditions in snowpack Runoff Areas, proceedings of the Western snow conference Reno, Nevada, U.S.A.: 164-175.
[18] UNDP. (2012). Kenya: adapting to climate variability in Arid and Semi-Arid Lands (KACCAL), project report Wilby, R. L., Orr, H. G., Hedger, M, Forrow D. and Blakmore, M. (2006a). Risks posed by climate variability to delivery of water framework directive objectives. Environ. Int., in press.
[19] Wang, H, Pan, Y., Chen, Y. (2017). Comparison of three drought indices and their evolutionary characteristics in arid and semi-arid regions of northwestern China, Atmospheric Science Letters, 18: 132-139.
[20] World food programme (WFP). (2011). Drought and famine in Horn of Africa.
[21] World Resources Institute (WRI). (2011). Kenya GIS data –world resources institute, retrieve from www.wri.org/resources/data-sets/kenya-gis-data on January 15, 2014.
[22] WRMA. (2010). Physiological survey in the upper Tana catchment, a natural resources management project report, Nairobi.
[23] Yannawut, U. and Laosuwan, T. (2017). Drought detection by application of Remote Sensing technology and vegetation phenology, Journal of ecological engineering, 18(6):115-121.
[24] Zeleke, T. T., Giorgi, F., Diro, G. T. and Zaitchik, B. F. (2017). Trend and periodicity of drought over Ethiopia. International Journal of Climatology doi:10.1002/joc.5122.
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    Raphael Muli Wambua, Benedict Mwavu Mutua, James Messo Raude. (2018). Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya. Hydrology, 6(2), 43-52. https://doi.org/10.11648/j.hyd.20180602.11

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    ACS Style

    Raphael Muli Wambua; Benedict Mwavu Mutua; James Messo Raude. Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya. Hydrology. 2018, 6(2), 43-52. doi: 10.11648/j.hyd.20180602.11

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    AMA Style

    Raphael Muli Wambua, Benedict Mwavu Mutua, James Messo Raude. Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya. Hydrology. 2018;6(2):43-52. doi: 10.11648/j.hyd.20180602.11

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  • @article{10.11648/j.hyd.20180602.11,
      author = {Raphael Muli Wambua and Benedict Mwavu Mutua and James Messo Raude},
      title = {Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya},
      journal = {Hydrology},
      volume = {6},
      number = {2},
      pages = {43-52},
      doi = {10.11648/j.hyd.20180602.11},
      url = {https://doi.org/10.11648/j.hyd.20180602.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hyd.20180602.11},
      abstract = {Drought and wet-event patterns in the Upper Tana River basin have significantly been changing due to variation of climatic and human-induced factors. This paper presents the analysis of drought and wet-events using Severity-Duration-Frequency (SDF) curves for the Upper Tana River basin, Kenya based on Surface Water Supply Index (SWSI). The extreme value EV1 (Gumbel) frequency distribution function was used to formulate SDF curves. The developed SDF curves were used to develop isoseverity maps for the basin. From the results, the event-probability show that likelihood of drought events increased linearly with increase in magnitude of SWSI while the return period of drought events increased exponentially with decrease in magnitude of SWSI. The findings show that the probability and magnitude, the return period and magnitude of drought have linear and exponential regression coefficients of 0.984 and 0.980 respectively. On the other hand the probability of wet-period events decreased linearly with increase in magnitude of SWSI while the return period of the events increased exponentially with increase in magnitude of SWSI with regression coefficients of the linear and exponential functions of 0.804 and 0.881 respectively. This indicates that both the drought and wet-events probability and magnitude, and the return period and magnitude have a strong correlation. Spatially, it was found that generally the river basin exhibit an increasing pattern in cumulative SWSI in south-eastern areas than the north-eastern and generally a more increase in extreme wet-events than droughts in the basin. The developed (SDF) curves are critical for design of hydrologic, hydraulic and water resources supply systems while the spatial event-patterns can be incorporated in prioritized mitigation of extreme events.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya
    AU  - Raphael Muli Wambua
    AU  - Benedict Mwavu Mutua
    AU  - James Messo Raude
    Y1  - 2018/06/12
    PY  - 2018
    N1  - https://doi.org/10.11648/j.hyd.20180602.11
    DO  - 10.11648/j.hyd.20180602.11
    T2  - Hydrology
    JF  - Hydrology
    JO  - Hydrology
    SP  - 43
    EP  - 52
    PB  - Science Publishing Group
    SN  - 2330-7617
    UR  - https://doi.org/10.11648/j.hyd.20180602.11
    AB  - Drought and wet-event patterns in the Upper Tana River basin have significantly been changing due to variation of climatic and human-induced factors. This paper presents the analysis of drought and wet-events using Severity-Duration-Frequency (SDF) curves for the Upper Tana River basin, Kenya based on Surface Water Supply Index (SWSI). The extreme value EV1 (Gumbel) frequency distribution function was used to formulate SDF curves. The developed SDF curves were used to develop isoseverity maps for the basin. From the results, the event-probability show that likelihood of drought events increased linearly with increase in magnitude of SWSI while the return period of drought events increased exponentially with decrease in magnitude of SWSI. The findings show that the probability and magnitude, the return period and magnitude of drought have linear and exponential regression coefficients of 0.984 and 0.980 respectively. On the other hand the probability of wet-period events decreased linearly with increase in magnitude of SWSI while the return period of the events increased exponentially with increase in magnitude of SWSI with regression coefficients of the linear and exponential functions of 0.804 and 0.881 respectively. This indicates that both the drought and wet-events probability and magnitude, and the return period and magnitude have a strong correlation. Spatially, it was found that generally the river basin exhibit an increasing pattern in cumulative SWSI in south-eastern areas than the north-eastern and generally a more increase in extreme wet-events than droughts in the basin. The developed (SDF) curves are critical for design of hydrologic, hydraulic and water resources supply systems while the spatial event-patterns can be incorporated in prioritized mitigation of extreme events.
    VL  - 6
    IS  - 2
    ER  - 

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

  • Division of Planning, Research and Innovation, Kibabii University, Bungoma, Kenya

  • Department of Soil Water and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya

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