Volume 6, Issue 2, June 2018, Page: 43-52
Analysis of Drought and Wet-Events Using SWSI-Based Severity-Duration-Frequency (SDF) Curves for the Upper Tana River Basin, Kenya
Raphael Muli Wambua, Department of Agricultural Engineering, Egerton University, Nakuru, Kenya
Benedict Mwavu Mutua, Division of Planning, Research and Innovation, Kibabii University, Bungoma, Kenya
James Messo Raude, Department of Soil Water and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Received: May 3, 2018;       Accepted: May 22, 2018;       Published: Jun. 12, 2018
DOI: 10.11648/j.hyd.20180602.11      View  696      Downloads  236
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.
Keywords
SDF Curves, Drought, Wet-Event, SWSI, Isoseverity, Return Period, Event-Probability, Upper Tana River Basin
To cite this article
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. Vol. 6, No. 2, 2018, pp. 43-52. doi: 10.11648/j.hyd.20180602.11
Copyright
Copyright © 2018 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.
Reference
[1]
Bak, B. and Kubiak-Wojcickak, K (2017). Impact of meteorological drought on hydrological drought in Turun (Cnentral polant) in the period 1971-2015, Journal of Water and Land Development, 32 (I-III): 3-12.
[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.
[13]
Leelaruban, N, Padmanabhan and Odour, P (2017). Examining the relationship between drought indices and ground water levels, water journal, 9(82):1-16.
[14]
Millington, N., Das, S. and Simonovic, S. P. (2011). The comparison of GEV, Log-Pearson Type 3 and Gumbel Distribution in Upper Thames River Watershed under global climate Models, Water Resources Research Report, Report No 077, London.
[15]
Mishra, A. K. and Singh, V. P. (2011). Drought modelling-A Review, Journal of Hydrology, 403(2011): 157-175.
[16]
NEMA. (2004). Kenya state of environment report: Chapter 7, fresh water, coastal and marine resources, Nairobi, Government printer.
[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.
Browse journals by subject