全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Detection of Spatial, Temporal and Trend of Meteorological Drought Using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the Upper Tana River Basin, Kenya

DOI: 10.4236/ojmh.2018.83007, PP. 83-100

Keywords: SPI, EDI, Drought-Detection, Man-Kendall, Drought-Prone Areas, Drought Frequency, Drought-Early Warning System, Upper Tana River Basin

Full-Text   Cite this paper   Add to My Lib

Abstract:

Drought events across the world are increasingly becoming a critical problem owing to its negative effects on water resources. There is need to understand on-site drought characteristics for the purpose of planning mitigation measures. In this paper, meteorological drought episodes on spatial, temporal and trend domains were detected using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the upper Tana River basin. 41 years (1980-2016) monthly precipitation data from eight meteorological stations were used in the study. The SPI and EDI were used for reconstruction of the drought events and used to characterize the spatial, temporal and trend distribution of drought occurrence. Drought frequency was estimated as the ratio of a defined severity to its total number of events. The change in drought events was detected using a non-parametric man-Kendall trend test. The main drought conditions detected by SPI and EDI are severe drought, moderate drought, near normal, moderate wet, very wet and extremely wet conditions. From the results the average drought frequency between 1970 and 2010 for the south-eastern and north-western areas ranged from 12.16 to 14.93 and 3.82 to 6.63 percent respectively. The Mann-Kendall trend test show that drought trend increased in the south-eastern parts of the basin at 90% and 95% significant levels. However, there was no significant trend that was detected in the North-western areas. This is an indication that the south-eastern parts are more drought-prone areas compared to the North-western areas of the upper Tana River basin. Both the SPI and the EDI were effective in detecting the on-set of drought, description of the temporal variability, severity and spatial extent across the basin. It is recommended that the findings be adopted for decision making for drought-early warning systems in the river basin.

References

[1]  Botai, C.M., Botai, J.O., Wit, J.P., Katlego, P.N. and Adeola, A.M. (2017) Drought characteristics over Western Cape Province, South Africa. Water Journal, 9, 1-16.
[2]  Rajput, P., Sinha, M.K., Verma, M.K. and Ahmad, I. (2014) Drought Hazard Assessment and Mapping in Upper Seonath Sub-Basin Using GIS. International Journal of Emerging Technology and Advanced Engineering, 4, 210-218.
[3]  Keyantash, J.A. and Dracup, J.A. (2004) An Aggregate Drought Index: Assessing Drought Severity Based on Fluctuations in the Hydrologic Cycle and Surface Water Storage. Journal of Water Resources Research, 40, 1-14.
https://doi.org/10.1029/2003WR002610
[4]  Mckee, T.B., Doesken, N.J. and Kleist, J. (1993) The Relationship of Drought Frequency and Duration to Time Scales. Proceedings of 8th Conference on Applied Climatology, Anaheim, 179-184.
[5]  Vicente-Serano, S.M., Begneria, S. and Lopez-Moreno, J.I. (2010) A Multi-Scalar Drought Index Sensitive to Global Warming, the Standardized Precipitation Evapotranspiration Index. Journal of Climatology, 23, 1696-1711.
https://doi.org/10.1175/2009JCLI2909.1
[6]  Palmer, W.C. (1965) Meteorological Drought Research Paper 45. Weather Bureau, Washington DC.
[7]  Park, J.H., Kim, K.B. and Chang, Y. (2014). Statistical Properties of Effective Drought Index (EDI) for Seoul, Busan, Daegu, Makpo in South Korea. Asia-Pacific Journal of Atmospheric Science, 50, 453-458.
[8]  Keetch, J.J. and Byuram, C.M. (1968) A Drought Index for Forest Fire Control, Res. Pap, SE-38. US Department of Agriculture, Forest Service, South Eastern Forest Experimental Station, Asheville.
[9]  Karamouz, M., Rasouli, K. and Nazi, S. (2009) Development of a Hybrid Index for Drought Prediction: Case Study. Journal of Hydrologic Engineering, 14, 617-627.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000022
[10]  Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J. and Reed, B.C. (2008) The Vegetation Drought Response Index (VegDRI). A New Integrated Approach for Monitoring Drought Stress in Vegetation. Geosciences and Remote Sensing, 45, 16-46.
[11]  Tsakiris, G. and Vangelis, H. (2005) Establishing a Drought Index Incorporating Evapo-Transpiration. European Water Journal, 9, 3-11.
[12]  Van-rooy, M.P. (1965) A Rainfall Anomaly Index (RAI), Independent of the Time and Space. Notos, 14, 43-48.
[13]  Bryant, S., Arnell, N.W. and Law, F.M. (1992) The Long-Term Context for the Current Hydrological Drought. Proceedings of the IWEM Conference on the Management of Scarce Water Resources, Brighton, 13-14 October 1992.
[14]  Gommes, R.A. and Petrassi, F. (1994) Rainfall Variability and Drought in Sub-Saharan Africa since 1960. FAO Agromet Report Series WP9, Rome.
[15]  Gonzalez, J. and Valdes, J. (2006) New Drought Frequency Index, Definitions and Evaporative Performance Analysis. Water Resources Research, 42, 333-349.
https://doi.org/10.1029/2005WR004308
[16]  Mckee, T.B. and Edwards, D.C. (1997) Characteristics of 20th Century Droughts in the United States at Multiple Time Scales. Journal of Atmospheric Science, 634, 97-92.
[17]  Bacanli, U.G., Firat, M. and Dikbas, F. (2008) Adaptive Neuro-Fuzzy Inference System for Drought Forecasting. Journal of Stochastic Environmental Research and Risk Assessment, 23, 1143-1154.
[18]  Belayneh, A. and Adamowski, J. (2012) Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks and Support Vector Regression. Journal of Applied Computational Intelligence and Soft Computing, 2012, Article ID: 794061.
[19]  Byun, H.R. and Wilhite, D.A. (1999) Objective Quantification of Drought Severity and Duration. Journal of Climatology, 12, 2747-2756.
https://doi.org/10.1175/1520-0442(1999)012<2747:OQODSA>2.0.CO;2
[20]  Markovic, R.D. (1965) Probability Functions of the Best Fit to Distributions of Annual Precipitation and Runoff Hydrology. Paper No. 8, Colorado State University, Fort Collins.
[21]  the Case of Wabi Shebele River Use of Gamma Distribution in Hydrological Analysis. Turkish Journal of Engineering Sciences, 24, 419-428.
[22]  Awass, A.A. (2009) Hydrological Drought Analysis-Occurrence, Severity and Risks, the Case of Wabi Shebele River Basin. Ethiopia PhD Thesis, Universität Siegen, Siegen.
[23]  Morid, S., Smakhtin, V. and Moghaddasi, M. (2006) Comparison of Seven Meteorological Indices for Drought in Iran. International Journal of Climatology, 26, 971-985.
https://doi.org/10.1002/joc.1264
[24]  IFAD (2012) Upper Tana Catchment Natural Resource Management Project. Report, East and Southern Africa Division, Project Management Department.
[25]  Cassiamani, C., Morgillo, A., Marchesi, S. and Pavan, V. (2007) Monitoring and Forecasting Drought on a Regional Scale: Emilia Romagna Region. Water Science and Technology, 62, 29-48.
[26]  Mishra, A.K., Desai, V.R. and Singh, V.P. (2007) Drought Forecasting Using a Hybrid Stochastic and Neural Net-Work Models. Journal of Hydrological Engineering, 12, 626-638.
https://doi.org/10.1061/(ASCE)1084-0699(2007)12:6(626)
[27]  Smakhtin, V.U. and Hughes, D.A. (2007) Automated Estimation and Analysis of Meteorological Drought Characteristics from Monthly Rainfall Data. Journal Environmental Modelling and Software, 22, 880-890.
[28]  Bulu, A. and Aksoy, H. (1998) Low Flow and Drought Studies in Turkey. Proceedings of Low Flows Expert Meeting, Belgrade, 10-12 June 1998.
[29]  Roudier, P. and Mahe, G. (2010) Study of Water Stress and Droughts with Indicators Using Daily Data on Bani River, Niger Basin, Mali. International Journal of Climatology, 30, 1689-1705.
[30]  Morid, S., Smakhtin, V. and Bargherzadeh, K. (2007) Drought Forecasting Using Artificial Neural Networks and Time Series of Drought Indices. International Journal of Climatology, 27, 2103-2111.
https://doi.org/10.1002/joc.1498
[31]  Buishand, T.A. (1982) Some Methods for Testing the Homogeneity of Rainfall Records. Journal of Hydrology, 58, 11-27.
https://doi.org/10.1016/0022-1694(82)90066-X
[32]  Hirsh, R.M., Slack J.R. and Smith, R.A. (1982) Techniques of Trend Analysis for Monthly Water Quality Data. Water Resources Research, 18, 107-121.
[33]  Pettitt, A.N. (1979) A Non-Parametric Approach to Change Point Problem. Journal of Applied Statistics, 28, 126-135.
https://doi.org/10.2307/2346729
[34]  Kendall, M.G. (1962) Rank Correlation Methods. Hafner Publishing Co. Ltd., New York.
[35]  Mahajan, D.R. and Dodamani, B.M. (2015) Trend Analysis of Drought Events over Upper Krishna Basin in the Maharashtra. Journal of Aquatic Procedia, 4, 1250-1257.
[36]  Sneyers, R. (1990) On the Statistical Analysis of Series of Observations. World Meteorological Organization (WMO), Technical Note No. 143, Geneva, 192.
[37]  Mishra, S.S. and Nagarajan, R. (2011) Spatio-Temporal Drought Assessment in Tel River Basin Using Standardized Precipitation Index (SPI) and GIS. Journal of Geomatics, Natural Hazards and Risk, 2, 79-93.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133