Investigating the effect of hydro-climatic homogeneous regions on priority of the best- fit probability distributions for daily raifall analysis in Iran
Subject Areas : Article frome a thesisSomayeh Mohammadi Jouzdani 1 , Hossein Malekinezhad 2 , Ali Dolati 3
1 - Ph.D. Student of Watershed Management Sciences and Engineering, Department of Natural Resources eng. Faculty of Agriculture and Natural Resources, Hormozgan university, Bandar-e-Abbas,
Iran
2 - Associate Professor, Department of Watershed & Range Management, Faculty of Natural Resources and Desertification, Yazd University, Iran
3 - Associate Professor, Department Of Statistics, Faculty of Science, Yazd University , Iran.
Keywords: Watershed, Maximum daily rainfall Probability distribution, Hydrologic homogeneous regions,
Abstract :
Choosing the methods and techniques of statistic and probability science for predicting the variable with certain return period is very important in hydrologic analyses. Maximum daily rainfall is one of the important hydrologic variables that have a basic role in flood magnitude and peak. More accurately prediction of maximum daily rainfall can help us for better planning and management of water resources and flood control. The purpose of this study is determination the suitable probability distribution for maximum daily rainfall in throughout of Iran and also in identified hydro-climatic homogeneous regions. For this purpose, the data of 46 Sinoptics and four Climatologic stations were used. After data prossecing, EasyFit software was used to identify the best-fit distribution. Kolmogrov-Smirnov test was carried out in order to select the best fit probability distribution. The homogeneous regions were determined using the cluster Analysis technique with Ward method based on six parameters; Altitude, the mean annual rainfall, the mean of maximum daily rainfall, the mean of rainfall in winter, spring and autumn.Then five region were obtaned. Then, frequency of suitable probability distributions for maximum daily rainfall in every homogeniuse area were determinated and compared with its in throughout the country. The results showed the Wakeby distribution is most suitable distributions for estimating the maximum daily rainfall in both cases. But the next probability distributions in every homogenius region were different from each other.
1) Alizadeh, A. 2007 Principles of Applied Hydrology. Imam Reza University Press. 22: 76 -80 (In Persian).
2) Bakhtiari, S. 2004. Atlas of Iranʼs Cosmography. Institute of Geography and Cartography of Cosmography (In Persian).
3) Behboodian, j. 2003. Nonparametric Methods. Payame Noor University Press (In Persian).
4) Bonnin, G.M., Martin, D., Lin, B., Parzybok, T., Yekta, M. and Riley, D., 2006. Precipitation-frequency atlas of the United States. NOAA atlas, 14(2): 1-65.
5) Burn, D.H. and Goel, N.K., 2000. The formation of groups for regional flood frequency analysis. Hydrological Sciences Journal, 45(1): 97-112.
6) Chapman, T., 1998. Stochastic modelling of daily rainfall: the impact of adjoining wet days on the distribution of rainfall amounts. Environmental modelling & software, 13(3-4): 317-324.
7) Ho, M.K. and Yusof, F., 2013. Determination of best-fit distribution and rainfall events in Damansara and Kelantan, Malaysia. Matematika, 29: 43-52.
8) Houghton, J.C., 1978. Birth of a parent: The Wakeby distribution for modeling flood flows. Water Resources Research, 14(6):1105-1109.
9) Jahanbakhsh, S. and Torabi, S. 1383. Investigation and prediction of temperature and precipitation changes in Iran. Journal of Geographical Research. 19 (3): 125-104 (In Persian).
10) Khedmati, H., Manshoori, M., Heydarizade M. and Sedghi, H. 2010. Zonation and Estimation of Flood Discharge in Unguaged Sites Located in South-East Basins of Iran Using a Combination of Flood Index and MultiVariable Regression Methods (Sistan and Baluchistan, Kerman, Yazd and Hormozgan Provinces). Journal of Water & Soil. Vol. 24 (3): 593-609 (In Persian).
11) Matalas, N.C., Slack, J.R. and Wallis, J.R., 1975. Regional skew in search of a parent. Water Resources Research.
11(6):815-826.
12) Mirshahi, B., Onof, C. and Wheater, H., 2008, September. Spatialtemporal daily rainfall simulation for a semi-arid area in Iran: a preliminary evaluation of generalised linear models. In Sustainable Hydrology for the 21st Century, Proceedings of the 10th BHS National Hydrology Symposium, Exeter, UK :145-52.
13) Modarres, R. and Sarhadi, A., 2011. Statistically-based regionalization of rainfall climates of Iran. Global and Planetary Change, 75(1-2): 67-75.
14) Mohammadi Jouzdani, S. 2012. Investigating the most appropriate probability distributions for estimating maximum daily rainfalls and peak flood discharges in Iran. Master Thesis for Watershed Management. Faculty of Natural Resources and Desertification, Yazd University (In Persian).
15) Mosaedi, A. and Ghabaeesoogh, M.
2011 Correction of the Standardized Precipitation Index (SPI) based on the selection of the most appropriate probability distribution function. Water and Soil Journal (Agricultural Sciences and Technology). Vol. 25, (5): 1206-1216 (In Persian).
16) Naghavi, B. and Yu, F.X., 1995. Regional frequency analysis of extreme
554 تاثیر مناطق همگن هیدرو-اقلیمی بر تعیین بهترین توزیع احتمالاتی برای بارشهای حداکثر روزانه
precipitation in Louisiana. Journal of Hydraulic Engineering, 121(11): 819-827.
17) Nasrabadi, A., Massoudian, A. and Asakereh, H. 2014. Identification and Spatial Distribution of Iran's Daily Precipitation Patterns. Journal of Applied Geosciences Research, 14 (33): 237-255 (In Persian).
18) Park, J.S., Jung, H.S., Kim, R.S. and Oh, J.H., 2001. Modelling summer extreme rainfall over the Korean peninsula using Wakeby distribution. International journal of climatology, 21(11):.1371-1384.
19) Poorafshari, F. 2010 . Investigation of suitable probability distributions for minimum, average and maximum discharges using L moment method (Caspian Sea case study), Master thesis of Watershed Management. Faculty of Natural Resources. Tarbiat Modarres University (In Persian).
20) Raziei, T. and Shokoohi, A. 2011. Identification of the best fitted probability distribution function on precipitation data in different climate regimes of Iran, to calculate Standardized precipitation index (SPI) at different time scales. The first national conference on drought and climate change. Karaj, Research Center for Dehydration and Drought in Agriculture and Natural Resources (In Persian).
21) Sharma, M.A. and Singh, J.B. 2010. Use of probability distribution in rainfall analysis. New York Science Journal, 3(9):40-49.