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Multivariate and Cluster Analysis of Hydrologic Indices: a Case Study of Karun Watershed, Khuzestan Province, Iran

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Multivariate and Cluster Analysis of Hydrologic Indices: a Case Study of Karun Watershed, Khuzestan Province, Iran International Journal of Research Studies in Science, Engineering and Technology Volume 5, Issue 2, 2018, PP 4-13 ISSN : 2349-476X Multivariate and Cluster Analysis of Hydrologic Indices: A Case Study of Karun Watershed, Khuzestan Province, Iran Elham Karimian1, Reza Modares1, Saeid Soltani1, Saeid Eslamian2, Kaveh Ostad-Ali-Askari3*, Vijay P. Singh4, Nicolas R. Dalezios5 1Department of Natural Resources Engineering, Isfahan University of Technology, Isfahan, Iran. 2Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran. 3*Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. (Corresponding Author) 4Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A and M University, 321 Scoates Hall, 2117 TAMU, College Station, Texas 77843-2117, U.S.A. 5Laboratory of Hydrology, Department of Civil Engineering, University of Thessaly, Volos, Greece & Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Athens, Greece. *Corresponding Author: Dr. Kaveh Ostad-Ali-Askari, Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. Email: [email protected] ABSTRACT Hydrological droughts usually affect large areas and minimum river flow is an appropriate index for the study of hydrological droughts. are commonly used for analyzing multivariate data? This study considered 35 hydrological indicators which were divided into 5 groups, including amplitude, duration, frequency, timing, variability, that characterized the various flow regime characteristics. Factor analysis and principal component analysis were used to calculate the hydrological indices for 14 stations of Karun watershed, Iran, and analyze the main components (PCAs). Then, the main components, the most important parameters of the Karun domain, which included A2, A4, D3, D5, V1, F and T, were determined and based on these indices, they were grouped by cluster analysis. Hydrological indicators were divided 6 groups which most of them near PC1 which then follow PC2 but at least of them follow other PC. The only group has positive axis include: D6, A2, A1 that Station 7 and 13 near it. Three groups hydrological indicators have positive PC2 and one group also negative and positive PC2.two groups has negative PC2.two groups of hydrological indicators has positive and 4 groups of hydrological indicators has negative. Keywords: Cluster Analysis, Component Analysis, Hydrological Drought, Hydrological Index INTRODUCTION are related to low flows. These indicators have been developed with different perspectives. The Drought is a natural and repetitive climatic rapid rise of hydrological indicators from 32 phenomenon. It occurs in virtually all parts of reported by Richter et al. (1996) to 171 reported the world. However, its characteristics vary by Olden and Puff (2003) and finally to 261 from one watershed to another. During reported by Monk et al. (2006) has complicated hydrological droughts, river flow and water in evaluation and resource management. Therefore, lakes and reservoirs behind dam’s decrease and water resources managers combined a few groundwater table also drops. indicators in a well-defined framework, which Hydrological regime characteristics are essential would help sustain management. Bonaiya for the assessment of river health and for water (2009) presented a feature of river's natural resource management (Puff et al. 1997). regime in eastern Canada. Using 175 river flow Therefore, it is hypothesized to develop an events from five eastern provinces of Canada, indicator that specifies the range (magnitude), Deleg et al. (2011) described a wide range of duration, frequency, timing and diversity of flow characteristics, such as magnitude (amplitude) natural hydrologic events of the regime. More and frequency of low flows, using multivariate than 200 indicators are currently available to analysis. They developed regional regression describe hydrologic regimes, of which about 70 equations for a number of low flow indicators, International Journal of Research Studies in Science, Engineering and Technology V5 ● I2 ● 2018 4 Multivariate and Cluster Analysis of Hydrologic Indices: A Case Study of Karun Watershed, Khuzestan Province, Iran which, as a function of drainage area, were used variable in the Ghar Aghaj River in the outskirts to compare flow regimes of different provinces and the Dokoh River upstream of the basin. In and regions. Khazaei et al. (2003) analyzed the outskirts of the Dez River in the north of the hydrological droughts in Gharehso River basin basin, which is from the highlands, erosion is in and concluded that the index of drought low to moderate amount, with a value of less incidence decreased with decreasing probability than 300 tons per square kilometer that is one of of occurrence. Nathan and McMahon (1990) areas with moderate erosion and in some areas conducted a regional analysis of minimum flows (Seyyed Shahid Abbaspour) it is high. in 184 sub-basins in Australia, using multivariate METHODS correlation analysis, cluster analysis, and principal component analysis. Samiei, Zahtabian, and Selection of Hydrometric Stations colleagues and Biabanaki compared multivariate For describing the characteristics of normal flow regression and low flow indexes, and found the regime in two watersheds of Karun, daily regression method to be better. It seems that due average flow at 14 hydrometric stations located to the use of more watershed characteristics in in 4 provinces was selected. The statistical determining regional equations, more accurate period of data was more than 30 years. Table 1 estimates of flow are obtained by multivariate shows the characteristics of hydrometric stations regression method. selected in the Karun watershed. The objective of this study is to employ several Selection and Calculation of Hydrological new hydrological indicators in Karun watershed, Indicators Iran, and identify the most relevant hydrologic indices for low flows, identify similarities and There are more than 200 hydrological regional differences by using multivariate indicators, of which 67 are directly related to statistical analysis, and use cluster analysis for low flows. In addition, four other indicators hydrologic indices. (average daily flow, average daily flow, annual average annual average and annual flow MATERIALS AND METHODS coefficient) due to their potential relationship Study Area with low-flow regimes were considered as part of low-flow regime. From these 71 hydrological Karun watershed is located between 30-00 and indicators, we selected 35 indicators and we 34-05 northern latitudes and 48-00 and 30-52 eastern longitudes, and covers parts of 7 calculated them. All of the variables used in provinces of Isfahan, Khuzestan, Chaharmahal Table 2 were divided into five groups to identify and Bakhtiari, Fars, Kohgiluyeh and Boyer the various characteristics of flow regime, Ahmad, and Lorestan and Central. The including: watershed is partly in the basin of the Persian Amplitude, Duration, Frequency, Timing, Gulf and the Oman Sea and consists of two Variability and Average of total data large rivers Karun and Dez that join together at the Shaloo bridge and form Great Karun. The Principal Component Analysis (PCA) altitude in the area varies from 0 to more than Component decomposition is a multivariate 4,400 meters. The lowest point of the area is the method that reduces the number of variables to southern margin of the Persian Gulf, and the several components and provides a summary of highest elevation is in the Zardukh and Dena the main data. The higher the internal Mountains. correlation between the variables is the lower The Karun watershed has a total area of the number of constructed components will be. 6,71,212 square kilometers and is bounded One of the ways of component analysis is the northwards by the watershed area of the rivers component weighting matrix. There are many Gharacheh, Saveh, Golpayegan and Zayandehrud, reasons for the importance of component by the western part of the Karkheh River basin, analysis. First, this method separates properties from the east to the Venus River, Maroon and that are dependent on other properties. Jarahi Rivers. About 69% of the area is Secondly, by increasing the number of mountainous and 31% plain and foothills. variables, the multi-variable regression equation Generally, the amount of sediment in the is increasingly uncontrollable, which can be watershed of the Great Karun branches ranges reduced by using component analysis from 28 to 1800 tons / km in a year, and is (Khorasanzadeh,1375) 5 International Journal of Research Studies in Science, Engineering and Technology V5 ● I2 ● 2018 Multivariate and Cluster Analysis of Hydrologic Indices: A Case Study of Karun Watershed, Khuzestan Province, Iran Flow Regime Classification The term flashiness reflects the frequency and rapidity of short term changes in stream flow Flow regime classification offers the source for (Baker et al, 2004). Stream flashiness is the hydrologic and ecologic educations. Bunn and stream flow response to storms. Streams that Arthington (2002) posed four guiding principles rise and fall quickly are considered flashier than regarding the effect of flow regimes on aquatic those that maintain a steadier flow (Fongers et biodiversity. Streamflow is a convenient al., 2007). quantity for classification purposes because it integrates
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