Mohammad Tahmoures; davud nikkami
Abstract
Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, ...
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Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, to determine the factors affecting sedimentation, the Urmia Lake watershed was selected as the study basin. After identifying 30 characteristics affecting the sedimentation of sub-basins of the study area, including hydrological, physiographic, geomorphological, geological and soil characteristics, climate, land use and vegetation as independent variables, the amount of sediment produced in each sub-basin. Was identified as a dependent variable. Using factor analysis, principal component analysis (PCA), cluster analysis and stepwise multivariate regression between selected independent variables and dependent variable using SPSS software Statistical relationship was obtained between sedimentation of sub-basins and watershed characteristics. According to the selected regression model, it is determined that the amount of sediment in the watershed of Lake Urmia to five factors of agricultural land area (rainfed, irrigated and orchards), the area of sub-basins, the total area of erosion and Quaternary structures, average discharge The annual and basin form factor depends on the fact that these five factors control 89% of the sediment production changes in the selected sub-basins, which is significant at the 5% confidence level. In general, the factors affecting erosion and sedimentation of the Urmia Lake watershed can be divided into three groups: human factors and land use change, geology and physiography.
majid kazemzadeh; arash malekian
Abstract
One of the most important dynamic ecosystems is river, awareness of spatio-temporal water quality changes of which is necessary. In this research, we studied the spatiotemporal water quality changes using three techniques of Cluster analysis (CA), Discriminant analysis (DA) and Principal Component analysis ...
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One of the most important dynamic ecosystems is river, awareness of spatio-temporal water quality changes of which is necessary. In this research, we studied the spatiotemporal water quality changes using three techniques of Cluster analysis (CA), Discriminant analysis (DA) and Principal Component analysis (PCA) in the Aji-Chai watershed over 1981-2010. Applying clustering, we identified three homogeneities clusters. Stations which were labeled in the first cluster showed that they are located in the upstream of Aji-Chi River. In comparison with other stations, these stations showed better water quality and the lowest changeability. DA methods significantly determined the three functions which described about 73.50, 20.30 and 3.40% of total variances. In the other word, in general three functions described the 97.20% of the total variances. Also the DA methods revealed the HCO-3, SAR, Na+, SO42- and Ca2+ were the most important parameters affecting upon water quality, based on which it's possible to seperate homogenous clusters. Finally, the results of PCA showed that the first two factors were the most important factors of water quality changes in the Aji-Chai River Watershed. These factors described about 78.75 and 14.71% of the variances, respectively.
sahar sabaghzade; Mohammad Zare; Mohamad Hosein Mokhtari
Abstract
Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. ...
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Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. The Landsat 8 data of May 2013 and field studies coinciding with field imaging in Marac (South Khorasan province) were used. Tamarix plant biomass measured in 30 random plots of 11 vegetation indices including DVI, IPVI, NDVI, PVI, RVI, SAVI, TSAVI, WDVI, and Tasselcap were used to estimate biomass of Tamarix.Then, using cluster analysis, vegetation indices were divided into three groups among which SAVI, RVI , and IPVI were chosen. The results showed that indexes which consider soil factors are more accurate than other measures. In this study, biomass map was prepared using the SAVI index.
nasim arman; Ali Salajegheh; Sadat Feiznia; Hassan Ahmadi; Jamal Ghoddousi; ali kiani rad
Abstract
Identification of homogenous watershed sub basins allows generalization of environmental study results. For this purpose, first available data for 27 selected watersheds in North Alborz regarding 21 variables including physiographic and climatic characteristics was gathered. The most important factors ...
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Identification of homogenous watershed sub basins allows generalization of environmental study results. For this purpose, first available data for 27 selected watersheds in North Alborz regarding 21 variables including physiographic and climatic characteristics was gathered. The most important factors impacting upon soil erosion and sediment yield were equivalent rectangular length, mean annual precipitation, rock susceptibility, aspect and drainage density which were identified using factor analysis (Principle Component Analysis : PCA) and a 80.72 percent variation of data was observed (KMO =0.516). For determination of homogenous region, different methods of cluster analysis (hierarchical, K-means and two step clustering) were used and three homogeneous regions were specified. Discriminant function analysis was employed and confirmed the results of cluster analysis in homogenous region. On the other hand, based on these five factors, a discriminant function was defined and canonical correlation, chi-square, wilks’ lambda values revealed that three homogenous regions were quite separate.
Jamal Mosaffaie; Davoud Akhzari; Saeed Rashvand; Javad Ataei
Abstract
One of the important parameters in the design of flood control structures is to determine flood peak discharge for various return periods. A primary issue of planners in the face with flood is lack of data or insufficient data. One of the most reliable strategies is generalizing the results from sites ...
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One of the important parameters in the design of flood control structures is to determine flood peak discharge for various return periods. A primary issue of planners in the face with flood is lack of data or insufficient data. One of the most reliable strategies is generalizing the results from sites with observed data to ungauged locations. The main goal of this study is regional flood frequency analysis using multiple regression method for Qazvin province of Iran. 8 out of 23 existing hydrometric station were removed because of the short-term statistics and construction of storage dam at upstream. The results of factor analysis showed that perimeter, equivalent diameter, time of concentration, length of main waterway and area were the main variables affecting flood magnitude. The remaining 15 stations were divided into two homogenous regions using cluster analysis. Homogeneity of these two regions was confirmed using homogeneity and heterogeneity tests of L-moments. Based on the best-fit criteria of Zdist, GNO distribution with the statistic of 0.29 has the best fit for the entire region but for one and two homogeneous regions, GLO and GPA distributions with the statistics equal to 0.09 & 1.56 have the best fit respectively. After calculating parameter values for selected distributions, discharges with different return periods were estimated for all stations. Then, regression relations were obtained between peak discharge and factors affecting flood peak for each return periods at two homogeneous regions. Peak discharges at ungauged locations can be estimated for different recurrence interval using these relationships.
