Irrigation
A. Mosaedi; E. Ramezanipour; M. Mesdaghi; M. Tajbakhshian
Abstract
Introduction: Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. ...
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Introduction: Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. Therefore, finding the proper methods in sediment yield study in watersheds is essential in planning and management of land and water resources. Climatic characteristics, physiography, geology, and hydrology of basins are the most effective factors in producing and transporting sediments according to several sources, but the role and impact of some factors are more pronounced than the others in different areas. As a result, the objective of this study was to investigate and identify the most important climatic, physiographic, geological, and hydrological factors in several watersheds of the northeastern part of Iran, by applying Gamma Test (GT) and principal component analysis (PCA) techniques.Materials and Methods: In this study, the data of discharge flow and suspended sediment concentration, and daily flow discharge recorded in 15 hydrometric stations in Mashhad and Neyshbour restricts and required maps were provided from the Regional Water Company of Khorasan Razavi, Iran. After drawing statistical bar graph period of suspended sediment, daily discharge, annual precipitation, and relatively adequate data, stations with the longest period and with the lowest deficit data were selected to determine the common statistical periods. Therefore, in this study, the time period of 1983-1984 to 2011-2012 was selected, and the run test was applied to control data quality and homogeneity. Then, the most effective factors of sediment yield were determined by principal component analysis (PCA) and Gamma Test (GT).Results and Discussion: The results of the principal component analysis showed that 90 percent of the first five components justify the changes. Among the factors, area and gross gradient of the mainstream from the first component, the average annual flow rate of mainstream, meandering waterways of the mainstream from second component, and drainage density of third component were identified as the most important influencing factors on suspended sediment production. Ninety superior combinations of 1500 proposed combinations were obtained by Gamma Test to evaluate the effects of each parameter on suspended sediment yield. To determine the order of importance of the entered parameters, first, Gamma Test was performed on all 12 parameters. Gamma values of all cases for each proposed combination were compared. The results showed that the impact of these statistics was lowered by eliminating high gamma parameters and the removal of low values. The data analysis revealed that the low levels of gamma and high accuracy of ratio to find the desired outputs from entries. By lowering the gradient, the complexity of the model was lowered and more suitable model was provided. As a result, high levels of gradient represented the complexity of the final model. The results of the percentage values of each of the 12 variables were considered among the superior equations for estimating the suspended sediment composition. In this regard, the mean annual discharge, main channel length, area, average annual rainfall, and percentage of the outcrop of erosion sensitive rocks with a total of 63 percent of the proposed equations were the most important factors affecting the sediment yield in the study area. The average height parameter of area, the average and gross slope of the mainstream had the lowest presence among the optimized compounds.Conclusion: Based on the results of the principal component analysis, the two factors of basin area and gross slope of the mainstream were selected as the most important factors affecting the amount of annual suspended sediment load, respectively. Based on the results of the Gamma Test, 12 main variables affecting suspended sediment load were identified and the effect of each of them on the production and transport of suspended sediment was determined. Based on the comparison of the results of the two methods of PCA and GT, it can be concluded that if the purpose of research or study is to prepare a model with the highest accuracy in estimating suspended sediment load, the 12-variable model of GT includes factors related to physiographical, geological, climatic and hydrological factors are suggested. However, if the preparation of a model with appropriate accuracy and a limited number of input variables is considered, a 5-variable model derived from the PCA method is proposed. At the same time, if the purpose is to prepare a model with the least input variables and their easy access and calculation and initial estimation of suspended sediments, a bivariate model (based on basin area and gross slope of the mainstream factors) resulting from PCA is proposed. According to the results of the present study, it can be concluded that the study of more parameters has provided grounds for evaluating their importance in sediment yield. Finally, due to the correlation of many parameters with each other, a limited number of parameters that have a more important role in suspended sediment estimation, were selected. Another finding of this study is the increase in the accuracy of the sediment model’s preparation due to achieving more important and effective parameters in sediment yield and identifying them in order to investigate the best sediment management measures in watersheds. It is suggested that similar research should be done in other watersheds with different conditions in terms of climatic conditions, topography, geology, and so on.
parvane mohaghegh; Mahdi Naderi; jahangard mohammadi
Abstract
Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to ...
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Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran.
Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods.
Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively.
Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.
