Maryam Ansari; Iraj Jabbari; Farhang Sargordi
Abstract
1-IntroductionIran is one of the arid and semi-arid regions of the world with an average annual rainfall of 240 mm. The country is such arid that the average annual rainfall is less than 130 mm (Jafari and Tavili, 2013:149) in 65% of its regions; therefore, it has been facing a water shortage for a long ...
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1-IntroductionIran is one of the arid and semi-arid regions of the world with an average annual rainfall of 240 mm. The country is such arid that the average annual rainfall is less than 130 mm (Jafari and Tavili, 2013:149) in 65% of its regions; therefore, it has been facing a water shortage for a long time. Thus, due to the limitation of surface and groundwater resources in the country, particularly arid and semi-arid regions, it is necessary to identify the factors affecting the quality of water resources for protection to reduce the vulnerability of these resources. Among the various factors that cause water quality degradation, the type and material of rocks or geology are crucial in changing groundwater quality (Jehbez, 1994: 1). Accordingly, in this research, the efficiency of the GWR model was measured to determine the sources of water pollution by selecting the Izdakhvat basin as a sample of inland Zagros basin that has good but saline water resources; these areas received the most impact from a particular formation.2-MethodologyIzadkhaast catchment, code 2647, is one of the closed basins of the Mond River catchment located in Fars province. The area of this basin is 1371.3 square kilometers the height and plain of which is respectively, 879.6 and 491.7 square kilometers of the total area of the basin. The maximum and minimum height in the basin are, respectively, 2182 and 1029 meters.In this study, the geographical weight regression (GWR) model has been used to investigate the relationship between geological formations, water quality parameters, and spatial modeling. This method is based on processing the hydrological information (water quality data) and geology using the GIS technique. The required parameters were considered as model inputs; moreover, geological map 1:100000 sheets of ZarrinDasht, Jahrom, and Bezenjan Geological Survey was used to extract geological data as well as the obtained data of observation wells, Fars Regional Water Joint Stock Organization. As the water quality data is related to 14 observation wells in 2010 (due to the more complete data), which is among 16 quality parameter data, after examining the relationship between the parameters together, those who had the highest correlation and significant relationship with the EC parameter, were selected for statistical analysis. They were also selected to quantify the geological formations. For each well, Polygon Thyssen was drawn. The area of the formations in each of the polygons was extracted and added as an independent variable to the descriptive table of the desired file shape, and then they were analyzed for modeling in ARC GIS environments in the following steps:1- First, to enter the best model for execution in the GWR method, independent variables related to trial and error in the OLS method were analyzed so that the best model with a significant relationship between variables, i.e., P value less than 0.05, R2 more and lower AICc coefficient was selected.2- After selecting the best model, the Moran index was used to evaluate the spatial autocorrelation of the OLS model residues. This index measures the degree of clustering or dispersion of standard residues. The residues were used to test the reliability of the model in predicting local conditions by experimenting with spatial correlation.3- Finally, the variables selected from the OLS model were entered into the GWR model to achieve higher precision in spatial relationship analysis. The GWR recorded local changes by weighing more close observations than farther ones (Pratt and Chang, 2012:52).GWR outputs include local residuals as well as the results of R2 or the coefficient of determination, where R2 is the standard for determining the performance of multivariate regression models.3-Results and DiscussionAccording to the results of the OLS model, the sign of beta coefficients for Aghajari Formation (MPLa), alluvial deposits Qc, and QScg were negative. They indicated their inverse relationship with qualitative parameters. However, most of the qualitative parameters were directly and remarkably related to seasonal lakes, salt dome (Pc CHD), Champe member (Mchm), and mole member (Mmo) in the area, which indicated surface erosion and leaching of salt and gypsum from the surface by surface currents and their transfer to the low points of the basin, i.e., seasonal lakes. These formations have also shown themselves as Mahour and Badland hills due to their instability against further erosion.After selecting the best models, all the standard residues of the selected OLS models were examined to ensure the normal distribution of the data and to evaluate the spatial autocorrelation using the Moran index. All residuals in the selected OLS models were within the standard range, indicating a normal data distribution.Finally, to better understand the correlation between geological formations and water quality parameters in different parts of the basin, the variables selected from the OLS model were entered into the GWR model. The results of this model have been presented as spatial model maps for each parameter based on the results of coefficients of determination (R2).According to the maps, the highest correlation was related to the potassium parameter, and the lowest value was related to the chlorine parameter, while the other parameters also showed a very high correlation with independent variables. In most qualitative parameters such as sodium, potassium, chlorine, and electrical conductivity, the highest correlation was related to the west of the basin, which indicated the high impact of the salt diaper in the west of the basin on water resources and wells that are close to the points of lower quality than wells in higher and farther points. Low resistance and erosion of evaporative sediments were also contributed to this issue, as water sources in contact with evaporative sediments may contain large amounts of potassium, sodium, chlorine, and sulfate in an insoluble form.4-ConclusionsThe results of this study revealed that this model with high spatial variability determined the impact of different formations on water resources in various places and critical areas with the most negative effects. This significant model was a simple and enriched method for managing and planning in basins that do not have enough data.The results of this model also showed that evaporative sediments in the basin, including the salt dome in the west of the basin, were the most important formations of water quality degradation. Also, the significant relationship between water quality parameters and low points of the basin or seasonal lakes indicated the leaching and transport of these sediments to these points by running water. These formations have shown the faces of mounds and hills in the region due to their weakness.Keywords: Water Quality, Geology, GWR, Izadkhast basin 5-References Jafari, M., & Tavili, A. (2013). Reclamation of Arid lands, Tehran, Tehran University press, 4, 396 p.Jehbez, O. (1994). Hydrochemical evaluation of Sarvestan basin with an emphasis on the role of geological formations, MSc in Hydrology, University of Shiraz, 436 p.Pratt, B., & Changa, H. (2012). Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales, Journal of Hazardous Materials, 209–210, 48-58.
Mehdi Teimouri; Omid Asadi Nalivan
Abstract
1- Introduction Underground water is one of the most important water resources that plays an important role in providing water for agricultural and drinking activities in arid and semi-arid regions (Usamah and Ahmad, 2018, Wu et al., 2019, Kumar et al., 2019). Awareness of the quality of water resources ...
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1- Introduction Underground water is one of the most important water resources that plays an important role in providing water for agricultural and drinking activities in arid and semi-arid regions (Usamah and Ahmad, 2018, Wu et al., 2019, Kumar et al., 2019). Awareness of the quality of water resources is one of the most important requirements in managing, planning, and developing, protecting, and controlling water resources. Using multivariate statistical techniques helps researchers identify the most important factors affecting the quality of water systems and is a valuable tool for water resources management (Pasandidehfard et al., 2019). On the other hand, geostatistical methods are also capable of zoning water quality at the watershed level and can play an important role in completing the assessment of water quality (Ahmadi et al., 2019). The aim of this study is to evaluate the quality of groundwater used for drinking and farming in Hable-Rood Basin, analyze and interpret the quality of these resources using ArcGIS, and perform statistical tests to determine the role of land use and geology formations in water quality. 2-Methodology To do this research, 132 water sources including wells, springs, and Qanats were used during the statistical period of 2008-2018. The watershed can be divided into fifteen main categories in terms of geology. Hable-Rood watershed has 11 main land uses, which has the largest area of the watershed for pasture and the smallest area of the dams. The main components were analyzed (factor analysis) to understand the most important parameters affecting the water quality. This method weighs the components and expresses a special value for each of them (Finkler et al., 2016). Factor analysis has three stages of producing a correlation matrix from all variables (Pearson correlation method), extracting the main factors, and interpreting the results. Duncan's test was also used to check the significance level of parameters among land uses and the type of formations. Geostatistical methods were used for zoning water quality for drinking and farming purposes in the GIS. The spatial relationship of a random variable in the geostatistics was determined by the semivariogram (software GS +). The root mean square error (RMSE) method was used to assess the geostatistical methods and select the best method. It should be noted that the Schoeller diagram and Wilcox diagram were used for the drinking water zoning and agricultural water quality zoning, respectively. 3-Results and Discussion The results showed that the Cl, EC, TDS, Na, Ca, TH, and SO4 vary significantly in different land uses. The highest average was related to industrial areas within the watershed due to the release of industrial materials and the spread and diffusion of groundwater pollution. Also, the parameters of Cl, EC, TDS, TH, and SO4 differed significantly in varied formations. The trend of water quality changes shows the water quality impact of land use, and water quality has decreased sharply in the industrial area, low-yielding land, saline lands, agriculture, and residential areas. The EC parameter showed the highest correlation with TDS at 5% significance level, which is due to a high correlation with the effect of increasing EC on TDS. The pH parameter did not correlate with the other parameters. The factor analysis on the basis of water quality characteristics showed that 88.16% of the water quality variations among land uses were controlled by a single factor (TDS with a weight of 0.99). The factor analysis on the basis of water quality characteristics showed that 91.59% of water quality changes in the formations were determined by two factors (the first and the second factors with weight loads of 0.95 and 0.95 belonged to the TDS and EC parameters, respectively), and the variance percentages of each of factors 1 and 2 were 77.29 and 14.3%, respectively. 4- Conclusion In this research, the effects of geology and land use on groundwater quality were evaluated using multivariate statistical methods and geostatistical methods in ArcGIS. It was determined that some of the groundwater quality parameters were affected by land use and some of the other parameters were under the influence of the geology in the watershed. In general, however, it can be stated that in the first priority, the land use factor and human activities, and in the second priority, the geological factor affecting groundwater quality have the most significant effects. In the formation part of the geology, the dissolution of calcareous and dolomite formations, the chemical processes of salt dissolution, and evaporative formations are the main factors controlling groundwater chemistry in the region. Based on the results, multivariate statistical techniques and geostatistical methods have the ability to recognize factors affecting groundwater quality and the zoning of water quality for different uses and are, therefore, suggested for similar research.
