Ali Bakhtiarizadeh; Mohammad Najafzadeh; Sedigheh Mohamadi
Abstract
Introduction
The groundwater aquifer is one of the most vital resources, being considered more important in the countries (e.g., Iran) located in hot and dry areas. One of the ways to prevent contamination of groundwater resources is to focus on their vulnerability. So, a trustworthy assessment of groundwater ...
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Introduction
The groundwater aquifer is one of the most vital resources, being considered more important in the countries (e.g., Iran) located in hot and dry areas. One of the ways to prevent contamination of groundwater resources is to focus on their vulnerability. So, a trustworthy assessment of groundwater vulnerability is useful to determine the contamination points of the aquifer for effective protection and management of groundwater resources.
Materials and methods
In this research, DRASTIC index was applied to evaluate groundwater susceptibility while considering effects of land use and nitrate pollution for Kerman-Baghein Plain located in Kerman Province. In this regard, seven parameters including the depth of the water table, net feeding of aquifer, aquifer texture, surface soil texture, topography, impact of the vadose zone, and hydraulic conductivity were employed to calculate the DRASTIC index. It should be noted that this index has previously been applied by researchers for assessment of the vulnerability of the aquifer against groundwater pollution in different regions. However, despite the complexities in the underground water system and the opinions of experts in assigning the rank and weight of the parameters in this index and the difference in the conditions prevailing in the studied areas, it has always prompted researchers to take practical steps to improve this index. This improvement has been carried out in a number of studies by adding other parameters (e.g., land use and the effect of nitrate) to the parameters of the DRASTIC index. Therefore, in the present research, the parameters of land use and potential risk associated with land use have been used to compute the Composite DRASTIC index (CD) and Nitrate Vulnerability Index (NVI) in the Arc/GIS software environment, respectively. In this way, the CD index was obtained by adding the land use parameter to the DRASTIC index and the NVI index by multiplying the raster map of the potential risk rating related to the land use in the DRASTIC index. It should be noted that according to the land use map evaluation, this plain includes 54% of low-density pastures, 24% of irrigated agriculture, 10% of hand-planted forests, 6% of bare and desert lands, 5% of residential areas, and 1% of claypans. After the evaluation of three vulnerability criteria using all three indices DRASTIC, CD and NVI, their correlation with Ggroundwater Contamination Risk (GCR) was also investigated.
Results and discussion
The results revealed that the correlation of the DRASTIC index with the risk of underground water pollution is 8%, the CD index is 30% and the NVI index is 54%, with a probability of 99%, they show a significant correlation. The results indicated that the addition of the land use parameter caused to increase the correlation of vulnerability with the risk of groundwater pollution, and multiplying the potential risk associated with land use led to further increase of the correlation. As a result, the NVI index was selected as the superior index compared to the other two indices.
Conclusion
The results of the NVI index of Kerman-Baghin Plain indicated that this plain is divided into two categories including very low vulnerability with an area of 1528.07 km2 (75.52 %) having an NVI value of less than 70 and low vulnerability with an area of 495.33 km2 (24.48 %) having NVI value from 70 to 110. Taken together, in order to properly manage the groundwater resources and prevent the pollution of these resources, it is recommended to prohibit the establishment of industries and the cultivation of agricultural sector causing the pollution in areas with low vulnerability.
Sedigheh Mohamadi
Abstract
With regard to financial and technical problems normally measured sediment data are limited in developing countries; therefore a model that uses water discharge data as input can be a reliable option for estimates of sediment. Due to widely application of the variety of models to predict the suspended ...
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With regard to financial and technical problems normally measured sediment data are limited in developing countries; therefore a model that uses water discharge data as input can be a reliable option for estimates of sediment. Due to widely application of the variety of models to predict the suspended sediment, this study aims to determine optimal prediction model based on the amount of discharge flow gauging stations of Halilrood River including, Soltani, Henjan, Cheshmeh Aroos, Meydan and Konaruiyeh. In this regard, efficiency of some rating curves models including one-linear, two-linear and the intermediate categories ones (by and without coefficients as CF1, CF2 and FAO) and black box models including artificial neural networks and neural-fuzzy in modeling sediment were evaluated. The results of the evaluation of the model using the parameters of MAE and RMSE showed that neuro-fuzzy models in major hydrometric stations studied, including Pole Baft, Henjan and Konaruiyeh with an equivalent amounts of 35.07, 11958.74 and 34235.27 ton/day for MAE and 42.07, 28672.78 and 52735.92 ton/day for RMSE, respectively are the best models to simulate the suspended sediment. The artificial neural network model of radial basis function in Meydan with 384.83 ton/day MAE and 669 ton/day RMSE amounts is the optimal model. Also two-linear sediment rating curve resulted the best simulation in Cheshmeh Aroos Station with MAE and RMSE as 1.7 and 4.1 ton/day and one-linear sediment rating curve with CF1 correction in Soltani Station with MAE and RMSE 9723.2 and 41235.6 ton/day, respectively are the best. According to changes of efficiency of models with varying location of gauging stations, it can be concluded that ecological conditions and statistical community determine the optimal model of the suspended sediment simulation.