مجله محیط زیست و مهندسی آب
سال سوم شماره 3 (پاییز 1396)

Soil erosion is a phenomenon that leads to destruction and loss of valuable soil, especially under accelerated conditions. As the life of human societies requires water and soil, variety of biological and mechanical operations are practiced in order to prevent soil erosion and water loss, runoff control, and to improve the soil vegetative coverage. In order to evaluate the role of biological and mechanical watershed management projects on the erosion and sediments of Chehel Gazi Watershed, Sanandaj, the erosion and sediment of the watershed was determined using MPSIAC Model after identifying the performed operations. Then using paired T-test method, the impact of watershed practices on decreasing erosion and sediment was investigated. The results showed that specific sediment of watershed has decreased from 2.7 to 2.5 ton/hectare. Moreover, the level of higher sediment classes decreased and level of lower sediment classes increased. The statistical analyses showed that there is a significant difference between sedimentation before and after the watershed management project.

Increased generation of pollutants such as heavy metals is one of the serious and developing environmental issues threatening human society. Heavy metal pollution not only affects the physical and chemical properties of the soil but also it is dangerous to human health through entering into the food chain and finding its way into the groundwater. The present study was conducted to predict soil lead concentration, as one of the most important heavy metals, using readily available soil properties based on artificial neural network model. For this purpose, 63 soil samples were collected from 60-cm depth of the land surrounding Kashafrud River located in Mashhad City. Measured parameters included pH, electrical conductivity, particle size distribution, organic carbon, and Pb content in soil. The multilayer perceptron (MLP) as an artificial neural network model was used to predict the Pb concentration in soil. The performance of this model was assessed by the coefficient of determination (R2), mean absolute error (MAE), and also root mean square error (RMSE). The results showed that artificial neural network model is a suitable method to determine Pb concentration in soil rather than the direct laboratory measurement, which is an expensive and time-consuming method.

One of the most important issues in increasing the efficiency of modern irrigation systems is their field evaluation after exploiting. Irrigation efficiency is the main indicator in measuring and managing agricultural water consumption. This study was carried out in order to evaluate the hydraulic performance of center pivot irrigation system at Shahriar and Eshtehard cities in Tehran and Alborz Provinces respectively. In order to measure water consumption, the systems were evaluated using application efficiency (Ea) and potential application efficiency of low quarter (PELQ) indicators. Moreover, the irrigation distribution uniformity was evaluation through using coefficient of uniformity (CU) and distribution uniformity (DU). For calculating these indicators, the field measurement was used to collect water under each system. It was found that the Herman and Hein CU for the Shahriar and Eshtehard center pivot systems were 70.2 and 88% respectively and the DU for these systems were, 47.87 and 81.3 % respectively. The adequacy rates and Ea were 36 and 12% and 85, 81% respectively. In terms of efficiency, these systems are in good condition. The calculated PELQ is calculated 85.3 and 66.5% respectively, which are acceptable.

In this research, we used the meteorological parameters of Birjand Plain in South Khorasan Province during the 16-year period in order to evaluate the gamma test and to compare the accuracy of the model of least squares of the machine and experimental models to estimate evaporation. Using the gamma test method, among the parameters affecting evaporation, the optimal input parameters were determined for modeling the evapotranspiration from among 90 specified components. Seven superior components were obtained from other combinations. Then, the optimal combination was then evaluated using the least squares model of backup vector with various kernel functions and empirical methods. The results showed that the performance of the LSSVM-poly (polynomial) model, in combination with minimum temperature, average temperature, wind speed and sunny hours with performance indices (R2 = 0.9995 and RMSE = 995) has a higher accuracy compared to the kernel functions and other experimental methods.

Selection of low flow is necessary for water resource management, water supply planning, and watershed ecosystem restoration. In this study, FL approach was used, which is based on minimum flows and flow duration curve (Q80) categories. This approach was compared with the conventional 7Q10 approach used in low flow selection. For this purpose, the daily flow series from Gharesou catchment was applied. Low flow frequency analysis related to 7Q10 approach was done by fitting various distribution functions and using three frequency software, including HYFA, SMADA, and Easy fit. The results of both approaches show that low flows increased as time elapsed and the recurrence probability of low flow increased while the recurrence interval of low flow decreased as time elapsed. In addition, a low flow decline from 1971 to 2000 observed as 1.57 m3/s and 2.46 m3/s for 7Q10 and FL approaches respectively. The 7Q10 approach lead to selecting extremely low flows (approximately zero). The FL approach used in this study, could result in selecting low flows, considering role of the environment and minimum flow to protect the ecosystem of the catchment.

Climate is one of the most important and effective phenomenon of human life. Today's human, in order to develop industrial and municipal centers, and to increase food resources, needs to increase its information in the context of different climatic zones. The aim of this research was to achieve cluster classification by multivariate statistical methods. In this regard, 12 climatic elements were selected from 11 synoptic stations (isometropia) inside the East Azarbaijan Province for climatic zoning of the region. The maps and diagrams (plots) were plotted using SURFER and MATLAB software and data analysis was done using SPSS and MINITAB. The principal components analysis was performed for the average temperature, water vapor pressure difference between the maximum and minimum temperature and wind data. Moreover, in factor analysis with varimax rotation, three factors were obtained: humidity, average mean temperature, and minimum absolute temperature. The intensity of the factors was portrayed from North East to East and North West to South East. In the cluster analysis, three climatic zones were obtained.

Quality of water resources in each region is influenced by factors with natural or man-made origin suffers from physical, chemical, and biological changes. These changes create serious limitations for utilization of water resources Therefore, investigation of these resources is essential to maintain and improve their quality. The present study was carried out in order to classify groundwater quality of Kaboudarahang Plain using statistical techniques, including factor and hierarchical clustering analysis on qualitative data (14 parameters) for a 3-years period (1390-1392) through SPSS software. Factor analysis results showed that 81.74% of changes influenced by four components. Moreover, based on cluster analysis on 30 wells, the wells were classified to three groups. According to the classification, group I had the lowest and group III had the highest concentration of the water quality parameters. However, all groups were in C3S1class from the agricultural point of view.