Bahman Kavari; Yahya Esmaeilpour; Ali Akbar Mousavi; Ommolbanin Bazrafshan; Arashk Holisaz
Abstract
The main source of water in the Arsanjan plain is underground water, which has been exploited in the past with Aqueduct and now with numerous wells. For knowing about the quality conditions of these sources; multivariate statistical analysis and interpolation methods were used in three years with different ...
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The main source of water in the Arsanjan plain is underground water, which has been exploited in the past with Aqueduct and now with numerous wells. For knowing about the quality conditions of these sources; multivariate statistical analysis and interpolation methods were used in three years with different rainfall. Factor analysis determined the key indicators of underground water quality and mapping was done with interpolation methods. The maps were classified using the Jenks optimization method of classification and the area of each class in each year calculated. Based on the results of factor analysis, EC, TH and Sodium concentration were selected with factor loadings of 0.843, 0.889 and 0.991, respectively. The RBF interpolation method for the sodium parameter was suitable in all three years of the study. For parameters of EC and TH, RBF-MQ method and LIP method had the least error in 2014 and 2015. Mapping spatial changes of the three mentioned parameters showed that in 2015, when the rainfall was lower than the average, the area of the regions with low values decreased. Due to the quantity and quality of its changes, sodium concentration parameter has a good potential to be used as an indicator of changes of the quality of underground water in response to climatic or management factors. In general, it is suggested that in assessment of the groundwater quality of Arsanjan Plain, the proximity factor to Bakhtegan Salt Lake, in addition to factors related to climate and watershed, should be considered.
Shahab oddin Zarezade Mehrizi; Asadollah Khoorani; Javad Bazrafshan; Ommolbanin Bazrafshan
Abstract
Gamasiab River is one of the five main branches of the Karkheh River and plays a basic role in preserving the life and ecosystem of the region. The first step in the adoption of proper and sustainable methods for managing the water resources of the Gamasiyab river is to gain continuous knowledge of the ...
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Gamasiab River is one of the five main branches of the Karkheh River and plays a basic role in preserving the life and ecosystem of the region. The first step in the adoption of proper and sustainable methods for managing the water resources of the Gamasiyab river is to gain continuous knowledge of the quantitative and qualitative status of the water as well as its variations. The use of hydrological models is common to simulate quantitative and qualitative processes associated with the water cycle. One of the models that is widely used in the international level is the SWAT model. In this research, we evaluated the efficiency of SWAT model in simulation of Gamasiyab river flow. For running this model requires a DEM, soil map, land use map and slope classification. Initially, discharge data in daily step at Polchehr Hydrometric Station was calibration (1977-1995 AD) and then validation (1996-2005 AD) by using precipitation data of two synoptic stations and three weather stations and minimum and maximum temperature of two synoptic stations. Statistical coefficients including Nash-Sutcliff coefficient, R2, P-factor and R-factor for calibration period were 0.71, 0.73, 0.79, 1.36 respectively and for validation period were 0.57, 0.61, 0.71 and 1.34 respectively. These results indicate that the SWAT model has the ability to simulate the Gamasiyab River discharge and researchers can use this model to apply management scenarios in short time and low cost for better decision making.
Ommolbanin Bazrafshan; Ali Salajegheh; Ahmad Fatehi; Abolghasem Mahdavi; Javad Bazrafshan; Somayeh Hejabi
Abstract
Drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. This paper presents the performances of autoregressive integrated moving average (ARIMA), Direct multi-step neural network (DMSNN), ...
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Drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. This paper presents the performances of autoregressive integrated moving average (ARIMA), Direct multi-step neural network (DMSNN), Recursive multi-step neural network (RMSNN), Hybrid stochastic neural network of directive approach (HSNNDM) and Hybrid stochastic neural network of recursive approach(HSNNRM) with time scale monthly and seasonally for hydrology drought forecasting and SDI selected as predictor in the Karkheh river basin. The results shown performances of HNNDA was found to forecast hydrological drought with greater accuracy for SDI forecasting, so performances model in monthly scale was greater accuracy to seasonality scale.