Kh. Osati; A. Salajegheh; S. Arekhi
Abstract
Spatiality assessment of groundwater pollution is very important to determine water quality condition, pollution sources and management decisions. In this case, GIS and geostatistics methods can be useful tools. Spatiality of groundwater quality parameters, in relation with various land uses, can be ...
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Spatiality assessment of groundwater pollution is very important to determine water quality condition, pollution sources and management decisions. In this case, GIS and geostatistics methods can be useful tools. Spatiality of groundwater quality parameters, in relation with various land uses, can be very extremely. Therefore water samples from 52 wells in the Kurdan area were analyzed in this study. The results show that nitrate concentrations are less than maximum acceptable concentration in drinking water (i.e. 50 mg/L as nitrate recommended by ISIRI and WHO guideline values) except to one sample (2 percent of samples) in the study area. Various geostatistics methods, e.g. IDW (power 1-4), ordinary Kriging and RBF (five Kernel functions) were compared after assessing the variograms and the spatiality of nitrate samples. Then the model parameters were calibrated and through the specific methods, predicted and standard errors maps were prepared. Errors criteria show that Kriging is the best fitting model in the study area. Finally, probability map of NO3 concentrations exceeding the threshold value of 50 mg/L, is generated using the Indictor Kriging method. Spatiality of NO3 show that Nitrate concentration is increased where the rock type is permeable, land use is agriculture and slope is enough low to infiltrate polluted water into the wells. This research also tries to describe how to assess the spatiality of groundwater parameters by GIS.
Hojatollah Jalilian; Ghobad Rostamizad; Saleh Arkhi
Abstract
The necessity of using discharge data in hydrological designs and regional programming is an unavoidable matter. Unfortunately, malfunctioning of observation instruments, natural disasters and other problems sometimes result in incomplete or missing data. Having of data related to time series is necessary ...
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The necessity of using discharge data in hydrological designs and regional programming is an unavoidable matter. Unfortunately, malfunctioning of observation instruments, natural disasters and other problems sometimes result in incomplete or missing data. Having of data related to time series is necessary for every investigation about hydrometeorology. Thus missing data should be estimated in a proper way. There are different methods for generation of missing data which estimates the data with regard to particular parameters. In this study, we have used four methods including linear regression, multiple regression, Longbein method and Thomas-Fiering method for reconstruction of hydrometric data including monthly and annual discharge, sediment and water quality data. Results showed that among various methods, in 192 cases of reconstruction of monthly average discharge of 17 stations, Longbein and multiple regression methods in 33% and 27% of the cases have provided the best results, respectively. Two-variable regression in 8 of 17 stations had the best answer and it is suitable for estimating of annual average discharge. Also results indicated that we cannot use of relation between discharge-water quality and discharge-sediment to reconstruct data using above mentioned methods in the Sefidroud basin.
