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Optimized management of water resources, conservation and their quality increase is needful with data existence in basis of situation, amount and distribution of water chemical factors for example; electrical conductivity (EC) in determined geographical region. Accuracy of interpolation appropriate methods and variation map preparation of groundwater quality variables is independent to region conditions and existence of enough data. That is true selection of interpolation methods is basic and important step in management of groundwater resources. EC is one of the important indicators for groundwater quality evaluation. The objective of this research was to determine the most suitable interpolation method and their accuracy for analysis and checking spatial variation of groundwater EC amount in central regions of Guilan province, northern Iran. This investigation evaluated the inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), radial basis function (RBF) and ordinary kriging (OK) methods for estimation of groundwater EC in paddy fields. In IDW method, for variable estimation used power value 1-5 that power value equal 1 was exact. Gaussian model was the best one fitted on empirical semivariogram of variable data in OK method. Standard statistical performance evaluation criteria include root mean square error (RMSE), correlation coefficient (R) and mean absolute error (MAE) were used to control the accuracy of the prediction capability of the developed methods. Results showed that the best estimator was OK method which was the most exact with regard to other methods for estimation groundwater electrical conductivity.

Since particle size distribution (PSD) is a fundamental soil physical property, so determination of its accurate and continuous curve is important. Many models have been introduced to describe PSD curve, but their fitting capability in different textural groups have been rarely investigated. The aim of this study was to evaluate the fitting ability of 15 models on 2653 soil samples from 13 province of Iran, and to determine the best model among them for the PSD of all soil samples as well as for each soil textural group based on evaluation criteria. Results showed that the Weibull model was the most accurate model for all soil samples as well as for the clayey and loamy groups. After the Weibull, Fredlund, Rosin-Rammler and van Genuchten were the most accurate models. However, their differences were not significant (p B 0.05). Also, for the coarse texture group the S-shape model showed the better fit than the others. These results showed the performance of a particular model varies with the soil textural characteristics.

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