spatial interpolation

Precipitation is a parameter related to climate and research on it is facing problems due to the lack of continuous data. Accurate planning and management of water resources depends on the availability of continuous and accurate precipitation data at meteorological stations. Hence, estimation of missing rainfall data is important in order to obtain more reliable results. Various imputation methods have been proposed and developed by researchers to estimate missing values in daily and monthly rainfall data. In most situations, spatial interpolation techniques such as normal ratio and inverse distance methods are used for estimating missing rainfall values at a particular target station based on the available rainfall values recorded at the neighboring stations. Moreover, these two methods are found to be very useful in the case where the neighboring stations are very close and highly correlated with the target stations. In this study, several modifications and improvements have been proposed to these methods in order to estimate the missing rainfall values at the target station using the information from the nearby stations. The methods have been tested with different percentages of missing rainfall values and also with a radius range of 75 km to 150 km in the catchment area of South Balochestan. The result indicate that the performance of these modified methods improved the estimation of missing rainfall values at the target station based on the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).

Many studies have evaluated different methods to estimate evapotranspiration as one of the most importantparameters of water resources and irrigation management in recent decades. The result of these studies confirmed that the FAO Penman-Monteith (PM) is the standard method for calculating reference evapotranspiration. This method requires numerous meteorological data such as air temperature, solar radiation, air humidity and wind speed. Since in most regions of Iran, the weather station is not available or all of the meteorological data is not measured, hence investigation of methods to estimate evapotranspiration is necessary in these situations. In this study, data scarcity scenarios, (when one, two and three meteorological variables are not available: relative humidity, wind speed and sunshine hours), have been evaluated. Also, the FAO-56 manual recommended methods (PM-R), PM-CI (computation-then-interpolation), PM-IC (interpolation-then-calculation) and Hargreaves–Samani (HS) were compared using different statistical parameters. The results showed that in absence of wind speed (when one or two meteorological parameters are not available), the PM-IC method estimated more accurately (R2> 0.96), while precision of the PM-R method is higher (R2≈ 0.99) in absence of other weather parameters (sunshine hoursand air humidity). Also, the HS method, temperature-based methods (absence of three meteorological data), provided a more acceptable result (PBIAS = -3.091, ME = -0.156, MAE = 0.337) than other methods.

Soil saturated hydraulic conductivity (Ks) is one of the main parameters in water and solute transport studies in soil and irrigation and drainage projects designs. So, knowledge about the Ks spatial distribution pattern has great importance. The aim of present study was to predict spatial distribution pattern of Ks in the experimental field of Sistan Dam, Zabol University, using different interpolation methods. For this purpose, a set of 113 single ring Beerkan infiltration experiments were carried out over the study area in a grid of average distance about 80 m. The Ks was obtained through Beerkan calculating algorithms, BESTslope, BESTintercept and BESTsteady. The relative fitting errors were 5.19% and 9.39% for BESTslope and BESTintercept algorithms, respectively, that are satisfactory. The interpolation methods were compared using evaluation criteria such as the weighted determination coefficient (ωr2) and standard error (SE). Based on the results, the spatial correlation of Ks was moderate and it had the exponential structure. The results showed that Log Kriging (LOK) achieved the highest ωr2 and lowest SE values for estimating Ks over the study area with a dominant soil textures of loam and sandy loam. However, the difference between LOK and the other interpolation approaches was not significant. Moreover, among the interpolation methods, BESTsteady algorithm, which had the simplest calculating procedure, had the highest precision in estimation. So, according to the results, LOK with an exponential semivarigram model is suggested as the best interpolation method for predicting the spatial distribution of Ks, based on values obtained from the simple and applicable algorithm, i.e. BESTsteady, in loam and sandy loam soils.

Determining the spatial distribution of different contaminants in soil is essential for the pollution assessment and risk control. Interpolation methods are widely used to estimate the concentrations of the heavy metals in the unstudied sites. In this study, the performances of interpolation methods (inverse distance weighting, local polynomials and ordinary Kriging and radial basis functions) were evaluated to estimate the topsoil contamination with copper and nickel in Golestan Province. 216 surface soil samples were collected from Golestan province, and their Cu and Ni concentrations were measured. Soil contamination was determined using different interpolation methods. Cross validation was applied to compare the methods and estimate their accuracy. The results showed that all the tested interpolation methods have an acceptable prediction accuracy of the mean content for soil heavy metals. RBF-IMQ and IDW1 methods had the lowest RMSE, whereas RBF-TPS method with the largest RMSE estimated a larger size for the polluted area. The greater the weighting power, the larger the polluted area estimated by IDW. Compared with the ‘‘sample ratio over the pollution limits” method, the polluted areas of Cu and Ni were reduced by 8.38% and 6.14%, respectively.

Continuous soil quality evaluation in is necessary agricultural regions to sustainable soil management and profitable agricultural production. The objectives of this study were comprised of an evaluation of soil quality utilizing Integrated Quality Index (IQI) along with its spatial distribution in Qazvin province. IQI was calculated and its spatial changes analyzed using different interpolation methods via GIS software. Precision criteria MAE and RMSE, bias criteria MBE and correlation between measured vs. predicted values (r) were employed to determine a suitable interpolation method. The results indicated an IQI index of strong spatial correlation (Nugget/Sill = 0.38) with an effective range of 19.18 kilometers. The IQI spatial distribution is well fitted by the exponential model. Completely Regularized Spline was selected as a suitable interpolation method using evaluation criteria as well as existing trend in area. IQI model revealed the soil areas with moderate quality (grade III) as dominant.