Studies on the spatial variability of soil properties in forest stand scale are limited. In this study, we demonstrated spatial soil variability with use of Geostatistic technique in mixed beech forest soil. The main objectives were assessment of Geostatistic technique to improve efficiency soil study. Soil samples from 0 to 15 cm depth were collected within 120 locations, on a 50*50 m systematic randomized grid besides 15 transect basis over a 16 ha field. Spatial variability for soil pH and organic matter revealed anisotropic variogram due to same range effect and sill. A Geostatistical analysis showed that the soil properties (pH, organic matter) were spatially structured. The nugget-to-sill ratio indicates a strong spatial dependence for soil pH, and a moderate spatial dependence for organic matter. Interpolation using kriging showed a spatial similarity among the soil properties. Soil pH increased in the northwest quadrant of the research area where located in footslope. The result of cross validation with use of MAE, MBE and MSE revealed that the high accuracy of estimation for soil acidity and organic matter.  

Introduction Rainfall erosivity is defined as the aggregative power of the rain. If other effective features on soil erosion be considered constant then soil loss could be directly connected to rainfall erosivity. Rain erosion term was proposed by Wichmeier and Smith in 1978 to consider the effect of climate on raw erosion. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. Given the significant change in rainfall in time and space on the one hand and low rain-gauge stations to record rainfall on the other hand, the necessity of using Geostatistical methods for rainfall erosivity mapping is inevitable. Geostatistical methods use the spatial correlation between observations in the estimation processes. In these cases the spatial distribution pattern of rainfall erosivity can be produced using different methods of interpolation. Materials and Methods Study area Lorestan province is located in southwest of Iran and covers an area of 28249 square kilometers. It is located between the latitudes 32º 37' and 34º 22' N and the longitudes 46˚ 51ʹ and 50˚ 30ʹ E. The main objective of this research were: (1) analyze the spatial distribution of rainfall erosivity using two different interpolation methods namely ordinary Kriging and simple Kriging; (2) put forward the best interpolation method through cross-validation, construct the high resolution grid data of rainfall erosivity and provide the reliable information for relevant researches Monthly rainfall erosivity model In the first step, the precipitation data collected from 53 precipitation stations and Modified Fournier Index (MF) calculated based on Eq. (1) (1) Where MF is the modified Fournier index value (mm), pi is averagemonthly precipitation (mm) and P is average annual precipitation (mm). Then Eq. (2) and Eq. (3) were used to estimate rainfall erosivity or R-factor values (MJ mm ha-1 h-1 year-1). (2) (3) It is suggested that Eq. (2) be applied for locations with MF values less than 55 mm and Eq. (3) be used for locations with MF values greater than 55 mm. Geostatistical Methods In this article two interpolation techniques namely simple and ordinary Kriging were compared in GS+5. 1. 1 and ArcGIS10. 3 software’ s in order to determine which one describe better the spatial distribution of rainfall erosivity. Kriging methods assume that the spatial variation of a continuous climatic variable is too irregular to be modeled by a continuous mathematical function, and its spatial variation could be better predicted by a probabilistic surface. The predictions of Kriging-based methods are currently a weighted average of the data available at neighboring sampling points (weather stations). The weighting is chosen so that the calculation is not biased and the variance is minimal. A function that relates the spatial variance of the variable is determined using a semi-variogram model which indicates the semi variance between the climatic values at different spatial distances. Validation and techniques comparison The resulting maps from interpolation were compared by using a set of validation statistics include Mean error (ME), Mean Standardized Error (MSE) and the root mean square error (RMSE) by Eq. (4), (5) and (6) (4) (5) (6) Results and Discussion Based on the results, rainfall erosivity values varied from 11. 1 to 749. 5 MJmmha− 1 h− 1 y− 1. Differences between the simple and ordinary Kriging models regarding the validation statistics were narrow, but allowed for a comparison. The obtained results showed that ordinary Kriging with higher R2 and lower ME, MSE and RMSE had better precision in mapping rainfall erosivity. The spatial distribution of rainfall erosivity showed the areas along north-south of Lorestan province and central regions had higher values while lower rainfall erosivity was seen in the western and eastern areas of the study area. Conclusion The availability of high-quality environmental maps is a key issue for agricultural and hydrological management in many regions of the world. Produced rainfall erosivity map in this research can be used for estimation of soil loss by USLE model. Rainfall erosivity maps also can be suitable as guidance for soil conservation practices and identifying areas with the high potential of soil retention as an ecosystem service. Further research may be directed to find reliable erosivity indices which can be computed from daily precipitation data.

Precise mapping of the spatial distribution of salt-affected soils is prerequisite for effective management of these soils. This study was carried out for mapping soil salinity of 78000 hectares of Ardakan soils (0-30 and 0-100 cm) using 151soil samples which were taken based on hyper cube method. The secondary variables used in co-kriging method were ETM data, terrain analysis and EM38 readings. The best model was selected by means of cross validation and error evaluation methods, such as RMSE and ME methods. Results showed that cokriging method with EM38 data as a secondary variable was the best method for prediction of soil salinity (69.1, 30.55, 48.8 and 20.41, respectively). Results recommended EM38 as secondary data for mapping soil salinity. Additionally, results showed the largest amount of soil salinity in the north of the area and the smallest values in the areas with more elevation. The concavely shaped plain could help to move soluble salts toward the north of area in which the soils with highest electrical conductivity are found.

Soil erosion is one of the factors that threatened human life. Measuring soil erosion is always difficult. Lack of hydrometric stations in the majority of watersheds, to estimate soil erosion and deposition face with difficult. However, many researchers have tried to present empirical relationships in favor of weather conditions for the estimation of soil erosion and sedimentation. Among them, universal soil loss equation and its revised version are including models that have application worldwide. One of the main parameters of this model is a rainfall erosively factor that has the direct relationship with rainfall intensity. In this study, 22-year periods related to 16 climatology stations in the Ilam dam watershed were used for preparing rainfall erosivity factor map. After calculating a rainfall erosively factor (R factor) for all stations using kriging-based Geostatistic techniques and Geostatistic extension in ARC GIS 9.3 environment, the map of R for the whole watershed was prepared. Here, we have compared three kriging techniques: ordinary, simple, and universal Kriging. The obtained results show that simple kriging with a 67.92 Root Mean Square Error (RMSE) is the most proper interpolation technique. Furthermore, in compare to the RMSE, the Standard Error (SE) for calculating the amount of expectations, the simple, ordinary and universal kriging had underestimated than the expectation.

Biogas production from anaerobic digestion of livestock manure is one of valuable energy resources. The use of stirrer and mixing of substrate can improve biogas production rate in digesters. Present research was carried out in order to model and trend analysis of biogas production and mixing effect in a full scale batch reactor 1200 lit. With liquid volume of 800 lit using Geostatistic. Variograms were used for modeling biogas production in three states: (I) without stirring and (II), (III) stirring at 40 and 100 rpm, respectively. Then, the proposed models were evaluated by reproducing observed data using kriging. Cross validation and statistical indices such as determination coefficient and remaining sum of squares were the assessment criterion. Simulation results showed that biogas production trend under uniform flow condition had most data structural continuity with correlation of 0. 97 and in state of (III) with correlation of 0. 33 had the lowest data structural continuity.

Heavy metals, in particular mercury, are important for the public health due to their toxicity and endurance in the environment. In the present study, the distribution of mercury was surveyed along the coast of the Caspian Sea (Mazandaran province). Twelve stations were selected and from each station 5 samples were randomly collected followed by chemical analyses. An inverse weight-distance method was used to interpolate the distribution of mercury. The correlation among physicochemical characteristics of sediments, organic matter, electrical conductivity, acidity, and the distribution of mercury was investigated using the distribution map of all these parameters. Overlaying the distribution maps of mercury concentration and the measured sediment parameters indicated that the mercury concentration was accordance with organic content and electrical conductivity. Generally, the total amount of mercury in the investigated stations was lower than the maximum levels recommended by NOAA. The findings of the present study provide useful information about distribution of heavy metals along the coast of the Caspian Sea that can help monitoring and assessment of sediments in the region.

Introduction: Snow is an important hydrological phenomenon and snow water equivalent is suitable water resource in many parts of the world. Snow and snow water equivalent have a significant contribution in stream flow and groundwater recharge. For this reason, it is important modeling of snow accumulation and melting. …

Introduction: Salinity is a highly important problem in arid and semi- arid region. In Iran, about 235,000 km 2 (or 14.2% of the total area of the country) area is salt-affected, which is equivalent to about 50% of irrigated lands in Iran (Pazira 1999). Irrigating of these lands causes to transfer salts to the area of root growth and thus increases the osmotic pressure and reduces the absorption of the nutrient elements and product. Some of researchers believe that this process is one of the disasters that threats development of human societies. Esfahan province is exposed to the danger of salinity.

Due to the importance of groundwater resources in arid and semi arid regions, the qualitative assessment of these resources is so important. Pressurized irrigation as a suitable method for increasing irrigation efficiency and reducing water consumption, requires water quality analysis. Despite many activities of researchers in the preparation of groundwater quality maps by using Geostatistical methods, most of these studies have focused on the use of conventional kriging techniques that are not suitable for the preparation of vulnerable zones of contamination, In this study spatial variations of aquifer quality parameters and especially suitable areas for pressurized irrigation were investigated by using indicator Kriging method. For this purpose groundwater quality data from 27 wells in Birjand aquifer were studied during 2016. Qualitative parameters were evaluated including pH (acidity), EC (electrical conductivity), SAR (sodium absorption ratio), Na, NaCl, Cl and ClCO and HCO3, respectively. The allowed thresholds for use of these parameters were considered in pressurized irrigation with the proposed FAO limitaion. On the other hand, due to the importance of LangelierSaturation Index in sedimentation of drip irrigation system, this index was also evaluated for aquifer wells and unsuitable areas for pressurized irrigation in the aquifer were determined using the Geostatistical indicator kriging method and ArcGIS software. The results indicate suitable irrigation region are located in the eastern and central parts of the plain.

Introduction and Objective: Due to the difficulty of this process, determining the volume inventory of the forest is one of the most fundamental objectives and duties of forest management. It has always been connected to difficulties for the department in charge of implementation. Mapping of forest stand volume has an important role in sustainable forest management. In Iran’, s north forestry plans, the systematic random sampling method and arithmetic average method is used to estimate the stand volume and spatial relationships between inventory sample plots will not be considered. The aim of this study is to compare kriging method and ordinary one used to map stand volume of Haftkhal forestry plan. Material and Methods: For this purpose, we used 243 circular sample plots with 10R area and 150×200 meter grid (Forestry Department standard in Iran) for inventory. To evaluate the performance of the kriging-method-created map the approach of poisonous variograms, histograms, and their indices was first used as the referential map to determine whether the data were normal. The best parameters of Lag Size, Nugget, and Partial Sill were then tested using the remaining points and the conventional approach to create the necessary kriging map using 30% of the sample pieces (73 sample pieces) randomly separated as ground control points. Finally, the ideal mode was decided upon. The produced map was then verified using ground control sample pieces (73 sample pieces), and the amount of difference was measured by comparing the head volumes of the control sample pieces and those calculated using the Kriging method as corresponding points. Results: Map produced by the arithmetic mean method (ordinary method of Forestry Department) and maps of Kriging method was compared. Accuracy of kriging map was controlled by using ground samples plots. The results showed that Root Mean Square error (RMSe) of the best model was 30 percent and Bias was 0. 11 percent. Using SAS software showed the correlation coefficient between kriging map and ground sample plots and corresponding relationship was at the level of 99 percent, but the correlation between arithmetic mean method was not corresponded even in 95% probability level. T-test, using this software, also showed similar results. Conclusion: In conclusion, Kriging capability is superior to conventional methods for estimating forest stand volume.