interpolation

Most of the water needs in arid and semi-arid regions is supplied through groundwater, so it is important to study the quantitative and qualitative changes of these waters and the factors affecting them. Land use changes can cause changes in groundwater quality. Here, land use changes in the period 2003 to 2020 in Damghan watershed were studied using Landsat 8, 7 and 5 satellite images, eCognition software and GIS. 1760 wells data in Semnan province were used for intermediation and accurate preparation of groundwater quality maps and extraction of geostatistical maps. Also, the information of 58 deep wells inside the watershed was used to analyze and study the groundwater quality changes. The results showed that the area of rangelands, forests and agricultural lands decreased and the area of gardens, urban areas, barren lands and surface water resources (due to the construction of the dam) increased. During this period, groundwater quality changes included Cl increased by 1.01 mEq, SO₄-2, decreased by 0.34 mEq, HCO3- increased by 0.46 mEq, Mg increased by 0.32 mEq, Ca decreased by 2.25 mEq, Na increased 3.58 mEq, electrical conductivity increased  by 44.81 μmohs / cm, Total dissolved solids increased by103.88 mg / l and pH increased by 0.09. These changes were evaluated with R software and the results showed that changes in groundwater quality are related to land use changes.

Introduction:

Climate change and global warming are the most important challenges in sustainable development, which is due to increased concentration of greenhouse gases in the atmosphere. Carbon dioxide is a major component of greenhouse gases. In order to reduce atmospheric carbon dioxide and to create a balance in the content of greenhouse gases, atmospheric carbon must be absorbed in organic forms (Dieleman et al., 2015; Lichtfouse, 2009). For this aim, a study was done in agricultural lands of Gorgan in order to estimate the carbon sequestration potential of soybean plant (Glycine max L.).

This research was carried out in 150 soybean fields of Gorgan township and sampled by quadrate 0.5Í0.5 m2 as random method, during 2016-2017. The soybean shoot and root organs were individually harvested and transferred to the laboratory. An electric burn furnace method was used to determine the carbon sequestration potential in soybean organs (including pods, seeds, stems, leaves and roots). Also, the amount of shoot to root and harvest index were estimated to determine the net primary production based on carbon content in the above ground organ, below ground organ and total plant, and the carbon allocation coefficients in each soybean organ. Then, using different interpolation methods, the spatial distribution of carbon sequestration of the plant organs was investigated in ArcGIS software. All data were analyzed by SPSS software.

The results showed that Kriging method was the best model for carbon interpolation and distribution of carbon sequestration potential in agricultural lands of Gorgan township. The amount of stored carbon was obtained as 579.64 kg.ha-1 in leaves, 744.81 kg.ha-1 in stem, 881.16 kg.ha-1 in seeds, 340.16 kg.ha-1 in pods and 540.21 kg.ha-1 in root. Also, according to harvest index (32%) and grain yield (3461.13 kg.ha-1), other indexes were calculates as the ratio of shoot to root about 4.30, the total net primary carbon production 6734.8 kg.ha-1, above-ground net primary carbon production 4867.2 kg.ha-1 and below-ground net primary carbon production about 1867.63 kg.ha-1. Also, the shares of the allocation coefficients of economic organs, stems, leaves, root and root secretions were 0.23, 0.49, 0.61 and 0.12, respectively. The zoning results showed that the total stored carbon potential in soybean plant was highest in the eastern, north and southeastern regions of the surveyed area, and the central, western and southwestern parts of this township had the lowest stored carbon potential. Also, the amount of carbon sequestration potential (total above ground and below ground organs) was as 3085.98 kg.ha-1 in this study.

 The highest proportion of carbon was allocated to the shoot organs, and the carbon of root exudates was also lower than other plant organs. In this study, it has been found that the potential of carbon sequestration was different in soybean plant organs and some variable such as agronomical management, soil and climatic condition can affect on its contents.AcknowledgementsWe are thankful to Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Agriculture Services Centers of Gorgan and soybean farmers for all their companions and supports.

Forestry plans have been influential on tree species and planting in different ways, regulating tree species diversity. Investigation of the changes in tree species diversity and preservation livestock sustainability by using forestry inventory can be an appropriate tool for decision makers in management. Indicators such as the Shannon Wiener and Simpson indices can be used to study the variations in tree species diversity in the forest. In this research, the  Watson series forestry inventory was used in 2004 and 2014. In the aforementioned years, 369 circular sampling plots were taken with an intensity of 3.3% and using a 200 * 150 m inventory grid with GPS. The Simpson and Shannon Wiener indices were calculated in sampling plots pieces; then, Kriging ability was investigated  using histogram, QQ plot, semivariogram and RMSE, and data normalization. The land maps of these indices were analyzed in 2004, 2014; also, their change detections were prepared. The results showed that during the 10-year period of the implementation of the forestry plan, the Simpson and Shannon Wiener species diversity indices were increased by 30%, and about 60% of the area remained unchanged; also, about 11% of the area had reduced species diversity.

This study was aimed to predict the potential distribution of Ferula ovina (Boiss) in Feridoonshar, in the western part of Isfahan province, and to produce species response curves in relation to the environmental variables using the Generalized Linear Model (GLM). The presence and absence of the species in 278 sites (including 138 presence sites & 140 absence sites) were collected using random stratified sampling. Digital elevation model was used to produce elevation classes, aspect and slope maps. Seventy soil profiles and 10 climate stations data were used to produce 31 environmental maps including climate and soil maps using kriging methods. According to the results, the presence of Ferula ovina was correlated with silt percent, average annual temperature, elevation, organic matter content, soil saturation percentage and CaCO3 content. The produced species distribution model had high accuracy, as indicated by calculated Kappa coefficient (0.79) and ROC area under curve plots (0.83). The result of this study can be, therefore, used in the rehabilitation and restoration of this valuable species in the rangeland ecosystems.

Study of the spatial variations of sediment and its influencing factors, is essential to control it. In this study, the spatial variations of production characteristics and sediment concentration in a seasonal stream located in an erosional pediment, studied. The location of plots at the 50 meters intervals selected and experiments done in 10 locations (with 3 replications). Then a rainfall with rainfall intensity of 1.4 mm/min and duration of 10 minutes, created using rainfall simulator and in the vicinity of each plot, surface soil samples were taken. The statistical analysis performed then using the cokriging method, the spatial distribution of characteristics prepared. The results showed that for sediment concentration variable among 10 locations, was no significant difference, but for the sediment load variable it was significant. The results of fitting experimental models on semivariograms, showed that the best fitted model to the spatial pattern of sediment concentration was gaussian and for sediment load was exponential model, respectively. Also two mentioned variables had strong spatial patterns. The results of the validation of applied model showed that the point-cokriging method has low accuracy to estimate sediment concentration, but for estimation of sediment load, the method has higher accuracy.

Optimization the number of synoptic stations in the estimation of rainfall is an important step in terms of reducing the maintenance cost and saving in the data collection. The main objective of this study was to determine the optimal number of synoptic stations to estimate the amount of rainfall in Iran country. Accordingly, the amount of rainfall of synoptic stations related to a common 14-year period was received from the National Weather Service and the performances of five different interpolation methods were evaluated. Based on the results of radial basis function (RBF), with a margin of error of 0.63, this method was selected as the most appropriate method in fitting the data. Studies show by eliminating the synoptic stations in PCA method the estimation error of RMSE increases from 0.48 to 0.52 related to the case that all synoptic stations were used and in the radial basis function interpolation method decreases from 0.63 to 0.55 which indicates the suitability of this method in the optimization of synoptic stations. The results indicate that removing 34 and 22 points from the network of synoptic stations in Iran respectively in the PCA method and interpolation method of radial basis, the resulting error is acceptable.

Accurate analysis and interpolation of heavy metals, especially arsenic concentrationin ground watercan play a significant role in planning and continuous monitoring of water resources. The analysis may also prevent human health issues.The purpose of this paper was to evaluate newly published Sugeno type fuzzy inference system as an interpolation method for estimating the amount of arsenic in Khoy Aquifer. It is done by assessinginverse distance weighting (IDW), Kriging (simple, ordinary and universal), Cokriging and Sugeno type fuzzy inference system. The results after optimization of the influencing factors in the interpolation methods indicated that RMSE, due to the low density and odd arrangement of wells, for all of the interpolation methods is high. Among the methods that did not use the auxiliary data, soft computing and IDW, with 53ppb and 56ppb RMSE respectively, lays better estimation than Kriging methods.Chlorine (Cl), Sodium (Na) and Iron (Fe) were used as auxiliary data. These data improved the accuracy of kriging and fuzzy methods by 46% and 51% than single univariate methods. The main cause of 19% improvement of the fuzzy method is attributed to its independence of normal distribution. The results showed that fuzzy Sugeno in the modeling of interpolation is more flexible and easier to execute (in terms of both user’s knowledge and in software development).

Climatic change can impose physiological constraints on species and can therefore affect species distribution. Bioclimatic predictors، including annual trends، regimes، thresholds and bio-limiting factors are the most important independent variables in species distribution models. Water and temperature are the most limiting factors in arid ecosystem in central Iran. Therefore، mapping of climatic factors in species distribution models seems necessary. In this study، we describe the extraction of 20 important bioclimatic variables from climatic data and compare different interpolation methods including inverse distance weighting، ordinary kriging، kriging with external trend، cokriging، and five radial basis functions. Normal climatic data (1950-2010) in 26 synoptic stations in central Iran were used to extract bioclimatic data. Spatial correlation، heterogeneity and trend in data were evaluated using three models of semivariogram (spherical، exponential and Gaussian) and the best model was selected using cross validation. The optimum model for bioclimatic variables was assessed based on the root mean square error and mean bias error. Exponential model was considered to be the best fit mathematical model to empirical semivariogram. IDW and cokriging were recognised as the best interpolating methods for average annual temperature and annual precipitation، respectively. Use of elevation as an auxiliary variable appeared to be necessary for optimizing interpolation methods of climatic and bioclimatic variables.