Cokriging Research Articles

Acid volatile sulphide estimation using spatial sediment covariates in the Eastern Upper Gulf of Thailand: Multiple geostatistical approaches

Summary Acid volatile sulphide (AVS), one of the most reactive phases in sediments, is a crucial link in explaining a dynamic biogeochemical cycle in a marine ecosystem. Research gaps exist in describing the spatial variation of AVS and interconnections with sediment covariates in the Eastern Upper Gulf of Thailand. Measurements of AVS and auxiliary parameters followed the standard protocol. A comparison of ordinary kriging (OK), cokriging (CK), and regression kriging (RK) performance was evaluated based on the mean absolute error (MAE) and root mean square error (RMSE). The concentrations of AVS ranged from 0.003 to 0.349 mg g−1 sediment dry weight. Most parameters contained short range spatial dependency except for oxidation–reduction potential (ORP) and pH. The AVS tended to be both linearly and non-linearly related to ORP and readily oxidisable organic matter (ROM). The RK model, using inputs from the tree-based model, was the most robust of the three kriging methods. It is suggested that nonlinear interactions should be taken into account when predicting AVS concentration, and it is expected that this will further increase the model accuracy. This study helps establish a platform for ecological health and sediment quality guidelines.

Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging

Soil heavy metals exhibit significant spatiotemporal variability and are strongly correlated with other soil heavy metals. Thus, other heavy metals can be used to improve the accuracy of predictions when performing spatiotemporal predictions of soil heavy metals within a given area. In this study, we propose the spatiotemporal cokriging (STCK) method to enable the use of historical sampling points and co-variables in the spatial prediction of soil heavy metals. Moreover, experimental spatiotemporal (ST) semivariogram and ST cross-semivariogram computational methods, a fitting strategy to the ST semivariogram and ST cross-semivariogram models based on the Bilonick model, and the STCK interpolation algorithm are introduced; these methods are based on spatiotemporal kriging (STK) and cokriging (CK). The data used in this study consist of measurements of soil heavy metals from 2010 to 2014 in Wuhan City, China. The results show that the behavior of predictions of the concentrations of heavy metals in soils is physically more realistic, and the prediction uncertainties are slightly smaller, when STCK is used with greater numbers of co-variables and neighboring points.

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash–Sutcliffe Coefficient (NSE), the coefficient of determination (R2), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K)

Soil macronutrients (i.e. nitrogen (N), phosphorus (P), and potassium (K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks (ANN) and two geostatistical methods (geographically weighted regression (GWR) and cokriging (CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil (0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration (n = 84) and validation (n = 22). Chemical and physical variables including clay, pH and organic carbon (OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model (with coefficient of determination R 2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model (with R 2 = 0.612 and RMSE = 0.0094%), and the GWR model (with R 2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients (N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information

Abstract. Unconsolidated sediment cover thickness (D) above bedrock was estimated by using a publicly available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias. However, if information about the horizontal distance to the nearest bedrock outcrop (L) is included, does the spatial estimation of D improve? This idea was tested by comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis showed only minor differences between OK and CK with respect to differences between estimation and true values. However, the CK results gave in general less estimation variance compared to the OK results. All observations were declustered and transformed to standard normal probability density functions before estimation and back-transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite the noisy character of the observations, the analysis demonstrated that L can be used as secondary information to reduce the estimation variance of D.

Hybrid kriging methods for interpolating sparse river bathymetry point data

ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.

Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey

The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest area in the semi - arid region of Black Sea backward region of Turkey. Some of the soil properties, including total nitrogen, SOC, soil organic matter, and bulk density were measured on a grid with a 50 m sampling distance on the top soil (0–15 cm depth). Accordingly, a total of 120 samples were taken from the four adjacent land uses. Data was analyzed using geostatistical methods. The methods used were: Block kriging (BK), co - kriging (CK) with organic matter, total nitrogen and bulk density as auxiliary variables and inverse distance weighting (IDW) methods with the power of 1, 2 and 4. The methods were compared using a performance criteria that included root mean square error (RMSE), mean absolute error (MAE) and the coefficient of correlation (r). The one - way ANOVA test showed that differences between the natural (0.6653 ± 0.2901) - plantation forest (0.7109 ± 0.2729) areas and the grassland (1.3964 ± 0.6828) - cultivated areas (1.5851 ± 0.5541) were statistically significant at 0.05 level (F = 28.462). The best model for describing spatially variation of SOC was CK with the lowest error criteria (RMSE = 0.3342, MAE = 0.2292) and the highest coefficient of correlation (r = 0.84). The spatial structure of SOC could be well described by the spherical model. The nugget effect indicated that SOC was moderately dependent on the study area. The error distributions of the model showed that the improved model was unbiased in predicting the spatial distribution of SOC. This study's results revealed that an explanatory variable linked SOC increased success of spatial interpolation methods. In subsequent studies, this case should be taken into account for reaching more accurate outputs.

Spatial Interpolation of Annual Runoff in Ungauged Basins Based on the Improved Information Diffusion Model Using a Genetic Algorithm

Prediction in Ungauged Basins (PUB) is an important task for water resources planning and management and remains a fundamental challenge for the hydrological community. In recent years, geostatistical methods have proven valuable for estimating hydrological variables in ungauged catchments. However, four major problems restrict the development of geostatistical methods. We established a new information diffusion model based on genetic algorithm (GIDM) for spatial interpolating of runoff in the ungauged basins. Genetic algorithms (GA) are used to generate high-quality solutions to optimization and search problems. So, using GA, the parameter of optimal window width can be obtained. To test our new method, seven experiments for the annual runoff interpolation based on GIDM at 17 stations on the mainstream and tributaries of the Yellow River are carried out and compared with the inverse distance weighting (IDW) method, Cokriging (COK) method, and conventional IDMs using the same sparse observed data. The seven experiments all show that the GIDM method can solve four problems of the previous geostatistical methods to some extent and obtains best accuracy among four different models. The key problems of the PUB research are the lack of observation data and the difficulties in information extraction. So the GIDM is a new and useful tool to solve the Prediction in Ungauged Basins (PUB) problem and to improve the water management.

Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer

ABSTRACTThe Aerosol Optical Depth (AOD) retrieved from satellite remote sensing measurements such as from MISR and MODIS, both onboard the Terra platform, are widely used for studying regional and global patterns of aerosol loading. Aerosol products from these sensors are also used for analyzing feedbacks and relationship between aerosols and climatic variables including clouds, precipitation, and radiation fluxes. Several statistical techniques leading to the understanding of such relationships, including empirical orthogonal function and temporal trend extraction methods, require spatially complete AOD data records. Inherent to remote sensing of aerosols, cloud cover significantly affects aerosol retrievals and results in missing data across the AOD products. This paper demonstrates widely-used geostatistical techniques, such as Co-Kriging (CK) and Regression Kriging (RK), for spatially-filling missing data in the MISR AOD product for the period 2001–2013. Among the unique characteristics of this data-filling algorithm is that it utilizes additional AOD information obtained from MODIS. The mean accuracy of the predicted MISR AOD using CK method is estimated to be 0.05, globally. The gap-filled MISR AOD data are also compared with 131 ground-based Aerosol Robotic Network (AERONET) stations, located around the world. It is found that Root Mean Squared Error of the gap-filled AOD dataset and the original MISR AOD product with respect to AERONET data are 0.143. The gap-filled AOD dataset can be used in applications where the presence of missing values is undesirable such as for global/regional aerosol variability and trend analysis.

Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region

Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (ECa), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The ECa measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg−1, whereas ECa varied from 6.7 to 14.7 mS m−1. These two variables were significantly correlated (r = −0.6, p < 0.05); hence, ECa was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and ECa. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.

Characterizing Geothermal Surface Manifestation Based on Multivariate Geostatistics of Ground Measurements Data

Mt. Wayang Windu is one of geothermal field located in West Java, Indonesia. The characterization of steam spots at surface manifestation zones based on the soil physical measurements of the area is presented in this study. The multivariate geostatistical methods incorporating the soil physical parameter data were used to characterize the zonation of geothermal surface manifestations. The purpose of this study is to evaluate the performance of spatial estimation method of multivariate geostatistics using Ordinary Cokriging (COK) to characterize the physical properties of geothermal surface manifestations at Mt. Wayang Windu. The COK method was selected because this method is favorable when the secondary variables has more number than the primary variables. There are four soil physical parameters used as the basis of COK method, i.e. Electrical Conductivity, Susceptibility, pH, and Temperature. The parameters were measured directly at and around geothermal surface manifestations including hot springs, fumaroles, and craters. Each location of surface manifestations was measured about 30 points with 30 x 30 m grids. The measurement results were analyzed by descriptive statistics to identify at the nature of data. The correlation among variables was analyzed using linear regression. When the correlation coefficient among variables is higher, the estimation results is expected to have better Linear Coregionalization Model (LCM). LCM was used to analyze the spatial correlation of each variable based on their variogram and cross-variogram model. In oder to evaluate the performance of multivariate geostatistical using COK method, a Root Mean Square Error (RMSE) was performed. Estimation result using COK method is well applicable for characterizing the surface physics parameters of radar images data.

Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy

Spatial variability of soil properties is complex in the hilly areas of subtropical China that are heavily influenced by human activities. Large datasets are needed for achieving accurate spatial estimates of soil properties in these areas, and it can be difficult due to the budget and time restrictions. Soil diffuse reflectance contains the integrated information of soil properties, thus can provide useful auxiliary variables to improve the spatial estimation of soil properties. This study aimed to improve the spatial estimation of soil organic matter (OM) in a typical subtropical hilly area of China, with principal component (PC) extracted from soil visible and near-infrared (vis-NIR) reflectance spectroscopy used as co-variable. Spatial estimation was performed using ordinary kriging (OK) and co-kriging (CK). A set of 125 samples collected from soil surface layer was used. To simulate the under-sampling situation of target variable, a subset was constructed by randomly sampling 50% of the total samples. The results showed that compared to OK, CK provided the decreased root mean square error (RMSE) by about 45% in cross validations and improved estimation map with much lower estimation variances. This study indicates that for the soil properties and functional attributes that are well correlated with soil reflectance and are sparsely sampled, combined use of soil reflectance spectroscopy technique and multivariate geostatistical method will provide a powerful solution for accurate spatial estimation of soil OM.

Spatial variability of soil organic matter using remote sensing data

Estimation of soil organic matter (SOM) at unsampled locations is crucial in agronomical and environmental studies. In this study, the ability of geostatistical methods such as ordinary kriging (OK), simple kriging (SK) and cokriging (CK) and hybrid geostatistical methods such as regression-simple kriging (RSK)/-ordinary kriging (ROK) and artificial neural network-simple kriging (ANNSK)/-ordinary kriging (ANNOK) was evaluated to predict SOM content. To this end, a set of 100 soil samples were collected from 0 to 15cm depth of agricultural soils in Selin plain, northwest of Iran. The organic carbon was measured using Walkley–Black method. An auxiliary variable was provided by remote sensing data (Landsat 7 ETM+). Three performance criteria including mean error (ME), root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of the derived models. The results showed that the ANN model that used principal components (PCs) as input variables, performed better than the multiple linear regression (MLR) model. The hybrid geostatistical methods, which include ANNOK, ANNSK, ROK and RSK provided more reliable predictions than the geostatistical methods, which include SK, OK and CK. In general, the best prediction method for the estimation of SOM spatial distribution was the ANNOK model, which had the smallest RMSE (0.271%) and the highest R2 (0.633). It was concluded that information from Landsat ETM+ imagery is potential auxiliary variables for improving spatial prediction, monitoring SOM and development of high quality SOM maps, which is the primary step in site-specific soil management.

Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China

In a sewage irrigation area of northwest China, 52 topsoil samples were collected to measure the contents of arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). To identify their sources, multivariate statistics and geostatistics were applied to separate pedogenic elements (As and Mn) from anthropogenic elements (Cr, Cu, Hg, Ni, Pb and Zn). The accumulation of soil Hg was mainly attributed to long-term sewage irrigation, whereas Cr, Ni and Zn were mainly from industrial activities and dust deposition. In addition to the impacts of industry and dust, traffic-related factors were the main sources of Pb and Cu contamination. Based on the relationships of heavy metals with various soil properties and reflectance spectra, co-kriging (CK) was used to improve the interpolation of heavy metals. Comparatively, soil spectra were more suitable as covariates due to their ease and low-cost of collecting as features.

Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River

Daily nitrate-nitrogen (NO3-N) loads in the Raccoon River, Iowa, were estimated using Ordinary kriging (OK), Cokriging (CK), and a standard rating curve method (LOADEST) based on a dataset of 3451 measurements of NO3-N concentration collected over 19 years. The CK estimation utilizes the temporal correlation of NO3-N load with daily discharge and honors the measured points to improve estimation relative to regression based models. Loads were estimated using the observed concentrations and three subsets of the measured data that correspond to three frequencies (weekly, biweekly, and monthly). Results indicated that daily NO3-N loads were best estimated by CK using measured loads with daily discharge. Daily load estimates produced by OK using weekly data matched well with measured values, but discrepancies emerged when samples were collected less frequently, e.g., biweekly and monthly. For the entire 19-year dataset, compared to measured loads, the estimated total NO3-N load decreased using OK when samples were collected monthly, but increased using CK. Load estimation using the seven-parameter LOADEST model did not perform well for the Raccoon River because the correlation of NO3-N concentration to river discharge was poor. For the site studied, weekly and biweekly sampling may be sufficient to estimate daily NO3-N loads with CK when daily discharge data is available.

강우장 추정을 위한 공간보간기법의 적용성 평가

In recent, the natural disaster like localized heavy rainfall due to the climate change is increasing. Therefore, it is important issue that the precise observation of rainfall and accurate spatial distribution of the rainfall for fast recovery of damaged region. Thus, researches on the use of the radar rainfall data have been performed. But there is a limitation in the estimation of spatial distribution of rainfall using rain gauge. Accordingly, this study uses the Kriging method which is a spatial interpolation method, to measure the rainfall field in Namgang river dam basin. The purpose of this study is to apply KED(Kriging with External Drift) with OK(Ordinary Kriging) and CK(Co-Kriging), generally used in Korea, to estimate rainfall field and compare each method for evaluate the applicability of each method. As a result of the quantitative assessment, the OK method using the raingauge only has 0.978 of correlation coefficient, 0.915 of slope best-fit line, and 0.957 of and shows an excellent result that MAE, RMSE, MSSE, and MRE are the closest to zero. Then KED and CK are in order of their good results. But the quantitative assessment alone has limitations in the evaluation of the methods for the precise estimation of the spatial distribution of rainfall. Thus, it is considered that there is a need to application of more sophisticated methods which can quantify the spatial distribution and this can be used to compare the similarity of rainfall field.

Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements

The form error estimation under various machining conditions is an essential step in the assessment of product surface quality generated in machining processes. Coordinate measuring machines (CMMs) are widely used to measure complicated surface form error. However, considering measurement cost, only a few measurement points are collected offline by a CMM for a part surface. Therefore, spatial statistics is adopted to interpolate more points for more accurate form error estimation. It is of great significance to decrease the deviation between the interpolated height value and the real one. Compared to univariate spatial statistics, only concerning spatial correlation of height value, this paper presents a method based on multivariate spatial statistics, co-Kriging (CK), to estimate surface form error not only concerning spatial correlation but also concerning the influence of machining conditions. This method can reconstruct a more accurate part surface and make the estimation deviation smaller. It characterizes the spatial correlation of machining errors by variogram and cross-variogram, and it is implemented on one of the common features: flatness error. Simulated datasets as well as actual CMM data are applied to demonstrate the improvement achieved by the proposed multivariate spatial statistics method over the univariate method and other interpolation methods.

Investigação geoestatística para um adequado mapeamento do nível do lençol freático: o caso de Bordéus (França)

Methodologies have been developed to establish realistic water-table maps using geostatistical methods: ordinary kriging (OK), cokriging (CoK), collocated cokriging (CoCoK), and kriging with external drift (KED). In fact, in a hilly terrain, when piezometric data are sparsely distributed over large areas, the water-table maps obtained by these methods provide exact water levels at monitoring wells but fail to represent the groundwater flow system, manifested through an interpolated water table above the topography. A methodology is developed in order to rebuild water-table maps for urban areas at the city scale. The interpolation methodology is presented and applied in a case study where water levels are monitored at a set of 47 points for a part urban domain covering 25.6 km2 close to Bordeaux city, France. To select the best method, a geographic information system was used to visualize surfaces reconstructed with each method. A cross-validation was carried out to evaluate the predictive performances of each kriging method. KED proves to be the most accurate and yields a better description of the local fluctuations induced by the topography (natural occurrence of ridges and valleys).

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran province, northern Iran

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km2 grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.