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Showing 2 results for Cokriging
Study of the Spatial Variability of Soil Salinity in Ramhormoz Area (Khuzestan) Using Geostatistical Theory Il. Cokriging
Jahangard Mohammadi,
Volume 3, Issue 1 (4-1999)
Abstract
The analysis of the EC data set indicated that the spatial distribution of EC data of different depths are closely related to one another. It means that they are spatially cross correlated on one another and can be considered to be co-regionalized. It also implies that EC values at a particular depth contain useful information about the other depths which can be used to improve their estimation. In this research, we aimed to investigate the effects of using relevant ancillary information in the estimation procedure. To do this, cokriging was used. To evaluate this algorithm as a potential tool for mapping EC, its performance on the independent test data was evaluated and compared with the results obtained from studies using kriging. The results of the co-regionalization of EC at different depths indicated that cokriging the salinity data, although more rigorous from theoretical point of view, displayed no advantage over independent ordinary kriging at each depth. The results confirmed that cokriging improves little over ordinary kriging if the primary and auxiliary variables are almost equally sampled and all the variograms are identical. Also, ordinary kriging showed to be quite self-consistent since the predicted average salinity profile over the three depths was almost identical to the one predicted by cokriging. Considering the complexity of the cokriging and the LMC modeling, it is clear that there is no gain in using co-regionalization.
Evaluation of Spatial Variations of Soil Infiltration and its Models Parameters Using Geostatistics (A case study: Mansour Abad Plain)
R. Azadikhah, M. Sedghiasl, E. Adhami, H. R. Owliaie, A. Karami, Sh. Saadipour,
Volume 23, Issue 2 (9-2019)
Abstract
The aim of this study was to evaluate the spatial distribution of soil infiltration using geostatistics methods in a regional scale on 400 hectares of Mansour Abad Plain, in Larestan region, Fars Province. Sampling and parameters measurement were done for 78 points in a regular grid with a distance of 100*100 meters; for these variables, the best variogram model between linear, exponential, Gaussian and spherical models with the highest R2 and the lowest error was determined using GS+ and ArcGIS software. In this study, soil infiltration (cm/min) using the double ring method and some other soil properties including soil electrical conductivity (dS/m), pH, saturation percentage (%SP), particle size percentage (sand, silt and clay), and calcium (meq/lit), magnesium (meq/lit), sodium (meq/lit) were measured and determined. The spatial distribution of Kostiakov and Philip models parameters and theri zoning were determined using the geostatistic method. The results showed that, among different soil properties, the final infiltration rate had a high degree of variability in the study area, and the decision was based on the usual averaging methods, which could have a lot of error. Among applied infiltration models, Kostiakov model and Philip model were the best empirical and physical infiltratin models, respectively, in the studied area. The best semivariogram model for the steady state infiltration rate was Philip model, with the coeficients of S and A, and a coefficient of Kostiakove model was gaussian; for the b coefficient, Kostiakove model was exponential. Spatial structure of the final infiltration rate, a and b coefficients of Kostiakove model, and S and A coefficients of the Philip model, was strong. The best interpolation method for the final infiltration rate was cokriging with the cofactor of silt percentage, for the S coefficient of Philip model was inverse distance weighting (IDW); for a and b coefficients of Kostiakove model, kriging and IDW were suitable, respectively.

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