Mapping Water Table Depth Using Geophysical and Environmental Variables
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Abstract:
Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.Keywords:
Inverse distance weighting
Data set
Table (database)
Geostatistics
This study attempted to characterize the spatial distributions of soil pH and electrical conductivity (ECe) of coastal fields in the Miyandoroud region, northern Iran, for three soil layer depths by assessing spatial variability and comparing different interpolation techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and conditional simulations (CS). Three soil composite samples were collected from 0–50, 50–100, and 100–150 cm depths at 105 sampling sites. At all three soil depths, pH and ECe were best fitted by exponential and spherical models, respectively. Nugget effects were higher for soil ECe data sets compared with soil pH at all three soil depths showing soil ECe had a spatial variability in small distances. The prediction accuracy of the interpolation methods indicated that the minimum error for all data sets was achieved with the OK method, except for pH at 50–100 cm depth, and the CS technique revealed the largest error. The effect of different numbers of simulations (100, 500 and 1000) in the CS interpolation method resulted not in a realistic mapping for the soil ECe and pH. Considering the high importance of irrigated agriculture in the Caspian Sea coastal areas, more subsoil salinity build-up and groundwater salinity monitoring plans are needed as a prerequisite for sustainable agricultural production systems of the future.
Inverse distance weighting
Subsoil
Interpolation
Soil test
Geostatistics
Soil horizon
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Abstract Data from 545 rainfall gauges were used to interpolate the spatial distribution of annual rainfall in South Africa. Several spatial interpolation methods (inverse distance weighting (IWD), ordinary kriging, universal kriging, cokriging) were tested by variation analyses and cross-validation to determine the most suitable one. The best results were achieved by ordinary kriging, whereby the setting of the parameters was determined through sensitivity analyses. The median of the errors turned out to be 61 mm (11%). The interpolation errors were generally small for the interior of the country and high for coastal and mountainous regions.
Inverse distance weighting
Interpolation
Geostatistics
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Citations (29)
Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.
Inverse distance weighting
Data set
Table (database)
Geostatistics
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Citations (122)
Geostatistics is widely used in soil science and has become an important tool to characterize soil properties.Semivariograms and Kriging play a principal role in this field.Semivariograms can be used to analyze the spatial patterns of physical or chemical attributes of soil.Kriging spatial interpolation is often employed in experiment design and soil sampling strategy.On the based of the analysis of spatial correlation between the observations from diffevent places,Kriging technique is also used to estimate the unsampled positions.Recently,spatial interpolation is widely used to assess soil quality and environmental capacity,and stochastic simulation to assess the uncertainty of soil properties.
Geostatistics
Interpolation
Spatial Dependence
Variogram
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