Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan

2008 ◽  
Vol 22 (22) ◽  
pp. 4477-4489 ◽  
Author(s):  
Cheng-Shin Jang ◽  
Shih-Kai Chen ◽  
Lin Ching-Chieh
Keyword(s):  
Groundwater Quality ◽  
Indicator Kriging ◽  
Alluvial Fan ◽  
Multiple Variable

scholarly journals Interpolation of groundwater quality parameters with some values below the detection limit

2011 ◽  
Vol 15 (9) ◽  
pp. 2763-2775 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Detection Limit ◽  
Maximum Likelihood ◽  
Groundwater Quality ◽  
Likelihood Estimation ◽  
Quality Parameters ◽  
Indicator Kriging ◽  
West Germany ◽  
Marginal Distributions ◽  
Copula Models ◽  
Measuring Device

Abstract. For many environmental variables, measurements cannot deliver exact observation values as their concentration is below the sensitivity of the measuring device (detection limit). These observations provide useful information but cannot be treated in the same manner as the other measurements. In this paper a methodology for the spatial interpolation of these values is described. The method is based on spatial copulas. Here two copula models – the Gaussian and a non-Gaussian v-copula are used. First a mixed maximum likelihood approach is used to estimate the marginal distributions of the parameters. After removal of the marginal distributions the next step is the maximum likelihood estimation of the parameters of the spatial dependence including taking those values below the detection limit into account. Interpolation using copulas yields full conditional distributions for the unobserved sites and can be used to estimate confidence intervals, and provides a good basis for spatial simulation. The methodology is demonstrated on three different groundwater quality parameters, i.e. arsenic, chloride and deethylatrazin, measured at more than 2000 locations in South-West Germany. The chloride values are artificially censored at different levels in order to evaluate the procedures on a complete dataset by progressive decimation. Interpolation results are evaluated using a cross validation approach. The method is compared with ordinary kriging and indicator kriging. The uncertainty measures of the different approaches are also compared.

scholarly journals Review of the Kriging Technique Applications to Groundwater Quality

2021 ◽  
Vol 27 (12) ◽  
pp. 23-32
Author(s):  
Hayat Azawi ◽  
May Samir Saleh
Keyword(s):  
Groundwater Quality ◽  
Quality Indicator ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Disjunctive Kriging ◽  
Kriging Method ◽  
Lognormal Kriging ◽  
Groundwater Contaminants ◽  
Simple Kriging

Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.

Using Multiple-Variable Indicator Kriging for Evaluating Soil Quality

1993 ◽  
Vol 57 (3) ◽  
pp. 743-749 ◽  
Author(s):  
Jeffrey L. Smith ◽  
Jonathan J. Halvorson ◽  
Robert I. Papendick
Keyword(s):  
Soil Quality ◽  
Indicator Kriging ◽  
Multiple Variable

Groundwater Quality Assessment in Haraz Alluvial Fan, Iran

2014 ◽  
Vol 2 (10) ◽  
pp. 346-360 ◽  
Author(s):  
Atikeh Afzali ◽  
Kaka Shahedi ◽  
Mahmoud Habib Nezhad Roshan ◽  
Karim Solaimani ◽  
Ghorban Vahabzadeh
Keyword(s):  
Groundwater Quality ◽  
Quality Assessment ◽  
Alluvial Fan ◽  
Groundwater Quality Assessment

Combining natural background levels (NBLs) assessment with indicator kriging analysis to improve groundwater quality data interpretation and management

2016 ◽  
Vol 569-570 ◽  
pp. 569-584 ◽  
Author(s):  
Daniela Ducci ◽  
M. Teresa Condesso de Melo ◽  
Elisabetta Preziosi ◽  
Mariangela Sellerino ◽  
Daniele Parrone ◽  
...  
Keyword(s):  
Groundwater Quality ◽  
Data Interpretation ◽  
Indicator Kriging ◽  
Quality Data ◽  
Natural Background ◽  
Background Levels ◽  
Kriging Analysis

scholarly journals Assessing groundwater quality for irrigation using indicator kriging method

2014 ◽  
Vol 6 (4) ◽  
pp. 371-381 ◽  
Author(s):  
Masoomeh Delbari ◽  
Meysam Amiri ◽  
Masoud Bahraini Motlagh
Keyword(s):  
Groundwater Quality ◽  
Indicator Kriging ◽  
Kriging Method

Evaluation of groundwater qualities in a paddy-dominated alluvial fan

2015 ◽  
Vol 15 (6) ◽  
pp. 1236-1243 ◽  
Author(s):  
Y. Yoshioka ◽  
K. Nakamura ◽  
H. Horino ◽  
T. Nakano ◽  
K. C. Shin ◽  
...  
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
River Water ◽  
Sustainable Use ◽  
Shallow Groundwater ◽  
Dilution Effect ◽  
Alluvial Fan ◽  
Environmental Standards ◽  
Irrigation Period ◽  
Dissolved Ions

Analytically assessing groundwater quality is indispensable for sustainable use of groundwater and its effective pollution controls. A large volume of groundwater is stored in the Tedori River alluvial fan one of which of the predominant land uses being irrigated paddy fields. Much groundwater under the fan is used for drinking and industrial purposes. For assessing agricultural activities at the paddy and upland fields on groundwater quality during an irrigation period, multiple water quality items were measured in several water types, including groundwater, river water, and paddy water. Water quality indicators, such as major dissolved ions, a number of trace elements, and some isotopes were measured. The concentrations of nutrients and some elements related to the environmental standards indicated that pollution in the groundwater in the fan was not severe. Concentrations of the tracers (Mg, Na, δD, δ18O) in the shallow groundwater were low along the Tedori River and increased with distance from the river; this trend would be caused by dilution effect by the river water. It was also shown that the paddy field also affects groundwater quality by the infiltration of irrigation water.

scholarly journals Interpolation of groundwater quality parameters with some values below the detection limit

2011 ◽  
Vol 8 (3) ◽  
pp. 5263-5299 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Detection Limit ◽  
Maximum Likelihood ◽  
Groundwater Quality ◽  
Likelihood Estimation ◽  
Quality Parameters ◽  
Indicator Kriging ◽  
West Germany ◽  
Marginal Distributions ◽  
Copula Models ◽  
Measuring Device

Abstract. For many environmental variables, measurements cannot deliver exact observation values as their concentration is below the sensibility of the measuring device (detection limit). These observations provide useful information but cannot be treated in the same manner as the other measurements. In this paper a methodology for the spatial interpolation of these values is described. The method is based on spatial copulas. Here two copula models – the Gaussian and a non-Gaussian v-copula are used. First a mixed maximum likelihood approach is used to estimate the marginal distributions of the parameters. After removal of the marginal distributions the next step is the maximum likelihood estimation of the parameters of the spatial dependence including values below the detection limit into account. Interpolation using copulas yields full conditional distributions for the unobserved sites and can be used to estimate confidence intervals, and provides a good basis for spatial simulation. The methodology is demonstrated on three different groundwater quality parameters, i.e. arsenic, chloride and deethylatrazin, measured at more than 2000 locations in South-West Germany. The chloride values are artificially censored at different levels in order to evaluate the procedures on a complete dataset. Interpolation results are evaluated using a cross validation approach. The method is compared with ordinary kriging and indicator kriging. The uncertainty measures of the different approaches are also compared.

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