Application and evaluation of universal kriging for optimal contouring of groundwater levels

Kambhammettu, B.V.N.P.; Allena, P.; King, James P.

doi:10.1007/s12040-011-0075-4

Cited by 39 publications

(19 citation statements)

References 7 publications

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“…It means that for example when we want to predict the values of locations using universal kriging, the mean of random field must be unknown and inconstant [1]. If the desired variable does not have normal distribution, nonlinear kriging should be used or we should convert variable distribution to the normal distribution using data transformation methods [2].…”

Section: Methodsologymentioning

confidence: 99%

Estimation method of spatial geostatistical data : Application to rainfall data

Erfanian

Barati²

2017

IJASP

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Restriction of water resources for agricultural and non-agricultural purposes has caused major difficulties and rainfall is considered as one of the most important water resources. Therefore, predicting rainfall and estimating its rate monthly or annually for each region as one of the most important atmospheric parameters, is of particular importance in optimized usage of water resources. In this paper in addition to the presenting application of novel statistical methods, prediction of rainfall amount has been performed for the entire map of Iran. In this analysis, data of average rainfall of 108 pluviometry stations in different cities of Iran have been used and zoning of rainfall has been prepared for the country.

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Section: Methodsologymentioning

confidence: 99%

Estimation method of spatial geostatistical data : Application to rainfall data

Erfanian

Barati²

2017

IJASP

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“…Some authors also take the temporal component of monitoring networks into account (i.e., frequency of measurements based on groundwater fluctuations; e.g., Ahmadi & Sedghamiz, ; Cameron & Hunter, ; Chandan & Yashwant, ; Kambhamettu et al, ; Nunes et al, ; Theodossiou & Latinopoulos, ). However, the results are always a compromise between the spatial and temporal component.…”

Section: Introductionmentioning

confidence: 99%

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Ohmer

Liesch

Goldscheider

2019

Water Resources Research

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Effective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross-validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently-used cross-validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross-validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN.

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“…Geostatistical method is a class of statistics used to analyze and predict the values associated with the spatial and temporal phenomena (ESRI, 2013), which has been applied to groundwater studies by many researchers from time to time. Kambhammettu et al (2011) applied universal kriging to create a continuous surface of water table elevation for Carslbad area with alluvial aquifer situated in the SE of New Mexico, USA. A generalized MATLAB code was developed to generate omnidirectional and directional semivariogram.…”

Section: Introductionmentioning

confidence: 99%

Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: A case study in Wainganga Sub-basin, India

Chandan

Yashwant

2017

Chin. Geogr. Sci.

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Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation (GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology (L i RDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System (GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and L i RDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.

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Application and evaluation of universal kriging for optimal contouring of groundwater levels

Cited by 39 publications

References 7 publications

Estimation method of spatial geostatistical data : Application to rainfall data

Estimation method of spatial geostatistical data : Application to rainfall data

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: A case study in Wainganga Sub-basin, India

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