Design of Groundwater Level Monitoring Network with Ordinary Kriging

Yang, Fengli; Cao, Shuyou; Liu, Xingnian; Yang, Kejun

doi:10.1016/s1001-6058(08)60066-9

Cited by 62 publications

(28 citation statements)

References 11 publications

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“…Results obtained from geostatistical analysis showed that groundwater depth varied spatially in different climatic conditions. Yang et al (2008) discussed the kriging approach combined with hydrogeological analysis (based on GIS) for the design of groundwater level monitoring network. The effect of variogram parameters (that is, the sill, nugget effect and range) on network was analyzed.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical study of the spatial variability of groundwater parameters in Afikpo and Ohaozara, Southeastern Nigeria

Iduma¹,

Abam²,

Uko³

2017

Int. J. Water Res. Environ. Eng.

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Abortive boreholes and parched wells, ascribed to the difficulty in understanding the hydrogeology of the aquifer by water borehole drillers, pose great concern to the people of the region. Mapping the spatial variability of water table depth (WTD) (m) and aquifer thickness H (m) is a vital step in optimal utilization of groundwater resources. Thus, the aim of this paper is to investigate the spatial variability of the groundwater parameters, H (m) and WTD (m) in the study area located in Nigeria, using geostatistical method of Ordinary Kriging, based on data estimated from interpreted results of fifty (50) Schlumberger Vertical Electrical Sounding (VES) curves. To attain this aim, the spatial variability of the groundwater parameters was analyzed. The result shows that the difference in directional behavior is not significant. Thus, the WTD and H were assumed as isotropic, and experimental semivariograms of logH and log(WTD) were calculated and modeled with the GS + software. It was found that, H and (WTD) data are moderately spatially correlated over the study area, and the spatial structures follow exponential model for H and spherical model for (WTD). According to the generated maps of kriged estimates of logH and log(WTD), the southern part of the study area with higher prolific aquiferous zone, shows higher kriged H-values, relative to the northern zone. The variation in the distribution of kriged WTD-values in the study region is asymmetrical. These results compare favorably in the trend patterns of distribution of the parameter values, with contour maps of a previous study in the region that indicates the distribution of H and WTD parameters. The parameters of the semivariogram models used for the analysis of the data, give insight into the spatial pattern of the groundwater parameters, H and WTD. This knowledge has improved the ability to understand the hydrogeology of the aquifer. The generated spatial variability maps of H and WTD will assist water resource managers and policymakers in the development of guidelines in judicious management of groundwater resources for drinking purposes in the study area.

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Section: Introductionmentioning

confidence: 99%

Geostatistical study of the spatial variability of groundwater parameters in Afikpo and Ohaozara, Southeastern Nigeria

Iduma¹,

Abam²,

Uko³

2017

Int. J. Water Res. Environ. Eng.

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“…The second type of optimization sampling design model includes statistical approaches that describe the spatial structure of a monitoring variable via statistical modeling and then use this information to design the network. For example, methods based on geostatistics aim to minimize the average kriging prediction-error variance; they have been widely used to design groundwater monitoring networks (Cameron and Hunter, 2002;Yeh et al, 2006;Nunes et al, 2007;Yang et al, 2008;Dhar and Datta, 2009;Nowak et al, 2010;Junez-Ferreira and Herrera, 2013). Yang et al (2008) used the average kriging standard deviation as a criterion to determine the density of the groundwater-level monitoring network in the Chaiwopu Basin, Xinjiang Uygur Autonomous Region, China.…”

Section: Introductionmentioning

confidence: 99%

“…For example, methods based on geostatistics aim to minimize the average kriging prediction-error variance; they have been widely used to design groundwater monitoring networks (Cameron and Hunter, 2002;Yeh et al, 2006;Nunes et al, 2007;Yang et al, 2008;Dhar and Datta, 2009;Nowak et al, 2010;Junez-Ferreira and Herrera, 2013). Yang et al (2008) used the average kriging standard deviation as a criterion to determine the density of the groundwater-level monitoring network in the Chaiwopu Basin, Xinjiang Uygur Autonomous Region, China. In addition, Dhar and Datta (2009) proposed a methodology for the global optimal design of groundwater-quality monitoring networks using a linear mixed-integer formulation that incorporates ordinary kriging within the decision model formulation for spatially estimating contaminant concentration values.…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of groundwater-level monitoring sites in the Zhangye Basin, Northwest China

Ran

et al. 2015

Journal of Hydrology

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Keywords:Groundwater Temporal stability Mean of surface with non-homogeneity Heihe River Basin s u m m a r y An optimized groundwater monitoring network is essential for agricultural irrigation that uses groundwater resources. This technical note presents a new scheme to optimize the existing groundwater-level monitoring network in the Zhangye Basin, China. The scheme integrates kriging theory, spatial stratification and temporal stability analysis. The optimized results indicate that the number of additional observation wells in each stratum is correlated with the number of existing wells, the stratum areas and the temporal stability of the groundwater-level spatial pattern. These findings are consistent with expectations. The effectiveness of the proposed method is demonstrated, and its implementation is simple, robust and flexible.

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“…The semivariogram, kriging variance, and standard errors provide valuable information about the predictability of the designed network. However, these techniques are often used in purely spatial sampling problems such as designing a network for groundwater monitoring (Yang et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Solar irradiance monitoring network design using the variance quadtree algorithm

Yang

Reindl²

2015

Renewables

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Our aim is to determine the optimal placement of solar irradiance monitoring stations for renewable energy integration into electricity grids. Hourly SUNY satellite-derived irradiance over a rectangular grid of 34°to 44°N, 100°t o 110°W with a 0.1°resolution are used in this work. The variance quadtree algorithm is used to identify the regions with high spatio-temporal variations. The densities of monitoring stations over different regions therefore follow the empirical variation. The network design is compared to the results from the so-called "L-method". A discussion based on the network's predictive performance is also presented. We show that the unique design solution obtained using the L-method cannot capture the spatio-temporal variations embedded in irradiance random fields. A robust design should consider both the design requirements and functionalities of the monitoring network.

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Design of Groundwater Level Monitoring Network with Ordinary Kriging

Cited by 62 publications

References 11 publications

Geostatistical study of the spatial variability of groundwater parameters in Afikpo and Ohaozara, Southeastern Nigeria

Geostatistical study of the spatial variability of groundwater parameters in Afikpo and Ohaozara, Southeastern Nigeria

Optimal selection of groundwater-level monitoring sites in the Zhangye Basin, Northwest China

Solar irradiance monitoring network design using the variance quadtree algorithm

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