Network design for groundwater monitoring - a case study

Prakash, Mayank; Singh, V. S.

doi:10.1007/s002540050474

Cited by 43 publications

(24 citation statements)

References 4 publications

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“…Bardossy and Lehmann, 1998); (ii) designing sampling networks and improving their efficiency (e.g. Pardo-Iguzquiza, 1998;Prakash and Singh, 2000); and (iii) stochastic modelling in order to understand the overall response of heterogeneous flow systems (e.g. Kitanidis and Lane, 1985).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of pore‐water pressures in residual soil slopes in a tropical climate

Rezaur¹,

Rahardjo²,

Leong³

2002

Earth Surf Processes Landf

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It is critical to understand and quantify the temporal and spatial variability in hillslope hydrological data in order to advance hillslope hydrological studies, evaluate distributed parameter hydrological models, analyse variability in hydrological response of slopes and design efficient field data sampling networks. The spatial and temporal variability of field-measured pore-water pressures in three residual soil slopes in Singapore was investigated using geostatistical methods. Parameters of the semivariograms, namely the range, sill and nugget effect, revealed interesting insights into the spatial structure of the temporal situation of pore-water pressures in the slopes. While informative, mean estimates have been shown to be inadequate for modelling purposes, indicator semivariograms together with mean prediction by kriging provide a better form of model input. Results also indicate that significant temporal and spatial variability in porewater pressures exists in the slope profile and thereby induces variability in hydrological response of the slope. Spatial and temporal variability in pore-water pressure decreases with increasing soil depth. The variability decreases during wet conditions as the slope approaches near saturation and the variability increases with high matric suction development following rainfall periods. Variability in pore-water pressures is greatest at shallow depths and near the slope crest and is strongly influenced by the combined action of microclimate, vegetation and soil properties.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of pore‐water pressures in residual soil slopes in a tropical climate

Rezaur¹,

Rahardjo²,

Leong³

2002

Earth Surf Processes Landf

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“…Prakash and Singh (2000) used the OK technique and determined optimum number of observation wells that can be added to the existing network of the study area for monitoring spatial distribution of groundwater level. Kumar et al (2005) have emphasized the use of geostatistics for better management and conservation of water resources and sustainable development of any area.…”

Section: Introductionmentioning

confidence: 99%

Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Adhikary

Dash

2014

Appl Water Sci

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Accurate and reliable interpolation of groundwater depth over a region is a pre-requisite for efficient planning and management of water resources. The performance of two deterministic, such as inverse distance weighting (IDW) and radial basis function (RBF) and two stochastic, i.e., ordinary kriging (OK) and universal kriging (UK) interpolation methods was compared to predict spatio-temporal variation of groundwater depth. Pre-and postmonsoon groundwater level data for the year 2006 from 110 different locations over Delhi were used. Analyses revealed that OK and UK methods outperformed the IDW method, and UK performed better than OK. RBF also performed better than IDW and OK. IDW and RBF methods slightly underestimated and both the kriging methods slightly overestimated the prediction of water table depth. OK, RBF and UK yielded 27.52, 27.66 and 51.11 % lower RMSE, 27.49, 35.34 and 51.28 % lower MRE, and 14.21, 16.12 and 21.36 % higher R 2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.

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“…This two-point ''covariation'' (or covariance) as a function of separation or ''scale'' (Goovaerts 1997) is referred to as spatial structure. These spatial structures, as identified in geostatistical analysis, help identify autocorrelation, reveal patterns in data series, identify major ongoing processes , predict unsampled locations via kriging (Deutsch and Journel 1998;Neilsen and Wendroth 2003) and design of experiments (Fagroud and van Meirvenne 2002), and monitoring networks (Prakash and Singh 2000). They also help in selecting proper data analysis method, in data interpretation (Lambert et al 2004), and in provision of more rigorous assessment of uncertainty (Papritz and Dubois 1999).…”

Section: Geostatistical Analysismentioning

confidence: 99%

Spatial relationship between soil hydraulic and soil physical properties in a farm field

Biswas¹,

Cheng²

2009

Can. J. Soil. Sci.

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The relationship between soil properties may vary with their spatial separation. Understanding this relationship is important in predicting hydraulic parameters from other soil physical properties. The objective of this study was to identify spatially dependent relationships between hydraulic parameters and soil physical properties. Regularly spaced (3-m) undisturbed soil samples were collected along a 384 m transect from a farm field at Smeaton, Saskatchewan. Saturated hydraulic conductivity, the soil water retention curve, and soil physical properties were measured. The scaling parameter, van Genuchten scaling parameter α (VGα), and curve shape parameter, van Genuchten curve shape parameter n (VGn), were obtained by fitting the van Genuchten model to measured soil moisture retention data. Results showed that the semivariograms of soil properties exhibited two different spatial structures at spatial separations of 20 and 120 m, respectively. A strong spatial structure was observed in organic carbon, saturated hydraulic conductivity (Ks), sand, and silt; whereas a weak structure was found for VGα and VGn. Correlation circle analysis showed strong spatially dependent relationships of Ks and VGα; with soil physical properties, but weak relationships of θs and VGn with soil physical properties. The spatially dependent relationships between soil physical and soil hydraulic parameters should be taken into consideration when developing pedotransfer functions. Key words: Spatial relationship, geostatistics, linear coregionalization model, principal component analysis, pedotransfer function

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Network design for groundwater monitoring - a case study

Cited by 43 publications

References 4 publications

Spatial and temporal variability of pore‐water pressures in residual soil slopes in a tropical climate

Spatial and temporal variability of pore‐water pressures in residual soil slopes in a tropical climate

Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Spatial relationship between soil hydraulic and soil physical properties in a farm field

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