1995
DOI: 10.1021/es950335u
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Reducing the Sampling Frequency of Groundwater Monitoring Wells

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Cited by 28 publications
(24 citation statements)
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“…Ordinary kriging was used to model the spatial variability of dry bulk density. Webster and Oliver (2001) and Johnson et al (1996) reported that the ordinary kriging of a single variable is the most robust and frequently used method for its accurate accounting of data fluctuations and consideration of a global trend over the study region.…”
Section: Analysis Proceduresmentioning
confidence: 99%
“…Ordinary kriging was used to model the spatial variability of dry bulk density. Webster and Oliver (2001) and Johnson et al (1996) reported that the ordinary kriging of a single variable is the most robust and frequently used method for its accurate accounting of data fluctuations and consideration of a global trend over the study region.…”
Section: Analysis Proceduresmentioning
confidence: 99%
“…• Based on current information, GTS (Cameron and Hunter, 2002), the 3-tiered monitoring optimization approach by Parsons (Nobel, 2003), MAROS (Aziz et al, 2003), and Cost Effective Sampling (Johnson et al, 1996) perform various spatial and temporal redundancy analyses for LTM. However, they do not perform data tracking for site-wide targets and do not use mathematical optimization.…”
Section: Advantages and Limitations Of The Technologymentioning
confidence: 99%
“…In some cases, the attempt has simply been to reconstruct the direction of a linear trend, either upward, downward, or flat. Others have tried to reconstruct the slope of the trend (again assumed to be linear), perhaps standardized by an action level or regulatory limit (Johnson et al, 1996;Cameron and Hunter, 2002).…”
Section: Temporal and Spatial Redundancymentioning
confidence: 99%
“…Johnson et al (1996) normalized their trend estimates by a regulatory limit or action level, but the trends were still linear. More complicated trends have typically been ignored, or perhaps only the linear component has been estimated.…”
Section: More Accurate Temporal Optimizationmentioning
confidence: 99%
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