Increasing Confidence in Mass Discharge Estimates Using Geostatistical Methods
Article first published online: 10 MAY 2010
Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association
Volume 49, Issue 2, pages 197–208, March/April 2011
How to Cite
Cai, Z., Wilson, R. D., Cardiff, M. A. and Kitanidis, P. K. (2011), Increasing Confidence in Mass Discharge Estimates Using Geostatistical Methods. Groundwater, 49: 197–208. doi: 10.1111/j.1745-6584.2010.00709.x
- Issue published online: 22 FEB 2011
- Article first published online: 10 MAY 2010
- Received October 2009, accepted March 2010.
Mass discharge is one metric rapidly gaining acceptance for assessing the performance of in situ groundwater remediation systems. Multilevel sampling transects provide the data necessary to make such estimates, often using the Thiessen Polygon method. This method, however, does not provide a direct estimate of uncertainty. We introduce a geostatistical mass discharge estimation approach that involves a rigorous analysis of data spatial variability and selection of an appropriate variogram model. High-resolution interpolation was applied to create a map of measurements across a transect, and the magnitude and uncertainty of mass discharge were quantified by conditional simulation. An important benefit of the approach is quantified uncertainty of the mass discharge estimate. We tested the approach on data from two sites monitored using multilevel transects. We also used the approach to explore the effect of lower spatial monitoring resolution on the accuracy and uncertainty of mass discharge estimates. This process revealed two important findings: (1) appropriate monitoring resolution is that which yielded an estimate comparable with the full dataset value, and (2) high-resolution sampling yields a more representative spatial data structure descriptor, which can then be used via conditional simulation to make subsequent mass discharge estimates from lower resolution sampling of the same transect. The implication of the latter is that a high-resolution multilevel transect needs to be sampled only once to obtain the necessary spatial data descriptor for a contaminant plume exhibiting minor temporal variability, and thereafter less spatially intensely to reduce costs.