Using spatial models and kriging techniques to optimize long‐term ground‐water monitoring networks: a case study

Abstract

SUMMARYIn a pilot project, a spatial and temporal algorithm (geostatistical temporal-spatial or GTS) was developed for optimizing long-term monitoring (LTM) networks. Data from two monitored ground-water plumes were used to test the algorithm. The primary objective was to determine the degree to which sampling, laboratory analysis, and/ or well construction resources could be pared without losing key statistical information concerning the plumes. Optimization of an LTM network requires an accurate assessment of both ground-water quality over time and trends or other changes in individual monitoring wells. Changes in interpolated concentration maps over time indicate whether ground-water quality has improved or declined. GTS separately identifies temporal and spatial redundancies. Temporal redundancy may be reduced by lengthening the time between sample collection. Spatial redundancy may be reduced by removing wells from the network which do not significantly impact assessment of ground-water quality.Part of the temporal algorithm in GTS involves computation of a composite temporal variogram to determine the least redundant overall sampling interval. Under this measure of autocorrelation between sampling events, the lag time at which the variogram reaches a sill is the sampling interval at which same-well measurements lack correlation and are therefore non-redundant. The spatial algorithm assumes that well locations are redundant if nearby wells offer nearly the same statistical information about the underlying plume. A well was considered redundant if its removal did not significantly change: (i) an interpolated map of the plume; (ii) the local kriging variances in that section of the plume; and (iii) the average global kriging variance. To identify well redundancy, local kriging weights were accumulated into global weights and used to gauge each well's relative contribution to the interpolated plume map. By temporarily removing that subset of wells with the lowest global kriging weights and re-mapping the plume, it was possible to determine how many wells could be removed without losing critical information.Test results from the Massachusetts Military Reserve (MMR) indicated that substantial savings in sampling, analysis and operational costs could be realized by utilizing GTS. Annual budgetary savings that would accrue were estimated at between 35 per cent and 5 per cent for both LTM networks under study.

Land Use/Cover Change Impacts on Water Table Change over 25 Years in a Desert-Oasis Transition Zone of the Heihe River Basin, China

“…Geostatistics can be used for the better management and conservation of water resources and sustainable development of any area [20,35], and geostatistical methods are good tools for water resources management and can effectively be used to derive the long term trends of the groundwater [20]. Several studies used geostatistics for optimizing a groundwater monitoring network [21,[36][37][38][39]. Christakos performed the geostatistical analysis on water table elevation of about 70 wells in Kansas [40].…”

Adaptive Long-Term Monitoring at Environmental Restoration Sites

“…• 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.…”

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

“…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.…”