Abstract
AbstractEffective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross‐validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently‐used cross‐validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross‐validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN.
Highlights
Groundwater is an important, yet spatially extensive, concealed, and inaccessible resource
We found that the global cross‐validation error statistics are not suitable to compare the performance of different sampling designs, they allow rough conclusions about the quality of the groundwater level monitoring network (GLMN)
Since a priori known groundwater surfaces are used, the CV errors and the “real” prediction errors based on the GLMN can be computed
Summary
Groundwater is an important, yet spatially extensive, concealed, and inaccessible resource. An effective groundwater monitoring network (GMN) is important, as systematic data collected at observation wells provide a crucial understanding of the dynamics and quality of the hydrogeological system. A GMN is defined by a spatial arrangement of monitoring sites and a temporal sampling frequency (Loaiciga et al, 1992). Economic considerations most strongly influence the number and location of monitoring wells. Since a high spatial resolution is usually associated with disproportionate costs, often only domains of high water management importance are adequately monitored. The design, that is, the selection of the location and number of the monitoring wells, is a vital part of any study involving modeling and prediction based on spatial data. We focus on the regional groundwater level as a monitoring parameter
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