Effects of Spatiotemporal Constraints on Geostatistical Analysis of Groundwater Depth

Amit Hellman, Gilboa Pe'er, Maribeth L. Kniffin, Ronnie Kamai

Research output: Contribution to journalArticlepeer-review

Abstract

Geostatistical evaluation of the groundwater depth (GWD) in California's South Coast hydrologic region, and its sensitivity to different spatiotemporal assumptions, is presented in this paper. We obtain a pseudo-stationary representation of the groundwater depth, using the publicly available, online database from the GAMA GeoTracker project, while tracking the associated uncertainty throughout the process. We create nine different sub-datasets, using different temporal constraints, such as seasonal partitioning and different long-term variability filtering criteria. The geostatistical analysis and comparison between the different maps highlight the trade-off between spatial and temporal accuracy. For example, when moving to stricter filtering criteria, despite removing a large number of sites from the interpolation, the root mean squared error (RMSE) calculated in the analysis either decreased or only slightly increased. This suggests that the long-term variability filter is a good representation of the GWD accuracy and that the cross-validation RMSE captures both the stability effect as well as spatial density of the measurement points. We further find that the point-specific standard error is strongly correlated with the associated GWD prediction and that the mean relative error is approximately 60% of the prediction. Hence, it is highly recommended to account for such error in a forward-engineering application, by introducing a GWD distribution rather than a single value into the analysis. Finally, we analyze seasonal fluctuations in the study region and find that they are on average 2.5 m with a standard deviation of 8 m.

Original languageEnglish
Pages (from-to)225-241
Number of pages17
JournalGround Water
Volume60
Issue number2
DOIs
StatePublished - 1 Mar 2022

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