TY - JOUR
T1 - Effects of Spatiotemporal Constraints on Geostatistical Analysis of Groundwater Depth
AU - Hellman, Amit
AU - Pe'er, Gilboa
AU - Kniffin, Maribeth L.
AU - Kamai, Ronnie
N1 - Publisher Copyright:
© 2021 National Ground Water Association.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85119320361&partnerID=8YFLogxK
U2 - 10.1111/gwat.13147
DO - 10.1111/gwat.13147
M3 - Article
C2 - 34741299
AN - SCOPUS:85119320361
SN - 0017-467X
VL - 60
SP - 225
EP - 241
JO - Ground Water
JF - Ground Water
IS - 2
ER -