A Probabilistic Model of Aquifer Susceptibility to Earthquake-Induced Groundwater-Level Changes

Abstract

A probabilistic model for earthquake-induced persistent groundwater-level response as a function of peak ground velocity (PGV) has been constructed using a catalog of monitoring well observations spanning multiple earthquakes. The regional-scale, multi-site, multi-earthquake investigation addresses the occurrence and absence of hydraulic responses to large earthquakes spanning almost a decade of seismic shaking. Persistent groundwater-level changes, or absences of change, have been quantified in 495 monitoring wells in response to one or more of 11 recent New Zealand earthquakes larger than Mw 5.4 that occurred between 2008 and 2017. A binary logistic regression model with random effects has been applied to the dataset using three predictors: earthquake shaking (PGV), degree of hydrogeological confinement (monitoring well depth), and rock strength (site-average shear-wave velocity). Random effects were included as a partial proxy for variations in monitoring wells’ susceptibilities to earthquake-induced persistent water-level changes. Marginal probabilities have been calculated as a function of PGV and related to modified Mercalli intensity (MMI) levels using a New Zealand-specific MMI–PGV relationship that enables the likelihood of persistent water-level changes to be expressed for MMIs of II–VIII. This study capitalizes on one of the largest catalogs of earthquake hydrological observations compiled worldwide and is the first attempt at incorporating seismic and hydrogeological factors in a common probabilistic description of earthquake-induced groundwater-level changes. This modeling framework provides a more generalizable approach to quantifying responses than alternative metrics based on epicentral distance, magnitude, and seismic energy density. It has potential to enable better comparison of international studies and to inform practitioners making engineering or investment decisions to mitigate risk and increase the resilience of water-supply infrastructure.

Epilogue

“…Mohr et al (2017) also found that PGV was the best ground motion predictor of streamflow changes. Weaver et al (2020) further found that PGV is better correlated with water level changes in wells than other measures of ground motion. Taken together, it appears that earthquake-enhanced permeability in the shallow crust may be closely related to the seismic energy density and may explain a broad spectrum of hydrologic responses that occur in the intermediate and far fields.…”

“…where r is in km and e in J/m 3 . We stress that this relation was constructed on the basis of ground motion data for southern California (Wang 2007) and is thus regionspecific and may not apply to other areas such as New Zealand (Weaver et al 2020).…”

“…6; Wang 2007) is sometimes applied to different parts of the world to study the earthquake-induced hydrologic responses. As emphasized previously, this relation was developed specifically from the seismic data for southern California and its application to other regions, if not verified, may lead to error, such as in New Zealand (Weaver et al 2020). Given the current availability of dense seismic networks in many parts of the world, the development of region-specific empirical relations between seismic energy density and responses may be worthwhile.…”

Different Sensitivities of Earthquake‐Induced Water Level and Hydrogeological Property Variations in Two Aquifer Systems

“…Weaver, Arnold, et al. (2020) used a random normally distributed variable as a proxy for variable well susceptibility, and although it could be fitted to a large data set of observations, the factor of well structure appears unpredictable.…”

“…Prior studies, however, have never shown a clear relationship between screen length or depth and the amplitude of earthquake-induced water level changes (Figures S3a and b in the Supporting Information). Weaver, Arnold, et al (2020) used a random normally distributed variable as a proxy for variable well susceptibility, and although it could be fitted to a large data set of observations, the factor of well structure appears unpredictable.…”