Concerns over unconventional oil and gas (UOG) development
persist,
especially in rural communities that rely on shallow groundwater for
drinking and other domestic purposes. Given the continued expansion
of the industry, regional (vs local scale) models are needed to characterize
groundwater contamination risks faced by the increasing proportion
of the population residing in areas that accommodate UOG extraction.
In this paper, we evaluate groundwater vulnerability to contamination
from surface spills and shallow subsurface leakage of UOG wells within
a 104,000 km2 region in the Appalachian Basin, northeastern
USA. We test a computationally efficient ensemble approach for simulating
groundwater flow and contaminant transport processes to quantify vulnerability
with high resolution. We also examine metamodels, or machine learning
models trained to emulate physically based models, and investigate
their spatial transferability. We identify predictors describing proximity
to UOG, hydrology, and topography that are important for metamodels
to make accurate vulnerability predictions outside their training
regions. Using our approach, we estimate that 21,000–30,000
individuals in our study area are dependent on domestic water wells
that are vulnerable to contamination from UOG activities. Our novel
modeling framework could be used to guide groundwater monitoring,
provide information for public health studies, and assess environmental
justice issues.