  Soil and groundwater contamination is a pervasive problem at thousands of
locations across the world. Contaminated sites often require decades to
remediate or to monitor natural attenuation. Climate change exacerbates the
long-term site management problem because extreme precipitation and/or shifts
in precipitation/evapotranspiration regimes could re-mobilize contaminants and
proliferate affected groundwater. To quickly assess the spatiotemporal
variations of groundwater contamination under uncertain climate disturbances,
we developed a physics-informed machine learning surrogate model using U-Net
enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential
Equations (PDEs) of groundwater flow and transport simulations at the site
scale.We develop a combined loss function that includes both data-driven
factors and physical boundary constraints at multiple spatiotemporal scales.
Our U-FNOs can reliably predict the spatiotemporal variations of groundwater
flow and contaminant transport properties from 1954 to 2100 with realistic
climate projections. In parallel, we develop a convolutional autoencoder
combined with online clustering to reduce the dimensionality of the vast
historical and projected climate data by quantifying climatic region
similarities across the United States. The ML-based unique climate clusters
provide climate projections for the surrogate modeling and help return reliable
future recharge rate projections immediately without querying large climate
datasets. In all, this Multi-scale Digital Twin work can advance the field of
environmental remediation under climate change.
