Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models

TL;DR

This paper introduces a physics-informed machine learning surrogate model, U-FNO, for rapid, accurate groundwater contamination prediction under uncertain climate scenarios, aiding environmental remediation efforts.

Contribution

It develops a multi-scale digital twin combining U-FNO and climate clustering to efficiently simulate groundwater flow and transport with climate uncertainties.

Findings

U-FNO accurately predicts groundwater contamination dynamics from 1954 to 2100.

The climate clustering reduces data dimensionality and enables quick future climate projections.

The model enhances environmental remediation strategies under climate change.

Abstract

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.