Climate Resilience Assessment Framework for Soil and Groundwater Contamination and Waste Disposal Cells: Demonstration at the 118 Department of Energy's Legacy Sites Across the US
Abstract
This study aims to develop a comprehensive climate resilience assessment framework for soil and groundwater contamination and waste disposal cells, and to demonstrate at the Department of Energy (DOE)'s 118 legacy sites distributed across the US. Although most of the sites have been remediated, they require a long-term institutional control over several decades due to residual contaminants, and many have permanent near-surface waste disposal cells. Climate change - through precipitation/evapotranspiration regime shifts or extreme precipitation events - can have a significant impact on residual contaminants and disposal cell performance at these sites. To meet this challenge, we are developing a framework to integrate (1) existing climate data and model projections, (2) data science and machine learning (ML) to automate data processing and knowledge discovery, and (3) institutional knowledge from the sites through interviews and surveys.
The major component is an open-source python package - pypi.org/climate-resilience - to download, process, analyze and visualize climate datasets at all the sites via Google Earth Engine. The climate projections are the Coupled Model Intercomparison Project (CMIP) climate models with different Representative Concentration Pathways (RCP) scenarios throughout 2100. The algorithms quantify hydroclimate variables and their changes in the future at these sites such as the annual frequency of extreme temperature, extreme precipitation and Standardized Precipitation-Evapotranspiration Index (SPEI). In addition, we have developed specific algorithms to (1) compute probable maximum precipitation which has been used for designing disposal cells based on multiple methods and considering regional effects, (2) calculate the future Canadian Fire Weather Index, accounting wind speed, fuel aridity and weather conditions, and (3) identify climate drivers and relevant time-scale for groundwater contaminant concentrations through ML. In addition, the survey and interviews have documented the past observations of climate change impacts such as the erosional damages of disposal cells, increased contaminant concentrations after flooding as well as identified key remediation infrastructure and disposal cell features.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGC22G0671W