Publications

Analysis of Great Lakes Water Balance Components: uncertainty reduction, trend detection, and projections for the future

Published in Technical report as a supplement to the 2023 Cumulative Impact Assessment, 2022

Ensuring continued access to freshwater is an essential part of managing consumptive use, withdrawals and diversions in the Great Lakes system. This is primarily accomplished through the Great Lakes Compact Agreement, administered by the Great Lakes and St. Lawrence Governors and Premiers Regional Body and Compact Council. We used the latest findings from the Large Lake Statistical Water Balance Model to better understand historical trends in precipitation, evaporation, runoff and inter-lake flow. These findings will be incorporated into the next iteration of Cumulative Impact Assessments in support of informed decision making by the Great Lakes and St. Lawrence Governors and Premiers.

Gelman Site 1,4-Dioxane Groundwater Contamination Plume Modeling and Forecasting

Published in Master’s practicum, University of Michigan, 2022

Groundwater systems are intrinsically heterogeneous with dynamic spatio-temporal patterns, which cause significant challenges in quantifying and mapping their complex processes. However, accurate forecasting of regional groundwater contamination is commonly needed to identify its spatio-temporal dynamic that helps the public anticipate the timing and severity of potential groundwater quality issues and possibly serve as an early warning system. This study focuses on modeling a plume of 1,4-dioxane originating from the Gelman site beneath the city of Ann Arbor, Michigan. It proposed a novel methodology to consider the spatially and temporally irregular and uncertain nature of groundwater contamination data to analyze the historical trends of dioxane concentration and predict its transportation: 1. A random forest interpolation model was deployed to fill in or extend fragmented time series data gaps among all the monitoring wells; 2. Mann-Kendall test was applied to evaluate the trend of dioxane concentrations at various wells; 3. An automated time series machine learning (AutoTS) package was utilized to predict the best future values forecasts; and 4. An R-based Shiny web application was designed to allow visualization and quantification of dioxane contamination analytical data. This research introduced a novel framework for filling spatial and temporal data sampling gaps in groundwater contamination to offer an effective and promising way to predict future plume concentration and spatial distribution.

Recommended citation: Luo, Y. Gelman Site 1,4-Dioxane Groundwater Contamination Plume Modeling and Forecasting. https://dx.doi.org/10.7302/4314