Yifan Luo

Yifan Luo

Posts by Collection

portfolio

projects

Investigating uncertainty associated with the Great Lakes water balance using the Large Lake Statistical Water Balance Model

The Laurentian Great Lakes represent the largest system of lakes on Earth, and contain roughly 20% of all the world’s fresh surface water. Our team leads research aimed at understanding short- and long-term changes in Great Lakes water level variability, including pathways through which climate change impacts the major components of the Great Lakes water balance. This research extends into other Great Lakes regional projects, including model and dataset development for water quantity and quality management.

Improving the representation of groundwater in foundational Great Lakes hydrologic and hydrodynamic models and datasets

Groundwater plays a critical role in the water balance, however the groundwater component of the hydrologic cycle is frequently overlooked at basin scales because it is difficult to observe and quantify.  We address this problem through a novel framework that combines existing hydrological models and data sets with groundwater flux estimates across Earth’s largest system of lakes; the Laurentian Great Lakes.  Aside from serving as a template for combining surface and ground water data and models, the Laurentian Great Lakes recently transitioned from a period characterized by water scarcity (water levels on the lakes were persistently below average from 1998 through 2013) to extreme water abundance (all-time high water levels were set in 2017 and 2019).  Throughout this transition, we know of no comprehensive data record or modeling system that explicitly linked changes in observed land surface hydrology with critical subsurface groundwater processes.  In addition to utilizing our novel framework for combining existing surface and ground water data sets and model simulations, we will also demonstrate the impact of ground water representation in existing lake physics models that serve as the basis for understanding lake evaporation, circulation patterns, and pollutant fate and transport dynamics.

Gelman site 1,4-dioxane groundwater contamination plume modeling and forecasting

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, including the following key components:

  • A random forest interpolation model was deployed to fill in or extend fragmented time series data gaps among all the monitoring wells;
  • The Mann-Kendall test was applied to evaluate the trend of dioxane concentrations at various wells;
  • An automated time series machine learning (AutoTS) package was utilized to predict the best future values forecasts; and
  • 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.

Assessing uncertainty and impacts of climate change in historical estimates of the Great Lakes water balance

The Laurentian Great Lakes and St. Lawrence River basin comprises the largest freshwater system on Earth, containing about one-fifth of the world’s surface fresh water. However, the Great Lakes basin has recently experienced some rapid shifts between high and low in some water balance components. In order to resolve the regional water budget in the Great Lakes over an extended historical period, and provide insight moving forward in our changing climate, we incorporated the Large Lake Statistical Water Balance Model (L2SWBM) to adequately quantify uncertainty and reconcile the discrepancies between model- and measurement-based estimates of each water balance component from various datasets. L2SWBM contextualizes and reduces uncertainty while closing the water balance over consecutive historical periods. The model assimilates multiple datasets for each hydrologic component (i.e., over-lake precipitation, over-lake evaporation, runoff, connecting channel flows, and inter-basin diversions) and runs millions of iterations to reconstruct potential historical water budgets. Using observed and modeled data of water balance components through the historical record, the L2SWBM can be used to iteratively solve the coefficient values and estimate a range of reasonable uncertainties in individual components that are faithful to the water balance. After applying the L2SWBM, uncertainty was significantly reduced in the Great Lakes water balance by approximately 10-40% depending on original estimates.

publications

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

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.

talks

teaching

Principles of GIS (EAS 531)

Graduate Student Instructor, University of Michigan, Ann Arbor, 2021

  • Taught two 2.5-hour lab-based classes for 44 graduate-level students and provided instructional assistance on GIS software application
  • Created clear and concise instructions and datasets prior to the lab and provided written and verbal feedback on written assignments and presentations
  • Held office hours three hours per week to provide students with additional support on software and lab assignments

Diverse Farming Systems: Theory & Practice (EAS 553)

Graduate Student Instructor, University of Michigan, Ann Arbor, 2021

  • Assisted 33 grad students in completing assignments involving data collection, analysis, research papers, and group presentations
  • Guided students through the process of designing, implementing, and presenting group projects
  • Administered exams to students, evaluated exams, and maintained records on students’ academic progress