USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.
Samantha K Oliver, PhD
Samantha Oliver is a Hydrologist with the Upper Midwest Water Science Center.
Samantha Oliver received her PhD in Limnology and Freshwater Sciences from the University of Wisconsin-Madison, where she did continental-scale water quality research at the Center for Limnology. Prior to her Phd, she completed a Masters degree in Integrated Biosciences at the University of Minnesota-Duluth, where her research focused on nutrient transport of migrating zooplankton in Lake Superior. She joined the Wisconsin Water Science Center in 2017 first as a student in the Pathways program, and then as a hydrologist. Currently, she contributes to a variety of projects across the center, including long-term trends in stream water quality in urban and agricultural watersheds of Milwaukee and Madison, the biological relevance of contaminants in Great Lakes tributaries, the effects of best management practices on runoff from farm fields (edge-of-field monitoring), and the consequences of airport deicers in surface water runoff. Samantha also spends part of her time with the Data Science branch in the Integrated Information Dissemination Division (IIDD) as an instructor for the Intro to R course and contributer to various projects including predictions of lake water temperature.
Science and Products
Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest
Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota
“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Model predictions for heterogeneous stream-reservoir graph networks with data assimilation
Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation
Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations
Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)
Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)
Data to support water quality modeling efforts in the Delaware River Basin
Predicting water temperature in the Delaware River Basin
Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes
Data release: Process-based predictions of lake water temperature in the Midwest US
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.
Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects
GRiMeDB: The Global River Database Methane Database of concentrations and fluxes
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes
Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions
Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers
Teams, networks, and networks of networks advancing our understanding and conservation of inland waters
Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?
Multi-task deep learning of daily streamflow and water temperature
Machine learning for understanding inland water quantity, quality, and ecology
Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
ToxMixtures: A package to explore toxicity due to chemical mixtures
Science and Products
Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest
Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota
“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Model predictions for heterogeneous stream-reservoir graph networks with data assimilation
Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation
Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations
Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)
Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)
Data to support water quality modeling efforts in the Delaware River Basin
Predicting water temperature in the Delaware River Basin
Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes
Data release: Process-based predictions of lake water temperature in the Midwest US
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.
USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.