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
Filter Total Items: 25
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored
Authors
Farshid Rahmani, Chaopeng Shen, Samantha K. Oliver, Kathryn Lawson, Alison P. Appling
Physics-guided recurrent graph model for predicting flow and temperature in river networks
This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of
Authors
Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Steven L. Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar
Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characterist
Authors
Meredith A. Holgerson, Rachel A. Hovel, Patrick T. Kelly, Lauren E Bortolotti, Jennifer A. Brentrup, Amber R Bellamy, Samantha K. Oliver, Alexander J Reisenger
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method,
Authors
Jared Willard, Jordan Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-b
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Inconsistent browning of northeastern U.S. lakes despite increased precipitation and recovery from acidification
Multiple studies have reported widespread browning of Northern Hemisphere lakes. Most examples are from boreal lakes that have experienced limited human influence, and browning has alternatively been attributed to changes in atmospheric deposition, climate, and land use. To determine the extent and possible causes of browning across a more geographically diverse region, we examined watercolor and
Authors
Jean-Francois Lapierre, Sarah M. Collins, Samantha K. Oliver, Emily H. Stanley, Tyler Wagner
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning archite
Authors
Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison P. Appling, Samantha K. Oliver, Chaopeng Shen
Heterogeneous stream-reservoir graph networks with data assimilation
Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected by weather patterns (such as solar radiation) and water flowing through the stream network. Additionally, stream temperature can be substantially affected by water releases from man-made reservoirs to downstream segments
Authors
Shengyu Chen, Alison P. Appling, Samantha K. Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart, Xiaowei Jia
Partial differential equation driven dynamic graph networks for predicting stream water temperature
This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple
Authors
Tianshu Bao, Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Taylor T. Johnson
Primary sources of polycyclic aromatic hydrocarbons to streambed sediment in Great Lakes tributaries using multiple lines of evidence
Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread and potentially toxic contaminants in Great Lakes (USA/Canada) tributaries. The sources of PAHs are numerous and diverse, and identifying the primary source(s) can be difficult. The present study used multiple lines of evidence to determine the likely sources of PAHs to surficial streambed sediments at 71 locations across 26 Gre
Authors
Austin K. Baldwin, Steven R. Corsi, Samantha K. Oliver, Peter L. Lenaker, Michelle A. Nott, Marc A. Mills, Gary A. Norris, Pentti Paatero
Process-guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework
Authors
Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob Aaron Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J. A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar
Spatial and temporal variation of ecosystem properties at macroscales
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observat
Authors
Patricia A. Soranno, Tyler Wagner, Sarah M. Collins, Jean-Francois Lapierre, Samantha K. Oliver
Science and Products
Filter Total Items: 25
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored
Authors
Farshid Rahmani, Chaopeng Shen, Samantha K. Oliver, Kathryn Lawson, Alison P. Appling
Physics-guided recurrent graph model for predicting flow and temperature in river networks
This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of
Authors
Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Steven L. Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar
Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characterist
Authors
Meredith A. Holgerson, Rachel A. Hovel, Patrick T. Kelly, Lauren E Bortolotti, Jennifer A. Brentrup, Amber R Bellamy, Samantha K. Oliver, Alexander J Reisenger
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method,
Authors
Jared Willard, Jordan Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-b
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Inconsistent browning of northeastern U.S. lakes despite increased precipitation and recovery from acidification
Multiple studies have reported widespread browning of Northern Hemisphere lakes. Most examples are from boreal lakes that have experienced limited human influence, and browning has alternatively been attributed to changes in atmospheric deposition, climate, and land use. To determine the extent and possible causes of browning across a more geographically diverse region, we examined watercolor and
Authors
Jean-Francois Lapierre, Sarah M. Collins, Samantha K. Oliver, Emily H. Stanley, Tyler Wagner
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning archite
Authors
Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison P. Appling, Samantha K. Oliver, Chaopeng Shen
Heterogeneous stream-reservoir graph networks with data assimilation
Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected by weather patterns (such as solar radiation) and water flowing through the stream network. Additionally, stream temperature can be substantially affected by water releases from man-made reservoirs to downstream segments
Authors
Shengyu Chen, Alison P. Appling, Samantha K. Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart, Xiaowei Jia
Partial differential equation driven dynamic graph networks for predicting stream water temperature
This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple
Authors
Tianshu Bao, Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Taylor T. Johnson
Primary sources of polycyclic aromatic hydrocarbons to streambed sediment in Great Lakes tributaries using multiple lines of evidence
Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread and potentially toxic contaminants in Great Lakes (USA/Canada) tributaries. The sources of PAHs are numerous and diverse, and identifying the primary source(s) can be difficult. The present study used multiple lines of evidence to determine the likely sources of PAHs to surficial streambed sediments at 71 locations across 26 Gre
Authors
Austin K. Baldwin, Steven R. Corsi, Samantha K. Oliver, Peter L. Lenaker, Michelle A. Nott, Marc A. Mills, Gary A. Norris, Pentti Paatero
Process-guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework
Authors
Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob Aaron Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J. A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar
Spatial and temporal variation of ecosystem properties at macroscales
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observat
Authors
Patricia A. Soranno, Tyler Wagner, Sarah M. Collins, Jean-Francois Lapierre, Samantha K. Oliver