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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

Lake Huran

Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Many Midwestern lakes are experiencing warming water temperatures as a result of climate change. In general, this change is causing coldwater fish species such as cisco and coolwater species such as walleye to decline. Meanwhile, warmer water species such as largemouth and smallmouth bass are increasing as temperatures warm. However, some fish populations are more vulnerable to these...
By
Climate Adaptation Science Centers
Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Many Midwestern lakes are experiencing warming water temperatures as a result of climate change. In general, this change is causing coldwater fish species such as cisco and coolwater species such as walleye to decline. Meanwhile, warmer water species such as largemouth and smallmouth bass are increasing as temperatures warm. However, some fish populations are more vulnerable to these changes than
Learn More
Overhead view of a series of lakes surrounded by green crops.

Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Lakes, reservoirs, and ponds are central and integral features of the North Central U.S. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. External impacts – such as climate change – can have significant impacts to these important parts of the region’s landscape. Understanding the responses of lakes to these drivers is...
By
Climate Adaptation Science Centers
Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Lakes, reservoirs, and ponds are central and integral features of the North Central U.S. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. External impacts – such as climate change – can have significant impacts to these important parts of the region’s landscape. Understanding the responses of lakes to these drivers is critical fo
Learn More
Clear blue lake with green trees in the background and blue sky with clouds reflected in the water

“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and...
By
Climate Adaptation Science Centers
“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and usable data and
Learn More
Filter Total Items: 14

Model application: modeling lake ice phenology in Minnesota, 1980-2018 Model application: modeling lake ice phenology in Minnesota, 1980-2018

Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased towards few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational record, but these approaches produce large errors when estimating ice phenology for individual lakes. Our associated manuscript explores machine...
By
Water Resources Mission Area

Compilation of multi-agency water temperature observations for U.S. streams, 1894-2022 Compilation of multi-agency water temperature observations for U.S. streams, 1894-2022

This data release collates stream water temperature observations from across the United States from four data sources: The U.S. Geological Survey's National Water Information System (NWIS), Water Quality Portal (WQP), Spatial Hydro-Ecological Decision Systems temperature database (EcoSHEDS), and the U.S. Fish and Wildlife's NorWeST stream temperature database. These data were compiled...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin

Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling...
By
Water Resources Mission Area

Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs...
By
Water Resources Mission Area

Model predictions for heterogeneous stream-reservoir graph networks with data assimilation Model predictions for heterogeneous stream-reservoir graph networks with data assimilation

This data release provides the predictions from stream temperature models described in Chen et al. 2021. Briefly, various deep learning and process-guided deep learning models were built to test improved performance of stream temperature predictions below reservoirs in the Delaware River Basin. The spatial extent of predictions was restricted to streams above the Delaware River at...
By
Water Resources Mission Area

Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation

This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that made real-time predictions in the Delaware River Basin during 2021. The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7) at five sites. This...
By
Water Resources Mission Area

Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations

Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from atmospheric signals. Including observed reservoir releases as inputs to models can improve water temperature predictions below reservoirs, but many reservoirs are not well-observed. This data release contains...
By
Water Resources Mission Area

Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release) Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the...
By
Water Resources Mission Area

Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022) Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each...
By
Water Resources Mission Area

Data to support water quality modeling efforts in the Delaware River Basin Data to support water quality modeling efforts in the Delaware River Basin

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this...
By
Water Resources Mission Area

Predicting water temperature in the Delaware River Basin Predicting water temperature in the Delaware River Basin

Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish and mussel species. This data release supports a variety of flow and water temperature modeling efforts and provides the inputs and outputs of both machine learning and process-based modeling methods across 456...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes

Climate change and land use change have been shown to influence lake temperatures and water clarity in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles and optical habitat in 881 lakes in Minnesota during 1980-2018. The...
By
Water Resources Mission Area
Field Sampling Lake Mendota
Field Sampling Lake Mendota
Field Sampling Lake Mendota
Field Sampling Lake Mendota

Field Sampling Lake Mendota

Field Sampling Lake Mendota

USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.

By
Upper Midwest Water Science Center
Field Sampling Lake Mendota

Field Sampling Lake Mendota

Field Sampling Lake Mendota

USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.

By
Upper Midwest Water Science Center
Filter Total Items: 35

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance –...
Authors
Jeremy Diaz, Samantha Oliver, Galen Gorski
By
Water Resources Mission Area

Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by...
Authors
Sebastiano Piccolroaz, Senlin Zhu, Robert Ladwig, Laura Carrea, Samantha Oliver, Adam Piotrowski, Mariusz Ptak, Ryuichiro Shinohara, Mariusz Sojka, Richard Woolway, David Zhu
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

GRiMeDB: The Global River Database Methane Database of concentrations and fluxes GRiMeDB: The Global River Database Methane Database of concentrations and fluxes

Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) into the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere...
Authors
Emily Stanley, Luke Loken, Nora Casson, Samantha Oliver, Ryan Sponseller, Marcus Wallin, Liwei Zhang, Gerard Rocher-Ros
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent...
Authors
Jacob Zwart, Jeremy Diaz, Scott Hamshaw, Samantha Oliver, Jesse Ross, Margaux Sleckman, Alison Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan Read, Jeffrey M Sadler, Theodore Thompson, David Watkins, Elaheh White
By
Water Resources Mission Area, Science Synthesis, Analysis and Research Program, Advanced Research Computing (ARC)

Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes

Watersheds of the Great Lakes Basin (USA/Canada) are highly modified and impacted by human activities including pesticide use. Despite labeling restrictions intended to minimize risks to nontarget organisms, concerns remain that environmental exposures to pesticides may be occurring at levels negatively impacting nontarget organisms. We used a combination of organismal-level toxicity...
Authors
Samantha Oliver, Steven R. Corsi, Austin Baldwin, Michelle Nott, Gerald T. Ankley, Brett R. Blackwell, Daniel Villeneuve, Michelle Hladik, Dana W. Kolpin, Luke Loken, Laura DeCicco, Michael T. Meyer, Keith Loftin
By
Water Resources Mission Area, Central Plains Water Science Center, Kansas Water Science Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow...
Authors
Jacob Zwart, Samantha Oliver, William Watkins, Jeffrey Sadler, Alison Appling, Hayley Corson-Dosch, Xiaowei Jia, Vipin Kumar, Jordan Read
By
Water Resources Mission Area

Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers

To help meet the objectives of the Great Lakes Restoration Initiative with regard to increasing knowledge about toxic substances, 223 pesticides and pesticide transformation products were monitored in 15 Great Lakes tributaries using polar organic chemical integrative samplers. A screening-level assessment of their potential for biological effects was conducted by computing toxicity...
Authors
Luke Loken, Steven R. Corsi, David Alvarez, Gerald T. Ankley, Austin Baldwin, Bradley Blackwell, Laura DeCicco, Michelle Nott, Samantha Oliver, Daniel Villeneuve
By
Ecosystems Mission Area, Water Resources Mission Area, Columbia Environmental Research Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Teams, networks, and networks of networks advancing our understanding and conservation of inland waters Teams, networks, and networks of networks advancing our understanding and conservation of inland waters

Networks are defined as groups of interconnected people and things, and by this definition, networks play a major role in the science of inland waters. In this article, we bring the latest social network research to understand and improve inland waters science and conservation outcomes. What we found is that relationships matter. Different teams and networks have different objectives and...
Authors
Emily Read, Jennifer Cross, Nicole Herman-Mercer, Samantha Oliver, Catherine O’Reilly
By
Water Resources Mission Area, Upper Midwest Water Science Center

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality? Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water...
Authors
Charuleka Varadharajan, Alison Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo Lima, Juliane Mueller, Samantha Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Sadler, Helen Weierbach, Jared Willard, Zexuan Xu, Jacob Zwart
By
Water Resources Mission Area

Multi-task deep learning of daily streamflow and water temperature Multi-task deep learning of daily streamflow and water temperature

Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi-task DL. A multi-task...
Authors
Jeffrey Sadler, Alison Appling, Jordan Read, Samantha Oliver, Xiaowei Jia, Jacob Zwart, Vipin Kumar
By
Water Resources Mission Area

Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature

This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to...
Authors
Xiaowei Jia, Shengyu Chen, Yiqun Xie, Haoyu Yang, Alison Appling, Samantha Oliver, Zhe Jiang
By
Water Resources Mission Area

Machine learning for understanding inland water quantity, quality, and ecology Machine learning for understanding inland water quantity, quality, and ecology

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring...
Authors
Alison Appling, Samantha Oliver, Jordan Read, Jeffrey Sadler, Jacob Zwart
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

ToxMixtures: A package to explore toxicity due to chemical mixtures ToxMixtures: A package to explore toxicity due to chemical mixtures

The ToxMixtures R-package includes a set of functions to analyze, visualize, and organize measured chemical concentration data as it relates to chemical mixtures. ToxMixtures combines outputs from the R-package toxEval with open access sources on molecular biology to provide information on potential biological effects of chemicals detected in environmental matrices. To use ToxMixtures...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Science and Products

Lake Huran

Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Many Midwestern lakes are experiencing warming water temperatures as a result of climate change. In general, this change is causing coldwater fish species such as cisco and coolwater species such as walleye to decline. Meanwhile, warmer water species such as largemouth and smallmouth bass are increasing as temperatures warm. However, some fish populations are more vulnerable to these...
By
Climate Adaptation Science Centers
Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Fish Habitat Restoration to Promote Adaptation: Resilience of Sport Fish in Lakes of the Upper Midwest

Many Midwestern lakes are experiencing warming water temperatures as a result of climate change. In general, this change is causing coldwater fish species such as cisco and coolwater species such as walleye to decline. Meanwhile, warmer water species such as largemouth and smallmouth bass are increasing as temperatures warm. However, some fish populations are more vulnerable to these changes than
Learn More
Overhead view of a series of lakes surrounded by green crops.

Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Lakes, reservoirs, and ponds are central and integral features of the North Central U.S. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. External impacts – such as climate change – can have significant impacts to these important parts of the region’s landscape. Understanding the responses of lakes to these drivers is...
By
Climate Adaptation Science Centers
Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota

Lakes, reservoirs, and ponds are central and integral features of the North Central U.S. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. External impacts – such as climate change – can have significant impacts to these important parts of the region’s landscape. Understanding the responses of lakes to these drivers is critical fo
Learn More
Clear blue lake with green trees in the background and blue sky with clouds reflected in the water

“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and...
By
Climate Adaptation Science Centers
“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes

Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and usable data and
Learn More
Filter Total Items: 14

Model application: modeling lake ice phenology in Minnesota, 1980-2018 Model application: modeling lake ice phenology in Minnesota, 1980-2018

Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased towards few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational record, but these approaches produce large errors when estimating ice phenology for individual lakes. Our associated manuscript explores machine...
By
Water Resources Mission Area

Compilation of multi-agency water temperature observations for U.S. streams, 1894-2022 Compilation of multi-agency water temperature observations for U.S. streams, 1894-2022

This data release collates stream water temperature observations from across the United States from four data sources: The U.S. Geological Survey's National Water Information System (NWIS), Water Quality Portal (WQP), Spatial Hydro-Ecological Decision Systems temperature database (EcoSHEDS), and the U.S. Fish and Wildlife's NorWeST stream temperature database. These data were compiled...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin

Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling...
By
Water Resources Mission Area

Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs...
By
Water Resources Mission Area

Model predictions for heterogeneous stream-reservoir graph networks with data assimilation Model predictions for heterogeneous stream-reservoir graph networks with data assimilation

This data release provides the predictions from stream temperature models described in Chen et al. 2021. Briefly, various deep learning and process-guided deep learning models were built to test improved performance of stream temperature predictions below reservoirs in the Delaware River Basin. The spatial extent of predictions was restricted to streams above the Delaware River at...
By
Water Resources Mission Area

Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation Data to support near-term forecasts of stream temperature using process-guided deep learning and data assimilation

This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that made real-time predictions in the Delaware River Basin during 2021. The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7) at five sites. This...
By
Water Resources Mission Area

Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations

Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from atmospheric signals. Including observed reservoir releases as inputs to models can improve water temperature predictions below reservoirs, but many reservoirs are not well-observed. This data release contains...
By
Water Resources Mission Area

Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release) Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the...
By
Water Resources Mission Area

Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022) Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each...
By
Water Resources Mission Area

Data to support water quality modeling efforts in the Delaware River Basin Data to support water quality modeling efforts in the Delaware River Basin

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this...
By
Water Resources Mission Area

Predicting water temperature in the Delaware River Basin Predicting water temperature in the Delaware River Basin

Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish and mussel species. This data release supports a variety of flow and water temperature modeling efforts and provides the inputs and outputs of both machine learning and process-based modeling methods across 456...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes

Climate change and land use change have been shown to influence lake temperatures and water clarity in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles and optical habitat in 881 lakes in Minnesota during 1980-2018. The...
By
Water Resources Mission Area
Field Sampling Lake Mendota
Field Sampling Lake Mendota
Field Sampling Lake Mendota
Field Sampling Lake Mendota

Field Sampling Lake Mendota

Field Sampling Lake Mendota

USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.

By
Upper Midwest Water Science Center
Field Sampling Lake Mendota

Field Sampling Lake Mendota

Field Sampling Lake Mendota

USGS scientists prepare to sample lake bed sediment on Lake Mendota, WI.

By
Upper Midwest Water Science Center
Filter Total Items: 35

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance –...
Authors
Jeremy Diaz, Samantha Oliver, Galen Gorski
By
Water Resources Mission Area

Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by...
Authors
Sebastiano Piccolroaz, Senlin Zhu, Robert Ladwig, Laura Carrea, Samantha Oliver, Adam Piotrowski, Mariusz Ptak, Ryuichiro Shinohara, Mariusz Sojka, Richard Woolway, David Zhu
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

GRiMeDB: The Global River Database Methane Database of concentrations and fluxes GRiMeDB: The Global River Database Methane Database of concentrations and fluxes

Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) into the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere...
Authors
Emily Stanley, Luke Loken, Nora Casson, Samantha Oliver, Ryan Sponseller, Marcus Wallin, Liwei Zhang, Gerard Rocher-Ros
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent...
Authors
Jacob Zwart, Jeremy Diaz, Scott Hamshaw, Samantha Oliver, Jesse Ross, Margaux Sleckman, Alison Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan Read, Jeffrey M Sadler, Theodore Thompson, David Watkins, Elaheh White
By
Water Resources Mission Area, Science Synthesis, Analysis and Research Program, Advanced Research Computing (ARC)

Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes

Watersheds of the Great Lakes Basin (USA/Canada) are highly modified and impacted by human activities including pesticide use. Despite labeling restrictions intended to minimize risks to nontarget organisms, concerns remain that environmental exposures to pesticides may be occurring at levels negatively impacting nontarget organisms. We used a combination of organismal-level toxicity...
Authors
Samantha Oliver, Steven R. Corsi, Austin Baldwin, Michelle Nott, Gerald T. Ankley, Brett R. Blackwell, Daniel Villeneuve, Michelle Hladik, Dana W. Kolpin, Luke Loken, Laura DeCicco, Michael T. Meyer, Keith Loftin
By
Water Resources Mission Area, Central Plains Water Science Center, Kansas Water Science Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow...
Authors
Jacob Zwart, Samantha Oliver, William Watkins, Jeffrey Sadler, Alison Appling, Hayley Corson-Dosch, Xiaowei Jia, Vipin Kumar, Jordan Read
By
Water Resources Mission Area

Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers

To help meet the objectives of the Great Lakes Restoration Initiative with regard to increasing knowledge about toxic substances, 223 pesticides and pesticide transformation products were monitored in 15 Great Lakes tributaries using polar organic chemical integrative samplers. A screening-level assessment of their potential for biological effects was conducted by computing toxicity...
Authors
Luke Loken, Steven R. Corsi, David Alvarez, Gerald T. Ankley, Austin Baldwin, Bradley Blackwell, Laura DeCicco, Michelle Nott, Samantha Oliver, Daniel Villeneuve
By
Ecosystems Mission Area, Water Resources Mission Area, Columbia Environmental Research Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

Teams, networks, and networks of networks advancing our understanding and conservation of inland waters Teams, networks, and networks of networks advancing our understanding and conservation of inland waters

Networks are defined as groups of interconnected people and things, and by this definition, networks play a major role in the science of inland waters. In this article, we bring the latest social network research to understand and improve inland waters science and conservation outcomes. What we found is that relationships matter. Different teams and networks have different objectives and...
Authors
Emily Read, Jennifer Cross, Nicole Herman-Mercer, Samantha Oliver, Catherine O’Reilly
By
Water Resources Mission Area, Upper Midwest Water Science Center

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality? Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water...
Authors
Charuleka Varadharajan, Alison Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo Lima, Juliane Mueller, Samantha Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Sadler, Helen Weierbach, Jared Willard, Zexuan Xu, Jacob Zwart
By
Water Resources Mission Area

Multi-task deep learning of daily streamflow and water temperature Multi-task deep learning of daily streamflow and water temperature

Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi-task DL. A multi-task...
Authors
Jeffrey Sadler, Alison Appling, Jordan Read, Samantha Oliver, Xiaowei Jia, Jacob Zwart, Vipin Kumar
By
Water Resources Mission Area

Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature

This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to...
Authors
Xiaowei Jia, Shengyu Chen, Yiqun Xie, Haoyu Yang, Alison Appling, Samantha Oliver, Zhe Jiang
By
Water Resources Mission Area

Machine learning for understanding inland water quantity, quality, and ecology Machine learning for understanding inland water quantity, quality, and ecology

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring...
Authors
Alison Appling, Samantha Oliver, Jordan Read, Jeffrey Sadler, Jacob Zwart
By
Water Resources Mission Area, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center

ToxMixtures: A package to explore toxicity due to chemical mixtures ToxMixtures: A package to explore toxicity due to chemical mixtures

The ToxMixtures R-package includes a set of functions to analyze, visualize, and organize measured chemical concentration data as it relates to chemical mixtures. ToxMixtures combines outputs from the R-package toxEval with open access sources on molecular biology to provide information on potential biological effects of chemicals detected in environmental matrices. To use ToxMixtures...
By
Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center
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