Dr. Jacob Zwart (he/him) is a data scientist for the USGS Water Resources Mission Area.
Jacob Zwart works within the Data Science Branch of the Water Resources Mission Area to develop aquatic ecosystem modeling techniques that provide timely information to stakeholders about important water resources across the nation. He uses his expertise in computational modeling, data assimilation, and limnology to help produce short-term forecasts of water quality at regional scales to aid in water resources decision making. Jacob’s research themes are: 1) improve understanding of aquatic biogeochemical processes and predicting how these processes may respond to future global change, 2) develop techniques to inject scientific knowledge into machine learning models to make accurate predictions of environmental variables (also known as “knowledge-guided machine learning”), and 3) advance methods for assimilating real-time observations into knowledge-guided machine learning models to improve near-term forecasts of water quality. Jacob also serves as a Peer Support Worker at USGS promoting awareness and education on topics and USGS policies for antiharassment, discrimination, biases, and scientific integrity, as well as providing peer-to-peer support for USGS employees.
Professional Experience
2021 – present: Data Scientist, Integrated Information Dissemination Division
2019 – 2021: Mendenhall Postdoctoral Fellow, Integrated Information Dissemination Division
2017 – 2019: National Science Foundation Earth Sciences Postdoctoral Fellow, Integrated Information Dissemination Division
2014 – 2017: National Science Foundation Graduate Research Fellow, University of Notre Dame
2012 – 2014: Research and Teaching Assistant, University of Notre Dame
Education and Certifications
Ph.D., Biological Sciences, University of Notre Dame, 2017
B.S., Biology, Calvin College, 2012
Honors and Awards
U.S. Geological Survey Mendenhall Postdoctoral Fellowship, 2019 – 2021
National Science Foundation Earth Sciences Postdoctoral Fellowship, 2017 – 2019
National Science Foundation Graduate Research Fellowship, 2014 – 2017
University of Notre Dame Linked Experimental Ecosystem Facility Research Grant, 2017
Exceptional Promise in Graduate Research Award, Ecological Society of America Aquatic Ecology Section, 2015
University of Notre Dame Center for Aquatic Conservation Graduate Fellow, 2014
University of Notre Dame Environmental Research Center Graduate Research Fellowship, 2013 – 2015
University of Notre Dame Environmental Research Center Graduate Mentoring Fellowship, 2012
Science and Products
Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks
Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.
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 approach in
National-scale, remotely sensed lake trophic status 1984-2020
Lake trophic status is a key water quality property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic status as a gauge of lake water quality, standardized and machine readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic status with reproducible, robust methods across time and space.
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 data rele
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 of 101 sit
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 data release
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 river rea
Data release: Process-based predictions of lake water temperature in the Midwest US
Climate change has been shown to influence lake temperatures 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 in 7,150 lakes in Minnesota and Wisconsin during 1980-2019. The data are organized into these items: Spatial data
Filter Total Items: 28
A synergistic future for AI and ecology
Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of glob
Authors
Barbara A. Han, Kush R. Varshney, Shannon L. LaDeau, Ajit Subramaniam, Kathleen C. Weathers, Jacob Aaron Zwart
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 observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley R. Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
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. In this us
Authors
Jacob Aaron Zwart, Samantha K. Oliver, William Watkins, Jeffrey Michael Sadler, Alison P. Appling, Hayley R. Corson-Dosch, Xiaowei Jia, Vipin Kumar, Jordan Read
Physics-guided recurrent neural networks for predicting lake water temperature
This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. M
Authors
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, Vipin Kumar
Physics-guided graph meta learning for predicting water temperature and streamflow in stream networks
This paper proposes a graph-based meta learning approach to separately predict water quantity and quality variables for river segments in stream networks. Given the heterogeneous water dynamic patterns in large-scale basins, we introduce an additional meta-learning condition based on physical characteristics of stream segments, which allows learning different sets of initial parameters for differe
Authors
Shengyu Chen, Jacob Aaron Zwart, Xiaowei Jia
Measurement and variability of lake metabolism
Aim: The aim of this article is to provide an overview of what contributes to lake metabolism, a brief overview of methods for estimating lake metabolism, and drivers of metabolism variability within and across lakes.Main concepts covered: In this article, we describe the key drivers of within and across lake variability in metabolism including lake morphometry, nutrients, light availability, temp
Authors
Jacob Aaron Zwart, Ludmila S Brighenti
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 quality and pred
Authors
Charuleka Varadharajan, Alison P. Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo R. Lima, Juliane Mueller, Samantha K. Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Michael Sadler, Helen Weierbach, Jared Willard, Zexuan Xu, Jacob Aaron Zwart
Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the rel
Authors
Mary Lofton, Jennifer A. Brentrup, Whitney S. Beck, Jacob Aaron Zwart, Ruchi Bhattacharya, Ludmila S Brighenti, Sarah H. Burnett, Ian M. McCullough, Bethel Steele, Cayelan C. Carey, Kathryn L Cottingham, Michael Dietze, Holly A. Ewing, Kathleen C. Weathers, Shannon L. LaDeau
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 scaling fac
Authors
Jeffrey Michael Sadler, Alison P. Appling, Jordan Read, Samantha K. Oliver, Xiaowei Jia, Jacob Aaron Zwart, Vipin Kumar
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 hypotheses; est
Authors
Alison P. Appling, Samantha K. Oliver, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart
Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches
Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon-14 (14C)-labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high-frequency in situ data such as free-water dissolved oxygen (O2). While there is
Authors
Noah R. Lottig, Joseph Phillips, Ryan D. Batt, Facundo Scordo, Tanner J. Williamson, Stephen R. Carpenter, Sudeep Chandra, Paul C. Hanson, Christopher T. Solomon, Michael J. Vanni, Jacob Aaron Zwart
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, these
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Science and Products
- Science
Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks
Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams. - Data
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 approach inNational-scale, remotely sensed lake trophic status 1984-2020
Lake trophic status is a key water quality property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic status as a gauge of lake water quality, standardized and machine readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic status with reproducible, robust methods across time and space.
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 data releMulti-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 of 101 sitData 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 data releasePredicting 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 river reaData release: Process-based predictions of lake water temperature in the Midwest US
Climate change has been shown to influence lake temperatures 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 in 7,150 lakes in Minnesota and Wisconsin during 1980-2019. The data are organized into these items: Spatial data - Publications
Filter Total Items: 28
A synergistic future for AI and ecology
Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of globAuthorsBarbara A. Han, Kush R. Varshney, Shannon L. LaDeau, Ajit Subramaniam, Kathleen C. Weathers, Jacob Aaron ZwartEvaluating 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 observationsAuthorsJacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley R. Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) WhiteNear-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. In this usAuthorsJacob Aaron Zwart, Samantha K. Oliver, William Watkins, Jeffrey Michael Sadler, Alison P. Appling, Hayley R. Corson-Dosch, Xiaowei Jia, Vipin Kumar, Jordan ReadPhysics-guided recurrent neural networks for predicting lake water temperature
This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. MAuthorsXiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, Vipin KumarPhysics-guided graph meta learning for predicting water temperature and streamflow in stream networks
This paper proposes a graph-based meta learning approach to separately predict water quantity and quality variables for river segments in stream networks. Given the heterogeneous water dynamic patterns in large-scale basins, we introduce an additional meta-learning condition based on physical characteristics of stream segments, which allows learning different sets of initial parameters for differeAuthorsShengyu Chen, Jacob Aaron Zwart, Xiaowei JiaMeasurement and variability of lake metabolism
Aim: The aim of this article is to provide an overview of what contributes to lake metabolism, a brief overview of methods for estimating lake metabolism, and drivers of metabolism variability within and across lakes.Main concepts covered: In this article, we describe the key drivers of within and across lake variability in metabolism including lake morphometry, nutrients, light availability, tempAuthorsJacob Aaron Zwart, Ludmila S BrighentiCan 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 quality and predAuthorsCharuleka Varadharajan, Alison P. Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo R. Lima, Juliane Mueller, Samantha K. Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Michael Sadler, Helen Weierbach, Jared Willard, Zexuan Xu, Jacob Aaron ZwartUsing near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relAuthorsMary Lofton, Jennifer A. Brentrup, Whitney S. Beck, Jacob Aaron Zwart, Ruchi Bhattacharya, Ludmila S Brighenti, Sarah H. Burnett, Ian M. McCullough, Bethel Steele, Cayelan C. Carey, Kathryn L Cottingham, Michael Dietze, Holly A. Ewing, Kathleen C. Weathers, Shannon L. LaDeauMulti-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 scaling facAuthorsJeffrey Michael Sadler, Alison P. Appling, Jordan Read, Samantha K. Oliver, Xiaowei Jia, Jacob Aaron Zwart, Vipin KumarMachine 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 hypotheses; estAuthorsAlison P. Appling, Samantha K. Oliver, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron ZwartEstimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches
Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon-14 (14C)-labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high-frequency in situ data such as free-water dissolved oxygen (O2). While there isAuthorsNoah R. Lottig, Joseph Phillips, Ryan D. Batt, Facundo Scordo, Tanner J. Williamson, Stephen R. Carpenter, Sudeep Chandra, Paul C. Hanson, Christopher T. Solomon, Michael J. Vanni, Jacob Aaron ZwartPhysics-guided machine learning from simulation data: An application in modeling lake and river systems
This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, theseAuthorsXiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read - News