Jacob Zwart
(He/him)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
Filter Total Items: 31
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
The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research
Following the 2020 “Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research” (DSOS; Meyer and Zwart 2020), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22–23 July 2021. DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d “Hacking Limnology” Workshop Series prior to the summ
Authors
Michael F. Meyer, Robert Ladwig, Jorrit Mesman, Isabella Oleksy, Carolina C. Barbosa, Kaelin M. Cawley, Alli N. Cramer, Johannes Feldbauer, Patricia Q. Tran, Jacob Aaron Zwart, Gregario A. Lopez Moreira, Muhammed Shikhani, Deviyani Gurung, Robert T. Hensley, Elena Matta, Ryan P. McClure, Thomas Petzoldt, Nuria Sanchez Lopez, Karline Soetaert, Mridul K. Thomas, Simon Nemer Topp, Xiao Yang
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
Projected changes of regional lake hydrologic characteristics in response to 21st century climate change
Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatially
Authors
Zachary J. Hanson, Jacob Aaron Zwart, Stuart E. Jones, Alan F. Hamlet, Diogo Bolster
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic res
Authors
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, 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
Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycl
Authors
Charuleka Varadharajan, Vipin Kumar, Jared Willard, Jacob Aaron Zwart, Jeffrey Michael Sadler, Helen Weierbach, Talita Perciano, Juliane Mueller, Valerie Hendrix, Danielle Christianson
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
Virtual summit: Incorporating data science and open science in aquatic research
No abstract available.
Authors
Michael F. Meyer, Jacob Aaron Zwart
Science and Products
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- Publications
Filter Total Items: 31
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; 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 ReadThe AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research
Following the 2020 “Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research” (DSOS; Meyer and Zwart 2020), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22–23 July 2021. DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d “Hacking Limnology” Workshop Series prior to the summAuthorsMichael F. Meyer, Robert Ladwig, Jorrit Mesman, Isabella Oleksy, Carolina C. Barbosa, Kaelin M. Cawley, Alli N. Cramer, Johannes Feldbauer, Patricia Q. Tran, Jacob Aaron Zwart, Gregario A. Lopez Moreira, Muhammed Shikhani, Deviyani Gurung, Robert T. Hensley, Elena Matta, Ryan P. McClure, Thomas Petzoldt, Nuria Sanchez Lopez, Karline Soetaert, Mridul K. Thomas, Simon Nemer Topp, Xiao YangPhysics-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 ofAuthorsXiaowei 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 KumarProjected changes of regional lake hydrologic characteristics in response to 21st century climate change
Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatiallyAuthorsZachary J. Hanson, Jacob Aaron Zwart, Stuart E. Jones, Alan F. Hamlet, Diogo BolsterPhysics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resAuthorsXiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, Vipin KumarGraph-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-bAuthorsXiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan ReadUsing machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cyclAuthorsCharuleka Varadharajan, Vipin Kumar, Jared Willard, Jacob Aaron Zwart, Jeffrey Michael Sadler, Helen Weierbach, Talita Perciano, Juliane Mueller, Valerie Hendrix, Danielle ChristiansonHeterogeneous 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 segmentsAuthorsShengyu Chen, Alison P. Appling, Samantha K. Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart, Xiaowei JiaPartial 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 multipleAuthorsTianshu Bao, Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Taylor T. JohnsonVirtual summit: Incorporating data science and open science in aquatic research
No abstract available.AuthorsMichael F. Meyer, Jacob Aaron Zwart - News