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
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 release: early warning indicators for harmful algal bloom assessments in the Illinois River, 2013 - 2020
Harmful algal blooms (HABs) have recently been observed in rivers, including the Illinois River in the Midwest United States. The Illinois River Basin has a history of eutrophication issues, primarily caused by the excessive loading of nitrogen and phosphorus from urban and agricultural sources. Recent events have seen the emergence of cyanobacterial harmful algal blooms in the area. This data rel
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.
Harmonized discrete and continuous water quality data in support of modeling harmful algal blooms in the Illinois River Basin, 2005 - 2020
Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood,
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 Lordville, NY, a
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
Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA
This dataset contains modeled daily lake area, volume, constituent mass, and biogeochemical rates for 3,692 lakes in the Northern Highlands Lake District (NHLD) for one retrospective model run (1986-2010) and 12 model runs under future climate scenarios. This dataset was created using published tools developed to simulate detailed hydrological and biogeochemical fluxes for thousands of lakes and r
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: 35
Near-term ecological forecasting for climate change action
A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term
Authors
Michael Dietze, Ethan P. White, Antoinette Abeyta, Carl Boettiger, Nievita Bueno Watts, Cayelan C. Carey, Rebecca Chaplin-Kramer, Ryan E. Emanuel, S.K. Morgan Ernest, Renato Figueiredo, Michael Gerst, Leah R. Johnson, Melissa A. Kenney, Jason S. McLachlan, Ioannis Ch. Paschalidis, Jody Peters, Christine R. Rollinson, Juniper Simonis, Kira Sullivan-Wiley, R. Quinn Thomas, Glenda M Wardle, Alyssa Willson, Jacob Aaron Zwart
The 2024 “Hacking Limnology” Workshop Series and Virtual Summit: Increasing inclusion, participation, and representation in the aquatic sciences
No abstract available.
Authors
Michael Frederick Meyer, Robert T. Hensley, Carolina C. Barbosa, Jonathan J Borelli, Johannes Feldbauer, Merritt Elizabeth Harlan, Burak Kuyumcu, Robert Ladwig, Jorrit Mesman, Rachel M. Pilla, Qinglong Zhang, Jacob Aaron Zwart, Ana I Ayala, Craig B Brinkerhoff, David Kneis, Daniel Mercado-Bettín, Cassandra Nickles, Donald C. Pierson, Patch Thongthaisong, Inne Vanderkelen
Human activities shape global patterns of decomposition rates in rivers
Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose
Authors
Scott Tiegs, Krista A. Capps, David M. Costello, John Paul Schmidt, Christopher J. Patrick, Jennifer Follstad Shah, Carri J. LeRoy, Vicenç Acuña, Ricardo Albariño, Daniel Allen, Cecilia Alonso, Patricio Andino, Clay Arango, Jukka Aroviita, Marcus Barbosa, Leon A. Barmuta, Colden V. Baxter, Brent Bellinger, Luz Boyero, Lyubov Bragina, Lee E. Brown, Andreas Bruder, Denise Bruesewitz, Francis Burdon, Marcos Callisto, Antonio G. Camacho, Cristina Canhoto, María Castillo, Eric Chauvet, Joanne Clapcott, Fanny Colas, Checo Colón-Gaud, Julien Cornut, Verónica Crespo-Pérez, Wyatt F. Cross, Joseph M. Culp, Michael Danger, Olivier Dangles, Elvira de Eyto, Alison Derry, Veronica Díaz-Villanueva, Michael Douglas, Arturo Elosegi, Andrea Encalada, Sally Entrekin, Rodrigo Espinosa, Verónica Ferreira, Carmen Ferriol, Kyla Flanagan, Alexander Flecker, Tadeusz Fleituch, André Frainer, Nikolai Friberg, Paul C. Frost, Erica A. Garcia, Liliana García-Lago, Pavel García Soto, Mark Gessner, Sudeep Ghate, Darren Giling, Alan Gilmer, Jose Goncalves, Rosario Gonzales, Manuel Graça, Mike Grace, Natalie A. Griffiths, Hans-Peter Grossart, François Guérold, Vlad Gulis, Pablo E. Gutiérrez-Fonseca, Luiz Hepp, Scott Higgins, Takuo Hishi, Joseph Huddart, John P. Hudson, Moss Imberger, Carlos Iñiguez-Armijos, Mark Isken, Tomoya Iwata, Dave Janetski, Andrea Kirkwood, Aaron A. Koning, Sarian Kosten, Kevin Kuehn, Hjalmar Laudon, Peter Leavitt, Aurea Lemes da Silva, Shawn Leroux, Peter Lisi, Richard Mackenzie, Amy M Marcarelli, Frank Masese, Peter B. McIntyre, Brendan G. McKie, Adriana Medeiros, Kristian Meissner, Marko Miliša, Shailendra Mishra, Yo Miyake, Ashley Moerke, Shorok Mombrikotb, Robert J Mooney, Tim Moulton, Timo Muotka, Junjiro N. Negishi, Vinicius Neres-Lima, Mika Nieminen, Jorge Nimptsch, Jakub Ondruch, Riku Paavola, Isabel Pardo, Edwin Peeters, Jesus Pozo, Aaron Prussian, Estefania Quenta, Brian Reid, John S. Richardson, Anna Rigosi, José Rincón, Geta Rîșnoveanu, Chris Robinson, Lorena Rodríguez-Gallego, Todd V Royer, James A. Rusak, Anna Santamans, Géza Selmeczy, Gelas Simiyu, Agnija Skuja, Jerzy Smykla, Ryan A. Sponseller, Kandikere Sridhar, Aaron Stoler, Christopher M. Swan, Franco Teixeira-de Mello, Jonathan Tonkin, Sari Uusheimo, Allison Veach, Sirje Vilbaste, Lena Vought, Chiao-Ping Wang, Jackson Webster, Paul B. Wilson, Stefan Woelfl, Guy Woodward, Marguerite A. Xenopoulos, Adam Yates, Chihiro Yoshimura, Catherine Yule, Yixin Zhang, Jacob Aaron Zwart
Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments
The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll
Authors
Sarah M. Stackpoole, Jacob Aaron Zwart, Jennifer L. Graham, Judson Harvey, Noah Schmadel, Jennifer C. Murphy
Fair graph learning using constraint-aware priority adjustment and graph masking in river networks
Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated
Authors
Erhu He, Yiqun Xie, Alexander Y. Sun, Jacob Aaron Zwart, Jie Yang, Zhenong Jin, Yang Wang, Hassan Ali Karimi, Xiaowei Jia
National-scale remotely sensed lake trophic state from 1984 through 2020
Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state 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 state with reproducible, robust methods across time and space. We used La
Authors
Michael Frederick Meyer, Simon Nemer Topp, Tyler Victor King, Robert Ladwig, Rachel M. Pilla, Hilary A. Dugan, Jack R. Eggleston, Stephanie E. Hampton, Dina M Leech, Isabella Oleksy, Jesse Cleveland Ross, Matthew V Ross, R. Iestyn Woolway, Xiao Yang, Matthew R. Brousil, Kate Colleen Fickas, Julie C Padowski, Amina Pollard, Jianning Ren, Jacob Aaron Zwart
Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere
In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved org
Authors
Jessica R. Corman, Jacob Aaron Zwart, Jennifer Klug, Denise A Bruesewitz, Elvira de Eyto, Marcus Klaus, Lesley B. Knoll, James A. Rusak, Michael J. Vanni, María Belén Alfonso, Rocio Luz Fernandez, Huaxia Yao, Kari Austnes, Raoul-Marie Couture, Heleen A. de Wit, Jan Karlsson, Alo Laas
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 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 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
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.
Data release: early warning indicators for harmful algal bloom assessments in the Illinois River, 2013 - 2020
Harmful algal blooms (HABs) have recently been observed in rivers, including the Illinois River in the Midwest United States. The Illinois River Basin has a history of eutrophication issues, primarily caused by the excessive loading of nitrogen and phosphorus from urban and agricultural sources. Recent events have seen the emergence of cyanobacterial harmful algal blooms in the area. This data rel
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.
Harmonized discrete and continuous water quality data in support of modeling harmful algal blooms in the Illinois River Basin, 2005 - 2020
Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood,
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 Lordville, NY, a
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
Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA
This dataset contains modeled daily lake area, volume, constituent mass, and biogeochemical rates for 3,692 lakes in the Northern Highlands Lake District (NHLD) for one retrospective model run (1986-2010) and 12 model runs under future climate scenarios. This dataset was created using published tools developed to simulate detailed hydrological and biogeochemical fluxes for thousands of lakes and r
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: 35
Near-term ecological forecasting for climate change action
A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term
Authors
Michael Dietze, Ethan P. White, Antoinette Abeyta, Carl Boettiger, Nievita Bueno Watts, Cayelan C. Carey, Rebecca Chaplin-Kramer, Ryan E. Emanuel, S.K. Morgan Ernest, Renato Figueiredo, Michael Gerst, Leah R. Johnson, Melissa A. Kenney, Jason S. McLachlan, Ioannis Ch. Paschalidis, Jody Peters, Christine R. Rollinson, Juniper Simonis, Kira Sullivan-Wiley, R. Quinn Thomas, Glenda M Wardle, Alyssa Willson, Jacob Aaron Zwart
The 2024 “Hacking Limnology” Workshop Series and Virtual Summit: Increasing inclusion, participation, and representation in the aquatic sciences
No abstract available.
Authors
Michael Frederick Meyer, Robert T. Hensley, Carolina C. Barbosa, Jonathan J Borelli, Johannes Feldbauer, Merritt Elizabeth Harlan, Burak Kuyumcu, Robert Ladwig, Jorrit Mesman, Rachel M. Pilla, Qinglong Zhang, Jacob Aaron Zwart, Ana I Ayala, Craig B Brinkerhoff, David Kneis, Daniel Mercado-Bettín, Cassandra Nickles, Donald C. Pierson, Patch Thongthaisong, Inne Vanderkelen
Human activities shape global patterns of decomposition rates in rivers
Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose
Authors
Scott Tiegs, Krista A. Capps, David M. Costello, John Paul Schmidt, Christopher J. Patrick, Jennifer Follstad Shah, Carri J. LeRoy, Vicenç Acuña, Ricardo Albariño, Daniel Allen, Cecilia Alonso, Patricio Andino, Clay Arango, Jukka Aroviita, Marcus Barbosa, Leon A. Barmuta, Colden V. Baxter, Brent Bellinger, Luz Boyero, Lyubov Bragina, Lee E. Brown, Andreas Bruder, Denise Bruesewitz, Francis Burdon, Marcos Callisto, Antonio G. Camacho, Cristina Canhoto, María Castillo, Eric Chauvet, Joanne Clapcott, Fanny Colas, Checo Colón-Gaud, Julien Cornut, Verónica Crespo-Pérez, Wyatt F. Cross, Joseph M. Culp, Michael Danger, Olivier Dangles, Elvira de Eyto, Alison Derry, Veronica Díaz-Villanueva, Michael Douglas, Arturo Elosegi, Andrea Encalada, Sally Entrekin, Rodrigo Espinosa, Verónica Ferreira, Carmen Ferriol, Kyla Flanagan, Alexander Flecker, Tadeusz Fleituch, André Frainer, Nikolai Friberg, Paul C. Frost, Erica A. Garcia, Liliana García-Lago, Pavel García Soto, Mark Gessner, Sudeep Ghate, Darren Giling, Alan Gilmer, Jose Goncalves, Rosario Gonzales, Manuel Graça, Mike Grace, Natalie A. Griffiths, Hans-Peter Grossart, François Guérold, Vlad Gulis, Pablo E. Gutiérrez-Fonseca, Luiz Hepp, Scott Higgins, Takuo Hishi, Joseph Huddart, John P. Hudson, Moss Imberger, Carlos Iñiguez-Armijos, Mark Isken, Tomoya Iwata, Dave Janetski, Andrea Kirkwood, Aaron A. Koning, Sarian Kosten, Kevin Kuehn, Hjalmar Laudon, Peter Leavitt, Aurea Lemes da Silva, Shawn Leroux, Peter Lisi, Richard Mackenzie, Amy M Marcarelli, Frank Masese, Peter B. McIntyre, Brendan G. McKie, Adriana Medeiros, Kristian Meissner, Marko Miliša, Shailendra Mishra, Yo Miyake, Ashley Moerke, Shorok Mombrikotb, Robert J Mooney, Tim Moulton, Timo Muotka, Junjiro N. Negishi, Vinicius Neres-Lima, Mika Nieminen, Jorge Nimptsch, Jakub Ondruch, Riku Paavola, Isabel Pardo, Edwin Peeters, Jesus Pozo, Aaron Prussian, Estefania Quenta, Brian Reid, John S. Richardson, Anna Rigosi, José Rincón, Geta Rîșnoveanu, Chris Robinson, Lorena Rodríguez-Gallego, Todd V Royer, James A. Rusak, Anna Santamans, Géza Selmeczy, Gelas Simiyu, Agnija Skuja, Jerzy Smykla, Ryan A. Sponseller, Kandikere Sridhar, Aaron Stoler, Christopher M. Swan, Franco Teixeira-de Mello, Jonathan Tonkin, Sari Uusheimo, Allison Veach, Sirje Vilbaste, Lena Vought, Chiao-Ping Wang, Jackson Webster, Paul B. Wilson, Stefan Woelfl, Guy Woodward, Marguerite A. Xenopoulos, Adam Yates, Chihiro Yoshimura, Catherine Yule, Yixin Zhang, Jacob Aaron Zwart
Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments
The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll
Authors
Sarah M. Stackpoole, Jacob Aaron Zwart, Jennifer L. Graham, Judson Harvey, Noah Schmadel, Jennifer C. Murphy
Fair graph learning using constraint-aware priority adjustment and graph masking in river networks
Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated
Authors
Erhu He, Yiqun Xie, Alexander Y. Sun, Jacob Aaron Zwart, Jie Yang, Zhenong Jin, Yang Wang, Hassan Ali Karimi, Xiaowei Jia
National-scale remotely sensed lake trophic state from 1984 through 2020
Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state 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 state with reproducible, robust methods across time and space. We used La
Authors
Michael Frederick Meyer, Simon Nemer Topp, Tyler Victor King, Robert Ladwig, Rachel M. Pilla, Hilary A. Dugan, Jack R. Eggleston, Stephanie E. Hampton, Dina M Leech, Isabella Oleksy, Jesse Cleveland Ross, Matthew V Ross, R. Iestyn Woolway, Xiao Yang, Matthew R. Brousil, Kate Colleen Fickas, Julie C Padowski, Amina Pollard, Jianning Ren, Jacob Aaron Zwart
Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere
In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved org
Authors
Jessica R. Corman, Jacob Aaron Zwart, Jennifer Klug, Denise A Bruesewitz, Elvira de Eyto, Marcus Klaus, Lesley B. Knoll, James A. Rusak, Michael J. Vanni, María Belén Alfonso, Rocio Luz Fernandez, Huaxia Yao, Kari Austnes, Raoul-Marie Couture, Heleen A. de Wit, Jan Karlsson, Alo Laas
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 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 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