Alison Appling, PhD
Alison Appling, Ph.D., is a data scientist and ecologist who applies machine learning and other data-driven methods to predict and understand water resources dynamics.
Current Roles
- Project Manager: Nutrient Prediction Innovation and Evaluation (NPIE)
- Project Manager: Predictive Understanding of Multiscale Processes (PUMP)
- Task Lead: Advancing Machine Learning and Data Assimilation, within the PUMP Project
Alison studies the movement of energy, carbon, and nutrients through rivers, lakes, and floodplains to better predict and understand variations in water quality over space and time.
As a machine learning modeler and biogeochemist, Alison seeks modeling advances that bring together scientific knowledge and data-driven models, using approaches including process-guided deep learning and differentiable hydrology.
As a data scientist, Alison conducts analyses in ways that are reproducible, efficient, and transparent, and Alison has developed tools and workflows to support others in these goals.
In leadership roles, Alison plans projects and organizes teams to deliver relevant, timely, high-quality products; challenges individuals to excel in their projects and careers; and coordinates across projects to realize the Water Mission Area’s vision of fit-for-purpose, integrated tools for modeling water quantity and quality across the nation.
Alison is a member of the Analysis and Prediction Branch in the Integrated Modeling and Prediction Division in the Water Resources Mission Area. Alison is on the USGS career track called Equipment Development Grade Evaluation (EDGE).
Professional Experience
Development Ecologist and Data Scientist, U.S. Geological Survey, 2019-Present
Ecologist, U.S. Geological Survey, 2016-2019
Postdoctoral Fellow, USGS Powell Center and University of Wisconsin-Madison. Mentors: E. H. Stanley, J. S. Read, E. G. Stets, and R. O. Hall, 2015-2016
Postdoctoral Associate, University of New Hampshire. Mentor: W. H. McDowell, 2013-2015
Postdoctoral Associate, Duke University. Mentor: J. B. Heffernan, 2012-2013
Ph.D. Student and Teaching Assistant: Organismal Diversity, Aquatic Field Ecology, and General Microbiology, University Program in Ecology, Duke University, 2006-2012
Research Technician, Stanford University & Carnegie Institution of Washington, 2004-2006
Undergraduate Teaching Assistant: Programming Paradigms and Discrete Mathematics, Computer Science, Stanford University, 2001-2003
Education and Certifications
Ph.D. Ecology, 2012. Duke University, Durham, NC.
Connectivity Drives Function: Carbon and Nitrogen Dynamics in a Floodplain-Aquifer Ecosystem. Advisors: E. S. Bernhardt and R. B. Jackson
B.S. Symbolic Systems, 2004. Stanford University, Stanford, CA.
Science and Products
Filter Total Items: 21
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
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...
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...
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...
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...
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...
Filter Total Items: 37
Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling
This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling...
Authors
Arka Daw, R. Thomas, Cayelan Carey, Jordan Read, Alison Appling, Anuj Karpatne
Long-term change in metabolism phenology in north temperate lakes Long-term change in metabolism phenology in north temperate lakes
The phenology of dissolved oxygen (DO) dynamics and metabolism in north temperate lakes offers a basis for comparing metabolic cycles over multi-year time scales. Although proximal control over lake DO can be attributed to metabolism and physical processes, how those processes evolve over decades largely remains unexplored. Metabolism phenology may reveal the importance of coherence...
Authors
Robert Ladwig, Alison Appling, Austin Delany, Hilary Dugan, Qiantong Gao, Noah Lottig, Jemma Stachelek, Paul Hanson
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
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
Light and flow regimes regulate the metabolism of rivers Light and flow regimes regulate the metabolism of rivers
Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial...
Authors
Emily. Bernhardt, Philip Savoy, Michael Vlah, Alison Appling, Lauren E Koenig, Robert O Hall Jr., Maite Arroita, Joanna Blaszczak, Alice Carter, Matthew Cohen, Judson Harvey, James Heffernan, Ashley Helton, J.D. Hosen, Lily Kirk, William McDowell, Emily Stanley, Charles Yackulic, Nancy Grimm
Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory
Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from 0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting...
Authors
Kayla Glossner, Kathleen Lohse, Alison Appling, Zane Cram, Erin Murray, Sarah Godsey, Steve Van Vactor, Emma McCorkle, Mark Seyfried, Frederick B Pierson
Non-USGS Publications**
J. S. Read, J. I. Walker, A. P. Appling, D. L. Blodgett, E. K. Read, and L. A. Winslow. 2015. geoknife: reproducible web-processing of large gridded datasets. Ecography. https://doi.org/10.1111/ecog.01880
S.A. Sistla, A. P. Appling, A. M. Lewandowska, B. N. Taylor, and A. A. Wolf. 2015. Stoichiometric flexibility in response to fertilization along gradients of environmental and organismal nutrient richness. Oikos, 124: 949-959. https://doi.org/10.1111/oik.02385
A. P. Appling, M. C. Leon, and W. H. McDowell. 2015. Reducing bias and quantifying uncertainty in watershed flux estimates: The R package loadflex. Ecosphere 6(12):269. https://doi.org/10.1890/ES14-00517.1
A. P. Appling and J. B. Heffernan. 2014. Nutrient limitation and physiology mediate the fine-scale [de]coupling of biogeochemical cycles. The American Naturalist 184:384-406. https://doi.org/10.1086/677282
A. P. Appling, E. S. Bernhardt, and J. A. Stanford. 2014. Floodplain biogeochemical mosaics: a multidimensional view of alluvial soils. Journal of Geophysical Research: Biogeosciences 119:2013JG002543. https://doi.org/10.1002/2013JG002543
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Filter Total Items: 21
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
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...
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...
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...
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...
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...
Filter Total Items: 37
Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling
This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling...
Authors
Arka Daw, R. Thomas, Cayelan Carey, Jordan Read, Alison Appling, Anuj Karpatne
Long-term change in metabolism phenology in north temperate lakes Long-term change in metabolism phenology in north temperate lakes
The phenology of dissolved oxygen (DO) dynamics and metabolism in north temperate lakes offers a basis for comparing metabolic cycles over multi-year time scales. Although proximal control over lake DO can be attributed to metabolism and physical processes, how those processes evolve over decades largely remains unexplored. Metabolism phenology may reveal the importance of coherence...
Authors
Robert Ladwig, Alison Appling, Austin Delany, Hilary Dugan, Qiantong Gao, Noah Lottig, Jemma Stachelek, Paul Hanson
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
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
Light and flow regimes regulate the metabolism of rivers Light and flow regimes regulate the metabolism of rivers
Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial...
Authors
Emily. Bernhardt, Philip Savoy, Michael Vlah, Alison Appling, Lauren E Koenig, Robert O Hall Jr., Maite Arroita, Joanna Blaszczak, Alice Carter, Matthew Cohen, Judson Harvey, James Heffernan, Ashley Helton, J.D. Hosen, Lily Kirk, William McDowell, Emily Stanley, Charles Yackulic, Nancy Grimm
Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory
Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from 0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting...
Authors
Kayla Glossner, Kathleen Lohse, Alison Appling, Zane Cram, Erin Murray, Sarah Godsey, Steve Van Vactor, Emma McCorkle, Mark Seyfried, Frederick B Pierson
Non-USGS Publications**
J. S. Read, J. I. Walker, A. P. Appling, D. L. Blodgett, E. K. Read, and L. A. Winslow. 2015. geoknife: reproducible web-processing of large gridded datasets. Ecography. https://doi.org/10.1111/ecog.01880
S.A. Sistla, A. P. Appling, A. M. Lewandowska, B. N. Taylor, and A. A. Wolf. 2015. Stoichiometric flexibility in response to fertilization along gradients of environmental and organismal nutrient richness. Oikos, 124: 949-959. https://doi.org/10.1111/oik.02385
A. P. Appling, M. C. Leon, and W. H. McDowell. 2015. Reducing bias and quantifying uncertainty in watershed flux estimates: The R package loadflex. Ecosphere 6(12):269. https://doi.org/10.1890/ES14-00517.1
A. P. Appling and J. B. Heffernan. 2014. Nutrient limitation and physiology mediate the fine-scale [de]coupling of biogeochemical cycles. The American Naturalist 184:384-406. https://doi.org/10.1086/677282
A. P. Appling, E. S. Bernhardt, and J. A. Stanford. 2014. Floodplain biogeochemical mosaics: a multidimensional view of alluvial soils. Journal of Geophysical Research: Biogeosciences 119:2013JG002543. https://doi.org/10.1002/2013JG002543
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.