Alison Appling, PhD
Alison Appling, Ph.D., (she/her) 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: 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, she seeks modeling advances that bring together scientific knowledge and data-driven models. “Process-guided deep learning” and “differentiable hydrology” are two approaches on which she collaborates.
As a data scientist, she conducts analyses in ways that are reproducible, efficient, and transparent, and she has developed tools and workflows to support others in these goals.
In her leadership roles, she facilitates fluid skill sharing within teams and communities of practice, challenges individuals to excel in their projects and careers, and coordinates across projects to realize the Water Mission Area’s vision of broadly reusable, integrated tools for predicting water quantity and quality across the nation.
Alison is based in State College, PA, and is a member of the Analysis and Prediction Branch in the Integrated Modeling and Prediction Division in the Water Mission Area. She 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.
Coursework in computer science, decision analysis, logic, linguistics, and psychology.
Science and Products
Filter Total Items: 16
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 data are
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
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted d
Metabolism estimates for 356 U.S. rivers (2007-2017)
This data release provides modeled estimates of gross primary productivity, ecosystem respiration, and gas exchange coefficients for 356 streams and rivers across the United States. The release also includes the model input data and alternative input data, model fit and diagnostic information, spatial data for the modeled sites (catchment boundaries and site point locations), and potential predict
Filter Total Items: 30
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
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
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored
Authors
Farshid Rahmani, Chaopeng Shen, Samantha K. Oliver, Kathryn Lawson, Alison P. Appling
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
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method,
Authors
Jared Willard, Jordan Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, 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
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning archite
Authors
Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison P. Appling, Samantha K. Oliver, Chaopeng Shen
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
Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate
Authors
Matthew P. Miller, Marcelo L de Souza, Richard B Alexander, Lillian Gorman Sanisaca, Alexandre de Amorim Teixeira, Alison P. Appling
Process-guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework
Authors
Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob Aaron Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J. A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar
Detecting signals of large‐scale climate phenomena in discharge and nutrient loads in the Mississippi‐Atchafalaya River Basin
Agricultural runoff from the Mississippi‐Atchafalaya River Basin delivers nitrogen (N) and phosphorus (P) to the Gulf of Mexico, causing hypoxia, and climate drives interannual variation in nutrient loads. Climate phenomena such as El Niño–Southern Oscillation may influence nutrient export through effects on river flow, nutrient uptake, or biogeochemical transformation, but landscape variation at
Authors
Adrianne P Smits, Claire M Ruffing, Todd V Royer, Alison P. Appling, Natalie A. Griffiths, Rebecca Bellmore, Mark D Scheuerell, Tamara K Harms, J. B. Jones
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
- Data
Filter Total Items: 16
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 data areData 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 dataExploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted dMetabolism estimates for 356 U.S. rivers (2007-2017)
This data release provides modeled estimates of gross primary productivity, ecosystem respiration, and gas exchange coefficients for 356 streams and rivers across the United States. The release also includes the model input data and alternative input data, model fit and diagnostic information, spatial data for the modeled sites (catchment boundaries and site point locations), and potential predict - Publications
Filter Total Items: 30
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 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 ReadDeep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitoredAuthorsFarshid Rahmani, Chaopeng Shen, Samantha K. Oliver, Kathryn Lawson, Alison P. ApplingPhysics-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 KumarPredicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method,AuthorsJared Willard, Jordan Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, 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 ReadExploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning architeAuthorsFarshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison P. Appling, Samantha K. Oliver, Chaopeng ShenHeterogeneous 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. JohnsonApplication of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fateAuthorsMatthew P. Miller, Marcelo L de Souza, Richard B Alexander, Lillian Gorman Sanisaca, Alexandre de Amorim Teixeira, Alison P. ApplingProcess-guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling frameworkAuthorsJordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob Aaron Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J. A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin KumarDetecting signals of large‐scale climate phenomena in discharge and nutrient loads in the Mississippi‐Atchafalaya River Basin
Agricultural runoff from the Mississippi‐Atchafalaya River Basin delivers nitrogen (N) and phosphorus (P) to the Gulf of Mexico, causing hypoxia, and climate drives interannual variation in nutrient loads. Climate phenomena such as El Niño–Southern Oscillation may influence nutrient export through effects on river flow, nutrient uptake, or biogeochemical transformation, but landscape variation atAuthorsAdrianne P Smits, Claire M Ruffing, Todd V Royer, Alison P. Appling, Natalie A. Griffiths, Rebecca Bellmore, Mark D Scheuerell, Tamara K Harms, J. B. JonesNon-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.01880S.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.02385A. 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/ES14-00517.1A. 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/10.1086/677282A. 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.