Alison Appling, PhD, is a Data Scientist for the USGS Water Resources Mission Area.
I am passionate about the process of transforming data into understanding. I participate in that process in two ways: As an ecologist and biogeochemist, I study the movement of energy, carbon, and nutrients through rivers, lakes, and floodplains to better understand how those ecosystems function. As a science communicator, I build interactive web-based data visualizations that tell a broader public audience about USGS research and activities.
What these data-rich projects have in common is that they require a lot of creativity, expertise, and collaboration...but also a lot of grunt work, because our datasets are often large and messy. My focus as a data scientist is on minimizing the grunt work in the data transformation process. To do this, I find existing software tools that support efficient data pipelines, build new tools when needed, and identify common patterns that help scientists to use those tools effectively. I share these tools and ideas primarily through collaboration with research and visualization teams, and occasionally via formal training efforts.
Science and Products
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
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
Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?
The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and pred
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
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
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
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
Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes
Although seasonal patterns of ecosystem productivity have been extensively described and analyzed with respect to their primary forcings in terrestrial and marine systems, comparatively little is known about these same processes in rivers. However, it is now possible to perform a large‐scale synthesis on the patterns and drivers of river productivity regimes because of the recent sensor advances a
Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams
Drought is common in rivers, yet how this disturbance regulates metabolic activity across network scales is largely unknown. Drought often lowers gross primary production (GPP) and ecosystem respiration (ER) in small headwaters but by contrast can enhance GPP and cause algal blooms in downstream estuaries. We estimated ecosystem metabolism across a nested network of 13 reaches from headwaters to t
The metabolic regimes of 356 rivers in the United States
A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practic
Overcoming equifinality: Leveraging long time series for stream metabolism estimation
The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O2) concentrations. Because rivers and streams constantly exchange O2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K600) or solve for all
The metabolic regimes of flowing waters
The processes and biomass that characterize any ecosystem are fundamentally constrained by the total amount of energy that is either fixed within or delivered across its boundaries. Ultimately, ecosystems may be understood and classified by their rates of total and net productivity and by the seasonal patterns of photosynthesis and respiration. Such understanding is well developed for terrestrial
Pre-USGS Publications
L. A. Winslow, S. Chamberlain, A. P. Appling, and J. S. Read. 2016. sbtools: A Package Connecting R to Cloud-based Data for Collaborative Online Research. The R Journal, 8(1).
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
Science and Products
- Data
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 areExploring 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
Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?
The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predExploring 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 architeApplication 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 fateProcess-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 frameworkDetecting 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 atMetabolic rhythms in flowing waters: An approach for classifying river productivity regimes
Although seasonal patterns of ecosystem productivity have been extensively described and analyzed with respect to their primary forcings in terrestrial and marine systems, comparatively little is known about these same processes in rivers. However, it is now possible to perform a large‐scale synthesis on the patterns and drivers of river productivity regimes because of the recent sensor advances aEnhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams
Drought is common in rivers, yet how this disturbance regulates metabolic activity across network scales is largely unknown. Drought often lowers gross primary production (GPP) and ecosystem respiration (ER) in small headwaters but by contrast can enhance GPP and cause algal blooms in downstream estuaries. We estimated ecosystem metabolism across a nested network of 13 reaches from headwaters to tThe metabolic regimes of 356 rivers in the United States
A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practicOvercoming equifinality: Leveraging long time series for stream metabolism estimation
The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O2) concentrations. Because rivers and streams constantly exchange O2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K600) or solve for allThe metabolic regimes of flowing waters
The processes and biomass that characterize any ecosystem are fundamentally constrained by the total amount of energy that is either fixed within or delivered across its boundaries. Ultimately, ecosystems may be understood and classified by their rates of total and net productivity and by the seasonal patterns of photosynthesis and respiration. Such understanding is well developed for terrestrialPre-USGS Publications
L. A. Winslow, S. Chamberlain, A. P. Appling, and J. S. Read. 2016. sbtools: A Package Connecting R to Cloud-based Data for Collaborative Online Research. The R Journal, 8(1).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