Jordan S Read, PhD (Former Employee)
Science and Products
Filter Total Items: 15
Metabolism estimates for 356 U.S. rivers (2007-2017) 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...
Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast
Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use...
Projected shifts in fish species dominance in Wisconsin lakes under climate change Projected shifts in fish species dominance in Wisconsin lakes under climate change
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of...
Filter Total Items: 59
Machine learning for understanding inland water quantity, quality, and ecology 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...
Authors
Alison P. Appling, Samantha K. Oliver, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart
Final report: Understanding historical and predicting future lake temperatures in North and South Dakota Final report: Understanding historical and predicting future lake temperatures in North and South Dakota
Lakes, reservoirs, and ponds are central and integral features of the landscape of the North Central US. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. Lakes are warming, resulting in the loss of many native fish. In order to manage economically valuable fisheries and other ecosystem services provided by lakes, it is...
Authors
Jordan Read
Physics-guided machine learning from simulation data: An application in modeling lake and river systems 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...
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
LakeEnsemblR: An R package that facilitates ensemble modelling of lakes LakeEnsemblR: An R package that facilitates ensemble modelling of lakes
Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running...
Authors
Tadhg N. Moore, Jorrit P. Mesman, Robert Ladwig, Johannes Feldbauer, Freya Olsson, Rachel M. Pilla, Tom Shatwell, Jason J. Venkiteswaran, Austin D. Delany, Hilary Dugan, Kevin C. Rose, Jordan Read
Physics-guided recurrent graph model for predicting flow and temperature in river networks 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...
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
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in...
Authors
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, Vipin Kumar
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning 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)...
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 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...
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Heterogeneous stream-reservoir graph networks with data assimilation 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...
Authors
Shengyu Chen, Alison P. Appling, Samantha K. Oliver, Hayley R. Corson-Dosch, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart, Xiaowei Jia
Ecological forecasting—21st century science for 21st century management Ecological forecasting—21st century science for 21st century management
Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the...
Authors
John B. Bradford, Jake Weltzin, Molly L. McCormick, Jill Baron, Zack Bowen, Sky Bristol, Daren M. Carlisle, Theresa Crimmins, Paul C. Cross, Joe DeVivo, Mike Dietze, Mary Freeman, Jason Goldberg, Mevin Hooten, Leslie Hsu, Karen Jenni, Jennifer L. Keisman, Jonathan G. Kennen, Kathy Lee, David P. Lesmes, Keith A. Loftin, Brian W. Miller, Peter S. Murdoch, Jana Newman, Karen L. Prentice, Imtiaz Rangwala, Jordan Read, Jennifer Sieracki, Helen Sofaer, Steve Thur, Gordon Toevs, Francisco Werner, C. LeAnn White, Timothy White, Mark T. Wiltermuth
By
Ecosystems Mission Area, Water Resources Mission Area, Science Synthesis, Analysis, and Research Program, Contaminant Biology, Environmental Health Program, Science Analytics and Synthesis (SAS) Program, Central Plains Water Science Center, Eastern Ecological Science Center, Fort Collins Science Center, Maryland-Delaware-D.C. Water Science Center, National Wildlife Health Center, New Jersey Water Science Center, Pacific Island Ecosystems Research Center, Southwest Biological Science Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center
Process-guided deep learning predictions of lake water temperature 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...
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
Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate
Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad-scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake-specific and regional primary productivity and carbon fluxes...
Authors
Jacob Aaron Zwart, Zachary J Hanson, Jordan Read, Michael N. Fienen, Alan F. Hamlet, Diogo Bolster, Stuart E. Jones
Non-USGS Publications**
Watras CJ, M Morrow, K Morrison, S Scannell, S Yaziciaglu, JS Read, YH Hu, PC Hanson, TK Kratz. 2013. Evaluation of wireless sensor networks (WSNs) for remote wetland monitoring: Design and initial results. Environmental Monitoring and Assessment. doi:10.1007/s10661-013-3424-8
Read JS, KC Rose. 2013. Physical responses of small temperate lakes to variation in dissolved organic carbon concentrations. Limnology and Oceanography. 58: 921-931. doi:10.4319/lo.2013.58.3.0921[Link]
Youngblut ND, A Shade, JS Read, KD McMahon, RJ Whitaker. 2013. Lineage-specific responses to environmental change in microbial communities. Applied and Environmental Microbiology. 79: 39-47. doi:10.1128/AEM.02226-12
Read JS. Physical processes in small temperate lakes. 2012. PhD Dissertation, University of Wisconsin-Madison
Samal NR, DC Pierson, E Schneiderman, Y Huang, JS Read, A Anandhi, EM Owens. 2012. Impact of climate change on Cannonsville reservoir thermal structure in the New York City Water Supply. Water Quality Research Journal of Canada. 47: 389-405
Staehr PA, JPA Christensen, RD Batt, JS Read. 2012. Ecosystem metabolism in a stratified lake. Limnology and Oceanography. 57: 1317-133
Shade A, JS Read, ND Youngblut, N Fierer, R Knight, TK Kratz, NR Lottig, EE Roden, EH Stanley, J Stombaugh, RJ Whitaker, CH Wu, KD McMahon. 2012. Microbial communities are resilient after a whole-ecosystem disturbance. The ISME Journal. 6: 2153-2167. doi:10.1038/ismej.2012.66
Read JS, DP Hamilton, AR Desai, KC Rose, S MacIntyre, JD Lenters, R Smyth, PC Hanson, JJ Cole, PA Staehr, JA Rusak, DC Pierson, JD Brookes, A Laas, CH Wu. 2012. Lake-size dependency of wind shear and convection as controls on gas exchange. Geophysical Research Letters. 39: L09405. doi:10.1029/2012GL051886
Gaeta JW, JS Read, JF Kitchell, SR Carpenter. 2012. Eradication via destratification: Whole-lake mixing to selectively remove rainbow smelt, a cold-water invasive species. Ecological Applications. 22: 817-827
Kara EL, PC Hanson, DP Hamilton, M Hipsey, KD McMahon, JS Read, LA Winslow, J Dedrick, KC Rose, CC Carey, S Bertilsson, D Motta-Marques, L Beversdorf, T Miller, CH Wu, YF Hsieh, E Gaiser, TK Kratz. 2012. Time-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake from scales of hours to months. Environmental Modelling and Software. 35: 104-121
Read JS, DP Hamilton, ID Jones, K Muraoka, LA Winslow, R Kroiss, CH Wu, E Gaiser. 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling and Software. 26: 1325-1336
Shade A, JS Read, D Welkie, TK Kratz, CH Wu, KD McMahon. 2011. Resistance, resilience, and recovery: Aquatic bacterial dynamics after water column disturbance. Environmental Microbiology. 13: 2752-2767
Read JS, A Shade, CH Wu, A Gorzalski, KD McMahon. 2011. "Gradual Entrainment Lake Inverter" (GELI): A novel device for experimental lake mixing. Limnology and Oceanography: Methods. 9:14-25
**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: 15
Metabolism estimates for 356 U.S. rivers (2007-2017) 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...
Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast
Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use...
Projected shifts in fish species dominance in Wisconsin lakes under climate change Projected shifts in fish species dominance in Wisconsin lakes under climate change
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of...
Filter Total Items: 59
Machine learning for understanding inland water quantity, quality, and ecology 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...
Authors
Alison P. Appling, Samantha K. Oliver, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart
Final report: Understanding historical and predicting future lake temperatures in North and South Dakota Final report: Understanding historical and predicting future lake temperatures in North and South Dakota
Lakes, reservoirs, and ponds are central and integral features of the landscape of the North Central US. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. Lakes are warming, resulting in the loss of many native fish. In order to manage economically valuable fisheries and other ecosystem services provided by lakes, it is...
Authors
Jordan Read
Physics-guided machine learning from simulation data: An application in modeling lake and river systems 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...
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
LakeEnsemblR: An R package that facilitates ensemble modelling of lakes LakeEnsemblR: An R package that facilitates ensemble modelling of lakes
Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running...
Authors
Tadhg N. Moore, Jorrit P. Mesman, Robert Ladwig, Johannes Feldbauer, Freya Olsson, Rachel M. Pilla, Tom Shatwell, Jason J. Venkiteswaran, Austin D. Delany, Hilary Dugan, Kevin C. Rose, Jordan Read
Physics-guided recurrent graph model for predicting flow and temperature in river networks 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...
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
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in...
Authors
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Aaron Zwart, Michael Steinbach, Vipin Kumar
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning 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)...
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 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...
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Heterogeneous stream-reservoir graph networks with data assimilation 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...
Authors
Shengyu Chen, Alison P. Appling, Samantha K. Oliver, Hayley R. Corson-Dosch, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart, Xiaowei Jia
Ecological forecasting—21st century science for 21st century management Ecological forecasting—21st century science for 21st century management
Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the...
Authors
John B. Bradford, Jake Weltzin, Molly L. McCormick, Jill Baron, Zack Bowen, Sky Bristol, Daren M. Carlisle, Theresa Crimmins, Paul C. Cross, Joe DeVivo, Mike Dietze, Mary Freeman, Jason Goldberg, Mevin Hooten, Leslie Hsu, Karen Jenni, Jennifer L. Keisman, Jonathan G. Kennen, Kathy Lee, David P. Lesmes, Keith A. Loftin, Brian W. Miller, Peter S. Murdoch, Jana Newman, Karen L. Prentice, Imtiaz Rangwala, Jordan Read, Jennifer Sieracki, Helen Sofaer, Steve Thur, Gordon Toevs, Francisco Werner, C. LeAnn White, Timothy White, Mark T. Wiltermuth
By
Ecosystems Mission Area, Water Resources Mission Area, Science Synthesis, Analysis, and Research Program, Contaminant Biology, Environmental Health Program, Science Analytics and Synthesis (SAS) Program, Central Plains Water Science Center, Eastern Ecological Science Center, Fort Collins Science Center, Maryland-Delaware-D.C. Water Science Center, National Wildlife Health Center, New Jersey Water Science Center, Pacific Island Ecosystems Research Center, Southwest Biological Science Center, Upper Midwest Environmental Sciences Center, Upper Midwest Water Science Center
Process-guided deep learning predictions of lake water temperature 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...
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
Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate
Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad-scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake-specific and regional primary productivity and carbon fluxes...
Authors
Jacob Aaron Zwart, Zachary J Hanson, Jordan Read, Michael N. Fienen, Alan F. Hamlet, Diogo Bolster, Stuart E. Jones
Non-USGS Publications**
Watras CJ, M Morrow, K Morrison, S Scannell, S Yaziciaglu, JS Read, YH Hu, PC Hanson, TK Kratz. 2013. Evaluation of wireless sensor networks (WSNs) for remote wetland monitoring: Design and initial results. Environmental Monitoring and Assessment. doi:10.1007/s10661-013-3424-8
Read JS, KC Rose. 2013. Physical responses of small temperate lakes to variation in dissolved organic carbon concentrations. Limnology and Oceanography. 58: 921-931. doi:10.4319/lo.2013.58.3.0921[Link]
Youngblut ND, A Shade, JS Read, KD McMahon, RJ Whitaker. 2013. Lineage-specific responses to environmental change in microbial communities. Applied and Environmental Microbiology. 79: 39-47. doi:10.1128/AEM.02226-12
Read JS. Physical processes in small temperate lakes. 2012. PhD Dissertation, University of Wisconsin-Madison
Samal NR, DC Pierson, E Schneiderman, Y Huang, JS Read, A Anandhi, EM Owens. 2012. Impact of climate change on Cannonsville reservoir thermal structure in the New York City Water Supply. Water Quality Research Journal of Canada. 47: 389-405
Staehr PA, JPA Christensen, RD Batt, JS Read. 2012. Ecosystem metabolism in a stratified lake. Limnology and Oceanography. 57: 1317-133
Shade A, JS Read, ND Youngblut, N Fierer, R Knight, TK Kratz, NR Lottig, EE Roden, EH Stanley, J Stombaugh, RJ Whitaker, CH Wu, KD McMahon. 2012. Microbial communities are resilient after a whole-ecosystem disturbance. The ISME Journal. 6: 2153-2167. doi:10.1038/ismej.2012.66
Read JS, DP Hamilton, AR Desai, KC Rose, S MacIntyre, JD Lenters, R Smyth, PC Hanson, JJ Cole, PA Staehr, JA Rusak, DC Pierson, JD Brookes, A Laas, CH Wu. 2012. Lake-size dependency of wind shear and convection as controls on gas exchange. Geophysical Research Letters. 39: L09405. doi:10.1029/2012GL051886
Gaeta JW, JS Read, JF Kitchell, SR Carpenter. 2012. Eradication via destratification: Whole-lake mixing to selectively remove rainbow smelt, a cold-water invasive species. Ecological Applications. 22: 817-827
Kara EL, PC Hanson, DP Hamilton, M Hipsey, KD McMahon, JS Read, LA Winslow, J Dedrick, KC Rose, CC Carey, S Bertilsson, D Motta-Marques, L Beversdorf, T Miller, CH Wu, YF Hsieh, E Gaiser, TK Kratz. 2012. Time-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake from scales of hours to months. Environmental Modelling and Software. 35: 104-121
Read JS, DP Hamilton, ID Jones, K Muraoka, LA Winslow, R Kroiss, CH Wu, E Gaiser. 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling and Software. 26: 1325-1336
Shade A, JS Read, D Welkie, TK Kratz, CH Wu, KD McMahon. 2011. Resistance, resilience, and recovery: Aquatic bacterial dynamics after water column disturbance. Environmental Microbiology. 13: 2752-2767
Read JS, A Shade, CH Wu, A Gorzalski, KD McMahon. 2011. "Gradual Entrainment Lake Inverter" (GELI): A novel device for experimental lake mixing. Limnology and Oceanography: Methods. 9:14-25
**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.