Timothy O Hodson, Ph.D.
Timothy Hodson is a Hydrologist with the USGS Water Resources Mission Area.
Timothy Hodson earned his BS in Geology and Geophysics from the University of Wisconsin-Madison and his MS and PhD in Geology from Northern Illinois University. He is a hydrologist with the USGS Water Resources Mission Area in Urbana, Illinois, where his research applies probabilistic modeling, cloud computing, and information theory to study water resources.
Education and Certifications
Ph.D., Geology, Northern Illinois University
B.S., Geology and Geophysics, University of Wisconsin – Madison
Science and Products
Filter Total Items: 14
Modeled Nutrient and Sediment Concentrations from Major Rivers in Illinois Based on Continuous Monitoring from October 1, 2015, through September 30, 2022
During water years 2016–2022 (a water year begins on October 1 and ends on September 30), the U.S. Geological Survey (USGS), in cooperation with the Illinois Environmental Protection Agency (Illinois EPA), operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This data
Annual Nutrient Loads at Illinois EPA Ambient Water Quality Monitoring Network Sites, Water Years 1976–2021
This data release contains estimates of annual nitrogen and phosphorus loads, as well as loads of other constituents, from sites in the Illinois Environmental Protection Agency (Illinois EPA) Ambient Water Quality Monitoring Network. The loads were estimated using Weighted Regressions on Time, Discharge, and Season with Kalman filtering (WRTDS-K) and existing discrete water-quality data and discha
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations (ver. 2.0, December 2022)
This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Water Model Retrospective (v2.1) at streamflow benchmark locations (version 1.0) as defined by Foks and others (2022). Modeled hourly timesteps were converted to mean daily timesteps. Model error was determined by evaluating predicted daily mean streamflow versus
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations in the conterminous Un
This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations defined by Foks and others (2022). Model error was determined by eval
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating pr
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflo
Estimated nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 2021
During water years 2016-2021 (a water year begins on October 1 and ends on September 30 and is designated by the year in which it ends), the U.S. Geological Survey (USGS), in cooperation with the Illinois Environmental Protection Agency (Illinois EPA), operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State o
Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages)
A list of stream gages within the conterminous United States that will serve as the initial list of sites (version 1.0) used for streamflow benchmarking of hydrologic models. Sites within this list were chosen based on their presence in the GAGES-II dataset, their availability of modeled streamflow data from the most recent version of the National Hydrologic Model application of Precipitation-Runo
Modeled nutrient and sediment concentrations from the Des Plaines River at Route 53 at Joliet, Illinois, based on continuous monitoring from October 1, 2017, through September 30, 2020
During the 2018-20 water years, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, operated a continuous monitoring station on the Des Plaines River at Route 53 at Joliet, Illinois (USGS station 05537980), to better quantify nutrient and sediment loadings from the Greater Chicago Area to the Illinois River. This data release presents est
Mean squared logarithmic error in daily mean streamflow predictions at GAGES-II reference streamgages
This data release contains daily mean squared logarithmic error (MSLE), as well as several decompositions of the MSLE, for three streamflow models: nearest-neighbor drainage area ratio (NNDAR), a simple statistical model that re-scales streamflow data from the nearest streamgage; the version 3.0 calibration of the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Mod
WRTDS-K nutrient and sediment loads at Illinois EPA Ambient Water Quality Monitoring Network sites, from WY 1978 through 2020
This data release contains estimates of annual nitrate, suspended sediment, phosphorus, and chloride loads and uncertainty from sites in the Illinois Environmental Protection Agency (Illinois EPA) Ambient Water Quality Monitoring Network. The loads were estimated using Weighted Regressions on Time, Discharge, and Season with Kalman filtering (WRTDS-K) and existing discrete water-quality data and d
Modeled nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 2020
During water years 2016-2020, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This data release presents estimates of daily nitrate, suspended sediment, and phosphorus co
Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska
Seafloor geology and potential benthic habitats were mapped in West Arm, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, groundtruthed observations, and geological interpretations. The West Arm of Glacier Bay is a recently deglaciated fjord system under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope inst
Evaluating hydrologic model performance for characterizing streamflow drought in the conterminous United States
Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought
Authors
Caelan Simeone, Sydney Foks, Erin Towler, Timothy O. Hodson, Thomas M. Over
How to select an objective function using information theory
In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should pr
Authors
Timothy O. Hodson, Thomas M. Over, Smith Tyler, Lucy A. Marshall
Ratingcurve: A Python package for fitting streamflow rating curves
Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manual
Authors
Timothy O. Hodson, Keith James Doore, Terry A. Kenney, Thomas M. Over, Muluken Yeheyis
Nitrate-nitrogen and total phosphorus river loads
Nitrate-nitrogen and total phosphorus loads from the major rivers draining Illinois were updated through the 2021 water year (Figure 3.1). Beginning with the 2023 biennial update to the Illinois NLRS, nutrient loads were estimated using data from the U.S. Geological Survey continuous monitoring stations rather than the original Illinois EPA monitoring stations. To maintain consistency with previou
Authors
Timothy O. Hodson
Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data
Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval r
Authors
Timothy O. Hodson, Laura A. DeCicco, Jayaram Athreya Hariharan, Lee Stanish, Scott Black, J. S. Horsburgh
Spatial and temporal variations in phosphorus loads in the Illinois River Basin, Illinois USA
Total phosphorus (TP) loads in many rivers in the north-central United States have increased, including the Illinois River at Valley City, Illinois, USA, which increased 39% from the periods 1989–1996 to 2015–2019 despite efforts to reduce loads from point and nonpoint sources. Here, we quantify long-term variations in phosphorus (P) loads in the Illinois River and its tributaries and identify fac
Authors
Gregory F. McIsaac, Timothy O. Hodson, Momvcilo Markus, Rabin Bhattarai, Daniel Chulgi Kim
Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give argu
Authors
Timothy O. Hodson
Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20
The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and s
Authors
Colin S. Peake, Timothy O. Hodson
Mean squared error, deconstructed
As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakne
Authors
Timothy O. Hodson, Thomas M. Over, Sydney Foks
Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020
The State of Illinois is one of the leading contributors of nitrogen, phosphorus, and suspended sediment to the Mississippi River and the Gulf of Mexico. During water years 2016–20, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight major rivers in Illinois to better quantify nutrient and sediment loadings
Authors
Timothy O. Hodson, Paul J. Terrio, Colin S. Peake, David J. Fazio
Trends in nutrient and soil loss in Illinois rivers, 1978–2017
Nutrient and soil loss, defined herein as the loss of nutrients or soil to streams and other downstream receiving waters, affect watersheds around the globe. Although governments make large investments mitigating nutrient and soil loss through watershed management efforts, the efficacy of these efforts is often difficult to assess, in part because streamflow variability obscures the effects.This s
Authors
Timothy O. Hodson, Paul J. Terrio
Southern Salish Sea Habitat Map Series: Admiralty Inlet
In 2010 the Environmental Protection Agency, Region 10 initiated the Puget Sound Scientific Studies and Technical Investigations Assistance Program, designed to support research in support of implementing the Puget Sound Action Agenda. The Action Agenda was created in response to Puget Sound having been designated as one of 28 estuaries of national significance under section 320 of the U.S. Clean
Authors
Guy R. Cochrane, Megan N. Dethier, Timothy O. Hodson, Kristine K. Kull, Nadine E. Golden, Andrew C. Ritchie, Crescent Moegling, Robert E. Pacunski
Non-USGS Publications**
Physical processes in Subglacial Lake Whillans, West Antarctica: inferences from sediment cores
TO Hodson, RD Powell, SA Brachfeld, S Tulaczyk, RP Scherer, WS Team
Earth and Planetary Science Letters 444, 56-63
TO Hodson, RD Powell, SA Brachfeld, S Tulaczyk, RP Scherer, WS Team
Earth and Planetary Science Letters 444, 56-63
**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: 14
Modeled Nutrient and Sediment Concentrations from Major Rivers in Illinois Based on Continuous Monitoring from October 1, 2015, through September 30, 2022
During water years 2016–2022 (a water year begins on October 1 and ends on September 30), the U.S. Geological Survey (USGS), in cooperation with the Illinois Environmental Protection Agency (Illinois EPA), operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This data
Annual Nutrient Loads at Illinois EPA Ambient Water Quality Monitoring Network Sites, Water Years 1976–2021
This data release contains estimates of annual nitrogen and phosphorus loads, as well as loads of other constituents, from sites in the Illinois Environmental Protection Agency (Illinois EPA) Ambient Water Quality Monitoring Network. The loads were estimated using Weighted Regressions on Time, Discharge, and Season with Kalman filtering (WRTDS-K) and existing discrete water-quality data and discha
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations (ver. 2.0, December 2022)
This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Water Model Retrospective (v2.1) at streamflow benchmark locations (version 1.0) as defined by Foks and others (2022). Modeled hourly timesteps were converted to mean daily timesteps. Model error was determined by evaluating predicted daily mean streamflow versus
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations in the conterminous Un
This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations defined by Foks and others (2022). Model error was determined by eval
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating pr
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflo
Estimated nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 2021
During water years 2016-2021 (a water year begins on October 1 and ends on September 30 and is designated by the year in which it ends), the U.S. Geological Survey (USGS), in cooperation with the Illinois Environmental Protection Agency (Illinois EPA), operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State o
Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages)
A list of stream gages within the conterminous United States that will serve as the initial list of sites (version 1.0) used for streamflow benchmarking of hydrologic models. Sites within this list were chosen based on their presence in the GAGES-II dataset, their availability of modeled streamflow data from the most recent version of the National Hydrologic Model application of Precipitation-Runo
Modeled nutrient and sediment concentrations from the Des Plaines River at Route 53 at Joliet, Illinois, based on continuous monitoring from October 1, 2017, through September 30, 2020
During the 2018-20 water years, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, operated a continuous monitoring station on the Des Plaines River at Route 53 at Joliet, Illinois (USGS station 05537980), to better quantify nutrient and sediment loadings from the Greater Chicago Area to the Illinois River. This data release presents est
Mean squared logarithmic error in daily mean streamflow predictions at GAGES-II reference streamgages
This data release contains daily mean squared logarithmic error (MSLE), as well as several decompositions of the MSLE, for three streamflow models: nearest-neighbor drainage area ratio (NNDAR), a simple statistical model that re-scales streamflow data from the nearest streamgage; the version 3.0 calibration of the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Mod
WRTDS-K nutrient and sediment loads at Illinois EPA Ambient Water Quality Monitoring Network sites, from WY 1978 through 2020
This data release contains estimates of annual nitrate, suspended sediment, phosphorus, and chloride loads and uncertainty from sites in the Illinois Environmental Protection Agency (Illinois EPA) Ambient Water Quality Monitoring Network. The loads were estimated using Weighted Regressions on Time, Discharge, and Season with Kalman filtering (WRTDS-K) and existing discrete water-quality data and d
Modeled nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 2020
During water years 2016-2020, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight of the major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This data release presents estimates of daily nitrate, suspended sediment, and phosphorus co
Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska
Seafloor geology and potential benthic habitats were mapped in West Arm, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, groundtruthed observations, and geological interpretations. The West Arm of Glacier Bay is a recently deglaciated fjord system under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope inst
Evaluating hydrologic model performance for characterizing streamflow drought in the conterminous United States
Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought
Authors
Caelan Simeone, Sydney Foks, Erin Towler, Timothy O. Hodson, Thomas M. Over
How to select an objective function using information theory
In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should pr
Authors
Timothy O. Hodson, Thomas M. Over, Smith Tyler, Lucy A. Marshall
Ratingcurve: A Python package for fitting streamflow rating curves
Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manual
Authors
Timothy O. Hodson, Keith James Doore, Terry A. Kenney, Thomas M. Over, Muluken Yeheyis
Nitrate-nitrogen and total phosphorus river loads
Nitrate-nitrogen and total phosphorus loads from the major rivers draining Illinois were updated through the 2021 water year (Figure 3.1). Beginning with the 2023 biennial update to the Illinois NLRS, nutrient loads were estimated using data from the U.S. Geological Survey continuous monitoring stations rather than the original Illinois EPA monitoring stations. To maintain consistency with previou
Authors
Timothy O. Hodson
Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data
Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval r
Authors
Timothy O. Hodson, Laura A. DeCicco, Jayaram Athreya Hariharan, Lee Stanish, Scott Black, J. S. Horsburgh
Spatial and temporal variations in phosphorus loads in the Illinois River Basin, Illinois USA
Total phosphorus (TP) loads in many rivers in the north-central United States have increased, including the Illinois River at Valley City, Illinois, USA, which increased 39% from the periods 1989–1996 to 2015–2019 despite efforts to reduce loads from point and nonpoint sources. Here, we quantify long-term variations in phosphorus (P) loads in the Illinois River and its tributaries and identify fac
Authors
Gregory F. McIsaac, Timothy O. Hodson, Momvcilo Markus, Rabin Bhattarai, Daniel Chulgi Kim
Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give argu
Authors
Timothy O. Hodson
Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20
The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and s
Authors
Colin S. Peake, Timothy O. Hodson
Mean squared error, deconstructed
As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakne
Authors
Timothy O. Hodson, Thomas M. Over, Sydney Foks
Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020
The State of Illinois is one of the leading contributors of nitrogen, phosphorus, and suspended sediment to the Mississippi River and the Gulf of Mexico. During water years 2016–20, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight major rivers in Illinois to better quantify nutrient and sediment loadings
Authors
Timothy O. Hodson, Paul J. Terrio, Colin S. Peake, David J. Fazio
Trends in nutrient and soil loss in Illinois rivers, 1978–2017
Nutrient and soil loss, defined herein as the loss of nutrients or soil to streams and other downstream receiving waters, affect watersheds around the globe. Although governments make large investments mitigating nutrient and soil loss through watershed management efforts, the efficacy of these efforts is often difficult to assess, in part because streamflow variability obscures the effects.This s
Authors
Timothy O. Hodson, Paul J. Terrio
Southern Salish Sea Habitat Map Series: Admiralty Inlet
In 2010 the Environmental Protection Agency, Region 10 initiated the Puget Sound Scientific Studies and Technical Investigations Assistance Program, designed to support research in support of implementing the Puget Sound Action Agenda. The Action Agenda was created in response to Puget Sound having been designated as one of 28 estuaries of national significance under section 320 of the U.S. Clean
Authors
Guy R. Cochrane, Megan N. Dethier, Timothy O. Hodson, Kristine K. Kull, Nadine E. Golden, Andrew C. Ritchie, Crescent Moegling, Robert E. Pacunski
Non-USGS Publications**
Physical processes in Subglacial Lake Whillans, West Antarctica: inferences from sediment cores
TO Hodson, RD Powell, SA Brachfeld, S Tulaczyk, RP Scherer, WS Team
Earth and Planetary Science Letters 444, 56-63
TO Hodson, RD Powell, SA Brachfeld, S Tulaczyk, RP Scherer, WS Team
Earth and Planetary Science Letters 444, 56-63
**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.