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 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...
Annual Nutrient Loads at Illinois EPA Ambient Water Quality Monitoring Network Sites, Water Years 1976–2021 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...
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) 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...
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 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...
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 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...
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations 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...
Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska 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...
Filter Total Items: 13
Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States
Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these...
Authors
Rafael Daniel Vias, Birgir Hrafnkelsson, Timothy O. Hodson, Sölvi Rögnvaldsson, Axel Örn Jansson, Sigurdur M. Gardarsson
Evaluating hydrologic model performance for characterizing streamflow drought in the conterminous United States 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...
Authors
Caelan Simeone, Sydney Foks, Erin Towler, Timothy O. Hodson, Thomas M. Over
How to select an objective function using information theory 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...
Authors
Timothy O. Hodson, Thomas M. Over, Smith Tyler, Lucy A. Marshall
Ratingcurve: A Python package for fitting streamflow rating curves 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...
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 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...
Authors
Timothy O. Hodson
Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data 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...
Authors
Timothy O. Hodson, Laura A. DeCicco, Jayaram Athreya Hariharan, Lee Stanish, Scott Black, J. S. Horsburgh
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 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...
Annual Nutrient Loads at Illinois EPA Ambient Water Quality Monitoring Network Sites, Water Years 1976–2021 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...
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) 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...
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 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...
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 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...
Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations 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...
Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska 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...
Filter Total Items: 13
Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States
Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these...
Authors
Rafael Daniel Vias, Birgir Hrafnkelsson, Timothy O. Hodson, Sölvi Rögnvaldsson, Axel Örn Jansson, Sigurdur M. Gardarsson
Evaluating hydrologic model performance for characterizing streamflow drought in the conterminous United States 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...
Authors
Caelan Simeone, Sydney Foks, Erin Towler, Timothy O. Hodson, Thomas M. Over
How to select an objective function using information theory 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...
Authors
Timothy O. Hodson, Thomas M. Over, Smith Tyler, Lucy A. Marshall
Ratingcurve: A Python package for fitting streamflow rating curves 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...
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 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...
Authors
Timothy O. Hodson
Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data 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...
Authors
Timothy O. Hodson, Laura A. DeCicco, Jayaram Athreya Hariharan, Lee Stanish, Scott Black, J. S. Horsburgh
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.