Thomas Over
Thomas Over, is a Research Hydrologist, with the Central Midwest Water Science Center, located in Urbana, Illinois.
Tom has worked for the USGS, Central Midwest Water Science Center (formerly the Illinois Water Science Center) since 2001 (originally part-time, full-time beginning 2012). He works in areas of prediction of peak and continuous streamflow in ungaged basins by statistical regionalization, hydrologic and hydraulic modeling (HSPF, SWMM, HEC-RAS, HEC-HMS, PEST), disaggregation and scaling of precipitation and streamflow, streamflow measurement uncertainty, hydrometeorological data analysis (evaluation of gage and radar-based precipitation observations and forecasts, development of homogeneous weather databases), and effects of urbanization on streamflow.
Prior to his full-time appointment with the USGS he was an assistant research professor at Eastern Illinois University (EIU) in the Geology/Geography Department, a visiting assistant professor at University of Illinois at Urbana-Champaign in the Civil and Environmental Engineering Department, and an assistant professor at Texas A&M University in the Civil Engineering Department, where he taught courses in water resources engineering, engineering hydrology, and stochastic hydrology. His research at EIU was in the area of controls of soil moisture and soil hydrophobicity on wind erosion. His Ph.D. is from University of Colorado - Boulder, Geophysics Program - Hydrology Option, where his dissertation was on multifractal space-time scaling properties of precipitation fields. While at CU-Boulder, he worked with Brent Troutman in the USGS (Water) National Research Program on the effects of river basin structure on streamflow. Prior to Ph.D. studies, he worked as a consulting civil engineer.
Professional Experience
2020 to Present, Research Hydrologist, Central Midwest Science Center
2012 to 2020, Hydrologist, Illinois, Illinois-Iowa, Central Midwest Water Science Center
2001 to 2012, Hydrologist (part-time), Illinois Water Science Center
2000 to 2012, Assistant Research Professor (part-time), Department of Geology / Geography, Eastern Illinois University
2000 to 2001, Visiting Assistant Professor, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
1996 to 2000, Assistant Professor, Department of Civil Engineering, Texas A&M University
1988 to 1995, Research Assistant, University of Colorado, Boulder
1984 to 1988, Consulting Civil Engineer, Nolte and Associates, San Jose, Calif.
Education and Certifications
Ph.D., Geophysics Program/Hydrology - University of Colorado-Boulder, 1995
M.S., CIvil and Environmental Engineering - Stanford University, 1984
S.B., Civil and Environmental Engineering - Massachusetts Institute of Technology, 1983
Science and Products
Filter Total Items: 16
Peak Streamflow Data, Climate Data, and Results from Investigating Hydroclimatic Trends and Climate Change Effects on Peak Streamflow in the Central United States, 1921–2020
Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annu
Data for Estimating Peak-Flow Quantiles for Selected Annual Exceedance Probabilities in Illinois
The U.S. Geological Survey Central Midwest Water Science Center completed a report (Over and others, 2023) documenting the methods, results, and applications of an updated flood-frequency study for the State of Illinois. This data release contains data related to the analysis completed to determine peak-flow quantiles (flood frequency estimates) at streamgages in Illinois for 50-, 20-, 10-, 4-, 2-
Models, Inputs, and Outputs for Estimating the Uncertainty of Discharge Simulations for the Lake Michigan Diversion Using the Hydrological Simulation Program - FORTRAN Model
This data release contains the models and their inputs and outputs needed to reproduce the findings for the publication by Soong and Over (2022), "Effect of Uncertainty of Discharge Data on Uncertainty of Discharge Simulation for the Lake Michigan Diversion, Northeastern Illinois and Northwestern Indiana." These data were developed in cooperation with the U.S. Army Corps of Engineers, Chicago Dist
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
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 and observed streamflow statistics at managed basins in the conterminous U.S. from October 1, 1983 through September 30, 2016.
This data release contains values of 29 streamflow statistics computed from modeled and observed daily streamflows from October 1, 1983, through September 30, 2016 at 1,257 streamgages in the 19 study regions defined by Falcone (2011) covering the conterminous United States. The streamflow statistics were computed at GAGES-II non-reference streamgages (Falcone, 2011), determined to be affected by
Statistical daily streamflow estimates at GAGES-II non-reference streamgages in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow at 5,439 GAGES-II non-reference streamgages in 19 study regions across the conterminous United States from October 1, 1980 through September 30, 2017, using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drainage area ratio (MCDAR), nearest-neighbor nonlinear spatial interpolati
Modeled and observed streamflow statistics at reference basins in the conterminous United States from October 1, 1983, through September 30, 2016
This data release contains 29 streamflow statistics computed from modeled and observed daily streamflows from October 1, 1983, through September 30, 2016 at 1,114 streamgages in 19 study regions covering the conterminous United States. The streamflow statistics were computed at selected GAGES-II reference streamgages (Falcone, 2011) from daily streamflow observations (Russell and others, 2020), fr
Cross-validation results for five statistical methods of daily streamflow estimation at 1,385 reference streamgages in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow for 1,385 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980, through September 30, 2017. These estimates are provided for gages from mostly undisturbed watersheds as defined by Falcone (2011), using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drain
Statistical daily streamflow estimates at HUC12 outlets in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow at the outlets of more than 80,000 12-digit hydrologic units in 19 study regions across the conterminous U.S. from October 1, 1980 through September 30, 2017, using three statistical techniques: Nearest-Neighbor Drainage Area Ratio (NNDAR), Map-Correlation Drainage Area Ratio (MCDAR), and Ordinary Kriging of the logarithm
Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2017
This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspira
Streamflow, flow-duration curves, basin characteristics, and regression models of flow-duration curves for selected streamgages in the conterminous United States
This data release contains the input used and the output files interpreted in the publication 'Refinement of a Regression-Based Method for Prediction of Flow-Duration Curves of Daily Streamflow in the Conterminous United States'. This data release contains daily streamflow data for 1,378 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980 through September 30, 2013 from m
Filter Total Items: 32
Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020
This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend perio
Authors
Mackenzie K. Marti, 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
Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is t
Authors
Karen R. Ryberg, Thomas M. Over, Sara B. Levin, David C. Heimann, Nancy A. Barth, Mackenzie K. Marti, Padraic S. O'Shea, Christopher A. Sanocki, Tara J. Williams-Sether, Harper N. Wavra, T. Roy Sando, Steven K. Sando, Milan S. Liu
The consequences of neglecting reservoir storage in national-scale hydrologic models: An appraisal of key streamflow statistics
A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process-based and three statistical-transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics repre
Authors
Glenn A. Hodgkins, Thomas M. Over, Robert W. Dudley, Amy M. Russell, Jacob H. LaFontaine
Estimating peak-flow quantiles for selected annual exceedance probabilities in Illinois
This report presents the methods, results, and applications of an updated flood-frequency study for the State of Illinois. This study, which uses data through September 2017, updates two previous studies that used data through 1999 and 2009, respectively. Flood-frequency estimates are used for a variety of land-use planning and infrastructure design purposes, including for the hydraulic design of
Authors
Thomas M. Over, Mackenzie K. Marti, Padraic S. O'Shea, Jennifer B. Sharpe
Improvements to estimate ADCP uncertainty sources for discharge measurements
The use of moving boat ADCPs (Acoustic Doppler Current Profilers) for discharge measurements requires identification of the sources and magnitude of uncertainty to ensure accurate measurements. Recently, a tool known as QUant was developed to estimate the contribution to the uncertainty estimates for each transect of moving-boat ADCP discharge measurements, by varying different sampling configurat
Authors
José M. Díaz Lozada, Carlos M. García, Kevin Oberg, Thomas M. Over, Federico Flores Nieto
Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana
Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and un
Authors
David T. Soong, Thomas M. Over
Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16
Monitoring discharge in the Chicago Sanitary and Ship Canal is critical for the accounting done by the U.S. Army Corps of Engineers of the diversion of water from Lake Michigan to the Mississippi River Basin by the State of Illinois. The primary streamgage used for this discharge monitoring, the Chicago Sanitary and Ship Canal near Lemont, Illinois (U.S. Geological Survey station 05536890), is ope
Authors
Thomas M. Over, Marian Muste, James J. Duncker, Heng-Wei Tsai, P. Ryan Jackson, Kevin K. Johnson, Frank L. Engel, Crystal D. Prater
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
Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom
Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.In this work we pre
Authors
Giuseppe Formetta, Thomas M. Over, Elizabeth Stewart
Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves
In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 Septemb
Authors
William H. Farmer, Thomas M. Over, Julie E. Kiang
Non-USGS Publications**
Over, T.M., Soong, D.T., and Su, T.Y., “Evaluation of HSPF snow modeling parameters using NWS Coop and SNODAS snow data”, Proceedings, EWRI Watershed Management 2010, Madison, WI, August 2010
Hirpa, F., M. Gebremichael., and T.M. Over, “River flow fluctuation analysis: Effect of watershed area”, Water Resources Research, 46, W12529, doi:10.1029/2009WR009000, 2010.
Gebremichael, M., Rigon, R., Bertoldi, R., and T. M. Over, “On the scaling characteristics of observed and simulated spatial soil moisture fields”, Nonlin. Processes Geophys., 16, 141–150, 2009
Ravi, S. P. D’Odorico, T.M. Zobeck, and T.M. Over, “The effect of fire-induced soil hydrophobicity on wind erosion in a semiarid grassland: Experimental observations and theoretical framework”,Geomorphology, 105: 80-86, 2009.
Gebremichael, M., W.F. Krajewski, T.M. Over, Y.N. Takayabu, P. Arkin, and M. Katayama, “Scaling of tropical rainfall as observed by TRMM Precipitation Radar”, Atmospheric Research, 88 (3-4): 337-354, 2008.
Alfieri, L., P. Claps, P. D’Odorico, F. Laio, and T.M. Over, “An analysis of the soil moisture feedback on convective and stratiform precipitation”, J. Hydrometeorol. 9(2): 280-291, 2008.
Ravi, S., P. D’Odorico, T.M. Zobeck, T.M. Over, and S.L. Collins, “Feedbacks between fires and wind erosion in heterogeneous arid lands”, J. Geophys Res., 112, G04007, doi:10.1029/2007JG000474, 2007.
Over, T.M., E.A. Murphy, T.W. Ortel, and A.L. Ishii, “Comparisons between NEXRAD radar and tipping bucket gage rainfall data: A case study for DuPage County, Illinois”, Proceedings, ASCE-EWRI World Environmental and Water Resources Congress, Tampa, Florida, May 2007.
Ravi, S. P. D'Odorico, B.E. Herbert, T. M. Zobeck, and T.M. Over, “Enhancement of wind erosion by fire-induced water repellency”, Water Resour. Res., 42, W11422, doi:10.1029/2006WR004895, 2006.
Gebremichael, M., T.M. Over, and W.F. Krajewski, “Comparison of the scaling characteristics of rainfall derived from space-based and ground-based radar observations”, J. Hydrometeorology, 7: 1277-1294, 2006.
Ravi, S., T. M. Zobeck, T. M. Over, G. S. Okin, and P. D'Odorico, “On the effect of moisture bonding forces in air-dry soils on threshold friction velocity of wind erosion”, Sedimentology, 53: 597-609, 2006.
Rigon, R. G. Bertoldi, and T. M. Over, “GEOtop: A distributed hydrological model with coupled water and energy budgets”, J. Hydrometeorology, 7: 371-388, 2006.
Bertoldi, G., R. Rigon, and T. M. Over, “Impact of watershed geomorphic characteristics on the energy and water budgets”, J. Hydrometeorology, 7: 389-404, 2006.
Lee, S.-W., A. G. Klein, and T. M. Over, “A comparison of MODIS and NOHRSC snowcover products for simulating streamflow using the Snowmelt Runoff Model”, Hydrol. Proc., 19: 2951-2972, 2005.
Wang, H., C.-M.Wang, J.-H. Wang, D.-Y. Qin, Z.-H. Zhou, and T.M. Over, “Theory and practice of runoff space-time distribution”, Sci. China Ser. E-Eng. Mater. Sci., 47(Suppl S.): 90-105, 2004.
Kuzuha, Y., T. M. Over, K. Tomosugi, and T. Kishii, “Analysis of multifractal properties of temporal and spatial precipitation data in Japan”, J. Hydrosci. Hydraul. Eng., 22(2): 59-78, 2004.
Ravi, S., P. D’Odorico, T. M. Over, and T. M. Zobeck, “Effect of soil moisture on threshold velocity for wind erosion”, Geophys. Res. Let., 31(9): 1-4, 2004.
Lee, S.-W., A. G. Klein, and T. M. Over, “Effects of El Nino / Southern Oscillation on temperature, precipitation, snow water equivalent and resulting streamflow in the Upper Rio Grande River Basin”,Hydrol. Proc., 18(6): 1053-1071, 2004.
D’Odorico, P., J.-C. Yoo, and T. M. Over, “On the Assessment of ENSO-induced Patterns of Rainfall Erosivity in the Southwestern United States,” J. Climate, 14(21): 4230-4242, 2001.
Over, T. M., and V. K. Gupta, “A Space-Time Theory of Mesoscale Rainfall Using Random Cascades,” J. Geophys. Res., 101(D21): 26,319-26,331, 1996.
Gupta, V.K., S. L. Castro, and T. M. Over, “On Scaling Exponents of Spatial Peak Flows from Spatial Rainfall and River Network Geometry”, J. Hydrology, 187(1/2), 81-104, 1996.
Over, T.M., Modeling Space-Time Rainfall at the Mesoscale using Random Cascades, Ph.D. dissertation, University of Colorado-Boulder, 1995, 238 p. [Link]
Over, T.M., and V. K. Gupta, “Statistical Analysis of Mesoscale Rainfall: Dependence of a Random Cascade Generator on the Large-Scale Forcing,” J. Applied Meteorol., 33(12):1526-1542, 1994.
Karlinger, M.R., T. M. Over and B.M. Troutman, “Relating Thin and Fat-Fractal Scaling of River-Network Models,” Fractals, 2(4):557-565, 1994.
**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: 16
Peak Streamflow Data, Climate Data, and Results from Investigating Hydroclimatic Trends and Climate Change Effects on Peak Streamflow in the Central United States, 1921–2020
Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annu
Data for Estimating Peak-Flow Quantiles for Selected Annual Exceedance Probabilities in Illinois
The U.S. Geological Survey Central Midwest Water Science Center completed a report (Over and others, 2023) documenting the methods, results, and applications of an updated flood-frequency study for the State of Illinois. This data release contains data related to the analysis completed to determine peak-flow quantiles (flood frequency estimates) at streamgages in Illinois for 50-, 20-, 10-, 4-, 2-
Models, Inputs, and Outputs for Estimating the Uncertainty of Discharge Simulations for the Lake Michigan Diversion Using the Hydrological Simulation Program - FORTRAN Model
This data release contains the models and their inputs and outputs needed to reproduce the findings for the publication by Soong and Over (2022), "Effect of Uncertainty of Discharge Data on Uncertainty of Discharge Simulation for the Lake Michigan Diversion, Northeastern Illinois and Northwestern Indiana." These data were developed in cooperation with the U.S. Army Corps of Engineers, Chicago Dist
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
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 and observed streamflow statistics at managed basins in the conterminous U.S. from October 1, 1983 through September 30, 2016.
This data release contains values of 29 streamflow statistics computed from modeled and observed daily streamflows from October 1, 1983, through September 30, 2016 at 1,257 streamgages in the 19 study regions defined by Falcone (2011) covering the conterminous United States. The streamflow statistics were computed at GAGES-II non-reference streamgages (Falcone, 2011), determined to be affected by
Statistical daily streamflow estimates at GAGES-II non-reference streamgages in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow at 5,439 GAGES-II non-reference streamgages in 19 study regions across the conterminous United States from October 1, 1980 through September 30, 2017, using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drainage area ratio (MCDAR), nearest-neighbor nonlinear spatial interpolati
Modeled and observed streamflow statistics at reference basins in the conterminous United States from October 1, 1983, through September 30, 2016
This data release contains 29 streamflow statistics computed from modeled and observed daily streamflows from October 1, 1983, through September 30, 2016 at 1,114 streamgages in 19 study regions covering the conterminous United States. The streamflow statistics were computed at selected GAGES-II reference streamgages (Falcone, 2011) from daily streamflow observations (Russell and others, 2020), fr
Cross-validation results for five statistical methods of daily streamflow estimation at 1,385 reference streamgages in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow for 1,385 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980, through September 30, 2017. These estimates are provided for gages from mostly undisturbed watersheds as defined by Falcone (2011), using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drain
Statistical daily streamflow estimates at HUC12 outlets in the conterminous United States, Water Years 1981-2017
This data release contains daily time series estimates of natural streamflow at the outlets of more than 80,000 12-digit hydrologic units in 19 study regions across the conterminous U.S. from October 1, 1980 through September 30, 2017, using three statistical techniques: Nearest-Neighbor Drainage Area Ratio (NNDAR), Map-Correlation Drainage Area Ratio (MCDAR), and Ordinary Kriging of the logarithm
Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2017
This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspira
Streamflow, flow-duration curves, basin characteristics, and regression models of flow-duration curves for selected streamgages in the conterminous United States
This data release contains the input used and the output files interpreted in the publication 'Refinement of a Regression-Based Method for Prediction of Flow-Duration Curves of Daily Streamflow in the Conterminous United States'. This data release contains daily streamflow data for 1,378 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980 through September 30, 2013 from m
Filter Total Items: 32
Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020
This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend perio
Authors
Mackenzie K. Marti, 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
Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is t
Authors
Karen R. Ryberg, Thomas M. Over, Sara B. Levin, David C. Heimann, Nancy A. Barth, Mackenzie K. Marti, Padraic S. O'Shea, Christopher A. Sanocki, Tara J. Williams-Sether, Harper N. Wavra, T. Roy Sando, Steven K. Sando, Milan S. Liu
The consequences of neglecting reservoir storage in national-scale hydrologic models: An appraisal of key streamflow statistics
A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process-based and three statistical-transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics repre
Authors
Glenn A. Hodgkins, Thomas M. Over, Robert W. Dudley, Amy M. Russell, Jacob H. LaFontaine
Estimating peak-flow quantiles for selected annual exceedance probabilities in Illinois
This report presents the methods, results, and applications of an updated flood-frequency study for the State of Illinois. This study, which uses data through September 2017, updates two previous studies that used data through 1999 and 2009, respectively. Flood-frequency estimates are used for a variety of land-use planning and infrastructure design purposes, including for the hydraulic design of
Authors
Thomas M. Over, Mackenzie K. Marti, Padraic S. O'Shea, Jennifer B. Sharpe
Improvements to estimate ADCP uncertainty sources for discharge measurements
The use of moving boat ADCPs (Acoustic Doppler Current Profilers) for discharge measurements requires identification of the sources and magnitude of uncertainty to ensure accurate measurements. Recently, a tool known as QUant was developed to estimate the contribution to the uncertainty estimates for each transect of moving-boat ADCP discharge measurements, by varying different sampling configurat
Authors
José M. Díaz Lozada, Carlos M. García, Kevin Oberg, Thomas M. Over, Federico Flores Nieto
Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana
Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and un
Authors
David T. Soong, Thomas M. Over
Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16
Monitoring discharge in the Chicago Sanitary and Ship Canal is critical for the accounting done by the U.S. Army Corps of Engineers of the diversion of water from Lake Michigan to the Mississippi River Basin by the State of Illinois. The primary streamgage used for this discharge monitoring, the Chicago Sanitary and Ship Canal near Lemont, Illinois (U.S. Geological Survey station 05536890), is ope
Authors
Thomas M. Over, Marian Muste, James J. Duncker, Heng-Wei Tsai, P. Ryan Jackson, Kevin K. Johnson, Frank L. Engel, Crystal D. Prater
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
Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom
Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.In this work we pre
Authors
Giuseppe Formetta, Thomas M. Over, Elizabeth Stewart
Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves
In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 Septemb
Authors
William H. Farmer, Thomas M. Over, Julie E. Kiang
Non-USGS Publications**
Over, T.M., Soong, D.T., and Su, T.Y., “Evaluation of HSPF snow modeling parameters using NWS Coop and SNODAS snow data”, Proceedings, EWRI Watershed Management 2010, Madison, WI, August 2010
Hirpa, F., M. Gebremichael., and T.M. Over, “River flow fluctuation analysis: Effect of watershed area”, Water Resources Research, 46, W12529, doi:10.1029/2009WR009000, 2010.
Gebremichael, M., Rigon, R., Bertoldi, R., and T. M. Over, “On the scaling characteristics of observed and simulated spatial soil moisture fields”, Nonlin. Processes Geophys., 16, 141–150, 2009
Ravi, S. P. D’Odorico, T.M. Zobeck, and T.M. Over, “The effect of fire-induced soil hydrophobicity on wind erosion in a semiarid grassland: Experimental observations and theoretical framework”,Geomorphology, 105: 80-86, 2009.
Gebremichael, M., W.F. Krajewski, T.M. Over, Y.N. Takayabu, P. Arkin, and M. Katayama, “Scaling of tropical rainfall as observed by TRMM Precipitation Radar”, Atmospheric Research, 88 (3-4): 337-354, 2008.
Alfieri, L., P. Claps, P. D’Odorico, F. Laio, and T.M. Over, “An analysis of the soil moisture feedback on convective and stratiform precipitation”, J. Hydrometeorol. 9(2): 280-291, 2008.
Ravi, S., P. D’Odorico, T.M. Zobeck, T.M. Over, and S.L. Collins, “Feedbacks between fires and wind erosion in heterogeneous arid lands”, J. Geophys Res., 112, G04007, doi:10.1029/2007JG000474, 2007.
Over, T.M., E.A. Murphy, T.W. Ortel, and A.L. Ishii, “Comparisons between NEXRAD radar and tipping bucket gage rainfall data: A case study for DuPage County, Illinois”, Proceedings, ASCE-EWRI World Environmental and Water Resources Congress, Tampa, Florida, May 2007.
Ravi, S. P. D'Odorico, B.E. Herbert, T. M. Zobeck, and T.M. Over, “Enhancement of wind erosion by fire-induced water repellency”, Water Resour. Res., 42, W11422, doi:10.1029/2006WR004895, 2006.
Gebremichael, M., T.M. Over, and W.F. Krajewski, “Comparison of the scaling characteristics of rainfall derived from space-based and ground-based radar observations”, J. Hydrometeorology, 7: 1277-1294, 2006.
Ravi, S., T. M. Zobeck, T. M. Over, G. S. Okin, and P. D'Odorico, “On the effect of moisture bonding forces in air-dry soils on threshold friction velocity of wind erosion”, Sedimentology, 53: 597-609, 2006.
Rigon, R. G. Bertoldi, and T. M. Over, “GEOtop: A distributed hydrological model with coupled water and energy budgets”, J. Hydrometeorology, 7: 371-388, 2006.
Bertoldi, G., R. Rigon, and T. M. Over, “Impact of watershed geomorphic characteristics on the energy and water budgets”, J. Hydrometeorology, 7: 389-404, 2006.
Lee, S.-W., A. G. Klein, and T. M. Over, “A comparison of MODIS and NOHRSC snowcover products for simulating streamflow using the Snowmelt Runoff Model”, Hydrol. Proc., 19: 2951-2972, 2005.
Wang, H., C.-M.Wang, J.-H. Wang, D.-Y. Qin, Z.-H. Zhou, and T.M. Over, “Theory and practice of runoff space-time distribution”, Sci. China Ser. E-Eng. Mater. Sci., 47(Suppl S.): 90-105, 2004.
Kuzuha, Y., T. M. Over, K. Tomosugi, and T. Kishii, “Analysis of multifractal properties of temporal and spatial precipitation data in Japan”, J. Hydrosci. Hydraul. Eng., 22(2): 59-78, 2004.
Ravi, S., P. D’Odorico, T. M. Over, and T. M. Zobeck, “Effect of soil moisture on threshold velocity for wind erosion”, Geophys. Res. Let., 31(9): 1-4, 2004.
Lee, S.-W., A. G. Klein, and T. M. Over, “Effects of El Nino / Southern Oscillation on temperature, precipitation, snow water equivalent and resulting streamflow in the Upper Rio Grande River Basin”,Hydrol. Proc., 18(6): 1053-1071, 2004.
D’Odorico, P., J.-C. Yoo, and T. M. Over, “On the Assessment of ENSO-induced Patterns of Rainfall Erosivity in the Southwestern United States,” J. Climate, 14(21): 4230-4242, 2001.
Over, T. M., and V. K. Gupta, “A Space-Time Theory of Mesoscale Rainfall Using Random Cascades,” J. Geophys. Res., 101(D21): 26,319-26,331, 1996.
Gupta, V.K., S. L. Castro, and T. M. Over, “On Scaling Exponents of Spatial Peak Flows from Spatial Rainfall and River Network Geometry”, J. Hydrology, 187(1/2), 81-104, 1996.
Over, T.M., Modeling Space-Time Rainfall at the Mesoscale using Random Cascades, Ph.D. dissertation, University of Colorado-Boulder, 1995, 238 p. [Link]
Over, T.M., and V. K. Gupta, “Statistical Analysis of Mesoscale Rainfall: Dependence of a Random Cascade Generator on the Large-Scale Forcing,” J. Applied Meteorol., 33(12):1526-1542, 1994.
Karlinger, M.R., T. M. Over and B.M. Troutman, “Relating Thin and Fat-Fractal Scaling of River-Network Models,” Fractals, 2(4):557-565, 1994.
**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.