Barnhart, T. B., Tague, C. L., & Molotch, N. P. (2020). The counteracting effects of snowmelt rate and timing on runoff. Water Resources Research, 56, e2019WR026634.
Theodore B Barnhart
Biography
Theo received his PhD in geography from the University of Colorado in 2018 and studied how changes in snowmelt and land cover impacted the partitioning of precipitation between runoff and evapotranspiration. Theo works to improve hydrologic predictions using process-based hydrologic modeling and data-driven techniques. He is particularly interested in furthering our understanding of mountain and high-latitude hydrology and the distribution and variability of the mountain snowpack. To accomplish this, Theo uses hydrologic models, water balance analyses, and remote sensing.
Education:
PhD: Geography, 2018, The University of Colorado, Boulder, CO. Dissertation: The Response of Streamflow and Evapotranspiration to Changes in Snowmelt Across the Western United States.
MS: Geology, 2013, Idaho State University, Pocatello, ID. Thesis: Morphodynamics of the Selawik Retrogressive Thaw Slump, Northwest Alaska.
BA: Geology-Environmental Studies, cum laude, 2010, Whitman College, Walla Walla, WA. Thesis: Glaciomarine Sediment Flux and Transportation Mechanisms, Kronebreen/Kongsvegan, Kongsfjorden, Svalbard.
Please see his Google Scholar page for a list of peer-reviewed publications.
Science and Products
Date published: August 12, 2020
Status: Active Flow-Conditioned Parameter Grids
Flow-Conditioned Parameter Grids (FCPGs) are a way of storing the upstream average of datasets, such as precipitation or land cover type, for all points on the landscape.
Contacts: Theodore B Barnhart
Attribution: Water Resources, Wyoming-Montana Water Science Center
Date published: March 20, 2018
Status: Active Geospatial Research and Development to Understand Hydrologic Processes
All natural phenomena have a spatial component. Remote sensing, GIS, and geostatistical methods can be used to evaluate the spatial components of hydrologic phenomena and understand characteristics, such as water quality, streamflow, and hydraulics.
Contacts: Roy Sando , Theodore B Barnhart , Ryan R McShane , DeAnn M Dutton , Laura L Hallberg , Aaron J Heldmyer
Date published: August 21, 2020
National Hydrologic Model Alaska Domain parameter database, version 1
This data release contains input data for hydrologic simulations of the Alaska Domain application of the U.S. Geological Survey (USGS) Precipitation Runoff Modelling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The NHM Alaska Domain parameter database consists of 114 parameter files in ASCII format (CSV), two
Attribution: Wyoming-Montana Water Science Center
Date published: June 4, 2020
Geospatial Fabric for the National Hydrologic Model Alaska Domain, version 1
This metadata record documents a geospatial dataset for the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS) used to drive the National Hydrologic Model (NHM). The Alaska Geospatial Fabric v1 is the spatial representation of the hydrologic response units (HRUs) used for the PRMS NHM Alaska domain. These HRUs were generated using the twelve-digit Hydrologic Unit Code
Attribution: Wyoming-Montana Water Science Center
Date published: September 27, 2019
Data to Estimate Water Use Associated with Continuous Oil and Gas Development, Williston Basin, United States, 1980-2017
This U.S. Geological Survey (USGS) Data Release provides data to estimate water use associated with continuous oil and gas development in the Williston Basin during 1980-2017. Data included: 1. Data records from the national hydraulic fracturing chemical registry, FracFocus, including the state, county, latitude and longitude of each well, and the year and volume of water used for...
Attribution: Wyoming-Montana Water Science Center
Year Published: 2021
Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States
Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks,...
Jaeger, Kristin; Hafen, Konrad; Dunham, Jason B.; Fritz, Ken M.; Kampf, Stephanie K.; Barnhart, Theodore B.; Kaiser, Kendra E.; Sando, Roy; Johnson, Sherri L; McShane, Ryan R.; Dunn, Sarah BethAttribution: Washington Water Science Center, Water Resources
Year Published: 2020
Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17
This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of...
McShane, Ryan R.; Barnhart, Theodore B.; Valder, Joshua F.; Haines, Seth S.; Macek-Rowland, Kathleen M. ; Carter, Janet M.; Delzer, Gregory C.; Thamke, Joanna N.Attribution: Wyoming-Montana Water Science Center, Water Resources, Water Availability and Use Science Program
View Citation
McShane, R.R., Barnhart, T.B., Valder, J.F., Haines, S.S., Macek-Rowland, K.M., Carter, J.M., Delzer, G.C., and Thamke, J.N., 2020, Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17: U.S. Geological Survey Scientific Investigations Report 2020–5012, 26 p., https://doi.org/10.3133/sir20205012
Year Published: 2020
Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model
The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of subgrid or subwatershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey...
Sexstone, Graham A.; Driscoll, Jessica M.; Hay, Lauren; Hammond, John; Barnhart, Theodore B.
Year Published: 2019
Analytical framework to estimate water use associated with continuous oil and gas development
An analytical framework was designed to estimate water use associated with continuous oil and gas (COG) development in support of the U.S. Geological Survey Water Availability and Use Science Program. This framework was developed to better understand the relation between the production of COG resources for energy and the amount of water needed to...
Valder, Joshua F.; McShane, Ryan R.; Barnhart, Theodore B.; Wheeling, Spencer L.; Carter, Janet M.; Macek-Rowland, Kathleen M. ; Delzer, Gregory C.; Thamke, Joanna N.View Citation
Valder, J.F., McShane, R.R., Barnhart, T.B., Wheeling, S.L., Carter, J.M., Macek-Rowland, K.M., Delzer, G.C., and Thamke, J.N., 2019, Analytical framework to estimate water use associated with continuous oil and gas development: U.S. Geological Survey Scientific Investigations Report 2019–5100, 19 p., https://doi.org/10.3133/sir20195100.
Year Published: 2018
Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development
As the demand for energy increases in the United States, so does the demand for water used to produce many forms of that energy. Technological advances, limited access to conventional oil and gas accumulations, and the rise of oil and gas prices resulted in increased development of unconventional oil and gas (UOG) accumulations. Unconventional oil...
Valder, Joshua F.; McShane, Ryan R.; Barnhart, Theodore B.; Sando, Roy; Carter, Janet M.; Lundgren, Robert F.View Citation
Valder, J.F., McShane, R.R., Barnhart, T.B., Sando, R., Carter, J.M., and Lundgren, R.F., 2018, Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development: U.S. Geological Survey Scientific Investigations Report 2018–5027, 22 p., https://doi.org/10.3133/sir20185027.
Pre-USGS Publications
Barnhart, T.B., N.P. Molotch, B. Livneh, A.A. Harpold, J.F. Knowles, D. Schneider (2016), Snowmelt Rate Dictates Streamflow: Geophysical Research Letters, 43(15), 8006-8016
Harpold, A.A., J.A. Marshall, S.W. Lyon, T.B. Barnhart, B. Fisher, M. Donovan, K.M. Brubaker, C.J. Crosby, N.F. Glenn, C.L. Glennie, P.B. Kirchner, N. Lam, K.D. Mankoff, J.L. McCreight, N.P. Molotch, K.N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West (2015), Laser Vision: Lidar as a Transformative Tool to Advance Critical Zone Science: Hydrology and Earth System Science, 19, 2881-2897
Barnhart, T.B., and B.T. Crosby (2013), Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska: Remote Sensing, 5, 2813-2837
Date published: September 3, 2020
Flow-Conditioned Parameter Grid Tools
The Flow-Conditioned Parameter Grid (FCPG) Tools are a Python 3 library to make FCPGs for either two-digit Hydrologic Unit Code (HUC2) regions, four-digit Hydrologic Unit Code (HUC4) regions, or other geospatial tiling schemes. These tools can be used in a Linux-based high performance computing (HPC) environment or locally on your system.
Attribution: Wyoming-Montana Water Science Center
Date published: June 4, 2020
StreamStats Data Preparation Tools, version 4
A Python package to pre-process and hydro-enforce digital elevation models using hydrography features for use in the U.S. Geological Survey (USGS) StreamStats project.
Attribution: Wyoming-Montana Water Science Center
Date published: April 14, 2021
New Open-Source Projects Using NHDPlus
Two new open-source projects, Xstrm and FCPGtools, have been released that provide functionality based on versions of the NHDPlus. Both projects provide tools to summarize landscape characteristics using the older NHDPlus V2, and also will work with NHDPlus HR. Both projects are Python-based, and work on Linux-based High Performance Computing platforms and on Windows systems.