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Utah Energy Balance (UEB) Snow Model

The Utah Energy Balance (UEB) snow model is an energy balance snowmelt model developed by David Tarboton's research group - and updated over the years. The model uses a lumped representation of the snowpack and keeps track of water and energy balance. The model is driven by inputs of air temperature, precipitation, wind speed, humidity and radiation at time steps sufficient to resolve the diurnal cycle (hourly or six hourly). The model uses physically-based calculations of radiative, sensible, latent and advective heat exchanges. In the latest versions a force-restore approach is used to represent surface temperature, accounting for differences between snow surface temperature and average snowpack temperature without having to introduce additional state variables. Melt outflow is a function of the liquid fraction, using Darcy's law. This allows the model to account for continued outflow even when the energy balance is negative. Because of its parsimony (few state variables - but increasing with later versions) this model is suitable for application in a distributed fashion on a grid over a watershed.

To facilitate collaboration and open development of UEB, code has been posted in Github, and as of 8/14/22 I have set the license to MIT open source license which is less restrictive than the previously used GPL license. Feel free to fork and create or comment on issues. Updates suggested via pull requests are welcome, though I may be slow in responding. If you would like to contribute to and commit source code changes please email me (dtarb@usu.edu).

There are a number of versions available.

UEB C++ Version

https://github.com/dtarb/UEB

C++ version ported by Tseganeh Gichamo from the earlier Fortran version so as to be easier to use with NetCDF and parallel MPI libraries.

Papers using this version are

• Gichamo, T. Z., N. S. Sazib, D. G. Tarboton and P. Dash, (2020), "HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models," Environmental Modelling & Software, 125: 104623, https://doi.org/10.1016/j.envsoft.2020.104623. [Author PDF 3 MB]
• Gichamo, T. Z. and D. G. Tarboton, (2020), "UEB parallel: Distributed snow accumulation and melt modeling using parallel computing," Environmental Modelling & Software, 125: 104614, https://doi.org/10.1016/j.envsoft.2019.104614. [Author PDF 0.6 MB]
• Gichamo, T. Z. and D. G. Tarboton, (2019), "Ensemble Streamflow Forecasting using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations," Water Resources Research, 55(12): 10813-10838, https://doi.org/10.1029/2019WR025472. [Free to read, USU Digital Commons]

UEBFortran Grid Version

https://github.com/dtarb/UEBFortran

Version that added the capability to represent the melting of glaciers and adopted a structured file based input/output format using ASCII and netCDF files to facilitate its use in a NASA project and its incorporation into the EPA BASINS software.

Papers using this version are

• Sen Gupta, A. and D. G. Tarboton, (2016), "A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications," Environmental Modelling & Software, 84: 50-69, http://dx.doi.org/10.1016/j.envsoft.2016.06.014. [Author accepted manuscript]
• Sen Gupta, A., D. G. Tarboton, P. Hummel, M. E. Brown and S. Habib, (2015), "Integration of an energy balance snowmelt model into an open source modeling framework," Environmental Modelling & Software, 68: 205-218, http://dx.doi.org/10.1016/j.envsoft.2015.02.017. [Author accepted manuscript]
• Brown, M. E., A. E. Racoviteanu, D. G. Tarboton, A. Sen Gupta, J. Nigro, F. Policelli, S. Habib, M. Tokay, M. S. Shrestha, S. Bajracharya, P. Hummel, M. Gray, P. Duda, B. Zaitchik, V. Mahat, G. Artan and S. Tokar, (2014), "An integrated modeling system for estimating glacier and snow melt driven streamflow from remote sensing and earth system data products in the Himalayas," Journal of Hydrology, 519, Part B: 1859-1869, http://dx.doi.org/10.1016/j.jhydrol.2014.09.050. [Author accepted manuscript]
• Sen Gupta, A. and D. G. Tarboton, (2013), "Using The Utah Energy Balance Snow Melt Model To Quantify Snow And Glacier Melt In The Himalayan Region," Proceedings of the Western Snow Conference 81st Annual Meeting. Adaptive Water Management in a Changing Climate, Jackson Hole, Wyoming, April 15-18, p. 103-114, http://www.westernsnowconference.org/sites/westernsnowconference.org/PDFs/2013SenGupta.pdf. [PDF 1.8 MB]

UEBVeg (Posted 4/7/2012)

• Source Code [UEBVeg.zip]
• Example input [UEBVegExample.zip]

This version incorporates a parameterization of vegetation developed by Vinod Mahat. The improvements are described in

• Mahat, V., (2011), "Effect of Vegetation on the Accumulation and Melting of Snow at the TW Daniels Experimental Forest," Ph.D. Dissertation, Civil and Environmental Engineering, Utah State University, http://digitalcommons.usu.edu/etd/1078, 181 pp.
• Mahat, V. and D. G. Tarboton, (2012), "Canopy radiation transmission for an energy balance snowmelt model," Water Resour. Res., 48: W01534, http://dx.doi.org/10.1029/2011WR010438. [PDF 1.2 MB with notes on some corrections to published WRR version]. Correction http://dx.doi.org/10.1029/2012WR011964.

Updated documentation for this version has not been written. The input and output files are mostly the same as earlier versions - so the users guide for the original version is still a good starting point in working with UEBVeg. However users should refer to the code to be certain how inputs are being used and what the output is.

Version 2.2 (Posted around 2008)

• Source Code [UEB22.zip]
• Point version example input [reydemo.zip]
• Lumped version example input [Reylumped.zip]

This version adopted the Force-Restore approach for representing surface temperature and introduced a parameterization of the refreezing of liquid water near the surface. These and other improvements are described in

• You, J., (2004), "Snow Hydrology: The Parameterization of Subgrid Processes within a Physically Based Snow Energy and Mass Balance Model," PhD Dissertation, Civil and Environmental Engineering, Utah State University.
• Luce, C. H., (2000), "Scale Influences on the Representation of Snowpack Processes," PhD Dissertation, Civil and Environmental Engineering, Utah State University, 188 pp.

There is also a "lumped" version that applies the depletion curve parameterization developed in these dissertations.

Updated documentation for this new version has not been written. The input and output files are mostly the same as for the original version - so the users guide for the original version below is still a good starting point in working with UEB 2. However users should refer to the code to be certain how inputs are being used and what the output is.

Original Version (posted around 1997)

The Utah Energy Balance (UEB) snow model is an energy balance snowmelt model developed by David G. Tarboton, Charlie H. Luce, Tanveer G. Chowdhury and Tom H. Jackson for the prediction of snowmelt surface water input rates. The model uses a lumped representation of the snowpack with two primary state variables, namely, water equivalence and energy content relative to a reference state of water in the ice phase at 0 C. This energy content is used to determine snowpack average temperature or liquid fraction. Snow surface age is retained as a third state variable, used for the calculation of albedo. The model is driven by inputs of air temperature, precipitation, wind speed, humidity and radiation at time steps sufficient to resolve the diurnal cycle (hourly or six hourly). The model uses physically-based calculations of radiative, sensible, latent and advective heat exchanges. An equilibrium parameterization of snow surface temperature accounts for differences between snow surface temperature and average snowpack temperature without having to introduce additional state variables. Melt outflow is a function of the liquid fraction, using Darcy's law. This allows the model to account for continued outflow even when the energy balance is negative. Because of its parsimony (only three state variables) this model is suitable for application in a distributed fashion on a grid over a watershed.

Presentation