Application of a Physically-Based Distributed Snowmelt Model in Support of Reservoir Operations and Water Management

Project ID: 2264
Principal Investigator: Eric Rothwell
Research Topic: Water Supply Forecasting
Funded Fiscal Years: 2013, 2014 and 2015
Keywords: model, forecasting, snow-melt, snow water equivalent

Research Question

This project will focus on Reclamation's need for hydrologic modeling tools that can optimize reservoir management decisions to improve planning and management of water supplies.

We will apply the latest advances in snow accumulation and melt modeling to the Boise River Basin, which contributes inflows to three reservoirs. This will be the first application of a physically-based distributed snow model in an operational forecasting setting. Are these modeling techniques appropriate to operational needs and do the deliverable products improve reservoir management outcomes?

Need and Benefit

Current operational snowmelt-driven streamflow forecasts are derived from statistical relationships largely based on a combination of historic trends and calibrations to point observations of SWE or as-available satellite observations of snow-covered-area. These models rarely contain a physical basis. It has been shown that these models become unreliable when non-normal conditions are encountered.

Physically-based, distributed models require little to no calibration and are based on current and predicted conditions. The physical basis means that all mass and energy fluxes that affect the snowcover are numerically calculated based on the governing physics. These models are robust to non-normal climate conditions and ideal tools for evaluating streamflow responses to short-term extreme events such as rain-on-snow, the extended effects of unseasonable wet, dry, warm, or cold periods, and the long-term effects of climate warming.

Up until now the reasoning has been that the computational demands of modeling over large basins required simpler, parameterized models. A lack of driving data (i.e. mountain weather observations) for physically-based modeling has also been seen as an impediment to more complex solutions. Today however, computational capabilities have multiplied, efficient techniques for distributing limited observations have been developed, and gridded weather forecasts are readily available.

The proposed work will provide immediate benefits to Reclamation. Current maps of basin-wide SWE will answer the oft-asked questions, "How much snow is still up there and where is it?", "Where is the snow located?", "When will the snow come off, with the next warm spell, rain storm, etc.?" The up-to-date maps of snowcover cold content will provide managers data on how sensitive reservoir inflows will be to subsequent energy inputs. Upon completion, the potential role of physically-based snow modeling in the realm of operational river forecasting will be known. The physically-based foundations of these modeling tools will be directly applicable to basins throughout the region – basin-specific calibrations are not necessary. If successful, reservoir managers will have an advanced, modern tool for predicting inflows, optimizing water usage, and increasing flood protection.

The immediate benefit of this proposal is to the PN Region. However, a pilot study was completed in a small California watershed in 2008. If techniques can be effectively scaled for a large river basin like the Boise, they could be effective in any snow driven basin in the Western US.

Contributing Partners

Contact the Principal Investigator for information about partners.

Research Products

Bureau of Reclamation Review

The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.

Application of a Physically-Based Distributed Snowmelt Model in Support of Reservoir Operations and Water Management (final, PDF, 4.5MB)
By Eric Rothwell
Publication completed on September 30, 2015

Current operational snowmelt models to drive streamflow forecasts rarely contain a physical foundation and have been shown to be unreliable in non-normal conditions. In contrast, physically based, distributed models are robust to non-normal conditions and have the potential to improve reservoir management decisions by providing distributed snowpack properties. This project focused on applying the physically based, distributed snow model iSnobal in an operational setting by running the model in near real time. Snowpack results, such as spatially distributed snow water equivalent (SWE), susceptibility to melt, and the volume of liquid water delivered to the soil (snow melt or rain) were provided on a weekly basis for water years 2013 to 2015 to local area water managers. In 2015, iSnobal was loosely coupled to a hydrologic routing model and a short term weather forecasting model. The coupling provided a proof of concept 3-day streamflow forecast by using the weather forecast to drive the snow model and route melt water to the stream channels.

The following documents were not reviewed. Statements made in these documents are those of the authors. The findings have not been verified.

Improving Snowmelt Models to Improve Operations in a Shifting Climate (final, PDF, 1.0MB)
By Eric Rothwell (PI), Scott Havens (PI)
Publication completed on September 30, 2016

This bulletin summarizes the research results and potential application to Reclamation's mission.

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Last Updated: 6/22/20