Tracing Snowmelt’s Journey from the Peaks to the Valley of the Colorado River’s Headwaters

Snowpack and stream water isotope sampling demonstrate the importance of high-elevation snowmelt for the water supply of the Colorado River.

Sprenger Et Al 2024 Picture1

In years with relatively low maximum snow water equivalent (SWEmax, y-axis) and high air temperature during the snowmelt period (Tair, x-axis), the high-elevation contribution to streamflow is highest (blue).

[Reprinted under a Creative Commons Attribution 4.0 International License (CC BY 4.0) from Sprenger, M., et al. "Stream Water Sourcing from High-Elevation Snowpack Inferred from Stable Isotopes of Water: A Novel Application of D-Excess Values." Hydrology and Earth System Sciences 28 (7), 1711–23 (2024). DOI:10.5194/hess-28-1711-2024.]

The Science

The isotopic signal of water, a natural tracer, was measured in the snowpack and stream water for over 7 years in the mountainous headwaters of the Colorado River. Measurement data provided insights on the share of the water in the headwaters sourced from the highest elevations during the snowmelt period. In years with relatively little snowfall and warm air temperatures, the share of high-elevation snowmelt contributions to the headwaters was highest. Researchers detected the observed variations of high-elevation snowpack contributions during snowmelt in both small mountainous catchments and large watersheds in the Upper Colorado River.

The Impact

The Colorado River, providing the water supply to 40 million people, is mainly sourced by the snowmelt in the Rocky Mountains. To understand the potential of water availability changes, knowledge about the consequences of changes in snowpack and air temperature on the river’s headwaters is crucial. Data from the past 7 years demonstrate that an increase in the relative contributions from high-elevation snowmelt underlines the critical role mountains play in sustaining the water supply. Because snowpack at lower elevations will be impacted most by climate change, the snowmelt water from snowpack at the highest elevations will become more important to sustain ample water flow throughout the summer.

Summary

The Watershed Function Science Focus Area (SFA) measured stable water isotopes in the snowpack and headwater rivers in the Upper Colorado River basin over 7 years. These measurements enabled the multi-institutional team to relate the spatial variation in the snowpack isotope ratio along an elevation gradient with the snowmelt stream discharge and its isotopic composition based on mixing analyses. Results of this tracer-based method highlight the snowpack’s importance in the highest elevations of the Rocky Mountains for streamflow generation.

Connecting the U.S. Department of Energy–funded SFA efforts with the stream/river monitoring led by the U.S. Geological Survey allowed the team to scale up from the intensely measured headwaters to larger watersheds. Results suggest the temporal variation of high-elevation snowmelt contributions is transferrable to other snow-dominated mountainous regions. Changes in the stream water isotope dynamics during the snowmelt period could therefore be used to identify changes in the snow water equivalent (SWE) of the snowpack that would be challenging to observe with ground-based instrumentation or remote sensing.

Principal Investigator

Matthias Sprenger
Lawrence Berkeley National Laboratory
[email protected]

Co-Principal Investigator

Eoin Brodie
Lawrence Berkeley National Laboratory
[email protected]

Program Manager

Paul Bayer
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]

Funding

This work was supported by the U.S. Department of Energy’s Office of Science under contract DE-AC02- 05CH11231 as part of the Lawrence Berkeley National Laboratory Watershed Function SFA.

References

Sprenger, M., et al. "Stream Water Sourcing from High-Elevation Snowpack Inferred from Stable Isotopes of Water: A Novel Application of D-Excess Values." Hydrology and Earth System Sciences 28 (7), 1711–23  (2024). https://doi.org/10.5194/hess-28-1711-2024.