Each winter, snow accumulates in the higher elevations of the Willamette Valley. This natural reservoir serves to store a proportion of the winter precipitation, releasing into streams (and reservoirs) during the spring. Snow is the most climatically sensitive element of the annual water budget in the basin. Changes in precipitation, temperature, and forest cover will affect snow accumulation, while changes in temperature and forest cover will affect the rate of snowmelt. 

The snow modeling team examined the maximum snow water equivalent (SWE) over the period January 1-April 1 for each year for the Low Climate Change (LowClim), Reference Case (Reference), High Climate Change (HighClim), and Upland Wildfire Suppression (FireSuppress) scenarios. The LowClim scenario, in which temperatures increase only slightly and winter precipitation also slightly increases, shows an increase in seasonal maximum snow water equivalent of about 41% for elevations above 1200 m (3937 ft). Low elevation snow (500-1200 m, or 1640-3937 ft) varies from decade to decade but there is no trend over the 90-year period. The Reference scenario shows a steep decline in seasonal maximum SWE at both high and low elevations (74% and 94% losses, respectively). SWE declines even more steeply in the HighClim scenario with a loss of 90% of SWE above 1200 m (3937 ft) and a loss of 94% of SWE at the 500-1200 meter (1640-3937 ft) elevation zone. Hydrologic impacts indicate that the snowmelt contribution to spring discharge is lost as snowfall converts to rainfall in winter and the remaining snowpack melts earlier. In all but the LowClim scenario, we see an increase in winter flows as a result of these declines in SWE. While the total water storage from SWE is a relatively small proportion of the annual water budget (2.3-10.6%), the loss of snowpack has important implications for the timing of reservoir filling, spring and early summer high elevation soil moisture, forest health, and spring streamflow.


Snow Modeling in Brief

In the Willamette River Basin (WRB), most annual precipitation falls between November and March, with snowfall occurring mainly at elevations above about 1200 m (3937 ft). Sub-basins such as the McKenzie, North Santiam, and Middle Fork, whose headwaters contain substantial area above 1200 m (3937 ft), have more snow than those in the Coast range and elsewhere in the WRB.

We simulated the seasonal evolution of the mountain snowpack at a daily timestep within the Willamette Hydrology Model (WHM) portion of Willamette Envision. WHM is a modified version of the HBV model (Seibert, 1997) in which snow is computed using a degree-day model. Melt rate is governed by air temperature in excess of 0° C and moderated by a melt factor. Precipitation is partitioned into rain and snow by incorporating a temperature-based transition “ramp” rather than a single fixed temperature threshold thus allowing for mixed rain-snow events. The model accounts for the effects of canopy interception and snow sublimation from the canopy as a function of leaf area index (LAI), a measure of canopy cover. Last, we included a radiant energy term as a function of LAI, where both convective heat (as a function of air temperature) and radiant energy can affect snowmelt. Snowmelt is constrained by the amount of actual snow available in each IDU, and when snowmelt exceeds the water-holding capacity of the snowpack, melt is routed into the soil.

Analysis Approach

We selected three climate scenarios to examine the effects of climate and changing forest cover on snowpack. As described in the Climate section, the scenarios representing high, medium, and low levels of climate warming are referred to as HighClim, Reference, and LowClim, respectively. For each climate scenario, the WW2100 team examined snowpack across three elevation zones: 500 m (1640 ft) and below, 500-1200 m (1640-3937 ft), and 1200 m (3937 ft) and above. While April 1 SWE is a traditional metric used by streamflow forecasters, we found that under climate warming this metric does not account for snow water equivalent that melts prior to that date. A more hydrologically appropriate metric is the maximum winter snow water equivalent (MaxSWE). We computed MaxSWE for the January-April time period for elevation zones 500-1200 m (1640-3937 ft, or “low elevation snow”) and 1200 m (3937 ft) and above (“high elevation snow”). Because snow cover below 500 m (1640 ft) is only ephemeral and insignificant, we did not analyze changes in snowpack in that zone. Snowpack in individual winters typically varies from year to year, so our temporal analysis examined decadal and 30-year averages of SWE over the 90-year model run. We also analyzed correlations between snowpack, winter temperature, and winter precipitation on a yearly basis for each of the climate scenarios. In addition to the elevation zone analyses of SWE, we examined decadal-averaged SWE for sub-watersheds (HUC-12 level of detail).

Select Findings from Snow Analysis

As temperatures increase in WW2100 future scenarios (2010-2099), snow water equivalent varies in its response to temperature. SWE decreases in both the Reference and HighClim scenario, where temperatures increase 2.3 °C and 4.4 °C from the first to the ninth decade, respectively. However, SWE increases in the LowClim scenario for both low and high elevations because the temperature increase is only 0.05 °C, not enough to shift precipitation from snow to rain. There is also a slight increase in winter precipitation. Spatially, changes in snow cover mainly affect the McKenzie and North Santiam sub-basins, as well as high elevation portions of several other sub-basins (Fig. 1).

Snowpack Trends in the Three WW2100 Climate Scenarios

  • Under the LowClim scenario, snow water equivalent in the high elevation zone there is high interdecadal variability and maximum SWE increases by 41%. In the low elevation zone, there is high decadal variability but no significant increase in SWE. (Figs. 2 and 3).

  • Under the Reference and HighClim scenarios, snow water equivalent in the high elevation zone declines 74% and 90% under the Reference and HighClim scenarios, respectively (Figs. 4 and 5).

  • Snow water equivalent in the low elevation zone declines 94% under both the Reference and HighClim scenarios (Figs. 6 and 7). In the HighClim scenario, low elevation snow essentially disappears by mid-century.

  • Declines in SWE in the Reference and HighClim scenarios are driven by increases in winter temperatures rather than changes in precipitation.

Annual maximum SWE for early, middle and late 21st century decades (Figure by Nolin and Stephens)

Figure 1. Snow cover in early, mid, late decades of the study period for the Reference Case scenario.

Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, 500-1200 m.

Figure 2a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).

Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference scenario, above 500-1200m.

Figure 3a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference Case scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).

Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, 500-1200 m.

Figure 4a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).


Table 1. Correlations between Jan-Apr Maximum SWE, temperature, and precipitation for the three scenarios. Values with an asterisk are significant at p=-0.05.

  LowClim Reference HighClim

>1200 m
(>3937 ft)

500-1200 m
(1640-3937 ft)
>1200 m
(>3937 ft)
500-1200 m
(1640-3937 ft)
>1200 m
(>3937 ft)
500-1200 m
(1640-3937 ft)

T vs. MaxSWE

-0.59* -0.53* -0.46* -0.36* -0.48* -0.38*
P vs. MaxSWE 0.31* 0.22 0.19 0.19 0.18 0.21

Changes in Volumetric Water Storage in Snowpack

We computed the difference in total volumetric water storage in the snowpack using the first and last decades of the model run and combining snow storage in both the high and low elevations zones. The LowClim scenario sees an increase of 324,285 ac-ft (0.40 km3), the Reference scenario sees a decrease of 2,180,818 ac-ft ( 2.70 km3), and the HighClim scenario sees a decrease of 956,642 ac-ft ( 1.17 km3) from the first to the last decade. The larger decline in the Reference scenario is because there is a single outlier year of extremely high snowpack during the first decade of the Reference scenario that doesn’t occur in the other two scenarios.

Because we did not run a counterfactual case controlling for the effects of wildfire, we are not able to tease apart the effects of climate from those of wildfire on snowpack. However, our field measurements indicate that dense, low elevation forests tend to decrease snowpack due to both canopy interception (reducing accumulation on the ground) and thermal effects that lead to faster melt. At the highest elevations, forests are lower density and colder, so canopy interception and thermal effects are lower. In burned areas, our field studies show that decreased canopy increases snow accumulation, but deposition of charred debris on snow leads to earlier snowmelt by several weeks. Thus, we speculate that forest fires will lead to increased snow accumulation but earlier melt. Forest harvest, especially thinning, may allow greater retention of snowpacks, though higher winter temperatures cause considerable declines in total snowpack at all elevations.


Pacific Northwest mountain snowpack is highly temperature sensitive. In recent decades, warm temperature anomalies have led to significant declines in snow water equivalent. Differences between the LowClim, Reference, and HighClim scenarios show that relatively small changes in temperature and precipitation affect both the magnitude and direction of temperature change. In the LowClim scenario, the slight temperature increase is insufficient to convert precipitation from snowfall to rainfall; the 19.5% increase in winter precipitation leads to a 41% increase SWE from the first to the last decade of the 90-year period. In contrast, the Reference and HighClim scenarios see significant increases in winter temperature but no change in winter precipitation, thus driving the large declines in SWE. The SWE decreases are especially substantial at lower elevations, which see a disappearance of nearly all snow. Climate-driven changes in snow hydrology are considerable. Less clear are the impacts of changing forest cover on snowpacks.

Related Links & Publications

  • Cooper, M. G., Nolin, A. W., & Safeeq, M. (2016). Testing the recent snow drought as an analog for climate warming sensitivity of Cascades snowpacks. Environmental Research Letters, 11(8), 084009.

  • Sproles, E. A., T. R. Roth, and A. W. Nolin. (2016). Future Snow? A Spatial-Probabilistic Assessment of the Extraordinarily Low Snowpacks of 2014 and 2015 in the Oregon Cascades, The Cryosphere Discussion,

  • Nolin, A. W. (2016). Remote sensing of the cryosphere, In The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. D. Richardson, Ed., Wiley and Sons,

  • Gleason, K. E. and A. W. Nolin. (2016). Charred forests accelerate snow albedo decay: parameterizing the post-fire radiative forcing on snow for three years following fire, Hydrological Processes,

  • Safeeq, M. S. Shukla, I. Arismendi, G. E. Grant, S. L. Lewis, and A. Nolin. (2015). Influence of winter season climate variability on snow-precipitation ratio in the western United States, International Journal of Climatology,

Contributors to WW2100 Snow Research

  • Anne Nolin, OSU College of Earth, Ocean, and Atmospheric Sciences (lead)

  • David Conklin, Oregon Freshwater Simulations

  • Matthew Cooper, MS Student, Geography (Graduate: 2014; now at UCLA Geography)

  • Kelly Gleason, PhD Student, OSU Geography (Graduated: 2015; now at USGS Flagstaff, AZ)

  • Travis Roth, PhD Student, OSU Water Resources Science (Graduated: 2012, now at 

  • Eric Sproles, PhD Student, OSU Geography (Graduated: 2012, now at Centro de Estudios Avanzados en Zonas Áridas, La Serena, Chile)

  • David Turner, OSU Forest Ecosystems and Society

  • Kellie Vache, OSU Biological & Ecological Engineering


Seibert, J. (1997) Estimation of parameter uncertainty in the HBV model. Nordic Hydrology, 28 (4/5), 247-262.


Web page author: A. Nolin
Last updated: December 2016