TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-331-2017Future snow? A spatial-probabilistic assessment of the extraordinarily low
snowpacks of 2014 and 2015 in the Oregon CascadesSprolesEric A.eric.sproles@gmail.comhttps://orcid.org/0000-0003-1245-1653RothTravis R.NolinAnne W.Centro de Estudios Avanzados en Zonas Áridas, Universidad de La
Serena, Raul Bitran 1305, La Serena, ChileCollege of College of Earth, Ocean, and Atmospheric Sciences, Oregon
State University, 104 CEOAS Admin Bldg, Corvallis, OR 97331-5503, USAEric A. Sproles (eric.sproles@gmail.com)1February201711133134111March201621March201610November201615November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/331/2017/tc-11-331-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/331/2017/tc-11-331-2017.pdf
In the Pacific Northwest, USA, the extraordinarily low snowpacks of winters
2013–2014 and 2014–2015 stressed regional water resources and the
social-environmental system. We introduce two new approaches to better
understand how seasonal snow water storage during these two winters would
compare to snow water storage under warmer climate conditions. The first
approach calculates a spatial-probabilistic metric representing the
likelihood that the snow water storage of 2013–2014 and 2014–2015 would
occur under +2 ∘C perturbed climate conditions. We computed snow
water storage (basin-wide and across elevations) and the ratio of snow water
equivalent to cumulative precipitation (across elevations) for the McKenzie
River basin (3041 km2), a major tributary to the Willamette River in
Oregon, USA. We applied these computations to calculate the occurrence
probability for similarly low snow water storage under climate warming.
Results suggest that, relative to +2 ∘C conditions, basin-wide
snow water storage during winter 2013–2014 would be above average, while
that of winter 2014–2015 would be far below average. Snow water storage on
1 April corresponds to a 42 % (2013–2014) and 92 % (2014–2015)
probability of being met or exceeded in any given year. The second approach
introduces the concept of snow analogs to improve the anticipatory capacity
of climate change impacts on snow-derived water resources. The use of a
spatial-probabilistic approach and snow analogs provide new methods of
assessing basin-wide snow water storage in a non-stationary climate and are
readily applicable in other snow-dominated watersheds.
Introduction
In the Pacific Northwest (PNW), USA, mountain snowpacks during
the winters of 2013–2014 and 2014–2015 were at or near record lows and well
below 50 % of the historic median value (Mote et al., 2016; National
Resource Conservation Service, 2014, 2015b). For several decades the Natural
Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network has
provided measurements of snow water equivalent (SWE; the amount of water
contained within the snowpack) and meteorological data. These station-based
measurements have historically served as a proxy for basin-wide snow storage
and provide an effective SWE index for estimating streamflow; however under a
shifting climate these statistical relationships have also changed (Montoya
et al., 2014). The PNW's extremely low snowpacks and subsequent snow water
storage of 2013–2014 and 2014–2015 highlight the limitations of
location-specific measurements in a shifting climate.
Context map of the McKenzie River basin and its geographic
relationship to the Willamette River basin. The geographic locations of the
SNOTEL and other meteorological stations used as model forcings show the
altitudinal range of inputs. Inset (a) represents the area by
elevation for the McKenzie River basin. Inset (b) presents the
cumulative distribution functions (CDFs) for the elevation of the Willamette
and McKenzie River basins for elevations above 1000 m and is
separated into 50 m bins.
On 1 March 2015, 47 % of snow monitoring sites in the Willamette River
basin (WRB, 29 730 km2, Fig. 1) registered zero SWE, while snow was
still present at higher elevations. The absence of snow during the winter of
2014–2015 stands in contrast to cumulative winter precipitation, which was
at 83 % of normal (778 mm) for November–February (derived from
PRISM (Parameter-elevation Relationships on Independent Slopes Model) data
(Daly et al., 2008). While the concurrent drought in California received
substantial attention, the economic and environmental impacts in the PNW were
also profound. These two extremely low snowpacks in the PNW led to ski area
closures, recreation restrictions, municipal water limitations, severe
wildfires, low streamflows, nearly dry reservoirs, harmful algal blooms, and
high fish mortality (Associated Press, 2015; Morical, 2015; Oregon Department
of Fish and Wildlife, 2015; Meunier, 2015; Wang et al., 2015). These types of
externalities highlight the importance of mountain snow water storage and the
implications of snow drought.
Mountain snow water storage in the western Oregon Cascades and across the
western United States serves as vital inter-seasonal storage from cool, wet
winters with low water demand to hot, dry summers when demand peaks (Oregon
Water Supply & Conservation Initiative, 2008; United States Army Corps of
Engineers, 2001). The western Oregon Cascades form the eastern boundary of
the WRB (Fig. 1), and abundant winter precipitation falling in these
mountains (up to 3000 mmyr-1) sustains the 13th-highest
streamflow in the conterminous United States (Hulse et al., 2002). Even in
such a wet place, snowmelt is critically important. Brooks et al. (2012)
estimated that over 60–80 % of summer base flow in the Willamette River
derives from the snow zone at elevations over 1200 m, though this
elevational zone represents only 12 % of the land area and 15.6 % of
the annual precipitation in the basin.
The McKenzie River basin (MRB, 3041 km2) is a major tributary to the
WRB (Fig. 1) and is located in the main part of the Willamette's “at-risk”
snow zone (Nolin and Daly, 2006). Snowmelt in the MRB is critical to meeting
environmental and societal demands of the WRB, supplying almost 25 % of
the river's summer discharge at its confluence with the Columbia River near
Portland, Oregon (Hulse et al., 2002), despite only occupying 10 % of its
area. The hypsometry of the MRB and WRB is visually similar (Fig. 1b) and
statistically similar when tested using a two-parameter Kolmogorov–Smirnov
test for sample distribution (Young, 1977).
The maritime snowpacks of the MRB, WRB, and the PNW are deep
(> 1.5 m) and relatively warm (Sturm et al., 1995), and SWE
typically reaches its basin-wide maximum on approximately 1 April (Serreze et
al., 1999; Stewart et al., 2004). Nolin and Daly (2006) identified snow in
the WRB as climatologically “at-risk” since it typically accumulates at
close to 0 ∘C and can convert to rainfall with just a slight
increase in temperature. As a result of changes in circulation patterns and
warmer temperatures, there have been declines on 1 April SWE in the PNW
(Barnett et al., 2005; Kapnick and Hall, 2012; Luce and Holden, 2009; Mote,
2006; Mote et al., 2005; Service, 2004; Stoelinga et al., 2010), and peak
streamflow has shifted to earlier in the year (Fritze et al., 2011; Stewart,
2009).
These shifts in streamflow highlight the challenges of using
location-specific measurements of SWE for prediction in changing climate.
While SNOTEL sites provide valuable and robust data, they typically occupy a
limited elevation range that leads to an under-sampling of both the
high-elevation snow zone and the lower-elevation rain–snow transition zone
(Molotch and Bales, 2006; Montoya et al., 2014; Nolin, 2012). This limited
range holds true in the MRB, where the mean elevation is 1424 m and
the elevational range between the five stations is only 245 m.
Elevational shifts in snowpack accumulation due to observed temperature
increases make the past less representative of the future (Dozier, 2011;
Milly et al., 2008). Additionally, patterns of snow accumulation and melt in
the PNW vary as nonlinear functions of elevation, slope, aspect, and land
cover (Tennant et al., 2015). Augmenting point-based measurements of SWE with
metrics that effectively estimate snow water storage in a mountain landscape
would include calculations for normal and extreme years across elevations and
at the basin scale – especially under current climate trends (Dozier, 2011).
The dimensionless ratio of SWE to precipitation (SWE :P) represents
the proportion of snow water equivalent relative to cumulative precipitation
(snowfall plus rainfall) over a specified time interval (Serreze et
al., 1999). This ratio normalizes snow water storage by cumulative
precipitation, emphasizing the impacts of temperature on snowpack
accumulation and melt. When computed for 1 April, the time of year when
maximum basin-scale SWE is considered to occur, this ratio can be an
effective measure of the stages of accumulation and melt (Clow, 2010).
Understanding how relationships between snowpack, precipitation, and
temperature will be expressed at the basin scale is particularly important in
the maritime PNW. Physically based modeling studies of climate impacts in the
PNW describe reduced snow water storage and earlier streamflow across the
region (Elsner et al., 2010; Hamlet, 2011; Sproles et al., 2013). These
deterministic approaches provide a range of outputs of past and future
conditions. However these approaches stop short of an analog approach that
links an individual year from the past, particularly a low snow year, to
projected conditions.
Climate analogs serve as a useful device to examine potential impacts on
societally relevant predictands (e.g., forest health, environmental flows,
municipal water supply) and apply previous conditions to represent potential
future conditions (Hallegatte et al., 2007; McLeman and Hunter, 2010;
Ramírez-Villegas et al., 2011; Webb et al., 2013). For example,
Ramírez-Villegas et al. (2011) developed analogs of climate and
agricultural practices to identify prior climatic events that may provide
insights into the impacts of future climate change in both time and space.
Incorporating an analog approach allows planners and managers to develop
anticipatory capacity, the ability to better anticipate changing scenarios as
needs and context change over time (Nelson et al., 2008; Rhodes and Ross,
2009). Using the extremely low snow water storage of 2014–2015 as an
example, residents of the Willamette Valley raised concerns regarding the
safety and taste of domestic drinking water during the summer months. These
changes in water characteristics led public works departments to examine
future strategies and equipment to mitigate future water quality concerns
(Hall, 2015). From a hydrological perspective, this same analog approach is
also used in describing streamflow and is most commonly framed using
statistical metrics. For example, the spatial extent for a previous 100-year
flood event serves as an analog of floodplain dynamics and provides
anticipatory capacity for land use planners and water managers.
Based on the premise that future snow water storage conditions will resemble
previous winters that were warm, Luce et al. (2014) developed spatial and
temporal analogs of snow water storage sensitivity to temperature and
precipitation across the western United States using point-based SNOTEL data.
Similarly, Cooper et al. (2016) applied model-based analyses to compare the
winters of 2014 and 2015 to projected future conditions using individual
metrics of snowpack (snow disappearance date, date of peak SWE, and duration
of snow cover) at SNOTEL locations in the Oregon Cascades. This approach is
informative, even though point-based analysis in projected warmer conditions
may not represent basin-wide conditions (Dozier, 2011; Milly et al., 2008),
specifically as the rain–snow transition shifts towards higher elevations
(Nolin et al., 2012; Nolin and Daly, 2006).
Developing statistically valid analogs for snow water storage at the basin
scale requires a spatially explicit, probabilistic approach that calculates
the statistical likelihood of SWE across a topographically complex mountain
basin. For example, the question, “What is the likelihood that the snow
droughts of water years 2014 and 2015 will occur in
the future?”, can be addressed by developing statistical thresholds of SWE
and SWE :P with regards to time and location. This
spatial-probabilistic approach develops upper or lower limits of predicted
snow water storage conditions throughout a watershed. While probabilistic
approaches are common to streamflow hydrology, spatial approaches to
probabilistic questions are less common. A notable application of a spatially
based, probabilistic approach was developed by Graf (1984). This research
applied 107 years of channel migration records to calculate the probability
of subsequent erosion in a given parcel, creating a probabilistic map of
river movement. The map outlined the character of the river system that
identified areas where channel migration was more likely to occur. Margulis
et al. (2016) and Mote et al. (2016) characterized the extreme snow deficits
of 2015 across the western United States, but they did not compare this snow
drought to potentially warmer climatic conditions. Snow hydrology models can
readily incorporate climate change projections (Adam et al., 2009; Sproles et
al., 2013), and model outputs can be assessed using a spatial-probabilistic
framework that explicitly accounts for elevation.
This research introduces a physically based, spatial-probabilistic modeling
framework to compare the extraordinarily low snow winters of WYs 2014 and 2015
(WY: water year, defined as 1 October–30 September in the western
United States) in the context of warmer climatic conditions. Our approach
captures the spatial variability of mountain snow water storage under warmer
temperatures across decades by simulating the variability of SWE and
SWE :P at the basin scale for 23 WYs using +2 ∘C
conditions. These outputs are used to frame the snow water storage of
WYs 2014 and 2015 in the context of future snow and snow analogs. This
approach is intended to build anticipatory capacity for climate change
impacts in the PNW through snow analogs. While limited to the McKenzie River
basin (a well-studied watershed that is characteristic for maritime snow in
the WRB (Nolin and Daly, 2006), regional sensitivity to climate warming makes
PNW snowpack and snow water storage, and those in similar maritime climates,
acutely vulnerable to snow drought (Leibowitz et al., 2014; Nolin and Daly,
2006).
Specifically, we ask:
How does snow water storage from WYs 2014 and 2015 compare to snow
water storage under +2 ∘C conditions?
What is the probability that similar snowpacks and snow water storage
will occur in the future?
How does snow water storage during WYs 2014 and 2015 vary by elevation?
Research methods
Our approach applies a spatially distributed and physically based snow
hydrology model to compute probabilities of SWE and SWE :P for 23 WYs
under +2 ∘C winter conditions. We then model snow water storage
for WYs 2014 and 2015, which provide probabilistic context for snow water
storage during these two winters. Below we provide details on the study area
and specific methods used in this approach.
This study focuses on the McKenzie River basin. In addition to the MRB being
a major tributary to the Willamette River, it has a well-developed network of
meteorological stations associated with the HJ Andrews Long Term Ecological
Research site, four SNOTEL
stations, and four dams for flood control and hydropower; serves as the
primary source of domestic water for approximately 200 000 people; and is
home to federally protected salmonids, amphibians, and mussels. The MRB is
characterized by wet winters and dry summers, with average annual
precipitation ranging from 1000 to 3000 mm that follows the elevation
gradient (114–3147 m). Elevations between 1000 and 2000 m
comprise 42 % of the MRB's total area (Fig. 1a) and 93 % of the total
snow water storage in the MRB (Sproles et al., 2013). While elevations above
2000 m accumulate the most SWE per unit area, that zone comprises
only 1 % of total area and 6 % of the total snow water storage for
the MRB. In terms of volume, snow is the primary seasonal water storage
mechanism in the MRB with historic mean basin-wide snow water storage
(SWE × area; 1989–2009) of 1.26 km3 on 1 April (Sproles et
al., 2013), compared with total reservoir storage of 0.40 km3
(United States Army Corps of Engineers, 2016; United States Department of
Agriculture, 2016). By comparison, groundwater storage for the MRB was
estimated to be roughly 4 km3, with a mean transit time of 7 years
(Jefferson et al., 2006).
Spatially distributed values of precipitation and SWE were computed using
SnowModel (Liston and Elder, 2006a, b) for WYs 1989–2012. SnowModel is a
spatially distributed, process-based model that computes temperature,
precipitation, and the full winter season evolution of SWE including
accumulation, canopy interception, wind redistribution, sublimation,
evaporation, and melt. The model framework applied in this study is the same
as applied in Sproles et al. (2013), with the addition of a multi-layer
snowpack algorithm. Because the modeling framework is physically based and
spatially distributed, perturbations to temperature inputs will propagate
throughout the model, including absolute humidity and energy balance
calculations; thus maintaining the dependencies between snowpack and
temperature. WY 2005 was excluded due to prolonged regional temperature
inversions that were not resolved in the model (Sproles et al., 2013).
Model input data were derived from SNOTEL and station data within the study
area (six stations in total), nearly spanning the full elevation range of the
MRB (Fig. 1; Sproles et al., 2013). The 23-year set of model forcing data
includes winters with above-average, normal, and below-average snowpack;
positive, negative, and neutral El Niño–Southern Oscillation (ENSO)
climate patterns; and cool and warm phases of the Pacific Decadal Oscillation
(Brown and Kipfmueller, 2012). The model was run at a daily time step and
100 m grid resolution. In the calibration and validation phase, the model
was first calibrated to temperature and precipitation to ensure that the
model results were representative of these first-order inputs, with mean
Nash–Sutcliffe efficiencies (Legates and McCabe, 1999; Nash and Sutcliffe,
1970) of 0.80 and 0.97, respectively. The model was then calibrated for
physical snowpack conditions (mean Nash–Sutcliffe efficiency of 0.83 for
automated stations and 0.70 for field locations, and an overall spatial
accuracy of 82 % compared with Landsat fractional snow-covered area
(fSCA) data). For a detailed description of the model structure, calibration,
validation, and performance, please refer to Sproles et al. (2013).
Using the validated model, we increased temperatures by +2 ∘C and
re-ran the model over the same time frame and spatial domain. Projections for
future precipitation in the WRB and the PNW are highly uncertain (Safeeq et
al., 2016), and in the Oregon Cascades, temperature, not precipitation,
dominates the accumulation and melt cycles of snowpack (Sproles, 2012;
Sproles et al., 2013). Our delta increase in temperature is intended to be
straightforward and avoids the uncertainties associated with projections for
precipitation in the WRB and the PNW.
We extracted SWE and precipitation (P) data, and computed 5-day averages
for each centered on the first day of each month for January–June, for
every year in the model run, and for each grid cell in the model domain.
These 5-day mean values were used to minimize any effects from individual
events (melt, snowfall) while still capturing the overall snow water storage
characteristics at the beginning of the month.
Exceedance probability (EP) is a widely used hydrologic metric describing the
statistical likelihood that a value of a given magnitude or greater will
occur in a specified time period (e.g., annually) (Sadovský et al., 2012;
Salas and Obeysekera, 2013). Expressed as a percentage, it is calculated as
EP=mn+1×100,
where m is the rank of the data value (ranked from highest to lowest) and
n is the total number of data values (Dingman, 2002).
For example, 20 % EP (a low annual exceedance probability) is the
statistical likelihood that a value could be met or exceeded 20 % of the
time, or a one-in-five chance of occurring or being exceeded in any year. A
20 % EP represents a relatively large value. A 90 % EP (a high annual
exceedance probability) describes the statistical likelihood of a measurement
that would be met or exceeded in 90 % of the time and represents a
relatively low value. EP is commonly applied to point-based data such as a
stream gage or SNOTEL station. However, because mountain snow water storage
varies by elevation, slope, aspect, and land cover (Tennant et al., 2015), we
expanded point-based EP calculations to the watershed scale to include normal
and extreme years.
To accomplish a spatial perspective of exceedance probability, we applied
23 years of model output to compute the EP for the first of the month
(January–June) based upon the 5-day averaged SWE and SWE :P values
for each grid cell in the model domain. The dimensions of the model domain is
a grid of 759 rows × 1121 columns. In order to sort each grid cell
individually across the 23 data sets (years), the two-dimensional data sets
(759 rows × 1121 columns) were decomposed into 23 one-dimensional
vectors (1 × 850 839) and then combined to create a
23 × 850 839 matrix. The location information of each grid cell was
retained for subsequent mapping and analysis. For each year, the 23 values in
each row were sorted from highest to lowest. The 23×850 839 data
matrix was recomposed into 23 data matrices of dimension 759×1121,
creating a corresponding spatial exceedance probability matrix. This was
completed for each month (January–June).
To respond to the question, “How does snow water storage from WYs 2014
and 2015 compare to snow water storage under a warmer climate?,” we modeled
SWE and SWE :P using SnowModel with meteorological forcing data from
WYs 2014 and 2015 for the MRB, using the same stations as from our previously
validated model runs. These model runs were validated using the same methods
as described in Sproles et al. (2013). We then compared the snowpack metrics
from WYs 2014 and 2015 with model output from the 23 water years under a
+2 ∘C climate scenario.
Elevation is the most important physiographic variable in determining SWE in
this basin (Nolin, 2012), so we aggregated the data into 50 m elevation
bands (Fig. 1a). In each of these bands we computed snow water storage
(km3) and mean SWE :P (m/m). This allowed
us to understand the variation of snowpack properties by elevation, their
spatial probability of occurrence, and the statistical context for the
extraordinary snowpacks of WYs 2014 and 2015.
An important point to bear in mind is that the EP values were computed using
perturbed meteorological forcing data (+2 ∘C), while values for
WYs 2014 and 2015 were derived from unperturbed meteorological forcing data.
The total precipitation (a) and mean
temperatures (b) for the McKenzie River basin for water years 2014
and 2015, as compared to the 30-year normal (from the PRISM data sets). The
lower figure (c) represents basin-wide snow water storage for the
McKenzie River basin for water years 2014 and 2015 and the normals
(+2 ∘C) calculated from the 23 years used in this study. The
calculations for snowpack are 5-day averages centered on the first day of
each month.
Results
For context, historically in the MRB, 62 % of annual precipitation falls
in the November–March (N–M) time period, as calculated from 30-year PRISM
gridded climate normals of monthly precipitation (Daly et al., 2008). Within
that period, December–February (DJF) are historically the coldest and
wettest months (Daly et al., 2008). For N–M in WY 2014, precipitation was at
102 % of the 30-year normal (calculated from PRISM data), and temperatures
at SNOTEL stations in the MRB were 0.9 ∘C warmer than normal
(National Resource Conservation Service, 2015a). For the DJF period, WY 2014
monthly precipitation was 96 % of normal, and SNOTEL temperatures were
0.7 ∘C warmer than normal. During WY 2015, N–M precipitation was
81 % of the 30-year average, but temperatures in the snow zone were
2.7 ∘C warmer than average. For the DJF period of WY 2015, monthly
precipitation was 78 % of normal, and temperatures in the snow zone were
3.3 ∘C warmer than normal (National Resource Conservation Service,
2015a). To provide historical context, Fig. 2a and b graphically present the
30-year precipitation and temperature normals from the PRISM data sets as
compared to WYs 2014 and 2015. Figure 2c presents modeled basin-wide snow
water storage for WYs 2014 and 2015, as compared to the 23-year mean from the
+2 ∘C snowpack simulations.
The exceedance probability of basin-wide snow water storage under
+2 ∘C conditions. During 2014, snow water storage increased
considerably in March to reach above-average conditions. The snowpack during
the winter of 2015 was extremely low and never increased beyond
0.21 km3. The calculations are 5-day averages centered on the first
day of each month.
A warmer-than-normal January 2014 limited snowpack accumulation during the
early portion of the winter, and wetter-than-normal conditions in February
2014 accompanied by near normal mean temperatures increased basin-wide snow
water storage to near-average/above-average snowpack conditions (as compared
to a +2 ∘C perturbation) for the remainder of the season
(Fig. 2c). The warmer-than-normal conditions that persisted throughout
WY 2015 greatly inhibited seasonal snowpack accumulation, despite
above-average precipitation in March 2015 (Fig. 2c). For a more detailed long-term
climate analysis please refer to Abatzoglou et al. (2014) and Mote et
al. (2016).
Snow water storage
In the context of our exceedance probability framework, we see that the
1 April basin-wide snow water storage for WY 2014 falls between the 42 and
46 % EP, meaning that WY 2014 snow water storage is slightly above
average for a +2 ∘C model perturbation (Figs. 2, 3, 4, 5a, c).
Snowfall occurring after 1 April 2014 improved late-season snow water
storage, corresponding to 33 and 25 % EP for May and June, respectively
(Figs. 3, 4). In WY 2015 basin-wide snow water storage was well below
historical conditions, even when compared with +2 ∘C conditions.
1 April snow water storage for WY 2015 corresponds to 92 % EP (Figs. 3,
4, 5b, d). In that year, there was little late-spring snowfall, so, unlike WY
2014, basin-wide snow water storage did not increase (Fig. 3). WY 2015 was
also notable in that peak snow water storage occurred in January and was only
0.21 km3, corresponding to 79 % EP (Figs. 3, 4).
Volumetric snow water storage binned by 50 m elevation bands. The
corresponding basin-wide snow water storage (km3) for 2014 and 2015
is provided for each month. Larger snowpacks (lower exceedance probability)
have considerable contributions between 1000 and 1300 m. During 2014
and 2015, this elevation range had minimal snowpack, despite close-to-normal
precipitation. Note that, on the vertical axes, snow water storage below 500
and above 2500 m is not included for visual clarity. These
elevations contribute minimally to basin-wide snow water storage. The
calculations are 5-day averages centered on the first day of each month.
The spatial distribution of SWE on 1 April from water years 2014 and
2015 as compared to the corresponding EP. Both the distribution and magnitude
of SWE are strikingly similar. The calculations are 5-day averages centered
on the first day of each month.
Figure 4 shows the spatial exceedance probabilities for the +2 ∘C
model runs, aggregated into 50 m elevation increments (WY 2014, 42 % EP;
WY 2015, 92 % EP). For most years, the total amount of 1 April snow water
storage is greatest within the elevation range of 1300–1800 m.
However, in WY 2015 this mid-elevation zone (1300–1800 m),
representing 393 km2 (as calculated from the elevation data set), is
essentially snow-free (Fig. 4). Snow water storage in this elevation range is
critical for late-season runoff, as 1200 m represents the elevation
threshold for summer baseflow contributions (Brooks et al., 2012). From a
spatial perspective, Fig. 5 presents the distribution of SWE in the MRB in
WYs 2014 and 2015 on 1 April, as compared to the 46 and 92 % EP (as
compared to a +2 ∘C perturbation), respectively. These figures
show that snow water storage is almost entirely limited to the upper portions of
the basin and that the more spatially extensive mid-elevations where snow
accumulates historically are snow-free. In other words, in WYs 2014 and 2015,
the zone where snowmelt has historically contributed most to groundwater
recharge (Jefferson et al., 2008; Tague and Grant, 2009) shifted to rain.
Jefferson et al. (2008) showed that the recharge signal in the MRB varies
spatially and temporally, and that the location of the rain–snow transition
is the dominant control on recharge for at the watershed scale.
SWE :P
This elevation-dependent shift from rain to snow is evident in Fig. 6, where,
at an elevation of 1200 m, SWE :P is below 0.06 for the period
January to June in both WYs 2014 and 2015. This ratio does not exceed 0.20
until an elevation of 1500 m in WY 2014, which is still markedly
lower than the long-term mean SWE :P at the McKenzie SNOTEL site
(0.58, 1454 m). In WY 2015 this 0.20 threshold is not reached until
an elevation of 1750 m, approximately 300 m above the
highest-elevation SNOTEL site in the MRB, and thus was not captured in the
SNOTEL data. From February to May in WY 2014, SWE :P increased due to
late-season storms that added snow water storage and remained above 50 %
EP when compared with +2 ∘C conditions. From February to May in WY
2015, SWE :P never surpassed the 0.60 threshold and remained below
90 % EP when compared with +2 ∘C conditions.
The ratio of SWE :P binned by 50 m elevation bands. The
relationship between elevation and SWE :P is evident across all
exceedance probabilities. Under +2 ∘C simulations and in 2014 and
2015, roughly 1500 m is the elevation at which SWE :P begins
to increase substantially along the horizontal axis. Note that, on the
vertical axes, snow water storage below 500 and above 2500 m is not
included for visual clarity. These elevations contribute minimally to
basin-wide snow water storage. The calculations are 5-day averages centered
on the first day of each month.
Discussion and conclusion
The winters of 2014 and 2015 had very low snowpacks across the Pacific
Northwest due to higher-than-normal winter temperatures but average or
near-average precipitation (Fig. 2, Mote et al., 2016; National Resource
Conservation Service, 2014, 2015b), highlighting the sensitivity of the
region's snowpack to increased temperature. In the MRB snow zone, mean
temperatures (N–M) were 0.9 ∘C above the 30-year normal in WY 2014,
while in WY 2015 they were 2.7 ∘C above normal. These low snow years
persisted even under normal and slightly below normal N–M precipitation
(WY 2014, 102 %; WY 2015, 81 %). The SWE :P metric also
identifies increased temperature, rather than reduced precipitation, as the
primary reason for the diminished snow water storage of WYs 2014 and 2015,
especially at mid-elevations. At 1500 m, the April SWE :P
values for the 2 years are considerably different (Fig. 6; WY 2014,
SWE :P=0.22, 60 % EP; WY 2015, SWE :P=0.04, 95 %
EP).
As such, these two winters' extraordinarily low snowpacks offer an analog
perspective for projected future snow conditions in the MRB and, potentially,
the Willamette River basin. WY 2014 serves as a snow analog for slightly
warmer conditions (+1 ∘C), with an EP between 42 and 46 %,
while WY 2015 serves as a snow analog for conditions increasing beyond
2.5 ∘C with an EP of around 92 %. The volumetric difference
between the 2 years is considerable (0.56 km3), representing 1.4
times more than the total reservoir storage capacity of the MRB (United
States Army Corps of Engineers, 2016; United States Department of
Agriculture, 2016).
The SWE :P metrics across elevation bands provide a simple yet telling
description of precipitation phase (rainfall vs. snowfall) and the seasonal
evolution of snow water storage (accumulation and ablation). The shifts from
rain to snow seen in the modeled results highlight the limitations of a
monitoring network that occupies a limited range. In the MRB, the SNOTEL
stations occupy a mean elevation of 1424 m, with a range of only
245 m. During WYs 2014 and 2015, this limited range did not capture
zones with maximum snow water volume and were essentially below the
rain–snow transition (Figs. 4, 6). This same under-representation of
snowpack was found throughout the greater WRB, with 47 % of snow
monitoring sites registering zero SWE while snow was still present at higher
elevations on 1 March 2015.
As precipitation shifts from snow to rain, the SWE :P metric can
augment individual values of SWE and P to provide key information on shifts
in water storage throughout the course of a winter and valuable insights to
water resource managers in a non-stationary climate. For example, on 1 March,
when basin-wide SWE is typically approaching its maximum, both years are
essentially snow-free at 1200 m. A low SWE :P ratio in March
under normal winter precipitation conditions could indicate peak streamflow
has occurred or most likely would occur earlier in the year, which has
important implications for water resource management in subsequent months.
Low snow water storage and shifts in streamflow negatively impact water
quantity, water quality, hydropower operations, winter snow sports, and
summer recreation. In WY 2015, record low snow water storage led to summer
drought declarations, extreme fire danger, and modified hydropower operations
in the MRB. The typical consistent flow of the groundwater-fed McKenzie River
was at 63 % of August–September median flow (United States Geological
Survey, 2015). Hoodoo Ski Area, located at Santiam Pass, was open for only a
few weekends in WY 2014, and in WY 2015 they suspended operations in
mid-January, the shortest season in their 77-year history. In the adjacent
Santiam River basin (north of the MRB), diminished snow water storage and
less-than-anticipated spring rains in WY 2015 pushed the Detroit Reservoir
(storage capacity 0.35 km3) to historically low levels. In May,
harmful blue-green algae concentrations were above acceptable amounts by a
factor of 7, and July reservoir levels were approximately 21 m below
capacity. Concerns over the taste and safety of domestic drinking water in
the Willamette Valley prompted municipal water managers to explore options
for upgrading water treatment facilities.
At more broad timescales, the shift from snow to rain at mid-elevations could
also potentially impact groundwater recharge. The rain–snow transition is
the dominant control on recharge in the MRB and varies spatially and
temporally (Jefferson et al., 2008). Because groundwater storage is large and
transit times in the MRB are approximately 7 years (Jefferson et al., 2008),
the full impacts of WYs 2014 and 2015 on ground and surface water resources
are not yet known.
Water quality, energy production, and recreation externalities are not well
represented in deterministic models but become challenging realities that the
public faces in years with low snow. Intervention strategies can fail because
they lack adequate information about the impacts of climate change and are
not incorporated into deterministic physical models that play out at the
human scale (Ramírez-Villegas et al., 2011). Transitioning from purely
deterministic approaches (i.e., snow water storage is reduced by a certain
percentage) to ones that link climate and snow conditions with real-world
impacts provides a complementary perspective for mitigation and adaptation.
Our analog approach combines projected climate impacts with the extremely low
snow years of 2013–2014 and 2014–2015 for insights into improved management
in shifting conditions. Such an analog approach allows planners and managers
to develop adaptation and mitigation strategies that use the past to
demonstrate what did or did not work under climate stress and help build a
more informed understanding of ways to improve future planning efforts
(Ramírez-Villegas et al., 2011).
Climate change impacts are often expressed in probabilistic terms (Randall et
al., 2007), and so it is logically consistent to estimate snowpacks and snow
water storage in this manner. This research does not assume that the
probabilities presented here are based upon a precise representation of
future conditions, nor that future climates will be +2 ∘C warmer
every winter. We present these results as a way to frame the likelihood of
future basin-wide snow water storage in the context of our current
understanding of climate change. These probabilistic insights are then used
to identify WYs 2014 and 2015 as analog years for managers and decision
makers. Snow water storage in WY 2014 would be slightly above average for
+2 ∘C conditions, and snow water storage in WY 2015 would be very
low for +2 ∘C conditions, albeit not a record low. These analog years
thus provide guidance for adaptation strategies to mitigate potential
failures of existing management plans.
Our spatially explicit approach augments information from the existing SNOTEL
network. While SNOTEL data continue to play a key role for seasonal
streamflow forecasting under historic climatic conditions, these statistical
relationships have been changing (Montoya et al., 2014). While providing
modern scientific equipment, SNOTEL sites in the MRB occupy a limited range
(245 m) at the mid-elevations and may not capture basin-wide snow
water storage in warmer conditions. For example, in the MRB, all SNOTEL sites
in the MRB were snow-free for most of February to March 2015 and were,
therefore, incapable of providing predictive skill for water resource
management. Our basin-scale probabilistic approach provides a more complete
picture of water storage and captures the elevation variability absent in
point-based measurements.
The winters of WYs 2014 and 2015 demonstrate a considerable departure from
the stationary snow water storage conditions on which present-day management
plans are based. With continued current warmer climates, the snow water
storage conditions represented by these two winters are more likely to occur.
In the meantime, the value of spatially explicit probabilistic calculations
rests in the ability to better define the range of statistical outcomes of
subsequent winters that are representative of basin-wide conditions. Framing
the low snow water storage of WYs 2014 and 2015 as analogs of future snow
provides insights into potential climate impacts and externalities on social
and environmental systems. Together, probabilistic metrics and snow water
storage analogs can help build capacity to better anticipate hydrologic
changes in a warming climate.
Data availability
The data used in this article are available at
10.7267/N9V985ZN.
Acknowledgements
This research was funded in part by a NASA award entitled “New Metrics for
Snow in a Warming World: Indicators for the National Climate Assessment”
(proposal no. 14-INCA14-0089) and two awards from the National Science
Foundation (Doctoral Dissertation Improvement, BCS-0903118; Water
Sustainability and Climate, EAR-1039192). We gratefully acknowledge the
modeling guidance of Glen Liston. The authors would also like to thank the
associate editor, Ross Brown, and P. Mote and another anonymous reviewer for
their insightful comments that significantly improved the quality of the
manuscript.
Edited by: R. Brown
Reviewed by: P. Mote and one anonymous referee
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