RESULTS
4.1 Snow simulations
The hydrological model performance in simulating SWE is adequate for all
winter seasons at the snow pillow site in Reynolds Mountain (Figure 4),
which provides confidence that the model can represent snow melt and
other fluxes that cannot be easily measured. The Nash-Sutcliffe
efficiency (NSE ), normalized mean bias (NBIAS ), and root
mean squared error (RMSE ) are 0.95, -0.01, and 4.8 cm,
respectively. The cumulative snowfall shown in Figure 4 illustrates the
accumulation or loss of snow at the snow pillow site. There is a
pronounced difference between cumulative snowfall and peak SWE (Figure
4), indicating the snow depletion by midwinter melting, winter snow
redistribution, and sublimation during most of the simulation period.
Further diagnosis, using model outputs, suggests that melting processes
during midwinter and early spring are responsible for large snow
depletion due to a series of warm spells having a maximum air
temperature of ~10 ºC. For example, during 1983-84
winter, five warm spells (one week duration each) occurred with a mean
air temperature of 5 ºC that resulted in melting 176 mm of SWE before it
peaked on April 15, 1984.
The simulated spatial snow depth agrees well with the observed snow
depth from airborne lidar (Figure 5; Table 2). The spatial NSEbetween observations and simulations is 0.7 for all HRUs. The mean of
observed and simulated spatial snow depths are 0.99 m and 1.03 m,
respectively. The model captures the areas of snow sinks and sources
with reasonable error ranges (Figure 5). Both simulated and observed
snow depth maps provide the first sign of underlying spatial controls on
the distributed snow processes. The snowpack usually recedes markedly in
almost 50% of the basin by mid-March. Snowpack in HRUs with north
facing aspects and forest cover (Figure 2c, Figure 3), however, does not
reach the peak value (Figure 5). Table 2 shows that sagebrush and aspen
HRUs having north facing steep slopes have high snow depths as these
areas have slow snow depletion rates with low solar radiation and
receive transport of snow from other HRUs. For example, a total sunshine
period during 2008-2009 winter for north facing steep slopes in
sagebrush is 45 days, which is lower than that of the east facing
sagebrush regions (66 days). Aspen and riparian forest on flat-lying
areas in the valley bottom also experience high snow accumulations as
snow is redistributed from sagebrush and grassland areas to wooded areas
in both basins.
Modeled net blowing snow transport into the riparian forest is up to 62
mm, which is 10% of the 2008-09 total precipitation. In contrast, the
blowing snow transport from the sagebrush HRU is 32 mm. Thus, vegetation
type or height, aspect, slope, and topographic depressions play an
essential role in the spatial variability of snow accumulation. Previous
studies (Reba et al., 2011a; Kumar et al., 2013; Rasouli et al., 2015)
emphasized the impacts of vegetation induced blowing snow transport as a
major process responsible for the spatial variation of SWE. Vegetation,
aspect, and slopes are found as partial factors affecting the spatial
variation of snow accumulation at a regional scale in western North
America (Tennant et al., 2017).
4.2 Relation between climatic
teleconnection patterans and local
hydrology
The Pearson correlation coefficients were calculated between basin-scale
hydrological variables, including observed mean annual and winter air
temperatures, modeled annual rainfall, the ratio of modeled rainfall to
observed precipitation, modeled snowfall, observed annual runoff, the
ratio of annual runoff to total annual precipitation, observed peak SWE
and its timing in Reynolds Mountain, and teleconnection patterns (Figure
6). The partitioning of precipitation into rainfall and snowfall was
used to calculate the ratio of rainfall to total precipitation, which
showed the strongest relation with AAO in the same year with a
correlation coefficient of -0.7 (Figure 6a). The ratio of rainfall to
precipitation also has an intermediate positive correlation with SST
(Figure 6b) and a strong negative correlation with PNA in the preceding
year (Figure 6e). The climate teleconnection of NAO showed an
intermediate positive relation with winter air temperature and a
negative relation with the annual ROS runoff (Figure 6d).
Six major hydroclimatic phases with distinct climatic conditions were
identified based on the decomposed multiple year frequency time series
of daily precipitation (Figure 7). Each phase was classified into a wet
or dry (above or below average, respectively) span, lasting for three to
eight years. Characteristics of the identified hydroclimatic phases
(Figure 7) in relating the hydrological variables and climatic
teleconnection patterns are demonstrated in Figure 8. Phase (1) is a wet
and cold period under negative AO and SST from 1984 to 1986; phase (2)
is a dry and warm period from 1987 to 1994 under positive AO and NAO;
phase (3) is a wet and cold period from 1995 to 1999 under positive AAO;
phase (4) is a warm period from 2000 to 2003 under the SST transitioning
from negative to positive and positive PNA; phase (5) is in a transition
from warm to cold conditions under positive PNA and negative NAO from
2004 to 2011; and finally, phase (6) is a low flow period from 2012 to
2014 under negative PNA and positive NAO.
4.3 Time variation of rain on snow
(ROS)
events
The hydrological importance of ROS events in generating high flows and
its potential relation with NAO and AAO, warrants studying these events
in more detail for different snow regimes in Reynolds Mountain. Snowmelt
generates substantial runoff during ROS events. The ROS contribution to
total runoff, however, depends on (i) snow cover of the HRUs and (ii)
rainfall occurrence. Heterogeneity of snow cover due to topography and
redistribution of snow by wind controls the runoff during ROS events.
Snow transport to sinks and topographic depressions with drifted snow
can intensify snowmelt in spring, and early summer when the likelihood
of rainfall is high.
HRUs with drifted snow have deep snowpacks (Figure 5) and generate high
ROS runoff depths and contribute more than other HRUs to basin
streamflow. Figure 9 shows the annual runoff generated in four snow
regimes. HRUs were grouped into four blowing snow regimes (Rasouli et
al., 2015), including sink and source, and intercepted and sheltered
snow. These categories are based on topographic exposure and vegetation
height (Pomeroy et al., 1997). Blowing snow sink HRUs include drift
HRUs, riparian, and tall sage HRUs. The HRUs covered with short
vegetation were grouped as source HRUs. The forested landscapes were
divided into those that are subject to interception (coniferous fir) and
those that are cleared or have negligible winter interception capacity
(Deciduous aspen, Pomeroy et al., 2002). Runoff generated during ROS
events varies with blowing snow regimes in different hydroclimatic
phases (Figure 9). Forest landscapes with intercepted snow on canopies
generate larger ROS runoff than other snow regimes in all six
hydroclimatic phases (Figure 9a). The sheltered forest landscapes with
minimal blowing wind generated the highest ROS runoff during phase three
among all snow regimes, with 60% above normal (Figure 9a). This is
likely due to the above-normal precipitation (206 mm, 21%) and
below-normal winter air temperature (0.5 °C) during phase three, which
prolonged the snow cover period by 16 days above normal (Table 3) and
increased the frequency of the ROS events. In contrast to the sheltered
HRUs, the blowing snow source HRUs showed the lowest (40% below normal)
ROS runoff generation during phase two (Figure 9b). In phase two, annual
precipitation was 163 mm (17%) below normal and the winter air
temperature was 0.3°C warmer than the normal values (Table 3). Despite
the high rainfall ratio in this phase, the modeled ROS runoff averaged
for the entire basin was the lowest among the phases with 56 mm (31%)
below normal (Table 3 and Figure 9). This is because of a short period
of snow cover (21 days shorter than normal, Table 3) and the effect of a
strong positive phase of NAO in hydroclimatic phase two (Figure 8). A
mechanistic diagnosis on ROS runoff is critical as the mid-latitude
basins are expected to experience warmer conditions and precipitation
phase change from snow to rain in the future.
The observed runoff ratio, defined as the ratio of total annual runoff
to total annual precipitation, varies between 13% above normal in
phases one and three and 13% below normal in phase six (Table 3). Time
series of the runoff ratio (Figure 8) are consistent with the values
reported by Sridhar and Nayak (2010).
4.4 Synthesis of the hydrological
linkage to climate teleconnection patterns in hydroclimatic
phases
The time-averaged observed precipitation (snowfall and rainfall ratio),
mean annual and winter air temperatures, observed streamflow, modeled
ROS, observed peak SWE and observed snow cover duration in Reynolds
Mountain are reported in Table 3. The linkage of these variables in a
small basin to regional climatic teleconnection patterns was synthesized
for the six hydroclimatic phases as the following:
Phase one (1984-1986, cold and wet, high flow, negative phases of
AO and SST). In this phase, the observed peak SWE and annual runoff
were respectively 315 mm (63%) and 314 mm (57%) above normal, and the
observed runoff ratio was up by 13% (Table 3). The observed mean annual
air temperature was 0.6 ºC below normal, and annual precipitation was
301 mm above normal, which makes this phase the coldest and wettest
among the phases. The highest annual runoff and peak SWE were linked to
strong negative phases of SST and AO (Figure 8). The high SWE
accumulations and subsequent runoff generations were spatially
restricted to areas with drifted snow and north facing HRUs (Figure 10).
Runoff generated during ROS events were, however, quite low during phase
one across Reynolds Mountain.
Phase two (1987-1994, dry, positive phases of NAO and AO) . The
observed annual runoff and modeled ROS runoff were respectively 163 mm
(30%) and 56 mm (31%) below normal (Table 3). A strong positive phase
of NAO (Figure 8) locked the polar cold air in the Arctic region,
leading to a slightly warmer and drier than normal winter in the study
area in phase two, which restricted the generation of ROS runoff. As a
result, this decreased the annual runoff. The spatial variations of
modeled peak SWE and runoff were smaller than other wet phases, such as
phase one (Figure 10).
Phase three (1995-1999, cold, high ROS runoff, negative AO,
positive AAO). Mean annual air temperature was 0.6 ºC below normal, and
the annual ROS runoff was 57 mm (45%) above normal, the highest among
the six phases (Table 3). Similar to phase one, high SWE accumulations
and subsequent ROS runoff generations were spatially restricted to drift
and north facing HRUs (Figure 10). The runoff depth was quite higher
than peak SWE (Figure 10), indicating a substantial contribution from
rainfall induced events to total runoff. Despite the similarity in snow,
rain, and air temperature between this phase and phase one, a large
difference is observed in ROS runoff. The only difference between the
two phases is the type and phase of teleconnection patterns. Positive
AAO is likely the main reason for high ROS runoff in this phase.
Phase four (2000-2003, warm, positive PNA). The annual and winter
air temperatures were 0.7 °C and 0.5 °C above normal, respectively,
causing this phase to be the warmest among the six phases with
near-freezing winter temperatures (Table 3). Such hydrological responses
suggested that Reynolds Mountain is very sensitive to changes in winter
air temperature and warming of 0.5 ºC can shift winter temperatures from
below-freezing to above-freezing conditions, resulting in reduction of
the ROS runoff by 9% below the normal. A relatively low flow condition
in this phase is associated with the positive phase of PNA.
Phase five (2004-2011, normal, negative NAO, positive PNA) .
Similar to phase three, the runoff generated from ROS events is high in
this phase (36 mm (20%) above normal, Table 3), which is the second
largest contribution to annual runoff among the six phases. The runoff
amounts are higher than peak SWEs across the basin, indicating the
substantial contribution of rainfall to total annual runoff (Figure 10).
Positive PNA partly moderated the effect of negative NAO on air
temperature and precipitation. It, however, was not enough to offset the
effect of the negative NAO on runoff, and as a result, a slight increase
in annual runoff and a relatively large increase in the ROS runoff were
observed in this phase.
Phase six (2012-2014, dry, low flow, low runoff ratio, positive
NAO, negative PNA) . This phase represents an extreme hydroclimatic
condition. The observed annual precipitation was 194 mm (20%) below
normal and the observed mean annual air temperature was 0.5 ºC above
normal. Warm and dry conditions caused observed peak SWE to drop 275 mm
(55%) below normal, snow cover season to shorten 26 days below normal,
and observed annual runoff to drop 234 mm (43%). The NAO and PNA phases
are opposite of those in phase five, and as a result, there is an
opposite response of the basin to phase change. The radical changes
relative to phase five is associated with positive NAO, which led to
dry, low snow, and low flow conditions. This clearly explains the
important role of the phase of the climate teleconnection patterns in
altering hydrological conditions in small basins.