DISCUSSION
Through linking hydrological fluxes at local scales to regional climatic
teleconnection patterns beyond seasonal variations, we can better
understand local hydrological processes across multiple years (Prein et
al., 2015) and the nature of regional climate and hydrological extremes
(Langendijk et al., 2019). Regional climatic patterns can explain
nonlinear hydrological behaviors. For example, the hydroclimatic phases
one and three in this study had similar annual air temperature and
precipitation (<10% difference (Table 3)). Snow and ROS
runoff, however, showed substantially different responses to variations
of air temperature and precipitation. The observed peak SWE was 63%
above normal in phase one and only 24% above normal in phase three. The
modeled ROS runoff was 7% below normal in phase one, while it was 45%
above normal in phase three (Table 3). Phase one was influenced by
negative SST with La Niño events over 1984–1986, while positive AAO
influenced phase three. Similar local hydrology and different climate
teleconnection pattern (negative SST in phase one and negative AAO in
phase three) explained a 52% difference in the modeled ROS runoff
(Table 3). The same but opposite-sign teleconnections in phases five and
six resulted in different snow and runoff conditions, wet in phase five
and dry in phase six. This suggests that there is a strong linkage
between teleconnection patterns and local hydrological regimes despite a
scale mismatch. The different ROS runoff in phases one and three and the
snow and runoff regimes in phases five and six highlighted the role of
climate teleconnection patterns in dictating hydrological conditions at
local scales. This is consistent with the findings of Whitfield (2001),
which showed that small variations in climatic patterns can lead to
large hydrological responses. Bonsal and Shabbar (2008) reported that
low flow events in western Canada are associated with positive phases of
the PNA pattern. The positive NAO, however, is more influential than the
positive PNA pattern in decreasing the annual runoff and ROS runoff in
Reynolds Mountain (phases two and six, Table 3).
The modeled ROS runoff had large temporal and spatial variations (Figure
9). High variability of ROS runoff implied that not only the regional
climate teleconnections affected the local scale hydrology, but also
vegetation heterogeneity played an important role. The sheltered forest
landscapes with minimal blowing wind generated the highest ROS runoff
during phase three (Figure 9a), while the blowing snow source HRUs
showed the lowest ROS runoff in phase two. The above-normal
precipitation (206 mm, 21%) and the below-normal winter air temperature
(0.5 °C) during phase three prolonged the snow cover period by 8 days
above normal (Table 3) and increased the frequency of the ROS events in
the sheltered forest landscapes. Annual precipitation was 163 mm (17%)
below normal and the winter air temperature was 0.3 °C warmer than the
normal values (Table 3) in phase two, which led to the lowest ROS runoff
in blowing snow sources HRUs among the six phases (Table 3 and Figure
9). This is because of a short period of snow cover in the warm phase
and snow transport out of the source HRUs by blowing wind. The frequent
ROS events are correlated with the positive phase of SST and El-Niño
events (McCabe et al., 2007). Consistently, the hydroclimatic conditions
in phase one with a negative SST (Figure 9) and La Niño events showed
less frequent events by decreasing ROS runoff by 7% below normal (Table
3). A strong positive NAO in the hydroclimatic phases two and six
(Figure 8) affected the interannual variability of snow cover (Derksen
et al. 2008; Ge & Gong 2009; Bao et al. 2011) and locked the polar cold
air in the Arctic region (Francis & Vavrus, 2012; Cohen et al., 2014),
leading to a warmer, drier, and shorter than normal winter in the study
area. Warm and dry conditions restricted the generation of ROS runoff,
especially in HRUs with short vegetation (blowing snow source HRUs in
Figure 9b). Therefore, positive NAO and AAO have more pronounced effects
on ROS runoff than negative SST or positive PNA pattern. The positive
NAO decreased the ROS runoff by 26% below normal, while the positive
AAO increased the ROS runoff by 45% above normal (Table 3). A strong
negative correlation between AAO and rainfall ratio (0.70, Figure 6a)
indirectly showed the effect of AAO on the magnitude of ROS runoff by
changing precipitation phase from snowfall to rainfall.
Despite a small effect of the negative SST on ROS runoff, it had the
largest effect on annual peak SWE and runoff increase in Reynolds
Mountain and it elevated the observed peak SWE and annual runoff by 63%
and 57% above normal, respectively (phase one, Table 3). On the other
hand, the positive NAO had the largest impact on snow and runoff drop
and it decreased the observed peak SWE and annual runoff by 55% and
43%, respectively (phase six, Table 3), and the modeled ROS runoff by
31% (phase two). A high correlation between ROS runoff and NAO (Figure
6d) explained their potential relation.
The runoff generations in drift and north facing HRUs are sensitive to a
wet climate with warm air temperatures (Figure 10). The modeled peak SWE
is almost similar to a modeled runoff in these HRUs during dry phases
(e.g., phase two) while under wet conditions (e.g., phase one) annual
runoff is greater than peak SWE. The spatial variability of runoff under
low flow conditions is very similar to that in the low ROS conditions
(phase two and six in Figure 10). The highest annual runoff in Reynolds
Mountain occurred when rainfall ratio was near the long term average and
mean annual air temperature was 0.6 ºC cooler than the average (phase
one, Table 3). Under cold air temperatures, snowpack is usually deep and
sustains the baseflows in summer, which may increase the annual runoff.
Under warm air temperatures, however, we may not expect an increase in
annual runoff (i.e., phase six) as both rainfall proportion of
precipitation and ET will increase, which may cancel each other out
(Rasouli, 2017; Rasouli et al., 2019a).
As the detected hydroclimatic phases are temporally evolving, they can
be applied in short and medium range hydrological predictions. For
instance, based on the distance of the present-year precipitation from
the long term average (Figure 7) and because of a climatological
persistence, it is likely that annual precipitation will be below normal
for the following year after the sixth phase in Reynolds Mountain. Dutta
and Maity (2018) found that a temporally evolving hydroclimatic
teleconnection can improve the predictability of the monsoon rainfall at
local scales. The application of climate variability patterns as an
input to machine-learning methods has been shown to be skillful in
improving short-term (Rasouli, Hsieh, & Cannon, 2012) and long-term
(Rasouli, Nasri, Soleymani, Mahmood, Hori, & Torabi Haghighi, 2020)
streamflow forecasts. A numerical representation of governing
hydrological processes by a physically based model can help simulate and
forecast the hydrological processes in the present and future climates
under different phases of climate variability patterns such as AO
(Thompson & Wallace, 1998), NAO (Walker & Bliss, 1932), AAO (Thompson
& Wallace, 2000), SST in the Niño 3.4 region (Trenberth, 1997), and
PNA, (Blackmon, Lee, Wallace, & Hsu, 1984).
The uncertainty in modeling the high flows using the CRHM model and
accurate delineation of the hydroclimatic phases due to occasional
overlapping of the positive and negative phases of different
teleconnection patterns are the main limitations of this study and
should be taken into account when interpreting the results.