Main Text File
Quantifying daily versus annual contributions of snowmelt water to
streamflow using graphical and geochemical hydrograph separation
Abstract
In this study, we characterize the snowmelt hydrological response of
nine nested headwater watersheds in southeast Wyoming by separating
streamflow into three components using a combination of tracer and
graphical approaches. First, continuous records of specific conductance
(SC) from 2016 to 2018 were used to separate streamflow into direct
runoff and baseflow components. Then, diurnal streamflow cycles
occurring during the snowmelt season were used to graphically separate
direct runoff into quickflow, representing water with the shortest
residence time, and throughflow, representing water with longer
residence time in the soil column and/or regolith layers before becoming
streamflow. On average, annual streamflow was comprised of between 22%
to 46% baseflow, 7% to 14% quickflow, and 46% to 55% throughflow
across the watersheds. We then quantified hysteresis at both annual and
daily timescales by plotting SC versus discharge. Annually, most
watersheds showed negative, concave, anti-clockwise hysteretic direction
suggesting faster flow pathways dominate streamflow on the rising limb
of the annual hydrograph relative to the falling limb. At the daily
timescale during snowmelt-induced diurnal streamflow cycles, hysteresis
was negative, but with a clockwise direction implying that quickflow
peaks generated from the concurrent daily snowmelt, with shorter
residence times and lower specific conductance, arrive after throughflow
peaks and preferentially contribute on the falling limb of diurnal
cycles. Slope aspect and surficial geology were highly correlated with
the partitioning of streamflow components. South-facing watersheds were
more susceptible to early season snowmelt at slower rates, resulting in
less direct runoff and more baseflow contribution. Conversely,
north-facing watersheds had longer snow persistence and larger
proportions of direct runoff and quickflow. Watersheds with surficial
and bedrock geologies dominated by glacial deposits had a lower
proportion of quickflow compared to watersheds with large percentages of
metasedimentary rocks and glaciated bedrock.
Keywords: snowmelt, hydrology, streamflow, watershed, tracer, specific
conductance, hydrograph separation, hysteresis
INTRODUCTION
Rivers draining mountainous regions around the world are vital for
supplying water for both consumptive uses and ecosystem services. This
is especially true in regions like the Southern Rocky Mountains where
the surrounding basins are classified as semi-arid to arid and rely
predominantly on mountain runoff to meet water demands. Agriculture in
this area, for example, is considered at risk from changing snowmelt
patterns associated with global climate change (Qin et al., 2020). In
such systems, snow is the dominant form of precipitation, but warming
temperatures threaten to decrease annual snowpacks and shift seasonality
(IPCC, 2013). Since snowmelt is more efficient than rain at generating
streamflow (Li, Wrzesien, Durand, Adam, & Lettenmaier, 2017) and
recharging aquifers in many mountainous regions (Earman, Campbell,
Phillips, & Newman, 2006; Jasechko et al., 2014), reductions in
snowpack pose a potential disruption to the delivery of water to streams
and aquifers. Increases in temperature can reduce snowmelt rates
(Musselman, Clark, Liu, Ikeda, & Rasmussen, 2017) and snow water
equivalent (SWE) (Hamlet, Mote, Clark, & Lettenmaier, 2005), resulting
in earlier timing in the onset of streamflow in western North America
(Stewart, Cayan, & Dettinger, 2005) and subsequent decreases in summer
streamflow (Rood et al., 2008) – potentially leading to habitat loss
for endemic species (Lytle & Poff, 2004).
Changes in snowmelt are of course only part of the story for how
mountain rivers and streams respond hydrologically under climatic
shifts. The time it takes for water to move through a watershed and
arrive in streams, thus controlling the shape of the hydrograph, is the
result of interactions between precipitation inputs and the eco-physical
characteristics of the watershed (Singh, 1997). Variations in vegetation
composition (Molotch et al. , 2009; Vivoni et al. , 2008),
topography (Broxton, Troch, & Lyon, 2009; Webb, Fassnacht, & Gooseff,
2018), and the lithology and structure of underlying soil and geologic
formations (Mueller, Weingartner, & Alewell, 2013; Nippgen, McGlynn,
Marshall, & Emanuel, 2011) can influence the residence time and flow
pathways of water in concert thereby impacting both the quality and
quantity of water arriving in streams.
Detangling such interactions is particularly challenging in mountainous
regions given the complexity and remoteness of the landscapes (e.g.
Jantze, Laudon, Dahlke, & Lyon, 2015). Still, efforts to characterize
older groundwater contributions to streamflow (i.e. baseflow) in
snowmelt-dominated headwater mountains regions serve to increase our
understanding on how groundwater interacts with the environment and has
important implications for the vulnerability of water resources and
ecosystems under warming conditions (Miller, Buto, Susong, & Rumsey,
2016). The effect of the underlying geologic formations becomes most
apparent during dry weather and baseflow conditions, when direct runoff
has drained and streamflow comes from older groundwater sources (Cross,
1949). Understanding how groundwater contributions to streamflow are
affected by different geologies is needed given that increasing
temperatures have shown to reduce runoff efficiency (the ratio of runoff
to precipitation) in watersheds near our study area (McCabe, Wolock,
Pederson, Woodhouse, & McAfee, 2017).
Hydrologists have established numerous methods for separating
hydrographs for well over 150 years. For example, Hall (1968) provides a
thorough review on early attempts to model baseflow using graphical or
mathematical approaches. Graphical approaches continue to be popular
choices in data-limited regions as they only require observation of
streamflow (Mugo & Sharma, 1999; Reitz, Sanford, Senay, & Cazenas,
2017). Collection of specific source-water chemical and / or isotopic
composition allows for mass-balance (tracer-based) approaches for
separating hydrographs into various flow components (Pinder & Jones,
1969). Mass-balance approaches are considered more reliable,
particularly in mountainous regions, compared to graphical or
mathematical interpretations because they integrate watershed-specific
geochemical information (Miller, Susong, Shope, Heilweil, & Stolp,
2014). However, success using mass-balance methods to separate
hydrograph components hinges on the ability to accurately assign
concentrations to end-members. Combining graphical and tracer techniques
offers a potentially cost-effective approach that has proven beneficial
in identifying dominant underlying processes that would not have been
possible with one method alone (Kronholm & Capel, 2015; McNamara, Kane,
& Hinzman, 1997).
One example of where both tracer and graphical hydrograph separations
might be complimentary is in the snowmelt-dominated and seasonally arid
Snowy Range of southeast Wyoming, USA. In such systems, specific
conductance (SC), a measurement of the ionic content in water, provides
a strong natural tracer due to the large differences in concentration
observed between peak flow and low flow conditions (Miller et
al. , 2014). Further, SC can be measured continuously at
relatively low costs using automated loggers, provides the best
hydrograph separation results compared to individual chemical
constituents (Caissie, Pollock, & Cunjak, 1996), and has been used
successfully to separate baseflow in similar watersheds in the Southern
Rocky Mountains (Caine, 1989; Liu, Williams, & Caine 2004; Miller et
al., 2014; Rumsey, Miller, Susong, Tillman, & Anning, 2015). In
addition, given the strong diurnal responses of Rocky Mountain systems
to snowmelt “pulses”, graphical techniques might have utility to
isolate changes in the fast flow pathways as the snowmelt season
progresses and snowpack depletes (Buttle, Webster, Hazlett, &
Jefferies, 2019). Diurnal streamflow cycles have been previously
analyzed to gain process understanding on the hydrologic response to
snowmelt (Caine, 1992; Kobayashi, 1986; Kurylyk & Hayashi, 2017;
Loheide & Lundquist, 2009; Lundquist & Cayan, 2002; Lundquist &
Dettinger, 2005; Mutzner et al. , 2015; Pellerin et al. ,
2012; Woelber et al. , 2018).
The goal of this study is to assess the storage and release of snowmelt
by quantifying the relative proportions of quickflow, throughflow, and
baseflow contributions to streamflow. We do this for nine nested
watersheds of the Snowy Range in southeastern Wyoming, USA by estimating
how various sources contributed to discharge under different streamflow
conditions throughout the year (i.e. rising limb, peak flow, falling
limb, low flow). We used a common chemical mass-balance method to
separate baseflow from direct runoff (Pinder & Jones, 1969). Direct
runoff was further separated into throughflow and quickflow graphically
based on naturally occurring diurnal cycles in streamflow that reflect
rapid additions of direct runoff to streamflow induced by daily
fluctuations in temperature. Further, we compared SC-Q hysteresis at
both annual and daily timescales with watershed characteristics to
better elucidate runoff processes driving the streamflow partitioning.
Specifically, we address the following research questions for our
seasonally-arid, snow-dominated systems:
- How do the sources of streamflow differ between seasonal and annual
timescales?
- How do SC-Q relationships reflect the storage and release of snowmelt
water at daily and annual scales?
- What are the dominant watershed properties governing streamflow
partitioning and SC-Q relationships?
SITE DESCRIPTION AND DATA
COLLECTION
2.1 Site description
The Snowy Range is the northernmost extent of the Medicine Bow Mountain
Range within the Southern Rocky Mountains. We installed stream
monitoring sites at nine nested locations along Libby Creek and the
North Fork of the Little Laramie and their tributaries (Figure 1; Table
1). Data considered in this study were collected during three
consecutive water years (October to September) covering 2016 through
2018. Our study watersheds range in elevation from 2570 – 3619 m (8431
– 11874 ft) and areal extent from 0.53 to 63.0 km2.
The watersheds drain predominantly south and east towards Centennial, WY
about 50 km east of Laramie, WY.
Bedrock geology underlying the watersheds is primarily a mix of
metasedimentary and metavolcanic from the Early Proterozoic and glacial
deposits from the Pleistocene-Holocene (Figure S1; Table S1; Wyoming
State Geological Survey, 2014). Metasedimentary rocks from the Libby
Creek Group are most prevalent at higher elevations consisting of
metaclastic quartzite, politic and amphibole-schist, with minor traces
of marble and conglomerate. Metasedimentary and metavolcanic rocks are
common in GOLD100. Lower elevations of LIBB100 contain politic-schist,
gneiss, and amphibolite with minor amounts of marble and granite.
Glacial deposits consisting of unconsolidated, coarse-detrital gravel,
boulders, and sand are common in the region. Landslide deposits occur in
portions of NFLL100 containing intermixed glacial deposits, talus, and
rock-glacier deposits. The surficial geology is dominated by glacial
deposits lower elevations (Figure S2; Table 2; Case, Arneson, &
Hallberg, 1998). Glacial deposits consist of scattered slope wash,
residuum, grus, alluvium, colluvium, and landslide deposits. Glaciated
bedrock is more common at higher elevations mixed with scattered shallow
eolian, grus, colluvium, and alluvium deposits. Grus and residuum mixed
with alluvium dominate the surficial geology of GOLD100, an outlier
compared to the other study watersheds.
No detailed soil map has been constructed for our study watersheds. Munn
and Arneson (1998) performed a low-order assessment and found soils to
be highly variable, both regionally and locally with changes in slope
aspect, slope position, climate, and geology. Haplocryalfs and
Dystrocryepts occupy most of the area. Haplocryalfs tend to occur on
low-relief areas while Dystrocryepts occupy forests on glacial deposits
and on steep slopes. Cryaquepts and minor histisols are present in
riparian areas (Munn & Arneson, 1998). Seismic refraction surveys
performed in NONM100 indicate thin regolith at the top of hillslopes (1
m) thickening toward the bottom of the slope (3-4 m; Thayer et al.,
2018). A porosity transition within the regolith indicated by seismic
refraction surveys suggests the top layer of upper regolith has
relatively high hydraulic conductivity capable of draining quickly. A
novel bulk density optimization method performed in NONM100 supports the
decline in porosity with depth, which creates conditions that favor
shallow lateral flow through the upper regolith as a dominant streamflow
generation mechanism (Fullhart, Kelleners, Chandler, McNamara, &
Seyfried, 2019).
Evergreen forest is the main land cover classification in eight of our
nine study watersheds ranging from 30 % (LIBB100) to 99 % (NONM100)
coverage and is more common at lower elevations (Figure S3; Table S2;
Homer & Fry, 2016). Shrub/scrub and grassland/herbaceous coverages with
intermittent lakes and wetlands tend to dominate at higher elevations.
2.2 Data collection
2.2.1 Snow and climatological
monitoring
The study watersheds receive most of their annual precipitation in the
form of snow. Spring snowmelt, typically occurring in a relatively short
period of time after the snowpack has become ‘ripe’, is a main driver of
streams. Snow water equivalent (SWE) was recorded at six stations in our
study watersheds (Figure 1). The Brooklyn Lake SNOTEL (sitenumber = 367)
site is located within the boundaries of NASH200. According to 30-year
daily median records (1981-2010), maximum SWE occurs on April 30 with a
value of 59.2 cm. Mean water year total precipitation for Brooklyn Lake
is 89.7 cm, which results in a mean water-year snow fraction (maximum
SWE / total precipitation) of 66%. During the three-year period
presented in this study, maximum SWE was remarkably consistent from 2016
to 2018 at the six measurement sites (Table S3). On average, maximum SWE
ranged from about 32 cm at the lowest site (2684 m) to 72 cm at the
highest site (3244 m) (Table S3).
Snow depth in 2016 was calculated at 0.5-m resolution in our study
watersheds using LIDAR data based on two flights: snow-free in October
2014 and peak snow depth in April 2016 (Table S4). Monthly precipitation
and temperature data from PRISM were aggregated to watershed boundaries
to provide water-year (Oct. 1 – Sept. 30) mean watershed precipitation
and temperature estimates (Table S4) (PRISM Climate Group, 2019).
Water-year precipitation from PRISM (2016) was highly correlated to
LIDAR-derived snow depth and elevation (Pearson’s r = 0.98, 0.99,
respectively).
2.2.2 Stream monitoring
Stream stage (Level TROLL 500 Data Logger, In-Situ, Fort Collins, CO,
USA; accuracy = ± 0.05%, resolution = ± 0.005%), electrical
conductivity (ONSET HOBO U24, Onset Computer Corporation, Bourne, MA,
USA; accuracy = 5 µS/cm, resolution = 1 µS/cm), and temperature (ONSET
HOBO U24, Onset Computer Corporation, Bourne, MA, USA; accuracy = 0.1
°C, resolution = 0.01 °C) were measured instantaneously at 15 min
intervals at nine gaging locations in the Snowy Range from 2016 to 2018
(Figure 1). Streamflow was estimated by creating rating curves at each
gaging location that relate measured stream stage to repeated field
measurements of discharge using a handheld electromagnetic water flow
meter (OTT MF pro, OTT HydroMet, Loveland, CO, USA; accuracy = ± 2%).
Electrical conductivity was measured by automated loggers and converted
to specific conductance (SC) to account for differences in stream
temperature following methodology from the manufacturer.
Stream stage and electrical conductivity loggers were installed each
year prior to the main snowmelt period, but at slightly different dates
for each gaging site. To account for variability in record length we
calculated cumulative streamflow for each water year at each site and
only analyzed the records after 3% of annual streamflow had occurred.
This approximation allowed for consistent inter-site and inter-annual
comparisons. Each year of analysis concluded at the end of the water
year (September 30). Four periods were selected from the total
hydrograph to assess dominant controls of runoff generation at different
hydrologic conditions: the rising limb, peak flow, falling limb, and low
flow conditions. The rising limb encompasses the time between the
beginning of the snowmelt period of the hydrograph (defined in below in
‘Graphical quickflow separation’) and the date of maximum discharge. The
peak flow time period was selected based on the highest 10% of
streamflow values. The falling limb occurs between the date of maximum
stream discharge and the last day of the snowmelt period of the
hydrograph. It should be noted that based on these definitions peak flow
partially overlaps with the rising and falling limbs. The low flow time
period refers to the lowest 30% of streamflow recorded after the
snowmelt portion of the hydrograph.
METHODS
3.1 Hydrograph separation
3.1.1 Tracer-based
separation
We followed a chemical mass-balance methodology from Pinder & Jones
(1969) for a tracer-based approach to separate baseflow and direct
runoff from the total hydrograph:
\(C_{T}Q_{T}=\ C_{\text{BF}}Q_{\text{BF}}+C_{\text{DR}}Q_{\text{DR}}\)(1)
Where C and Q represent concentration and discharge,
respectively, and subscripts T , BF , and DR refer to
total, baseflow, and direct runoff, respectively. For this study, we use
SC as the “concentration” of interest in Eq. (1) (e.g. Caine, 1989;
Kobayashi, 1986; Miller et al. , 2014; Pilgrim, Huff, & Steele,
1979). The quantity of baseflow can be solved by rearranging Eq. (1):
\(Q_{\text{BF}}=\ Q_{T}\ \left[\frac{C_{T}-C_{\text{DR}}}{C_{\text{BF}}-C_{\text{DR}}}\right]\)(2)
Accurate interpretation of Eq. (2) requires that the following
assumptions are made (Sklash & Farvolden, 1979; Buttle, 1994): (1)
direct runoff and baseflow have different chemical composition, (2)
direct runoff and baseflow chemical compositions are constant in time
and space or the variability can be accounted for, (3) soil water
contribution to streamflow is small or has similar composition of direct
runoff, and (4) surface storage has a minor contribution to streamflow.
Total stream concentration (CT ) and discharge
(QT ) are obtained using in-situ measurements. The
baseflow concentration (CBF ) is typically
estimated from low flow stream conditions when total streamflow is
assumed to be entirely derived from groundwater. We followed suggestions
from Miller et al. (2014) and calculated CBF at
each watershed by selecting the 99th percentile of the
SC data to account for potential outliers. We assignedCDR a value of 21.6 µS/cm based on the mean value
measured from 13 snow and 10 snowmelt samples. The SC of these samples
ranged from 9.8 µS/cm to 42.1 µS/cm and the standard deviation across
all 23 samples was 10.5 µS/cm.
Uncertainty was quantified following methods presented by Genereux
(1998):
\(\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }W_{\text{BF}}=\sqrt{\left(\frac{f_{\text{BF}}}{C_{\text{DR}}-C_{\text{BF}}}W_{C_{\text{BF}}}\right)^{2}+\left(\frac{f_{\text{DR}}}{C_{\text{DR}}-C_{\text{BF}}}W_{C_{\text{DR}}}\right)^{2}+\left(\frac{-1}{C_{\text{DR}}-C_{\text{BF}}}W_{C_{T}}\right)^{2}}\)(3)
where WBF represents the uncertainty in baseflow
discharge to streamflow at the 95% confidence interval,fBF and fDR are the
fractions of total streamflow from baseflow and direct runoff,
respectively, \(W_{C_{\text{BF}}}\) is the standard deviation of the
highest 1% of measured SC concentrations multiplied by the t-value from
the Student’s t distribution, \(W_{C_{\text{DR}}}\) is the standard
deviation associated with observations of CDR (21.8
µS/cm) multiplied by the t-value, and \(W_{C_{T}}\) is the analytical
error in the SC measurement multiplied by the t-value.
3.1.2 Graphical separation
After separating total streamflow into baseflow and direct runoff using
the tracer-based approach, we applied a graphical hydrograph separation
technique based on the presence of natural snowmelt-induced diurnal
cycles to differentiate direct runoff into two components: throughflow
and quickflow. Quickflow runoff represents water that moves most rapidly
to the stream channel resulting in sharp rises of the daily hydrograph
(Hewlett & Hibbert, 1967; Buttle et al. , 2019). Quickflow is
generated during the snowmelt period as the result of daily fluctuation
in temperature creating diurnal snowmelt pulses (Caine, 1992). For this
reason, we constricted quickflow to only occur at times of peak snowmelt
based on when the moving-average seven-day runoff was greater than 0.5
times mean water year daily runoff (i.e. the snowmelt period). This
procedure approximated the time when snowmelt-induced diurnal cycles
were present. The residual of the direct runoff water not contributing
to quickflow was assumed to be throughflow, which represents delayed
flow interacting more with the subsurface, resulting in a longer travel
time.
A computer code was written to identify diurnal cycles by selecting
times when instantaneous streamflow curvature was at a daily maximum.
Quickflow volumes were calculated by subtracting the area created above
the diurnal snowmelt cycles from the total hydrograph. The diurnal cycle
(%) was then calculated by dividing the difference between daily
maximum and minimum streamflow by the daily maximum streamflow for each
day in the snowmelt period. Visual inspection of each hydrograph was
performed to ensure accurate snowmelt period selection and quickflow
volume calculation.
3.2 Hysteresis index
An index was calculated to quantify specific conductance-discharge
(SC-Q) hysteresis at annual and daily scales during snowmelt-induced
diurnal cycles. We adopted methodology from Lloyd, Freer, Johnes, and
Collins (2016) who suggest rescaling the SC and Q data through
minimum-maximum normalization to allow for an index that represents
changing dynamics of an event and permits comparison between events:
\(\text{Normalized\ }Q_{i}=\ \frac{Q_{i}-\ Q_{\min}}{Q_{\max}-\ Q_{\min}}\)(4)\(\text{Normalized\ }\text{SC}_{i}=\ \frac{\text{SC}_{i}-\ \text{SC}_{\min}}{\text{SC}_{\max}-\ \text{SC}_{\min}}\)(5)
where Qi and SCi represent discharge and specific conductance at time i ,Qmin and SCmin are the
minimum discharge and specific conductance, andQmaz and SCmax are the
maximum discharge and specific conductance. The hysteresis index (HI) is
calculated by subtracting the normalized SC on the falling limb from the
normalized SC on the rising limb for a particular flow percentile:
\(\text{HI}_{Q_{i}}=\ \text{Normalized\ SC}_{\text{i\ }_{\text{Rising\ limb}}}-\ \text{Normalized\ SC}_{\text{i\ }_{\text{Falling\ limb}}}\)(6)
At the annual scale, we calculated a hysteresis index for the
10th, 25th, and
50th percentile of discharge values. For example, HI10
was calculated by subtracting the tenth percentile of discharge
occurring during the falling limb from the tenth percentile of discharge
occurring during the rising limb.
The HI varies between -1 and 1, where the larger the absolute value
indicates a more circular hysteresis and the sign represents the
direction of the loop (positive for clockwise, negative for
counter-clockwise) (Lloyd et al. , 2016). To avoid spikes in the
data affecting the hysteresis index, we averaged the normalized SC for
values within 1% of each annual hysteresis index percentile (i.e. the
10th percentile of normalized discharge was expanded
to include the 9th through 11thpercentile values).
Similar methodology was applied to create a hysteresis index at the
daily scale. For this analysis, each date was rescaled from noon on the
particular date to noon on the subsequent date because snowmelt-induced
diurnal cycles begin rising in the late afternoon following a delay from
the time of peak daily solar maximum. The daily hysteresis index was
calculated for the 25th, 50th, and
75th percentile of normalized discharge values. Due to
many fewer data points compared to the annual datasets, we expanded the
buffer by averaging the normalized SC for values within 10% of each
daily hysteresis index percentile (i.e. the 25thpercentile of normalized discharge was expanded to include the
15th through 35th percentiles).
4. RESULTS
4.1 Tracer-based hydrograph
separation
Baseflow contributions to streamflow varied spatially and temporally but
were greatest at low flow conditions for all watersheds (Figure 2; Table
3). On average, total baseflow contributions to streamflow in the study
watersheds ranged from 22.1% (GOLD100) to 45.5% (NONM100). This range
of baseflow contribution is similar to other studies in the Upper
Colorado River Basin (Miller et al., 2014; Rumsey et al., 2015). During
the rising limb portion of the annual hydrograph, baseflow contributions
ranged from 14.2% (GOLD100) to 33.9% (NFLL200). Baseflow contributions
were lowest during peak flow conditions, ranging from 10.8% (GOLD100)
to 29.0% (NASH100). During the falling limb, proportions of baseflow
increased slightly and ranged from 13.0% (GOLD100) to 32.8% (NASH100).
Baseflow contributions to streamflow were greatest at low flow
conditions and ranged from 65.6% (NASH100) to 93.4% (GOLD100) (Table
3). The close proximity of NASH100 to Brooklyn Lake, which is one of the
largest lakes in the region and is located upstream of NASH100, likely
explains the smaller contribution of baseflow to total streamflow at low
flow conditions. Uncertainty was greater when proportions of direct
runoff were greater due to a smaller, and more variable, number of
snowmelt/snowpack samples used to define CDR in Eq. 3
(Table 3).
It is important to emphasize that most of the total streamflow in our
study watersheds was generated during a relatively brief period
associated with peak snowmelt. The highest 10% of recorded streamflow
values (i.e. peak flow conditions) were responsible for 37% (NASH200)
to 47% (GOLD100, LIBB200, LIBB400) of total streamflow in our study
watersheds (Table 4). During this time, streamflow was overwhelmingly
derived from direct runoff (Table 3). In contrast, the lowest 30% of
streamflow values recorded after peak flow (i.e. low flow conditions)
accounted for only 1.4% (LIBB100, LIBB200) to 3.0% (NFLL100) of total
streamflow (Table 4) and were primarily sourced from baseflow (Table 3).
However, baseflow contributions were responsible for a majority of total
streamflow generation on any particular day outside of the relatively
short snowmelt period.
4.2 Graphical hydrograph
separation
Direct runoff produced during diurnal snowmelt cycles (Figure 3)
resulted in quickflow contributions to total streamflow that ranged from
7% (NASH100) to 14% (LIBB100, LIBB200) annually (Table 4). Quickflow
was responsible for 15.4% (NASH100) to 27.7% (LIBB100) of direct
runoff during the snowmelt period and 11.4% (NASH100) to 22.1%
(LIBB200) of direct runoff annually for our study watersheds (Table 5).
When present, the mean snowmelt-induced diurnal streamflow cycle ranged
from 19.7% (NASH100) to 33.5% (LIBB100) of total streamflow during the
snowmelt period (Table 5). The mean magnitude of the diurnal cycle was
significantly (p < 0.05) greater for Libby Creek (LIBB100,
LIBB200, LIBB400) compared to Nash Fork and the North Fork of the Little
Laramie (NASH100, NASH200, NFLL100, NFLL200). Throughflow was the main
contributor to streamflow at all times except during low flow conditions
(Figure 4; Tables 4 & 5) when baseflow was dominant. The close
proximity of NASH100 to Brooklyn Lake, which drains directly to the Nash
Fork main channel, likely dampened the amplitude of the diurnal
fluctuations measured in the stream and resulted in the lowest
proportion of quickflow of all the watersheds considered.
4.3 Annual SC-Q hysteresis
Discharge (Q) and SC generally mirror each other in our study watersheds
with the highest Q values corresponding with the lowest SC (and vice
versa) implying dilution from snowmelt water with low SC at high Q
(Figure 5). Time lags between SC and Q at an annual scale result in
hysteresis which varies systematically across the different runoff
components. In NONM100, LIBB100, LIBB200, and LIBB400, SC-Q
relationships have a negative, concave, and anticlockwise hysteretic
behavior at annual timescales, resulting in negative annual hysteresis
indices (Figure 5d; Table 6), implying that SC concentration is greatest
in baseflow, followed by throughflow, and then quickflow (Evans &
Davies, 1998). The lack of annual hysteresis observed at GOLD100,
NASH100, and NFLL100 implies little to no time lag between annual peak
SC and annual peak Q suggesting relatively inert subsurface material
properties that result in little chemical evolution as water travels
through the soil and /or regolith layers. Thus throughflow and quickflow
result in similar geochemical alteration in these watersheds (Figure
5c).
All watersheds had a negative relationship between SC and Q on an annual
scale, implying recently melted snow (i.e. from the current snowmelt
season) is responsible for the majority of streamflow generation in a
given year. Seasonal snowmelt has been shown to be the dominant
streamflow generation mechanism in the western United States (e.g. Li et
al. , 2017) but differs from what has been shown using isotope
tracers in snowmelt-dominated watersheds located in lower-elevation,
humid areas (e.g. Buttle, 1994; Laudon, Sjoblom, Buffam, Seibert, &
Morth, 2007) where snowmelt mainly recharges groundwater and displaces
previously stored older water.
4.4 Daily SC–Q hysteresis
When distinct snowmelt-induced diurnal streamflow cycles are present, Q
and SC often demonstrated daily hysteresis. The shape of the diurnal
streamflow cycle was typically front-loaded, where streamflow rises
rapidly followed by more gradual decline (Figure 6a). The opposite was
true for SC in streamflow which exhibited a sharp drop followed by a
gradual increase (Figure 6a). This phenomenon was also demonstrated by
Kobayashi (1986) during snowmelt in the headwaters of the Uryu River,
Japan. In most observed cases for our study watersheds, SC was lower on
the falling limb of diurnal cycles than on the rising limb, resulting in
clockwise hysteresis and positive daily hysteresis indices (Figure 6b-c;
Table 6). The daily hysteresis index was largest on the rising limb of
the annual hydrograph for most watersheds (Figure 6b; GOLD100, NASH100,
NASH200, NFLL200, NONM100) and became smaller throughout the snowmelt
period during the falling limb of the annual hydrograph (Figure 6c). The
opposite was true for Libby Creek watersheds (LIBB100, LIBB200,
LIBB400), where the hysteresis index became larger as the snowmelt
period progresses.