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Hydrol. Earth Syst. Sci., 20, 5015–5033, 2016
www.hydrol-earth-syst-sci.net/20/5015/2016/
doi:10.5194/hess-20-5015-2016
© Author(s) 2016. CC Attribution 3.0 License.
The importance of snowmelt spatiotemporal variability for
isotope-based hydrograph separation in a high-elevation catchment
Jan Schmieder1, Florian Hanzer1, Thomas Marke1, Jakob Garvelmann2, Michael Warscher2, Harald Kunstmann2,
and Ulrich Strasser1
1Institute of Geography, University of Innsbruck, 6020 Innsbruck, Austria
2Institute of Meteorology and Climate Research – Atmospheric Environmental Research,
Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Correspondence to: Jan Schmieder (jan.schmieder@uibk.ac.at)
Received: 15 March 2016 – Published in Hydrol. Earth Syst. Sci. Discuss.: 11 May 2016
Revised: 15 November 2016 – Accepted: 26 November 2016 – Published: 19 December 2016
Abstract. Seasonal snow cover is an important temporary
water storage in high-elevation regions. Especially in re-
mote areas, the available data are often insufficient to ac-
curately quantify snowmelt contributions to streamflow. The
limited knowledge about the spatiotemporal variability of
the snowmelt isotopic composition, as well as pronounced
spatial variation in snowmelt rates, leads to high uncertain-
ties in applying the isotope-based hydrograph separation
method. The stable isotopic signatures of snowmelt water
samples collected during two spring 2014 snowmelt events
at a north- and a south-facing slope were volume weighted
with snowmelt rates derived from a distributed physics-
based snow model in order to transfer the measured plot-
scale isotopic composition of snowmelt to the catchment
scale. The observed δ18O values and modeled snowmelt rates
showed distinct inter- and intra-event variations, as well as
marked differences between north- and south-facing slopes.
Accounting for these differences, two-component isotopic
hydrograph separation revealed snowmelt contributions to
streamflow of 35 ±3 and 75 ±14 % for the early and peak
melt season, respectively. These values differed from those
determined by formerly used weighting methods (e.g., us-
ing observed plot-scale melt rates) or considering either the
north- or south-facing slope by up to 5 and 15 %, respec-
tively.
1 Introduction
In many headwater catchments, seasonal water availability
is strongly dependent on cryospheric processes and under-
standing these processes becomes even more relevant in a
changing climate (APCC, 2014; IPCC, 2013; Weingartner
and Aschwanden, 1992). The seasonal snow cover is an im-
portant temporary water storage in alpine regions. The tim-
ing and amount of water released from this storage is im-
portant to know for water resources management, especially
in downstream regions where the water is needed (drinking
water, snow making, hydropower, irrigation water) or where
it represents a potential risk (flood, drought). Environmental
tracers are a common tool to investigate the hydrological pro-
cesses, but scientific studies are still rare for high-elevation
regions because of the restricted access and high risk for field
measurements in these challenging conditions.
Two-component isotope-based hydrograph separation
(IHS) is a technique to separate streamflow into differ-
ent time source components (event water, pre-event water)
(Sklash et al., 1976). The event component depicts water that
enters the catchment during an event (e.g., snowmelt) and is
characterized by a distinct isotopic signature, whereas pre-
event water is stored in the catchment prior to the onset of
the event (i.e., groundwater and soil water, which form base-
flow) and is characterized by a different isotopic signature
(Sklash and Farvolden, 1979; Sklash et al., 1976). The tech-
nique dates back to the late 1960s (Pinder and Jones, 1969)
and was initially used for separating storm hydrographs in
humid catchments. The first snowmelt-based studies were
Published by Copernicus Publications on behalf of the European Geosciences Union.
5016 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
conducted in the 1970s by Dinçer et al. (1970) and Martinec
et al. (1974). These studies showed a large pre-event water
fraction (> 50%) of streamflow that changed the understand-
ing of the processes in catchment hydrology fundamentally
(Klaus and McDonnell, 2013; Sklash and Farvolden, 1979)
and forced a paradigm shift, especially for humid temperate
catchments. However, other snowmelt-based studies in per-
mafrost or high-elevation catchments (Huth et al., 2004; Liu
et al., 2004; Williams et al., 2009) revealed a large contribu-
tion of event water (>70 %), depending on the system state
(e.g., frost layer thickness and snow depth), catchment char-
acteristics, and runoff generation mechanisms.
Klaus and McDonnell (2013) highlighted the need to
quantify and account for the spatial variability of the iso-
tope signal of event water, which is still a vast uncertainty
in snowmelt-based IHS. In the literature inconclusive results
prevail with respect to the variation of the isotopic signal
of snowmelt. Spatial variability of snowmelt isotopic com-
position was statistically significant in relation to elevation
(Beaulieu et al., 2012) in a catchment in British Columbia,
Canada, with 500 m relief. Moore (1989) and Laudon et
al. (2007) found no statistical significant variation in their
snowmelt δ18O data, due to the low gradient and small el-
evation range (approximately 30 and 290m) in their catch-
ments, which favors an isotopically more homogenous snow
cover. The effect of the aspect of the hillslopes on isotopic
variability and IHS results in topographically complex ter-
rain has been rarely investigated. Dahlke and Lyon (2013)
and Dietermann and Weiler (2013) surveyed the snowpack
isotopic composition and showed a notable spatial variabil-
ity in their data, particularly between north- and south-facing
slopes. They conclude that the spatial variability of snowmelt
could be high and that the timing of meltwater varies with the
morphology of the catchment. Dietermann and Weiler (2013)
also concluded that an elevation effect (decrease of snow-
pack isotopic signature with elevation), if observed, is dis-
turbed by fractionation due to melt/refreeze processes during
the ablation period. Aspect and slope are therefore important
factors that affect the isotopic evolution of the snow cover
and its melt (Cooper, 2006). In contrast, there have been var-
ious studies that have investigated the temporal variability
of the snowmelt isotopic signal, e.g., with the use of snow
lysimeters (Hooper and Shoemaker, 1986; Laudon et al.,
2002; Liu et al., 2004; Maulé and Stein, 1990; Moore, 1989;
Williams et al., 2009). During the ablation season the iso-
topic composition of the snowpack changes due to percolat-
ing rain and meltwater, and fractionation caused by melting,
refreezing and sublimation (Dietermann and Weiler, 2013;
Lee et al., 2010; Unnikrishna et al., 2002; Zhou et al., 2008),
which leads to a homogenization of the isotopic profile of the
snowpack (Árnason et al., 1973; Dinçer et al., 1970; Stich-
ler, 1987) and an increase in heavy isotopes of meltwater
throughout the freshet period (Laudon et al., 2007; Taylor
et al., 2001, 2002; Unnikrishna et al., 2002). Therefore, the
characterization and the use of the evolving isotopic signal
of snowmelt water instead of single snow cores is crucial for
applying IHS (Taylor et al., 2001, 2002).
There have been various approaches to cope with the tem-
poral variability of the input signal. If one uses more than
one δ18O snowmelt sample for applying the IHS method,
it is important to weight the values with appropriate melt
rates, e.g., measured from the outflow of a snow lysimeter.
Common weighting methods are the volume-weighted av-
erage approach (VWA), as used by Mast et al. (1995), and
the current meltwater approach (CMW), applied by Hooper
and Shoemaker (1986). Laudon et al. (2002) developed the
runoff-corrected event water approach (runCE), which ac-
counts for both, the temporal isotopic evolution and tempo-
rary storage of meltwater in the catchment and overcomes
the shortcoming of the exclusion of residence times by VWA
and CMW. This method was also deployed in several other
snowmelt-based IHS (Beaulieu et al., 2012; Carey and Quin-
ton, 2004; Laudon et al., 2004, 2007).
Tracers have successfully been used in modeling stud-
ies to provide empirical insights into runoff generation pro-
cesses and catchment functioning (Birkel and Soulsby, 2015;
Birkel et al., 2011; Capell et al., 2012; Uhlenbrook and Lei-
bundgut, 2002), but the combined use of distributed mod-
eling and isotope tracers in snow-dominated environments
is rare. Ahluwalia et al. (2013) used an isotope and model-
ing approach to derive snowmelt contributions to streamflow
and determined differences between the two techniques of
2 %. Distributed modeling can provide areal melt rates that
can be used for weighting the measured isotopic composi-
tion of meltwater. Pomeroy et al. (2003) described the differ-
ences of insolation between north- and south-facing slopes
in complex terrain that lead to spatial varying melt rates of
the snowpack throughout the freshet period. The use of the
areal snowmelt data from models will likely reduce the un-
certainty that arises from the representativeness of measured
melt rates at the plot-scale.
The overall goal of our study was to quantify the con-
tribution of snowmelt to streamflow and hence to improve
the knowledge of hydroclimatological processes in high-
elevation catchments. This study aims to enhance the reli-
ability of isotope-based hydrograph separation by consider-
ing the distinct spatiotemporal variability of snowmelt and
its isotopic signature in a high-elevation study region. This
study has the following three objectives: (1) the estimation
of the spatiotemporal variability of snowmelt and its isotopic
composition, (2) the quantification of the impact of the spa-
tial variability in snowmelt rates and its isotopic composi-
tion on IHS, and (3) to assess the combined use of a physi-
cally based snowmelt model and traditional IHS to determine
snowmelt contributions to streamflow. Distributed melt rates
provided by a surface energy balance model were used to
weight the measured isotopic composition of snowmelt in or-
der to characterize the event water isotopic composition. Tra-
ditional weighting methods (e.g., using plot-scale observed
melt rates) were compared with the model approach.
Hydrol. Earth Syst. Sci., 20, 5015–5033, 2016 www.hydrol-earth-syst-sci.net/20/5015/2016/
J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5017
Figure 1. (a) Distribution of slope aspects in the study area; (b) study area (Rofen valley) with underlying orthophoto, sampling, and
measurement locations.
2 Study area
The 98 km2high-elevation catchment of the Rofenache
stream is located in the central eastern Alps (Oetztal Alps,
Austria), close to the main Alpine ridge. The basin ranges in
elevation from approximately 1900 to 3770m.a.s.l. The av-
erage slope is 25◦and the average elevation is 2930 m.a.s.l.
(calculated from a 50 m digital elevation model). A nar-
row riparian zone (< 100 m width) is located in the valley
floor. The predominantly south- (southeast) and north-facing
(north-northwest) slopes form the main valley (Fig. 1a),
which trends roughly from southwest to northeast (Fig. 1b).
The study area has a dry inner-alpine climate. Mean annual
precipitation is 800 mm yr−1, of which 44 % falls as snow.
The mean annual temperature at the gauging station in Vent
(1890 m.a.s.l., reference period: 1982–2003) is 2 ◦C. Sea-
sonal snow cover typically lasts from October to the end of
June at the highest regions of the valley.
The bedrock consists of mainly paragneiss and mica schist
and is overlain by a mantle of glacial deposits and thin soils
(< 1m). The bedrock outcrops and unconsolidated bare rocks
cover the largest part (42%) of the catchment (CLC, 2006).
Glaciers cover approximately a third of the Rofenache catch-
ment (35 %), while pastures and coniferous forests are lo-
cated in the lowest parts of the catchment and cover less
than 0.5 % (CLC, 2006). Sparsely vegetated areas and nat-
ural grassland cover 15 and 7.5%, respectively (CLC, 2006).
Besides seasonally frozen ground at slopes of various ex-
positions, permafrost is likely to occur at an elevation over
2600 m.a.sl. at the north-facing slopes (Haeberli, 1975). The
annual hydrograph reveals a highly seasonal flow regime.
The mean annual discharge is 4.5 m3s−1(reference period:
1971–2009) and is dominated by snow and glacier melt dur-
ing the ablation season, which typically lasts from May to
September. The onset of the early snowmelt season in the
lower part of the basin is typically in April.
3 Methods
3.1 Field sampling, measurements, and laboratory
analysis
The field work was conducted during the 2014 snowmelt sea-
son between the beginning of April and the end of June. Two
short-term melt events (3 days) were investigated to illustrate
the difference between early spring season melt and peak
melt. The events were defined as warm and precipitation-free
spells, with clear skies and dry antecedent conditions (i.e., no
precipitation was observed 48 h prior to the event). Low dis-
charge and air temperatures with a small diurnal variation
and low melt rates, as well as a snow-covered area (SCA) of
about 90 % in the basin (Fig. 2a), characterize the conditions
of the early melt event at the end of April (Fig. 3b). In con-
trast, the peak melt period at the end of June is characterized
by high discharge and melt rates, a flashy hydrograph, high
air temperatures with remarkable diurnal variations (Fig. 3c),
and a strongly retreated snow line (SCA: 66 %; Fig. 2c). Dis-
charge data are available at an hourly resolution for the gaug-
ing station in Vent and meteorological data are obtained by
two automatic weather stations (hourly resolution) located in
and around the basin (Fig. 1).
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5018 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
Figure 2. Comparison of observed and simulated snow distributions for (a, b) 5 May (MODIS scene) and (c, d) 10 June 2014 (Landsat
scene).
The stream water sampling for stable isotope analysis con-
sisted of pre-freshet baseflow samples at the beginning of
March, sub-daily samples (temporal resolution ranges be-
tween 1 and 4 h) during the two studied events and a post-
event sample in July as indicated in Fig. 3a (gray-shaded
area). Samples of snowmelt, snowpack, and surface over-
land flow (if observed) were collected at the south- (S1,
S2) and north-facing slope (N1, N2), as well as on a wind-
exposed ridge (Fig. 1b) using a snowmelt collector. At each
test site, a snow pit was dug to install a 0.1m2polyethy-
lene snowmelt collector at the ground–snowpack interface.
The snowmelt collector consists of a pipe that drains the
percolating meltwater into a fixed plastic bag. Tests yield a
preclusion of evaporation for this sampling method. Com-
posite daily snowmelt water samples (bulk sample) were col-
lected in these bags and transferred to polyethylene bottles
in the field before the onset of the diurnal melt cycle. Fur-
thermore, sub-daily grab melt samples were collected at S1
(on 23 April) and at N2 (on 7 June) to define the diurnal
variability of the respective melt event. Unfortunately further
sub-daily snowmelt sampling was not feasible. The pit face
was covered with white styrofoam to protect it from direct
sunlight. Stream, surface overland flow, and grab snowmelt
water samples were collected in 20 mL polyethylene bot-
tles. Snow samples from snow pit layers were filled in air-
tight plastic bags and melted below room temperature be-
fore being transferred into bottles. Overall, 144 samples were
taken during the study period. Snow water equivalent (SWE),
snow height, snow density, and various snowpack observa-
tions (wetness and hand hardness index) were observed be-
fore the onset of the diurnal melt cycle at the study plots
(Fig. 1). Mean SWE was determined by averaging five snow-
tube measurements within an area of 20 m2at each site. Daily
melt rates were calculated by subtracting succeeding SWE
values. Sublimation was neglected, as it contributes only a
small percentage (∼10 %) to the seasonal water balance in
high-altitude catchments in the Alps (Strasser et al., 2008).
All samples were treated by the guidelines proposed by
Clark and Fritz (1997) and were stored in the dark and kept
cold until analysis. The isotopic composition of the sam-
ples (δ18O, δD) was measured with cavity ring-down spec-
troscopy (Picarro L1102-i). Results are expressed in the delta
notation as parts per thousand relative to the Vienna Standard
Mean Ocean Water (VSMOW2). The mean laboratory pre-
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J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5019
Figure 3. (a) Daily precipitation, air temperature, and discharge at the outlet of the catchment during the complete study period; hourly
hydroclimatologic data of a 7-day period around the (b) early melt and (c) peak melt event. Gray-shaded areas indicate the investigated
events.
cision (replication of eight measurements) for all measured
samples was 0.06 ‰ for δ18O. Due to the covariance of δ2H
(δD) and δ18O (Fig. 5), all analyses were done with oxygen-
18 values.
3.2 Model description
For the simulation of the daily melt rates, the non-calibrated,
distributed, and physically based hydroclimatological model
AMUNDSEN (Strasser, 2008) was applied. Model features
include interpolation of meteorological fields from point
measurements (Marke, 2008; Strasser, 2008); simulation of
shortwave and longwave radiation, including topographic
and cloud effects (Corripio, 2003; Greuell et al., 1997); pa-
rameterization of snow albedo depending on snow age and
temperature (Rohrer, 1991); modeling of forest snow and
meteorological processes (Liston and Elder, 2006; Strasser et
al., 2011); lateral redistribution of snow due to gravitational-
(Gruber, 2007) and wind-induced (Helfricht, 2014; Warscher
et al., 2013) processes; and determination of snowmelt using
an energy balance approach (Strasser, 2008). Besides hav-
ing been applied for various other Alpine sites in the past
(Hanzer et al., 2014; Marke et al., 2015; Pellicciotti et al.,
2005; Strasser, 2008; Strasser et al., 2004, 2008), AMUND-
SEN has recently been set up and extensively validated for
the Oetztal Alps region (Hanzer et al., 2016). This setup
was also used to run the model in this study for the period
2013–2014 using a temporal resolution of 1 h and a spatial
resolution of 50 m. In order to determine the model perfor-
mance during the study period, catchment-scale snow distri-
bution by satellite-derived binary snow-cover maps and plot-
scale observed SWE data were used for the validation (cf.
Sect. 4.2). Therefore, the spatial snow distribution as simu-
lated by AMUNDSEN was compared with a set of MODIS
(500 m spatial resolution) and Landsat (30m resolution, sub-
sequently resampled to the 50 m model resolution) snow
maps with less than 10 % cloud coverage over the study area
using the methodology described in Hanzer et al. (2016).
Model results were evaluated using the performance mea-
sures BIAS, accuracy (ACC) and critical success index (CSI)
(Zappa, 2008). ACC represents the fraction of correctly clas-
sified pixels (either snow covered or snow free both in the
observation and the simulation). CSI describes the number of
correctly predicted snow-covered pixels divided by the num-
ber of times where snow is predicted in the model and/or
observed, and BIAS corresponds to the number of snow-
covered pixels in the simulation divided by the respective
number in the observation. ACC and CSI values range from
0 to 1 (where 1 is a perfect match), while BIAS values below
1 indicate underestimations of the simulated snow cover, and
values above 1 indicate overestimations. At the plot-scale,
observed SWE values were compared with AMUNDSEN
SWE values represented by the underlying pixels at the lo-
cation of the snow course. Catchment-scale melt rates are
calculated by subtracting two consecutive daily SWE grids,
neglecting sublimation losses, which is also done to achieve
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5020 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
observed melt rates at the plot scale. Subsequently, the digital
elevation model was used to calculate an aspect grid and fur-
ther to divide the catchment into two parts: grid cells with as-
pects ranging from ≥270 to ≤90◦were classified as “north
facing”, while the remaining cells were attributed to the class
“south facing”. Finally, these two grids were combined to de-
rive melt rates for the south-facing (melts)and for the north-
facing slope (meltn).
3.3 Isotopic hydrograph separation, weighting
approaches, and uncertainty analysis
IHS is a steady-state tracer mass balance approach, and sev-
eral assumptions underlie this simple principle, which are de-
scribed and reviewed in Buttle (1994) and Klaus and McDon-
nell (2013):
1. The isotopic compositions of event and pre-event water
are significantly different.
2. The event water isotopic signature has no spatiotempo-
ral variability, or variations can be accounted for.
3. The pre-event water isotopic signature has no spa-
tiotemporal variability, or variations can be accounted
for.
4. Contributions from the vadose zone must be negligible
or soil water should be isotopically similar to ground-
water.
5. There is no or minimal discharge contribution from sur-
face storage.
The focus of this study is on one of the assumptions: the spa-
tiotemporal variability of the event water isotopic signature
is absent or can be accounted for. The fraction of event water
(fe)contributing to streamflow was calculated from Eq. (1).
fe=Cp−Cs
Cp−Ce(1)
The tracer concentration of the pre-event component (Cp)is
the δ18O composition of baseflow prior to the onset of the
freshet period, constituted mainly by groundwater and po-
tentially by soil water, which was assumed to have the same
isotopic signal as groundwater. Tracer concentration Csis
the isotopic composition of stream water for each sampling
time. The isotopic compositions of snowmelt samples were
weighted differently to obtain the event water tracer concen-
tration (Ce)using the following five weighting approaches:
1. volume weighted with observed plot-scale melt rates
(VWO);
2. equally weighted, assuming an equal melt rate on north-
and south-facing slopes (VWE);
3. no weighting, only south-facing slopes considered
(SOUTH);
4. no weighting, only north-facing slopes considered
(NORTH);
5. volume weighted with simulated catchment-scale melt
rates (VWS).
Equation (2) is the VWS approach with simulated melt rates
for north- and south-facing slope as described in Sect. 3.2,
where Mis the simulated melt rate (in mm d−1),δ18O is the
isotopic composition of sampled snowmelt, and subscripts
s and n indicate north and south, respectively. For obtain-
ing the value of Cea daily time step (t) is used, considering
daily melt rates and the isotopic composition of the daily bulk
snowmelt samples.
Ce(t)=Ms(t)δ18Os(t)+Mn(t )δ18On(t)
Ms(t)+Mn(t) (2)
An uncertainty analysis (Eq. 3) was performed accord-
ing to the Gaussian standard error method proposed by
Genereux (1998):
Wfe=
"Cp−Cs
Cp−Ce2WCe#2
+"Cs−Ce
Cp−Ce2WCp#2
+"−1
Cp−Ce2WCs#2
1/2
,(3)
where Wis the uncertainty, Cis the isotopic composition,
fis the fraction, and the subscripts p, s, and e refer to the
pre-event, stream, and event component, respectively. This
assumes negligible errors in the discharge measurements and
the melt rates (modeled and observed). The uncertainty of
streamflow (WCs)is assumed to be equal to the laboratory
precision (0.06 ‰). For the uncertainty of the event compo-
nent (WCe), the diurnal temporal variability (standard devi-
ation) of the snowmelt isotopic signal (from one site and 1
day) was multiplied by the appropriate value of the two-tailed
t-table (dependent on sample number) and used for the event,
as proposed by Genereux (1998). This resulted in different
uncertainty values for the early melt event (WCe=0.2 ‰)
and the peak melt event (WCe=0.5‰). An error of 0.04 ‰
was assumed for the pre-event component (WCp), which re-
flects the standard deviation of two baseflow samples. A 95%
confidence level was used. Spatial variation in snowmelt and
its isotopic composition were not considered in this error cal-
culation method as they represent the hydrologic signal of
interest.
4 Results
4.1 Spatiotemporal variability of streamflow and stable
isotopic signature of sampled of water sources
Two major snowmelt pulses (mid-May and beginning of
June) and four less pronounced pulses between mid-March
Hydrol. Earth Syst. Sci., 20, 5015–5033, 2016 www.hydrol-earth-syst-sci.net/20/5015/2016/
J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5021
Figure 4. Linearly interpolated stream isotopic content of Rofenache for (a) the early melt and (b) the peak melt event. Dots indicate
measurements. Event and pre-event water contributions during (c) the early melt and (d) the peak melt event calculated with the VWS
approach.
Figure 5. Relationship between δ2H and δ18O of water sources
sampled during the snowmelt season of 2014 in the Rofen valley,
Austrian Alps.
to early May occurred during the snowmelt season (Fig. 3a).
Peak melt occurred at the beginning of June with maximum
daily temperatures and runoff of 15 ◦C and 18 mm d−1, re-
spectively. The following high flows were affected by rain
(Fig. 3a) and glacier melt due to the strongly retreated snow
line and snow-free ablation area of the glaciers in July. Di-
urnal variations in discharge were strongly correlated with
diurnal variations in air temperature (Fig. 3b and c) with
a time lag of 3–5 h for the early melt event and 2–3 h for
the peak melt event. An inverse relationship between stream-
flow δ18O and discharge was found for the early melt event
(Fig. 4a and c). Small diurnal responses of streamflow δ18O
were identified for both events, but were masked due to miss-
ing data during the recession of the hydrograph.
The quality control of the isotopic data was performed by
the δ2H–δ18O plot (Fig. 5), which indicated no shift in the
linear regression line and thus no secondary fractionation ef-
fects (evaporation) during storage and transport of the sam-
ples. The slope of the linear regression (slope =8.5, n=144,
R2=0.93) of the measurement data slightly deviates from
that of the global meteoric (slope =8) and local meteoric wa-
ter line (slope =8.1) based on monthly data from the Aus-
trian Network of Isotopes in Precipitation sampling site in
Obergurgl, which is located in an adjacent valley (reference
period: 1991–2014). The small deviation (visible in Fig. 5) of
the sampled water (i.e., snowpack and snowmelt) could indi-
cate fractionation effects induced by phase transition (i.e.,
melt/refreeze and sublimation). The significant differences
between the isotopic signatures of pre-event streamflow and
snowmelt water enabled the IHS.
Overall, the δ18O values ranged from −21.5 to −15.0 ‰,
while snowpack samples were characterized by the most neg-
ative and pre-event baseflow samples by the least negative
values. Snowpack samples showed a wide isotopic range,
while streamflow samples revealed the narrowest spread, re-
flecting a composite isotopic signal mixing of the water com-
ponents. Figure 6 shows the δ18O data of the water samples
grouped into different categories and split into early and peak
melt data. It shows the different δ18O ranges and medians of
the sampled water sources (Fig. 6a), as well as marked spa-
tiotemporal variations in the isotopic signal (Fig. 6b and c).
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5022 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
Figure 6. Jittered dot plots for δ18O of collected water samples split into (a) water sources, (b) stage of snowmelt and (c) spatial origin. Gray
circles indicate early melt samples and black circles peak melt samples. The gray and black line represents the median of early and peak melt
data, respectively. neis the number of early melt samples and npis the number of peak melt samples.
It is apparent that the snowpack δ18O values have a larger
variation compared to the snowmelt data due to homoge-
nization effects (Fig. 6a), as was also shown by Árnason et
al. (1973), Dinçer et al. (1970) and Stichler (1987). The me-
dian of the δ18O of snowmelt was higher than that of the
snowpack, which indicates fractionation. The median δ18O
of surface overland flow was higher than that of snowmelt
(Fig. 6a) for the early and peak melt period. Overall, the peak
melt δ18O values (Fig. 6b) were less variable and had a higher
median than the early melt values, because fractionation ef-
fects (due to melt/refreeze and sublimation) most likely al-
tered the isotopic composition of the snowpack over time (cf.
Taylor et al., 2001, 2002). One major finding was that the
δ18O values on the north-facing slope had a larger range and
a lower median compared to the opposing slope (Fig. 6c).
Samples from the wind-drift-influenced site (also south ex-
posed) were more depleted in heavy isotopes compared to
the south-facing slope samples (Fig. 6c).
In general, the average snowmelt and snowpack isotopic
composition was more depleted for the early melt period (Ta-
ble 1) and changed over time because fractionation likely al-
tered the snowpack and its melt. It is obvious that the iso-
topic evolution (gradually enrichment) on the south-facing
slope took place earlier in the annual melting cycle of the
snow, and indicates a premature snowpack concerning the
enrichment of isotopes and earlier ripening compared to the
north-facing slope.
Table 1 shows that meltwater sampling throughout the en-
tire snowmelt period is required to account for the tempo-
ral variation in the isotopic composition of the snowpack
(cf. Taylor et al., 2001, 2002). In detail, the snowpack and
snowmelt δ18O data highlighted a marked spatial inhomo-
geneity between north- and south-facing slopes throughout
the study period. The snowpack isotopic composition from
both sampled slopes was statistically different for the early
melt, but not for the peak melt (with Kruskal–Wallis test at
0.05 significance level), whereas the snowmelt δ18O showed
a significant difference throughout the study period (Fig. 7).
Sub-daily snowmelt samples (n=5) at S1 (23 April 2014)
had a range of 0.1 ‰ in δ18O, and the bulk sample (inte-
grating the entire diurnal melt cycle) was within the scat-
ter of those values (Fig. 8). The intra-daily variability of
snowmelt (n=3) at N2 (7 June 2014) was relatively higher
with values ranging from −17.9 to −18.1 ‰. The bulk sam-
ple (−17.9 ‰) was at the upper end of those values (Fig. 8).
Stream water isotopic composition was more enriched in
heavy isotopes during the early melt period and successively
became more depleted throughout the freshet period, result-
ing in more negative values during peak melt (Table 2). The
standard deviation and range of stream water δ18O during
early melt was higher and could be related to an increasing
snowmelt contribution throughout the event and larger diur-
nal amplitudes of snowmelt contribution compared to peak
melt (Table 2).
4.2 Snow model validation and snowmelt variability
Figure 9 shows the values for the selected performance mea-
sures based on the available MODIS and Landsat scenes
during the period March–July 2014. The results indicate a
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J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5023
Table 1. Average isotopic composition of snowpack and snowmelt with standard deviation for north- and south-facing slopes during the early
and the peak melt event. Values are averages of 3 consecutive days.
North-facing slope South-facing slope
Snowpack δ18O (‰) Snowmelt δ18O (‰) Snowpack δ18O (‰) Snowmelt δ18O (‰)
Early melt event −19.7 ±0.6 (n=12) −18.8 ±0.2 (n=3) −17.3 ±0.3 (n=4) −17.4 ±0.2 (n=8)
Peak melt event −17.6 ±0.4 (n=18) −17.9 ±0.1 (n=3) −17.9 ±0.1 (n=15) −17.1 ±0.0 (n=2)
Table 2. Descriptive statistics of streamflow isotopic composition at the outlet of the Rofenache during events of the snowmelt season 2014.
Pre-event Early melt Peak melt Post-event
Date 7 Mar 23–25 Apr 7–9 Jun 11 Jul
Average (δ18O ‰ ) −15.02 −15.97 −16.87 −15.09
Standard deviation (δ18O ‰) 0.04 0.16 0.05 –
Range (δ18O ‰) 0.05 0.50 0.20 –
Number of samples 2 17 30 1
Figure 7. Jittered dot plots for δ18O of (a) snowpack
and (b) snowmelt of north- and south-facing slopes. Gray circles
indicate early melt samples and black circles are for peak melt sam-
ples. The gray and black lines indicates the median of the early and
peak melt data, respectively. neis the number of early melt samples
and npis the number of peak melt samples.
reasonable model performance with a tendency to slightly
overestimate the snow cover during the peak melt season
(BIAS > 1). In general the CSI does not drop below 0.7, and
80 % of the pixels are correctly classified (ACC) through-
out the study period. Figure 2 shows the observed and sim-
ulated spatial snow distribution around the time of the two
events. Despite a higher SCA during the early melt season
(Fig. 2a and b) compared to the peak melt season (Fig. 2c
and d) one can see the overestimation of the simulated SCA
compared to the observed (MODIS/Landsat) SCA. Table 3
Figure 8. Comparison of snowmelt δ18O between the bulk sample
(dashed line) and sub-daily samples (circles) for the two sites (S1,
N2).
shows the observed and simulated SWE values at the plot
scale. The model slightly underestimated SWE during peak
melt, but generally appears to be in quite good agreement,
suggesting well-simulated snowpack processes. Throughout
the study period the model deviates by 13 % from the ob-
served SWE values, but the representativeness (small-scale
effects) of SWE values for the respective 50 m pixels should
be considered.
Snowmelt (observed and simulated daily losses of SWE)
showed a distinct spatial variation between the north-facing
and the south-facing slope for the early melt (23/24 April) pe-
riod, but less marked variations for the peak melt (7/8 June)
period (Fig. 10). Relative day-to-day differences are more
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5024 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
Table 3. Comparison of observed and simulated (represented by the underlying pixel) SWE values.
Site Date Stage of SWE [mm] Difference between
snowmelt observed and
season simulated SWE [%]
Observed Simulated
S1 23 Apr 2014 Early melt 141 151 7
N1 23 Apr 2014 Early melt 351 356 1
Wind 24 Apr 2014 Early melt 201 229 14
S1 25 Apr 2014 Early melt 113 78 −31
N1 25 Apr 2014 Early melt 270 293 9
N2 7 Jun 2014 Peak melt 594 477 −20
N2 8 Jun 2014 Peak melt 568 435 −23
N2 9 Jun 2014 Peak melt 537 390 −27
Mean deviation between observed and simulated SWE 13
Figure 9. Performance measures of (a) accuracy (ACC), (b) critical success index (CSI), and (c) BIAS as calculated by comparing AMUND-
SEN simulation results with satellite-derived (MODIS/Landsat) snow maps.
pronounced for the early melt season. Both simulated and
observed melt rates are higher for the peak melt event on the
south-facing slope, but not for the north-facing slope. Sim-
ulated melt intensity on the south-facing slope at the end of
April was twice the rate on the north-facing slope, while sim-
ulated melt rates were approximately the same for the op-
posing slopes during peak melt. Simulated (catchment scale)
snowmelt rates were markedly lower during the early melt
(23 and 24 April) on the north-facing slope compared to the
observed (plot scale) melt rates (Fig. 10a), but differences be-
tween them were small during peak melt for both slopes (7
and 8 June; Fig. 10).
4.3 Weighting techniques and isotope-based
hydrograph separation
Differences between the applied snowmelt weighting tech-
niques, induced by the high spatial variability of snowmelt
(Sect. 4.2), led to different event water isotopic compositions
(Ce)for the IHS analyses (Table 4). The event water compo-
nent was depleted in δ18O by roughly 0.3 ‰ for the second
day (24 April) of the early melt event compared to the pre-
ceding day, but inter-daily variation during the peak melt is
Table 4. Isotopic composition of the event water component for the
applied weighting techniques.
Event water isotopic composition
(δ18O ‰)
23 Apr 24 Apr 7 Jun 8 Jun
VWS −17.9 −18.2 −17.5 −17.5
VWO −18.3 −18.6 −17.4 −17.5
VWE −18.1 −18.3 −17.5 −17.5
NORTH −18.6 −18.8 −17.9 −17.9
SOUTH −17.6 −17.9 −17.1 −17.1
almost absent. Especially during early melt (23 to 24 April),
strong deviations between observed plot-scale melt rates and
distributed (areal) melt rates obtained by AMUNDSEN oc-
curred (Fig. 11), and led to more different event water iso-
topic compositions between the VWS and the VWO ap-
proach (Table 4).
The hydrograph and the results of the IHS applied with the
VWS method for the early and peak melt event are presented
in Fig. 4 and highlight the lower flow rates and higher pre-
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J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5025
Figure 10. Observed (plot scale) and simulated (catchment scale)
daily snowmelt on (a) the north-facing and (b) the south-facing
slope for the early melt (23/24 April) and peak melt (7/8 June).
Figure 11. Relative contribution of the north- and south-facing
slope δ18O values to the catchment average. VWS: volume
weighted with simulated (areal) melt rates. VWO: volume weighted
with observed (plot-scale) melt rates.
event fractions during early melt (Fig. 4c) and vice versa for
the peak melt period (Fig. 4d). The total runoff volume dur-
ing the peak melt period was approximately 6 times higher
than in the early melt period. The fractions of snowmelt (vol-
ume) estimated with the VWS approach were 35 and 75 %
with calculated uncertainties (95 % confidence level) of ±3
and ±14 % for the early and peak melt event, respectively.
The uncertainty calculated from Eq. (3) of the IHS applied
with the VWS method was higher (14 %) for the peak melt
Table 5. Discharge characteristics of the Rofenache for the early
and peak melt event.
Event
Early melt Peak melt
Date 23–25 Apr 7–9 Jun
Mean discharge 1.5 m3s−111.5 m3s−1
Peak discharge 1.9 m3s−117.4 m3s−1
Volume runoff 3.3 mm 20.7 mm
Mean-event water fraction 35 ±3 % 75 ±14 %
Peak-event water fraction 44 ±4 % 78 ±15 %
event than for the early melt event because the difference be-
tween isotopic composition of pre-event water and event wa-
ter was smaller than for the early melt event (uncertainty:
3 %) (cf. Tables 2 and 4).
Throughout the early melt event, the snowmelt fraction in-
creased from 25 to 44 % (Fig. 4c; Table 5). This trend mir-
rors the stream isotopic composition, which became more de-
pleted (Fig. 4a). Event water contributions during peak melt
were generally higher but had a smaller range (70 to 78 %;
Fig. 4d). Diurnal isotopic variations of stream water were
small for both events (Fig. 4a and b), and could not clearly
be obtained due to missing data on the falling limb of the
hydrographs.
The use of the different weighting approaches led to
strongly varying estimated snowmelt fractions of streamflow
(Fig. 12). Especially the differences between the SOUTH
and the NORTH approach during both investigated events
(up to 24 %), and the differences between the VWS and the
VWO approach (5 %) during early melt (Fig. 12a) are no-
table. Event water contributions estimated by the different
weighting methods ranged from 21–28 % at the beginning
of the early melt event up to 31–55% at the end of the
event (Fig. 12a, Table 6). Minimum event water contributions
during the peak melt were estimated at 60–84 % and max-
ima ranged between 67 and 94 % for the different weighting
methods (Table 6, Fig. 12b). Beside these intra-event varia-
tions in snowmelt contribution, the volumetric variations at
the event-scale were smaller and ranged between 28–40 and
66–90 %, for the early and peak melt event, respectively (Ta-
ble 6).
Considering only spatial variation of snowmelt isotopic
signatures (i.e., comparing the NORTH/SOUTH approach
with the VWE approach) for IHS led to differences in es-
timated event water fractions up to 7 and 14% for the early
and peak melt period, respectively (Table 6). However, con-
sidering only spatial variation in snowmelt rates (i.e., com-
paring the VWS/VWO approach with the VWE approach)
led to differences in event water fraction up to 3 and 2 % for
the early and peak melt period, respectively (Table 6).
Surface overland flow was not considered in the IHS anal-
yses, but if applied, it would most likely increase the cal-
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5026 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
Table 6. Event water contribution to streamflow based on the different weighting techniques. The error indicates the variability (standard
deviation) and the values in parentheses depict the range.
Event water contribution (%)
VWS VWO VWE NORTH SOUTH
Early melt event 35 ±6
(25–44)
30 ±4
(22–35)
33 ±5
(24–39)
28 ±3
(21–31)
40 ±9
(28–55)
Peak melt event 75±2
(70–78)
78 ±3
(71–82)
76 ±2
(70–78)
66 ±2
(60–67)
90 ±3
(84–94)
Figure 12. Comparison of the IHS results for the different weighting techniques used for (a) early melt and (b) peak melt. Scale of yaxis
in (b) differs from that in (a).
culated snowmelt fraction slightly. Furthermore, snowmelt
samples from the wind-exposed site were not used in the IHS
analyses because this site was only sampled on the south-
facing slope during early melt and is not representative for
the catchment due to its limited coverage. However, incorpo-
ration of this data would decrease the calculated snowmelt
fraction by approximately 2 %.
5 Discussion
5.1 Temporal variation in streamflow during the
melting season
Snowmelt is a major contributor to streamflow during the
spring freshet period in alpine regions and large amounts of
snowmelt water infiltrate into the soil and recharge ground-
water (Penna et al., 2014). The hydrological response of the
stream followed the variations of air temperature, as already
observed by Braithwaite and Olesen (1989) (Fig. 3a). The
observed time lags (Fig. 3b and c) between maximum daily
air temperature and daily peak flow are common in mountain
catchments (Engel et al., 2016; Schuler, 2002). During peak
melt, the flashy hydrograph revealed less variation in the tim-
ing of peak discharge of 7-day data (Fig. 3c) compared to the
early melt, as reported by Lundquist and Cayan (2002). The
increase in discharge coincides with decreasing streamflow
δ18O during the early melt event (Fig. 4a and c) and con-
firms the earlier findings of Engel et al. (2016), who identi-
fied inverse relationships between streamflow δ18O and dis-
charge during several 24h events in an adjacent valley on the
southern side of the main Alpine ridge, although their find-
ings rely on streamflow contributions from snow and glacier
melt. The lower stream water isotopic composition during
peak melt suggests a remarkable contribution of more de-
pleted snowmelt to streamflow and therefore confirms the re-
sults of the IHS.
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J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5027
5.2 Spatiotemporal variability of snowmelt and its
isotopic signature
The rate of snowmelt varies spatially in catchments with
complex topography (Carey and Quinton, 2004; Dahlke and
Lyon, 2013; Pomeroy et al., 2003). This was also demon-
strated for the Rofen valley in this study (Fig. 10, Table 3).
Snowmelt results from a series of processes (e.g., energy ex-
change between snow–atmosphere) that are spatially variable
– especially in complex terrain. This also becomes obvious
when comparing the snowmelt rates on 23 April 2014 in
Fig. 10a. Differences of observed and simulated snowmelt
rates might result from the non-representativeness of point
measurements for catchment averages and refer to the scale
issue of data collection. The peak melt period was charac-
terized by less spatial and day-to-day variation in observed
melt rates (Fig. 10). The modeled daily snowmelt during this
period was similar for north- and south-facing slopes, likely
because of higher melt rates but also a smaller snow-covered
area of the south-facing slope in contrast to the north-facing
slope during peak melt (Fig. 11). The model performance
was good for SWE (Table 3) and snow-cover extent (Figs. 2
and 9). The spatial variation of snowpack isotopic compo-
sition are significant, as can be seen in the differences for
north- and south-facing slopes, and also shown by Carey and
Quinton (2004), Dahlke and Lyon (2013), and Dietermann
and Weiler (2013) in their studied high-gradient catchments,
whereas there are unclear differences for the spatial varia-
tion of snowmelt isotopic signals in the literature. It is not
clear to which extent altitude is important, as Dietermann
and Weiler (2013) stated that a potential elevation effect (de-
crease in snowmelt δ18O with elevation) is likely to be dis-
turbed by melting processes (isotopic enrichment) depending
on catchment morphology (aspect, slope) during the ablation
period. Beaulieu et al. (2012) detected elevation as a pre-
dictor, which explained most of the variance they observed
in snowmelt δ18O from four distributed snow lysimeters.
Moore (1989) and Laudon et al. (2007) found no significant
difference of δ18O in their lysimeter outflows, which was
likely due to the small elevation gradient of their catchments
that favor an isotopically homogenous snowpack, whereas
Unnikrishna et al. (2002) found remarkable small-scale spa-
tial variability. An altitudinal gradient was not considered
in this study, but possible effects on IHS are discussed in
Sect. 5.6. The difference of snowmelt (not snowpack) iso-
topic signature between north- and south-facing slopes was
clearly shown in this study. The dataset is small, but reveals
clear differences induced by varying magnitudes and timing
of melt due to differences in solar radiation on the oppos-
ing slopes (Fig. 7). Temporal variability in snowmelt isotopic
composition is greater for the north-facing slope compared to
the south-facing slope (Fig. 7), which was also pointed out by
Carey and Quinton (2004) in their subarctic catchment. Ear-
lier homogenization in the isotopic profile of the snowpack
and earlier melt out are responsible for this phenomenon (cf.
Dinçer et al., 1970; Unnikrishna et al., 2002). Fractionation
processes likely controlled this homogenization of the snow-
pack between the two investigated melt events. The isotopic
homogenization of the snowpack on the south-facing slope
started earlier in the melting period and caused a smaller
spatial and temporal variation compared to the north-facing
snowpack, as was also reported by Unnikrishna et al. (2002)
and Dinçer et al. (1970). The differences between these in-
vestigated snowpacks were larger in the early melt season
than in the peak melt season. This affects the IHS results, es-
pecially because the snowmelt contributions from the south-
and north-facing slope – with marked isotopic differences
– were distinct. Due to melt, fractionation processes pro-
ceeded and the snowpack likely became more homogenous
throughout the snowmelt season. However, inter-daily vari-
ations of snowpack isotopic composition, especially for the
north-facing slope, were still observable during the peak melt
period. The gradual isotopic enrichment of the snowpack was
also observed for snowmelt, as described by many others
(Feng et al., 2002; Shanley et al., 2002; Taylor et al., 2001,
2002; Unnikrishna et al., 2002).
Intra-daily variations of snowmelt δ18O could be quanti-
fied for two sites (Fig. 8). At S1 on the south-facing slope
during the early melt event, the 0.1‰ range in δ18O (n=5)
was smaller than the range at N2 on the north-facing slope
during the peak melt event (n=3, range =0.2 ‰). This sub-
daily variability is markedly smaller than the differences be-
tween the investigated slopes (cf. Table 1), which ranged
from 0.8 ‰ (peak melt) to 1.4 ‰ (early melt). Unnikrishna
et al. (2002) described significant temporal variations of
snowmelt δ18O during large snowmelt events (peak melt).
However, these findings could not be confirmed within in
this study, probably due to the temporally limited data and
should be tested with a larger dataset. The bulk sample at S1
(23 April 2014) was isotopically closer to the sub-daily val-
ues compared to the bulk sample at N2 (7 June 2014) that was
at the upper range of the sub-daily samples (Fig. 8). There-
fore, one could argue that for the south-facing slope there is
a negligible uncertainty if one uses a single snowmelt value
(at one time) for IHS instead of using a bulk sample, but this
is not the case for the north-facing slope (Fig. 8, site N2).
Unfortunately the sample numbers are small, because more
frequent and more distributed sampling (at different sites)
was not feasible due to logistical issues. Hence, these re-
sults should be used with caution and should be investigated
in further studies. If the focus and the scale of the study is
not on the sub-daily variability, the authors recommend the
use of bulk samples, because these integrate (automatically
weighed with snowmelt rate) the diurnal variations.
5.3 Validity of isotopic hydrograph separation
The validity of IHS relies on several assumptions (cf.
Sect. 3.3; Buttle, 1994; Klaus and McDonnell, 2013).
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5028 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
The assumption that the isotopic composition of event
and pre-event water differ significantly (assumption 1) was
successfully proven, because the snowmelt isotopic values
were markedly lower than pre-event baseflow values (cf. Ta-
bles 2 and 4, Fig. 5). Spatiotemporal variations of event
water isotopic composition (assumption 2) were accounted
for by collecting daily and sub-daily samples during both
events throughout the freshet period and meltwater sampling
at a north- and south-facing slope, respectively. The spa-
tially variable input of event water was considered by di-
viding the catchment into two parts – a north- and a south-
facing slope. This study supports the findings of Dahlke and
Lyon (2013) and Carey and Quinton (2004), emphasizing
the highly variable snowpack/snowmelt isotopic composi-
tion in complex topography catchments due to enrichment.
The temporal variability of event water isotopic composition
was considered by using bulk daily samples, which integrate
snowmelt from the entire diurnal melting cycle, but smooth
out a sub-daily signal. Because the focus of this study was
more on the inter-event than the intra-daily scale, this ap-
proach seemed reasonably reliable. The spatiotemporal vari-
ability of the isotopic composition of pre-event water (as-
sumption 3) is a major limitation and could not be clearly
identified due to a lack of data and was therefore assumed to
be constant. Small differences between the pre-event samples
(−15.00 and −15.05 ‰ for δ18O) and post-event stream wa-
ter isotopic composition support this assumption (Table 2).
The assumption of soil water having the same isotopic com-
position as groundwater in time and space (assumption 4) is
critical. Some studies reveal no significant differences (e.g.,
Laudon et al., 2007), whereas others do (e.g., Sklash and Far-
volden, 1979). Isotopic differences between groundwater and
soil water were not considered due to a lack of data. Fur-
thermore, it is not known to which degree the vadose zone
contributes to baseflow in the study area. Winter baseflow
used in the analyses is assumed to integrate mainly ground-
water and partly soil water. Soil water could be hypothesized
to have a negligible contribution to baseflow during winter
due to the recession of the soil water flow in autumn and
frozen soils in winter. The assumption that no or minimal
surface storage occurs (assumption 5) is plausible because
water bodies like lakes or wetlands do not exist in the study
catchment and due to the steep topography detention stor-
age is likely limited. The transit time of snowmelt was as-
sumed to be less than 24 h. This short travel time is char-
acteristic for headwater catchments (Lundquist et al., 2005)
with high in-channel flow velocities, steep hillslopes, a high
drainage density with snow-fed tributaries, thin soils, most
snowmelt originating from the edge of the snow line (small
average travel distances), partly frozen soil, and observed
surface overland flow. The state-of-the-art method (runCE)
to include residence times of snowmelt in the event water
reservoir proposed by Laudon et al. (2002) was applied in
several IHS studies (Beaulieu et al., 2012; Carey and Quin-
ton, 2004; Petrone et al., 2007), but was not feasible due to
the short-term character and temporally limited data.
5.4 Hydrograph separation results and inferred runoff
generation processes
Large contributions from snowmelt to streamflow are com-
mon in high-elevation catchments. Daily contributions be-
tween 35 and 75 % in the Rofen valley are comparable to the
results of studies conducted in other mountainous regions,
mostly outside the European Alps. Beaulieu et al. (2012)
estimated snowmelt contributions ranging from 7 to 66%
at the seasonal scale for their 2.4 km2catchment and re-
ported contributions of 34 and 62 %, for the early melt and
peak melt, respectively. The hydrograph was dominated by
pre-event water during early melt in April (Fig. 4c), which
is in accordance with the results obtained by other IHS
studies (Beaulieu et al., 2012; Laudon et al., 2004, 2007;
Moore, 1989). The snowmelt contribution increased as the
freshet period progressed and peaked with high contribu-
tions at the beginning of June. Beaulieu et al. (2012) and
Sueker et al. (2000) reported comparable results for their
physically similar catchments during peak melt with 62 and
up to 76 % event water contributions to streamflow, respec-
tively. At the event-scale comparable studies are rare. Engel
et al. (2016) report a maximum daily snowmelt contribution
estimated with a three-component hydrograph separation of
33 % for an 11 km2southwest of the Rofen valley with sim-
ilar physiographic characteristics, but on the southern side
of the main Alpine ridge. It should be mentioned that in their
study, runoff was fed by three components (snowmelt, glacier
melt, and groundwater) and lower snowmelt contributions
were prevalent because most of the catchment area (69%)
was snow free.
Initial snowmelt events flush the pre-event water reservoir
as snowmelt infiltrates into the soil and causes the pre-event
water to exfiltrate and contribute to the streamflow. As the
soil and groundwater reservoir becomes gradually filled with
new water (snowmelt), the event water fraction in the stream
increases. The system is also wetter during peak melt. The
dominance of event water in the hydrograph is interpreted as
an outflow of pre-event water stored in the subsurface and
the gradual replenishment of the soil and groundwater reser-
voirs by event water. The higher water table – compared to
the early melt period – could cause a transmissivity feed-
back mechanism (Bishop, 1991). This is a common mech-
anism in catchments with glacial till (Bishop et al., 2011)
characterized by higher transmissivities and hence increased
lateral flow velocities towards to the surface. Runoff gen-
eration is spatially very variable in the study area. There
are areas (meadow patches between rock fields) where sat-
uration excess overland flow is dominant (observed mainly
at plots S1, S2, and Wind) and areas (with larger rocks
and debris) where rapid shallow subsurface flow can be as-
sumed (plot N2). Catchment morphology controls various
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J. Schmieder et al.: The importance of snowmelt spatiotemporal variability 5029
hydrologic processes and hence the shape of the hydrograph.
Upslope residence times of snowmelt are usually smaller
due to thin soils (observed during the field work), steeper
slopes (Sueker et al., 2000), and higher contributing areas
of glaciers with impermeable ice (Behrens et al., 1978), and
would be indicators for the more flashy hydrograph during
the peak melt season.
5.5 Impact of spatial varying snowmelt and its isotopic
composition on isotope-based hydrograph
separation and assessment of weighting approaches
Klaus and McDonnell (2013) stress in their review paper
the need to investigate the effects of the spatially varying
snowmelt and its isotopic composition on IHS. This study
quantified the impact of the spatially varying isotopic com-
position of snowmelt between north- and south-facing slopes
on IHS results for the first time. The IHS results were more
sensitive to the spatial variability of snowmelt δ18O than
to spatial variations of snowmelt rates (Table 6). This is
even more pronounced for the peak melt period, because
snowmelt rates were similar for the north- and south-facing
slope, probably due to a ripe snow cover throughout the
catchment. The difference in volumetric snowmelt contribu-
tion to streamflow at the event-scale determined using the
five different weighting methods for IHS is maximum 24%
(NORTH approach vs. SOUTH approach). The data show
that the variations between the weighting approaches (VWS,
VWO, and VWE) are higher throughout the early melt sea-
son (Table 6), because small-scale variability of snowmelt
and its isotopic composition are more pronounced in the
early melt season. Thus, the influence of spatial variability
of snowmelt and its isotopic composition on the event wa-
ter fraction calculated with IHS is larger during this time.
Melt rates strongly differ between the south- and the north-
facing slope (Fig. 11), which were deceptively gathered by
manually measured SWE, likely due to micro-topographic
effects. As the contributions from both slopes are used in
Eq. (3), they strongly influence the average isotopic com-
position of event water. The weighting method SOUTH (or
NORTH) represents the hypothetical and most extreme sce-
nario in which only one sampling site is used for the IHS
analysis. Because snowmelt is more enriched in δ18O and
closer to pre-event water isotopic composition on the south-
facing slope during peak melt, this scenario has the greatest
effect on IHS and leads to the strongest deviation in estimated
snowmelt fractions (up to 15 % overestimation compared to
the VWS approach). These scenarios (NORTH/SOUTH) are
theoretical and it is obvious that it is not recommended to
conduct a IHS analysis by using only samples from either
north- or south-facing slopes in catchments with complex
terrain. Similar to the VWE method, snowmelt isotopic data
were not volume weighted in other studies (e.g., Engel et al.,
2016) where snowmelt data were not available. This has a
more distinct effect on IHS during the early melt season be-
cause of the higher spatiotemporal variability in snowmelt
(and its isotopic composition) compared to the peak melt
season and led to a deviation in the snowmelt fraction in
streamflow of 2 and 3 % compared to the VWS and VWO ap-
proaches, respectively. These differences are small, because
the differing snowmelt and isotopic values offset each other
in this particular case (Table 6). Nevertheless, the results of
VWS are more correct for the right reason, because single
observed plot-scale melt rates do not represent distributed
snowmelt contribution at the catchment scale. Therefore, one
can hypothesize that distributed simulated melt rates enhance
the reliability of IHS, whereas plot-scale weighting intro-
duces a large error caused by the difficulty in finding loca-
tions that represent the average melt rate in complex terrain.
5.6 Limitations of the study
Collecting water samples in high-elevation terrain is chal-
lenging due to limited access and high risk (e.g., avalanches),
limiting high-frequency sampling. Hence, some limitations
are inherent in this study. Potential elevation effects on
snowmelt isotopic composition were not tested. The oppos-
ing sampling sites (S1–N1 and S2–N2) were at the same ele-
vation (Fig. 1). It was assumed that the differences in north-
and south-facing slopes were much greater than a possible
altitudinal gradient in snowmelt isotopic composition. This
hypothesis was not tested, but based on the results of other
studies (Dietermann and Weiler, 2013). However, account-
ing for a potential altitudinal gradient (decrease in snowmelt
δ18O with elevation) would lead to more depleted isotopic
signatures of event water and hence to lower event water frac-
tions.
Another disadvantage is that no snow survey was con-
ducted prior to the onset of snowmelt (peak accumulation)
to estimate spatial variability in bulk snow δ18O. Because
snowmelt is used for applying IHS, it is not clear to which de-
gree the spatial variability of the snowpack isotopic composi-
tion is important. Two-component isotope-based hydrograph
separation was successfully applied using the snowmelt and
baseflow endmembers, but potential contributions of glacier
melt were neglected (here defined as ice/firn melt). Because
glaciers in the catchment were still covered by snow during
the peak melt season, a significant contribution from ice/firn
melt was assumed to be unlikely. Nevertheless, negligible
amounts of basal (ice) meltwater could originate from tem-
perate glaciers. No samples could be collected during the re-
cession of the hydrograph (at night). Even though the spa-
tial variability of the event water signal was the focus of
the study, only temporal variability was considered in the
Genereux-based uncertainty analyses. Although the tempo-
ral variability of winter baseflow isotopic composition seems
to be insignificant, the sample number (n=2) could be too
small to characterize the pre-event component and should
be clearly investigated in future work. Penna et al. (2016)
used two approaches to determine the isotopic composition
www.hydrol-earth-syst-sci.net/20/5015/2016/ Hydrol. Earth Syst. Sci., 20, 5015–5033, 2016
5030 J. Schmieder et al.: The importance of snowmelt spatiotemporal variability
of pre-event water and described differences in the estimated
event water contributions during snowmelt events. They ad-
vise to take pre-event samples prior to the onset of the melt
season because pre-event samples taken prior to the onset of
the diurnal melt cycle could be affected by snowmelt wa-
ter from the previous melt pulses and therefore could lead
to underestimated snowmelt fractions and high uncertainties.
Furthermore, model results and observed discharges were as-
sumed to be free of error in the analyses. As pointed out, in-
strumentation and accessibility are major problems for high-
elevation studies. For this study it turned out that compos-
ite snowmelt samples were easier to collect, representing the
day-integrated melt signal. A denser network of melt collec-
tors would be desirable, as well as a snow lysimeter to gain
high-frequency data automatically. Representative samples
of the elevation zones and different vegetation belts could be
important too, especially in partly forested catchments with
a distinct relief (cf. Unnikrishna et al., 2002).
6 Conclusions
This study provides new insights into the variability of the
isotopic composition in snowmelt and highlights its impact
on IHS results in a high-elevation environment. The spa-
tial variability in snowmelt isotopic signature was considered
by experimental investigations on south- and north-facing
slopes to define the isotopic composition of the snowmelt
endmember with greater accuracy. This study clearly shows
that distributed snowmelt rates obtained from a model based
on meteorological data from local automatic weather sta-
tions affect the weighting of the event water isotopic sig-
nal, and hence the estimation of the snowmelt fraction in
the stream by IHS. The study provides a variety of relevant
findings that are important for hydrologic research in high-
alpine environments. There was a distinct spatial variability
in snowmelt between north- and south-facing slopes, espe-
cially during the early melt season. The isotopic composi-
tion of snowmelt water was significantly different between
north-facing and south-facing slopes, which resulted in a pro-
nounced effect on the estimated snowmelt contributions to
streamflow with IHS. The IHS results were more sensitive
to the spatial variability of snowmelt δ18O than to spatial
variation of snowmelt rates. The differences in the estimated
snowmelt fraction due to the weighting methods used for IHS
were as large as 24 %. This study also shows that it is hardly
possible to characterize the event water signature of larger
slopes based on plot-scale snowmelt measurements. Apply-
ing a distributed model reduced the uncertainty of the spa-
tial snowmelt variability inherent to point-scale observations.
Hence, applying the VWS method provided more reasonable
results than the VWO method. This study highlighted that the
selection of sampling sites has a major effect on IHS results.
Sampling at least north-facing and south-facing slopes in
complex terrain and using distributed melt rates to weight the
snowmelt isotopic composition of the differing exposures is
therefore highly recommended for applying snowmelt-based
IHS.
7 Data availability
Isotope and field data (i.e. snow observations) can be ob-
tained from the corresponding author upon request.
Acknowledgements. The authors wish to thank the Institute of
Atmospheric and Cryospheric Sciences of the University of Inns-
bruck, the Zentralanstalt für Meteorologie and Geodynamik, the
Hydrographic Service of Tyrol and the TIWAG-Tiroler Wasserkraft
AG for providing hydrological and meteorological data, the Amt
der Tiroler Landesregierung for providing the digital elevation
model, the Center of Stable Isotopes (CSI) for laboratory support,
as well as many other individuals, who have helped to collect
data in the field. We also thank the reviewers for their valuable
suggestions that have much improved the manuscript, and the
editor for the careful handling of the manuscript.
Edited by: I. van Meerveld
Reviewed by: S. Pohl and one anonymous referee
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