assessment of the timing of daily peak streamflow during ... · watersheds (barnett et al. 2005;...
TRANSCRIPT
Assessment of the Timing of Daily Peak Streamflow during the MeltSeason in a Snow-Dominated Watershed
XING CHEN, MUKESH KUMAR, AND RUI WANG
Nicholas School of the Environment, Duke University, Durham, North Carolina
ADAM WINSTRALaAND DANNY MARKS
Northwest Watershed Research Center, Agricultural Research Service, USDA, Boise, Idaho
(Manuscript received 19 August 2015, in final form 23 February 2016)
ABSTRACT
Previous studies have shown that gauge-observed daily streamflow peak times (DPTs) during spring
snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three
processes: 1) melt flux translation through the snowpack or percolation, 2) surface and subsurface flow ofmelt
from the base of snowpacks to streams, and 3) translation of water flux in the streams to stream gauging
stations. The goal of this study is to evaluate and quantify how these processes affect observed DPTs vari-
ations at the Reynolds Mountain East (RME) research catchment in southwest Idaho, United States. To
accomplish this goal, DPTs were simulated for the RME catchment over a period of 25 water years using a
modified snowmelt model, iSnobal, and a hydrology model, the Penn State Integrated Hydrologic Model
(PIHM). The influence of each controlling process was then evaluated by simulating the DPT with and
without the process under consideration. Both intra- and interseasonal variability in DPTs were evaluated.
Results indicate that the magnitude of DPTs is dominantly influenced by subsurface flow, whereas the
temporal shifts within a season are primarily controlled by percolation through snow. In addition to the three
processes previously identified in the literature, processes governing the snowpack ripening time are iden-
tified as additionally influencing DPT variability. Results also indicate that the relative dominance of each
control varies through the melt season and between wet and dry years. The results could be used for sup-
porting DPTs prediction efforts and for prioritization of observables for DPT determination.
1. Introduction
Snowmelt is the major source of groundwater re-
charge and streamflow inmountainous areas throughout
the western United States. More than 50%–70% of the
western U.S. water supply originates in snow-fed upland
watersheds (Barnett et al. 2005; Carroll et al. 2006;
DeWalle and Rango 2008). Accurate quantification of
the variability of streamflow from these watersheds can
be crucial for both stream ecology and sustainable
management of water supply resources.
Streamflow variability in the western United States
exhibits spatial and temporal dependencies. This study
examines the temporal dependencies, in particular,
those that occur at subdaily time scales (Lowry et al.
2010; Lundquist and Cayan 2002; Lundquist and
Dettinger 2005; Tobin et al. 2013). Subdaily streamflow
variations are important for fluvial processes and
aquatic ecology as well as reservoir operations aimed at
reducing the magnitude of high flows (Graf 1999; Poff
et al. 1997) and modulating downstream aquatic com-
munities (Bain et al. 1988) and water quality variables
such as stream temperature (Neumann et al. 2003) and
dissolved oxygen. The diurnal variations in streamflow
are mainly introduced by ice or snowmelt and evapo-
transpiration and have been the focus of many studies
(Caine 1992; Gribovszki et al. 2008, 2010; Johnson et al.
2013; Loheide 2008; Loheide and Lundquist 2009;
Lundquist and Dettinger 2005; Soylu et al. 2012). We
focus on assessing the intra- and interseasonal variations
a Current affiliation: WSL Institute for Snow and Avalanche
Research SLF, Davos Dorf, Switzerland.
Corresponding author address: Mukesh Kumar, Nicholas School
of the Environment, Duke University, 450 Research Dr., LSRC
A207A, Durham, NC 27708.
E-mail: [email protected]
AUGUST 2016 CHEN ET AL . 2225
DOI: 10.1175/JHM-D-15-0152.1
� 2016 American Meteorological Society
in daily streamflow peak times (DPTs) during the melt
season and identifying the process dependencies of
these variations. DPT is defined as the hour when
streamflow is the maximum within a day, and melt sea-
son is considered to span from the first to last day that
shows a melt-affected diurnal signal (characterized by a
rapid increase followed by a gradual decrease in daily
hydrograph; Lundquist and Cayan 2002).
DPTs during the snowmelt season can demonstrate
varied shifts. As the melt season progresses, DPTs may
occur earlier in the diurnal cycle (Caine 1992; Jordan
1983a,b), later (Grover and Harrington 1943; Lundquist
and Cayan 2002), or have no significant temporal shift
(Lundquist et al. 2005). Earlier/later DPTs indicate
shorter/longer time intervals between peak melt pulse
and peak subdaily streamflow. These disparate shifts
have been attributed to a combination of three primary
controls: 1) the percolation of liquid water through the
snowpack (Ambach et al. 1981; Caine 1992; Colbeck and
Davidson 1973; Dunne et al. 1976; Jordan 1983a; Pfeffer
et al. 1990), 2) the translation ofmeltwater from the base
of snowpack to the river channel (Caine 1992; Dunne
and Black 1971; Flerchinger et al. 1992; Kobayashi 1986;
Maulé and Stein 1990), and 3) the translation of water
flux in the river channel to the stream gauging station
(Lundquist and Dettinger 2005). These three controls
affect the time difference between the peakmelt pulse at
the snowpack surface due to energy inputs and peak
subdaily streamflow at the stream gauging station; for
example, peak melt pulse typically occurs in the early
afternoon, while peak subdaily streamflow generally
occurs late at night or even the following morning
(Lundquist and Dettinger 2005).
The first control affects the DPTs during the melt
season by altering percolation time of liquid water
through the snowpack as the melt season progresses.
During spring melt when snow cover densities exhibit
low variability, liquid water translation times are gen-
erally longer through thicker snowpacks than thinner
ones. At the beginning of the melt season when snow-
pack is generally thicker, the translation time is longer.
Refreezing of meltwater may further contribute to slow
effective percolation velocity early in the melt season
(Colbeck and Davidson 1973; Colbeck 1975; Pfeffer
et al. 1990). In contrast, the percolation time is reduced
during the late melting season as the snowpack thins
(Caine 1992; Jordan 1983a,b; Kobayashi and Motoyama
1985). Percolation rates may also be affected by the
evolution of preferential flow paths in the snowpacks
during the melt season (Marsh and Woo 1985; Marsh
and Pomeroy 1996).
The second control on shifts in the timing of DPTs is
associated with the time difference between surface
water input from the base of the snow cover and themelt
response signal in the stream. In larger watersheds that
span over a wide range of elevations, streamflow re-
sponse time will increase during the melt season as the
snow line retreats to higher elevations. This results in a
shift of DPTs to later in the day (Caprio 1966; Lundquist
and Cayan 2002; Lundquist and Dettinger 2005;
Lundquist et al. 2004, 2005). In watersheds with frac-
tured basalt in the subsurface, DPTs have been reported
to vary nonmonotonically depending on the snow dis-
tribution and distance of residual snow cover from the
stream gauging station (Flerchinger et al. 1992). In some
large watersheds with a highly heterogeneous snow
cover distribution, the delaying effect of retreating snow
line may offset the effect of percolation time change
within the snowpack, resulting in an essentially un-
changed DPTs through the melt season (Lundquist
et al. 2005).
The third control on changes in DPTs during the melt
season is caused by differences in daily average flow
velocity in the stream channel. Flow velocity varies with
streamflow volume, with higher velocities around the
streamflow peak and lower velocities late in the melt
period (Lundquist and Dettinger 2005).
As the snow cover amount and properties vary inter-
seasonally, the effects of each aforementioned control
on DPTs is expected to change from one year to the
next. Using 5 years of data in Martinelli snowpatch
(0.08 km2 area), Caine (1992) suggested that the DPT
occurred later in the day during years with larger basin-
scale snow depth. Similar conclusions were also drawn
by Lundquist and Dettinger (2005) based on data from
Marble Fork watershed.
The primary purpose of this paper is to evaluate the
role of the process controls on both intra- and inter-
seasonal variations in DPTs. Lundquist et al. (2005)
assessed the impacts of percolation time through the
snowpack and the travel time of meltwater in the river
channel on DPT shifts while assuming that travel times
between the base of the snowpack and the stream were
negligible [see paragraph 19 of Lundquist et al. (2005)].
However, this assumption cannot be universally applied,
especially in watersheds where streamflow has a large
groundwater contribution. To explore the impact of
percolation of liquid water through the snow cover and
translation of water from the base of the snow cover to
the stream and to evaluate the role of these and any
additional processes that may determine DPT variation,
we couple a snowmelt model, ISNOBAL, with a hy-
drology model, Penn State Integrated Hydrologic
Model (PIHM), to simulate the DPTs. The intra- and
interseasonal variations in both observed and modeled
DPTs are identified, and the capability of the coupled
2226 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
ISNOBAL–PIHM to estimate DPTs and its variations
during the melt season is evaluated. Through a series of
process-unmixing experiments, the coupled modeling
system is then used to isolate the role of individual
processes in determining DPTs. Aside from the three
previously reported controls, the physically based cou-
pled modeling framework allows us to also assess the
roles of additional processes on observed DPT varia-
tions. These experiments were used to answer the
following two questions: 1) How do DPTs vary intra-
seasonally and what processes are critical to de-
termine the magnitude of those variations? and
2) How doDPTs vary interseasonally and what are the
processes that control that variation?
This paper is organized as follows. Section 2 pres-
ents the study area, the modeling methodology, and
related calibration and validation details of the linked
model (ISNOBAL–PIHM). Section 3 describes the
design of process-unmixing experiments and the in-
formation obtained from these experiments. Section
4 describes the results from the process-unmixing
experiments and discusses process-dependent con-
trols on intra- and interseasonal variations of DPTs.
Section 5 summarizes the results and takeaways from
this study.
2. Setting and model details
a. Study area and relevant datasets
Reynolds Mountain East (RME), a snow-dominated
headwater catchment within the Reynolds Creek Ex-
perimental Watershed (RCEW; Marks 2001), was
selected for this study (Fig. 1). Elevations in RME
(0.38 km2) range from 2028 to 2137m above mean sea
level. Hillslope angles range from 08 to 21.48. Soil texturein the watershed ranges from loam to clay, with variably
fractured and altered basalt underneath. The watershed
is covered by dense willows and aspen near the riparian
zone and mixture of sparse Douglas fir, Vaseyana
sagebrush, scattered dry meadow, and aspen patches
farther away from the stream (Grant et al. 2004; Reba
et al. 2011a). Based on the 25-water-year (WY)1 dataset
presented in Reba et al. (2011a) (and discussed further
below), the monthly average temperature in the water-
shed ranges from 248C in December to 178C in July.
Over 70% of the precipitation occurs in the form of
snow during the winter months, with July–September
being typically very dry. WY precipitation at the snow
pillow site (filled circle in Fig. 1) varies from 584 to
1537mm with a WY average of 967mm. In contrast,
WY precipitation at the exposed site, which is only
around 350m away and is located in an exposed area
within a sagebrush community (filled triangle in
Fig. 1), varies from 454 to 1201mm with aWY average
of 779mm (Marks andWinstral 2001). The differences
in precipitation are due to complex snow accumula-
tion patterns produced by wind scour and drifting.
Heterogeneity in mass and energy inputs in this
FIG. 1. Land-cover distribution and observation sites in the RME watershed.
1 A water year runs from 1 Oct of the previous year through 30
Sep of the given WY and is used in regions dominated by winter
precipitation, like the western United States. In this paper, any
reference to years is to water years, with the year referring to the
calendar year of that spring’s snowmelt runoff.
AUGUST 2016 CHEN ET AL . 2227
watershed produce spatial–temporal differences in
melt production with exposed areas producing a
greater proportion of early spring melt and sheltered
regions contributing higher percentages in late spring
(Marks et al. 2002).
The aforementioned 25-WY (1984–2008) hydro-
climatic dataset (Reba et al. 2011a) was used to evaluate
DPT variations. Relevant datasets were hourly stream-
flow at the basin outlet (WYs 1984–2008), hourly
groundwater at three wells (WYs 2006–08), and hourly
soil moisture at one site (WYs 2005–08). Streamflow
from the RME watershed ranges from 125mm in the
driest WY to 1106mm in the wettest WY, with an av-
erage of 518mm for the 25-WY period. The runoff ratio
varies from 0.24 to 0.90, with an average runoff ratio of
0.61 for the 25-WY simulation period. The three years
containing fine temporal groundwater and soil moisture
data encompass a wide range of hydroclimatic condi-
tions, with 2006 and 2007 WYs being among the wettest
and driest five WYs, respectively, over the 25-WY
dataset (Reba et al. 2011a). WY 2008 was an average
year with precipitation of 856mm at the exposed site and
996mm at the sheltered site. All relevant topographic,
physiographic, and hydroclimatic datasets for WYs 1984
to 2008 are obtainable from the Northwest Watershed
Research Center, USDA (ftp://ftp.nwrc.ars.usda.gov/
public/RME_25yr_database). Detailed descriptions of
these datasets can be found in Hanson et al. (2001).
b. Modeling methodology
For this analysis, ISNOBAL was coupled to PIHM,
simulating snow accumulation, melt, and hydrologic
processes across the RME watershed for the 25-WY
simulation period. ISNOBAL is a grid-based two-layer
energy- and mass-balance snow model (Marks et al.
1999), forced with distributed fields of precipitation, air
temperature, net shortwave radiation, downwelling
thermal radiation, wind speed, and relative humidity.
ISNOBAL simulates snow states including snow tem-
perature, density, and snow water equivalent for each
layer at every time step. Snowmelt is initiated with ad-
ditional energy input once snow temperature in either of
the two layers reaches 08C. Melt water is retained in the
snowpack by capillary forces (surface tension) until
threshold saturation levels are reached. At this point,
surface tension can no longer retain any additional liq-
uid water against gravity and any additional energy in-
put results in meltwater percolating downward.
ISNOBAL delivers excess meltwater, or rain on bare
ground to the soil surface as surface water input (SWI)
for each hourly time step, assuming zero lag time.
ISNOBAL has been previously applied across sites with
varying snow and climate conditions in the United
States (Garen and Marks 2005; Link and Marks 1999;
Reba et al. 2011b; Winstral et al. 2009). In this work, a
lag function was incorporated in ISNOBAL to delay the
SWI fluxes using (Anderson 1976; Flerchinger 2000)
L5L
max
C2W1 1
, (1)
where L is the SWI delay time (h); C2 is an empirical
coefficient with the value 0.01m21;W is the depth of the
SWI flux (m); and Lmax is the maximum lag time (h),
calculated as
Lmax
5C1[12 exp(20:0253 d
s/r
s)], (2)
where C1 is the maximum allowable lag obtained from
best fit of experimental data (Anderson 1976),C15 10 h;
ds is snow cover depth (m); and rs is snow cover density
(kgm23).
Even though this ‘‘lag and route’’ approach calculates
the percolation time of excess liquid water without ac-
counting for the preferential flow paths (Marsh andWoo
1985; Marsh and Pomeroy 1996) and an explicit dis-
tinction of the snowpack into unsaturated and saturated
layers (Gray et al. 1985), it has been shown to be ef-
fective in simulating hourly outflow from the bottom of
snowpack (Barry et al. 1990). Equation (1) suggests that
for a ripe snowpack with a density of 480kgm23, depth
of 0.90m, and melt rate of 1.2mmh21, the liquid water
flux lag time at a point would be around 3.5 h. This
propagation time is similar to the one obtained using the
equations presented in Lundquist and Dettinger (2005),
which calculates a translation time of 3.7 h. Dunne et al.
(1976) reported the observed shift in lag time in the
range of 0.5–7.9 h from plots sampled at a range of as-
pects, vegetation cover, snow depth, and density. After
accounting for the delay in SWI flux translation through
snowpacks, the resulting adjusted SWI is input to PIHM
(Kumar 2009; Kumar et al. 2009; Qu and Duffy 2007).
PIHM is a physics-based distributed hydrologic
model, which employs a semidiscrete finite volume
formulation to simulate a range of processes, includ-
ing snowmelt, evapotranspiration [Penman–Monteith
equation (Allen et al. 1998)], interception [Rutter
model (Rutter et al. 1971)], overland flow [2D diffu-
sion wave equation (Gottardi and Venutelli 1993)],
unsaturated zone infiltration [1D approximation of
Richards’s equation (Richards 1931)], groundwater
flow (3D, Richards’s equation), and streamflow [1D
diffusive wave equation (Strelkoff 1970)]. It is to be
noted that one-way linking between ISNOBAL
and PIHM involved deactivating the temperature
index snowmelt utility in PIHM and replacing it with
2228 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
the physically based snowmelt SWI output from
ISNOBAL. Additionally, simulated ground evapora-
tion in PIHM is shut off at locations shielded by accu-
mulated snow, but evapotranspiration from protruding
vegetation still occurs. Energy exchanges at snow-free
locations remain the same. Both ISNOBAL and
PIHM are set to run at an hourly time step; however,
the spatial resolution of ISNOBAL and PIHM are
different. ISNOBAL was run on a total of 3978 10m310m structured grids in the model domain, while
PIHM has 101 unstructured triangular grids in the
same domain. SWI for each triangle grid contains the
sum of SWI output from ISNOBAL structured grids,
which has the centroid within the triangle. PIHM has
been previously applied across watersheds with dif-
ferent scales and climate conditions (Chen et al 2015;
Kumar and Duffy 2015; Shi et al. 2014; Yu et al. 2014).
The linked framework has been previously used in
Kumar et al. (2013) and Wang et al. (2013) to model
relevant hydrologic states and fluxes in RME.
c. Model calibration and validation
1) MODEL CALIBRATION
Model calibration is performed only in PIHM.
Though ISNOBAL is uncalibrated, the terrain-based
wind redistribution parameters tuned to topographic
and vegetation structure of the RME catchment as
presented by Winstral et al. (2009) were used for the
ISNOBAL simulation. The ability of the model to sim-
ulate snow distribution and total melt and surface water
output has already been demonstrated in Winstral and
Marks (2002). Reba et al. (2011b) showed that for an
uncalibrated ISNOBAL simulation, the average Nash–
Sutcliffe model efficiency (NSE) over the 25-WY period
for predicted versus measured SWE at the snow pillow
was 0.90, with a range of values between 0.74 and 0.98.
Calibration of parameters for PIHM simulations in-
volved nudging of hydrogeological parameters uni-
formly across the model domain (Refsgaard 1997) to
match the baseflow magnitude, groundwater head dis-
tribution, and decay rate of the hydrograph during the
recession period. Calibration experiments were per-
formed for two periods: 1) a dry period with no appre-
ciable antecedent recharge (10–25 October 2005) and
2) a wet and cold period with peak streamflow response
(caused by rainfall event) and negligible evapotranspi-
ration (from 30 December 2005 to 30 January 2006).
Streamflow during the first calibration period was as-
sumed to be predominantly base flow and controlled by
subsurface properties. It was assumed that streamflow
during the second calibration period was controlled by
both surface and subsurface properties. This strategy
ensured that calibration of the coupled snow and hy-
drology model did not depend on the ISNOBAL SWI
output. Hydrogeological parameters that were adjusted
during the calibration procedure included soil drainage
parameters, such as van Genuchten coefficients (Van
Genuchten 1980), porosity, and conductivity of the top
soil and the underlying subsurface. More details about
the calibration methodology are presented in Kumar
et al. (2013). It is to be noted that parameter optimiza-
tion did not include matching the magnitude or timing
of streamflow peak response at either daily or seasonal
scales.
2) MODEL VALIDATION
(i) Streamflow, groundwater, and soil moisturevalidation
Results of streamflow validation for the simulation
period 1984–2008 (Fig. 2a) showed NSE of 0.86 and
coefficient of determination r 2 of 0.93 for hourly data,
0.88 and 0.94 for daily data, 0.86 and 0.96 for monthly
data, and 0.86 and 0.96 for yearly data. The simulated
and observed annual runoff means during WYs 1984–
2008 were 528 and 468mm, respectively.
Groundwater table dynamics validation for the period
WY 2006–08 is presented in Fig. 2b. Though the ob-
served groundwater responses at wells 1 and 2 were
unusually steep, for example, varying by as much as
7.5m in a matter of 3 h, the model was able to capture
the general shape and structure of these dynamics across
all years where groundwater height data were available
[see Kumar et al. (2013) for details]. Figure 2b shows
that the model captures the slower dynamics equally
well. More detailed characterization of the subsurface,
finer spatial data and model grid resolution, and an au-
tomated calibration strategy may possibly help resolve
the discrepancies. It is possible that the simple repre-
sentation of macroporous flow behavior in the sub-
surface is not adequate for highly transient subsurface
flow systems.
Comparisons between observed soil moisture at
multiple locations has already been shown in Fig. 2 of
Kumar et al. (2013), where the simulated top soil satu-
ration magnitude and timing reasonably matches the
observation. Even though hourly soil moisture data exist
for the snow pillow site fromWYs 2005 to 2008, out of 67
melt days during this period only 6 exhibited a peak
signal. On these 6 days, average error between peak
hour in the observed soil moisture data and the modeled
lagged SWI flux was within an hour. On the rest of the
61 days, soil is either completely saturated during the
day or it saturates well before the melt peak is reached.
Despite using a nonlocalized calibration approach,
AUGUST 2016 CHEN ET AL . 2229
model predictions for streamflow, groundwater table,
and soil moisture variations can be considered adequate
and underscore the potential of the coupled ISNOBAL–
PIHM system for simulating hydrologic responses and
understanding the role of process controls on DPTs.
(ii) DPT validation
In this study, melt season DPTs were calculated from
simulated streamflow and compared to observed DPTs
for WYs 1984–2008. Days without a melt-affected (di-
urnal) signal were excluded from the analysis (i.e., the
hydrograph showed monotonic variations within a day).
Figure 3 shows the typical streamflow diurnal signals
during the melt season and corresponding DPTs. Per-
formance of the linked model was evaluated based on
the following two metrics: 1) presence/absence of an
observed diurnal signal in simulation results and 2) DPT
simulation.
A. PRESENCE /ABSENCE OF AN OBSERVED DIURNAL SIGNAL
IN SIMULATION RESULTS
For the 25-WY simulation period, a total of 811 and
595 days showed melt-affected signal in observed and
simulated streamflow, respectively, with 519 days (64%
of observed melt-affected days) showing melt-affected
signal in both observed and simulated streamflow. For
the wettest 5 years, which is defined as the 5 years with
the largest precipitation amount before themelt-out date
at the snowpillow site, a total of 195 and 169 days showed
melt-affected signal in observed and simulated stream-
flow, respectively, with 150 days (77% of observed melt-
affected days) showing melt-affected signal in both
observed and simulated streamflow data. In contrast, for
the driest 5 years, which is defined as the 5 years with the
smallest precipitation amount before the melt-out date
at the snow pillow site, a total of 124 and 57 days showed
FIG. 2. Modeled and observed (a) streamflow for WYs 1984–2008 and (b) groundwater
depth for WYs 2006–08 [light gray vertical lines in (b) demarcate period from 1 to 13 Apr in
WY 2006].
2230 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
melt-affected signal in observed and simulated stream-
flow, respectively, with 56 days (45% of observed melt-
affected days) showing melt-affected signal in both
observed and simulated streamflow. The results indicate
that the model could capture the days with diurnal signal
much more accurately in wet years than in dry years.
The observed disparity in accuracy between wet and
dry years is largely due to the effectiveness of the model
during the early melt period. Isolating the first 5 days of
the season withmelt-affected diurnal signals in observed
streamflow, modeled streamflow similarly exhibits a di-
urnal signal on 88%of the days in the wettest 5 years and
FIG. 4. Comparison of simulated and observed streamflow during the melt season for (a) a very wet, warm snow
season (WY 1996) and (b) a dry, cool snow season (WY 1999). See Reba et al. (2011b) for details on conditions
during WYs 1996 and 1999.
FIG. 3. Streamflow time series for WY 2006 during melt season. Also illustrated is the DPT
hour (after noon) for every day. Times in black indicate peak hour for days with distinct diurnal
signal. Times in gray boxes indicate peak hour for days with multiple peaks. All times are with
respect to the local noon. Note that negative value means time before noon, for example, 22
means 1000 local time (LT).
AUGUST 2016 CHEN ET AL . 2231
only 52% of the days in the driest 5 years. The inability
of the model to capture melt-affected streamflow in
early spring, especially during dry years, is highlighted in
Fig. 4. The streamflow simulation is fairly accurate
throughout the melt season for a very wet, warm snow
season (WY 1996, Fig. 4a). In contrast, Fig. 4b shows that,
for a dry, cool snow season (WY 1999), the model under-
estimates streamflow early in the melt season and over-
estimates streamflow later in the melt season. One possible
reason could be that PIHM did not simulate soil temper-
ature, and hence the consequent reduction in infiltration
capacity under frozen/near-frozen soil conditions was not
accounted for (Burt and Williams 1976; Flerchinger et al.
2006; Horiguchi and Miller 1983; Watanabe and Flury
2008). As a result, instead of surface runoff during the cool
WY 1999 snow season, the model overestimated recharge
to the subsurface, thus missing the melt-affected stream-
flow in early spring. These conclusions are consistent with
reported cold snow and soil temperatures during the WY
1999 snow season (see Reba et al. 2011b).
B. DPT MAGNITUDE
The DPTs were compared for the days when both ob-
served and simulated data showed a melt-affected signal.
Since both observed and modeled daily hydrographs may
often consist of almost flat and slightly noisy high-flow
periods lasting few hours (,4h) with very small hour-to-
hour variations, comparison of modeled and observed ab-
solute DPTs can sometimes be muddled by noise (Fig. 5).
Hence, comparisons were also performed against ‘‘mod-
eled Q1% peak hour,’’ which is defined as the closest hour
to the observed DPTs in which the modeled streamflow is
within the top 1% of the modeled daily streamflow range.
An identical observed DPT and modeled Q1% peak hour
indicates that the top 1% of modeled daily streamflow
occurred within the same hour as the observed daily peak.
In contrast, a larger difference between modeled DPT and
modeled Q1% DPT indicates a longer period of relatively
flat (and possibly noisy) hydrograph near the peak.
DPTs in all the figures presented here are with respect
to the local noon, that is, 9 indicates 9 h after noon,
which is 2100 local time (LT). DPT simulation results for
the 24 WYs are shown in Fig. 6. WY 1992 is not shown
here since themodel could not capture the diurnal signal
exhibited on any of the 5 days with an observed diurnal
signal during this extremely dry year. The r 2 value be-
tween modeled and observed DPTs for the 519 days
with diurnal signal in both simulated and observed
FIG. 5. Schematic of observed and modeled daily streamflow hydrographs with (a) identical observed and
modeled daily peak hour (here observed peak hour5modeled absolute peak hour5modeledQ1% peak hour) and
(b) different observed and modeled peak hour with very small variations near the top 1% peak discharge (here
observed peak hour 5 modeled absolute peak hour 2 1 5 modeled Q1% peak hour).
2232 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
FIG. 6. Observed and modeled daily peak time for 24 WYs ranging from WYs 1984 to 2008 (WY 1992 was excluded). The x axis
represents WY day, and the y axis indicates the daily peak time hour with respect to local noon. Note that negative value means time
before noon, for example, 24 means 0800 LT.
AUGUST 2016 CHEN ET AL . 2233
streamflows was 0.58, while r 2 between Q1% DPTs and
observed DPTs was 0.74. However, 130 of these days
had multimodal peaks because of rain or snow events
that interfered with the diurnal variation in energy
forcing to the snowpack. For days with unimodal peak,
r2 between modeled and observed DPTs was 0.61 and
between Q1% DPTs and observed DPTs was 0.80.
Figure 6 shows that both modeled and Q1% DPTs ap-
pear to follow the seasonal variations exhibited by the
observed data. The mean and variance of observed,
modeled, andQ1%DPTswere (5.8, 7.4), (5.6, 9.3), and (6.0,
7.7) h, respectively. Further analyses indicated that, of the
number of days considered in Fig. 6, years with more than
80% of days with j(observed DPT 2 modeled absolute
DPT)j and j(observed DPT2 modeledQ1% DPT)j beingsmaller than or equal to 1h are 6 and 14 years, respectively.
For the 24 years under consideration, more than 59% of
days had a difference between observed and modeled
DPTs of less than 1h, and more than 78% of days had this
difference in the range less than 2h. The corresponding
numbers for the difference between Q1% modeled and
observed DPTs were 76% and 88%, respectively.
Presented results indicate that the simulated hour of
peak discharge, especially the hour of the Q1% peak,
closely matched the hour of observed daily streamflow
peak during the snowmelt season, especially in wetter
years. However, ISNOBAL–PIHM was not as success-
ful in capturing diurnal variations in streamflow, espe-
cially early in the melt season during drier years.
3. Experiment design and details
a. Daily peak time and melt season duration
The initial day of the melt period, identified by the
melt-induced signal in streamflow, does not always
mean that snowmelt begins on this given day. For ex-
ample, analysis of the simulated daily SWI volume forWY
2006 indicates 12 days of melt before a melt-affected
streamflow signal was registered. Furthermore, it also
does not mean that the SWI volume on this day is neces-
sarily larger than that on the previous days. For example,
on the previous and following 5 days of initiation of the
melt period (13 April, day 195 of WY 2006), simulated
melt amount only differed marginally (Fig. 7). Simulation
results suggest that the melt-affected diurnal signal in this
year was expressed only after groundwater table was
shallow enough for it to substantially contribute to
streamflow. Before this, most of the melt recharges the
subsurface moisture deficit and groundwater. This expla-
nation is supported by the observed groundwater table and
streamflow data for WY 2006 (see Figs. 2, 3) where the
melt-affected signal in streamflow starts on 13 April (WY
day 195), only after the observed groundwater levels in
wells 1 and 2 have become shallow enough after un-
dergoing gradual increase from 1 to 13 April (this time
interval is highlighted by two circular dots in well 1 and 2
groundwater series in Fig. 2) in response to melt recharge.
Groundwater control on diurnal streamflow response is
further evident from diurnal streamflow fluctuations that
aremuch smaller (both in terms of volume and amplitude)
than melt (see Fig. 7). It is clear in Fig. 7 that before WY
day 195, the magnitude of SWI is comparable to that after
day 200; however, SWI at the beginning of the melt season
(before WY day 195) does not lead to substantial
streamflow as it does later in the melt season (e.g., after
WY day 200). Also, the observed streamflow magnitude
on the first melt-affected day is small and increases in
subsequent days (concomitantly with the increase in
groundwater height) even though a similar increasing
trend inmelt and SWI does not exist. This indicates a large
FIG. 7. Modeled daily total melt and streamflow during melt period in WY 2006.
2234 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
groundwater contribution to streamflow response
(Bengtsson 1982). In fact, simulation results suggest that
the groundwater contribution to streamflow is close to
100% during the melt season in the RME watershed. This
indicates that DPTs analyzed in this watershed are domi-
nantly controlled by subsurface flow processes, including
infiltration of SWI into the upper unsaturated zone and its
translation to groundwater.
b. Isolating the relative role of individual processcontrols on DPTs through process-unmixingexperiments
The variation of DPTs within a melt season has been
previously attributed to three processes (see section 1 for
detailed discussion).We hypothesize that the time for snow
cover ripening may also contribute to intraseasonal varia-
tions in DPTs. Therefore, in the ensuing analyses, we
consider the role of the following four processes on
DPT variations: 1) net incoming energy (turbulent
heat 1 radiation 1 advection) to the snowpack and
snowpack properties (depth1 temperature1moisture
content), which determines the snowpack ripening time; 2)
percolation rate of liquid water flux through the ripened
snowpack; 3) translation of meltwater output from the
bottom of the snowpack (SWI) to the stream channel
through surface and/or subsurface pathways; and 4) trans-
lation ofwater in the river channel to the streamflowgauge.
Notably, many other factors, such as snowpack and its
distribution and watershed properties, may also affect the
peak time, but they do so by indirectly influencing the
aforementioned four processes. The scope of this paper is
restricted to assessing the influence of the four basic pro-
cesses on DPTs.
To isolate the influence of each process on the timing
of daily peak streamflow, DPTs are evaluated with and
without the process under consideration. DPTs from dif-
ferent processes and relations between them are listed in
Table 1. It is to be noted that the peak time delay (P122P1)
caused by translationwithin a snowpack is not equivalent to
the average translation time through the snowpack. Instead,
(P122 P1) represents the time difference between instances
that receive the largest water flux in the two simulations and
hence is a function of bothmelt flux amount and the time of
translation. For example, between two cases with identical
spatial distribution of percolation times through the snow-
pack in awatershed, ifmelt contributions from thinner snow
(with relatively shorter percolation times) are larger than
that from deeper snow, then (P12 2 P1) will be smaller.
Conversely, if melt contributions are mostly from deeper
snowpacks through which the translation time is relatively
longer, (P12 2 P1) can be expected to be larger. Similarly,
(P123 2 P12) expresses the contribution from surface/
subsurface flow processes in delaying the peak time.
4. Results and discussion
The results are presented thematically following the
two questions outlined in section 1.
a. How do DPTs vary intraseasonally and what arethe key processes that determine its magnitude andvariations?
1) INTRASEASONAL DPT VARIATIONS
Within each melt season, DPTs generally exhibited a
shift to earlier in the diurnal cycle as the melt season
TABLE 1. Description of processes considered and process-unmixing approach.
Processes considered Streamflow DPT Process-unmixing approach
1 Net incoming energy to the snowpack
and snowpack characteristics that
determines the time it takes for
snowpack to ripen
P1 DPT of total melt flux over the watershed
from ISNOBALwithout accounting for
translation process through snow, i.e.,
by setting L 5 0 in Eq. (1)
P1 DPT from snowpack ripening
process
2 Percolation rate of liquid water flux
through the ripened snowpack
P12 DPT of liquid water flux (by accounting
for translation process in the snowpack)
over the watershed, i.e., by calculating
L in Eq. (1) based on snow density, melt
flux amount, and snow depth at each
grid cell
P12 2 P1 DPT delay from liquid water
flux percolation through
snowpack
3 Translation of melt flux output from
the bottom of the snowpack to the
stream channel through surface
and/or subsurface pathways
P123 DPT of streamflow simulated by linked
ISNOBALand PIHM, wherein the flow
translation process in the river channel
was discounted by setting Manning’s
roughness coefficient in the river to be
infinitesimally small
P123 2 P12 DPT delay from liquid water
flux translation through
surface/subsurface
pathways
4 Translation of water in the river
channel to the streamflow gauge
P1234 DPT from the linked ISNOBAL and
PIHM detailed in sections 2b and 2c
P1234 2 P123 DPT delay fromwater traveling
in river channel
AUGUST 2016 CHEN ET AL . 2235
progressed, that is, DPT on the last day of the melt
season was earlier than that on the start day. The change
in DPTs from the start to end of the melt season across
the 25-WY period was from 10 to 1 h in the observed
data, from 12 to 1h in the ISNOBAL–PIHM results, and
from 11 to 1h inQ1%modeled results. The shift of DPTs
to earlier in the day was often obscured by abrupt non-
monotonic day-to-day variations. For example, in WY
1993 (Fig. 6), the observed DPTs shifted from hour 12 to
3 (2400–1500 LT) between WY days 207 and 215,
abruptly shifting to hour 11 (2300 LT) on WY day 217
(identified by an arrow in Fig. 6) and then gradually
shifted earlier again to hour 4 (1600 LT) byWY day 232.
During the simulation period, abrupt shifts ($2 h) in
DPTs were observed on 40 days in the observation data,
with a maximum shift equal to 8 h. Comparatively,
abrupt shifts occurred on 43 days in the modeled
streamflow and on 36 days in Q1% modeled results,
with a maximum shift time of 5 and 6h, respectively.
2) INFLUENCE OF PHYSICAL CONTROLS ON
INTRASEASONAL PEAK TIME AND ITS
VARIATIONS
Using the strategy detailed in section 3b, the time of
peak streamflow (e.g., P1, P12, and P123) was obtained
after discounting the effect of individual process con-
trols. Results (Fig. 8) suggest that discounting the role of
individual processes affects both the timing of daily
simulated peak streamflow and its variation during the
season. The daily peak time was earlier, as expected, as
increasing number of process controls were discounted,
that is, P1 , P12 , P123 , P1234. In addition, the dif-
ference between P1, P12,P123, andP1234 was much larger
in the beginning of the season than at the end for each
year. However, the relative contribution of each process
control was observed to vary from day to day. In the next
section, we evaluate the role of each process control on
the intraseasonal timing and variation of DPTs.
(i) Role of net incoming energy and evolvingsnowpack properties
Increasing energy inputs and concomitant changes in
snowpack properties are expected to result in decreasing
DPTs. This is because as net energy input increases,
snow cover ripening may occur earlier during the day. In
addition, changing snowpack properties such as in-
creasing snowpack temperature and liquid water con-
tent and decreasing depth would require less energy to
ripen the snowpack, leading to earlier melt. The average
P1 difference from the start to end of the snow season
during the simulation period was 1.1 h. While in some
years P1 at the start of the season was up to 10h later
than that at the end, other years showed no differences.
Since a shift in P1 is a compound effect of both in-
creasing incoming energy and changing snowpack
properties, two additional ISNOBAL experiments were
performed to assess influences of the two factors.
To evaluate the influence of increasing net energy input
on the DPTs, ISNOBAL snow states for each day within
themelt seasonwere fixed to conditions on the first day of
melt.With fixed snow states and varying net energy input,
the average P1 difference from start to end of the season
during the simulation period was around 0.3h. Similarly,
to evaluate the influence of changing snowpack proper-
ties on the DPTs, ISNOBAL incoming energy for each
day within the melt season was fixed to conditions on the
first day of melt. With fixed incoming energy and varying
snowpack properties, the averageP1 difference from start
to end of the season during the simulation period was
around 0.7h. The results from the two additional exper-
iments indicate that evolving snowpack properties play a
more important role than the incoming energy in themelt
timing. In reality, the variation of P1 (shown in Fig. 8)
during the melt season is not monotonic, as it is de-
pendent on net energy fluxes and snowpack properties,
which exhibit strong variability. Sudden decreases in
temperature or radiation, or new snow events, which in-
crease albedo and snow depth, can lower the net energy
inputs and modulate temperature of the snowpack. In
fact, all 95 days showing a delayed streamflow peak time
in P1 had either large decreases in temperature or radi-
ation relative to the prior day. Of these 95 days, 55%
showed a concomitant increase in P1234. In summary, an
increasing trend in radiation and temperature, with con-
comitant increase in snowpack temperature andmoisture
content and decrease in snowpack depth during the melt
season, generally leads to a decreasing trend in P1.
However, the isolated impact of net energy input is only
marginal. Sudden decreases in radiation or temperature
(e.g., due to cloudiness) and precipitation eventsmay lead
to an abrupt P1 increase. Changes in P1234 could mirror
changes in P1. Evaluation of the isolated impact of cloud
and/or precipitation on P1234 is reserved for future study.
(ii) Role of snow percolation process
Delay in the DPTs (i.e., the time difference in DPTs
if a process’ contribution is considered or not) caused by
the process of snow percolation (P12 2 P1) is also ex-
pected to decrease as the melt season progresses be-
cause of decreasing snow depth and increasing liquid
water content. This is evident in Figs. 8 and 9, where the
difference between P12 and P1 is generally larger in the
beginning than the end of the melt season for all years.
The average (P12 2 P1) at the start and end of the melt
season over all years is 2.7 and 0.2 h, respectively.
However, the delay from translation of meltwater
2236 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
FIG. 8. Results of peak hour variation after discounting different processes for 24WYs:P1234 is the original modeled peak hour;P1 is the
peak hour of melt flux when translation time through snowpack is not accounted for, P12 is the peak hour from translated liquid water flux,
and P123 is based on the original model but assuming translation in the river does not happen. Detailed explanations of the four exper-
iments are presented in section 3a.
AUGUST 2016 CHEN ET AL . 2237
through the snowpack showed a nonmonotonic decrease
and could vary significantly from day to day. One ex-
ample of such day-to-day variance could be seen onWY
days 196 and 197 in WY 2006 (Fig. 9). On WY day 196,
P1-forced DPT was determined by an intense rain event
occurring late in the day at 1800 LT. This intense rain
event resulted not only in accelerated melt, but trans-
lation of rainwater through the snowpack, leading to a
reduction in snow translation delay (P12 2 P1) of 3 h
[based on Eq. (1)]. The following day (WY day 197),
where melt was forced primarily by diurnal energy fluxes,
the snow translation delay was 6h. The difference in the
time of meltwater translation through the snow between
WY days 196 and 197 was mainly caused by the intense
rain event on WY day 196 that increased the percolation
flux. Our analysis shows that a decreasing trend in snow
depth during the melt season generally leads to a de-
creasing trend in (P12 2 P1). However, timing of rain-on-
snow events during the day may interfere with this trend
and can lead to an abrupt increase in (P12 2 P1).
(iii) Role of surface/subsurface flow
The streamflow in RME watershed is controlled pri-
marily by subsurface processes (see section 3a). As the
melt season progresses, soil moisture storages become
increasingly saturated, water-table height increases, and
the contributions of subsurface processes to discharge
increase. Since the subsurface hydrologic conductivity in
the watershed decreases with depth, net increase in
water-table height during the melt season leads to a
consequent increase in effective hydraulic conductivity
of the watershed (Wildenschild and Jensen 1999; Yeh
and Harvey 1990; Quinton and Marsh 1998; Quinton
and Gray 2003). Though the groundwater table during
the melt period generally reduced after the peak melt, it
still remained higher than its level at the beginning of
melt season. In fact, the average difference in ground-
water table between the beginning and end of the melt
season across the simulation years was 2.1m. As a result,
the delay in timing of peak streamflow caused by sub-
surface flow (P123 2 P12) is expected to decrease. This is
evident in Fig. 8, where the difference between P123 and
P12 is generally larger in the beginning than the end of
the melt season. Average (P123 2 P12) at the start and
end of melt season over all years was 4.0 and 1.6 h, re-
spectively. However, (P123 2 P12) does not decrease
monotonically during the melt season. A closer look at
the melt flux peak and corresponding streamflow sug-
gested that mild increases in (P1232 P12) on consecutive
days were often observed, mostly because of a flat hy-
drograph around the peak (see section 4a). However, on
certain days, (P123 2 P12) exhibited jumps as large as
5 h (an abrupt increase during WY 1984 is highlighted
in Fig. 10). Interestingly, the abrupt jump identified in
WY 1984 occurred on days with very small differences
in melt and groundwater distributions. Further investi-
gation of WY 1984 melt flux and streamflow onWY day
236 revealed that both the original melt flux and trans-
lation of meltwater through the snow were trimodal on
this day, with the middle peak being the largest. How-
ever, daily streamflow showed only two peaks due to dry
antecedent groundwater conditions early in the day.
Because of the multiple melt recharge pulses during the
day, the difference in the hour of peak streamflow for
FIG. 9. Peak time and delay caused by different factors in WY 2006 (P1 is peak hour from melt
flux when translation time through snowpack is not accounted for, P12 2 P1 is peak hour delay
caused by snow translation process, P123 2 P12 is peak hour delay caused by ground translation
process, and P1234 2 P123 is peak hour delay caused by in channel translation process).
2238 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
days with multimodal melt flux could not be compared
to days with a single peak. Variations in (P123 2 P12)
were also affected by dynamic changes in subsurface
moisture and its distribution, which influenced in-
filtration capacity and drainage pathways. Our analysis
shows an increasing trend in effective hydraulic con-
ductivity due to a rising groundwater table during the
melt season, which generally leads to a decreasing trend
in (P123 2 P12). However, days with a multimodal melt
flux can exhibit abrupt jumps in (P123 2 P12) because of
dynamic changes in losses and subsurface storage.
(iv) Role of stream channel translation
Impacts of stream channel translation onDPTs during
melt season were generally within an hour because of
the small size of the RME catchment (0.4 km2) and the
limited stream channel (,500m). This is illustrated by
Fig. 8, which shows that P123 and P1234 overlap each
other on most of the days during the melt season. Re-
solving DPTs at finer time steps might reveal contribu-
tions from this process to be on the order of minutes for
days with overlapping P123 and P1234; however, to
maintain consistency in the analyses given that the
temporal resolution of the observation data is an hour,
only hourly difference in P123 and P1234 is reported here.
(v) Relative role of each process in delaying the timeof peak streamflow
Among the above four processes, P1 controls the
timing of melt flux while the other three processes
contribute to the delay in the translation of meltwater
from the snow through the soil and groundwater to the
stream channel and finally to the stream gauge. The
peak streamflow timing delays attributable to each
process are taken as differences in simulated peak flows
before and after including each process in the simula-
tions. Average delays due to percolation of flow through
the snowpack (P2), flow through the subsurface (P3),
and flow in the stream channel (P4) were 1.7, 2.3, and
0.2 h, respectively. The average delay values indicate
that subsurface translation played the dominant role in
delaying the peak times, with snow percolation being the
secondmost important factor. The influence of stream
translation process on the streamflow DPTs was negli-
gible compared to the other two factors, which is con-
sistent with the findings in Lundquist et al. (2005).
As noted in the previous sections, the delay effect
caused by the percolation and subsurface processes was
generally larger in the beginning of the melt season than
the end. To identify the relative contribution from each
process at different times during the melt season, the melt
period was evenly divided into three periods: 1) the early
melt period (deep snowpack 1 deep groundwater table),
2) the rapid melt period (rapidly reducing snowpack 1rapidly increasing groundwater table), and 3) the late
melt period (thin snowpack1 shallow groundwater table).
Average timing delay for DPTs during the three intervals
as caused by each process was calculated and compared.
Results indicate that the relative importance of indi-
vidual processes change during the melt season.
FIG. 10. (a) Peak time and delay caused by ground translation process (P123 2 P12) in WY
1984. (b) Original melt, melt after translation through snowpack, and streamflow time series
for WY 1984.
AUGUST 2016 CHEN ET AL . 2239
During the early melt period in the wettest 5 years,
average delay caused by snow translation and sub-
surface flow was 3.4 and 2.3 h, respectively. During this
period, translation through snow was the dominant
control in determining DPTs. In the rapid melt period,
delays caused by translation through snow and sub-
surface processes were 1.3 and 2.2 h, respectively. Dur-
ing this period, subsurface processes became the
dominant factor. In the late melt period, translations
through snow and the subsurface contributed evenly to
the delay in DPTs, with delay time being 1.2 h for both.
For the driest 5 years, subsurface processes were the
dominant factor for delay throughout the melt season.
Average delay caused by translation through the sub-
surface for the early melt period, rapid melt period, and
late melt period were 7.8, 4.7, and 2.7 h, respectively,
while corresponding delays caused by snow translation
were only 1.3, 0.7, and 0.6 h, respectively. The contri-
bution to DPT from translation through snow was gen-
erally larger in the early melt period, while the contribution
from the subsurface process was much more prominent
in the later periods when most of the snow had ablated.
The importance or controlling influence of a process on
changes to DPT during particular times of the melt sea-
son does not always mean that it is also the main de-
terminant on seasonal DPT variations, especially the
seasonal decreasing trend. Our analysis indicates that
while subsurface flow processes were more dominant in
determining the actual streamflow peak time, the de-
creasing trend in DPT was controlled primarily by the
snow translation process. Variations in net energy input,
on the other hand, mainly affected day-to-day variations
in DPT during the melt season.
b. How do DPTs vary interseasonally and what arethe processes that control this variation?
1) INTERSEASONAL DPT VARIATIONS
Seasonal average DPTs (for days exhibiting melt-
affected signal) showed significant variations among
years, ranging from 4.0 to 7.1 h after local noon in the
observed data, 3.2 to 7.6 h in the modeled absolute
DPTs, and 4.4 to 7.4 h in Q1% modeled results. These
variations are likely the result of interaction among the
controlling processes identified in section 3b.
It is important to recognize that the differences in
seasonal average DPT among individual years are also
influenced by the variations in the number and timing of
melt days between different years, which in turn are
controlled by the peak SWE, soil temperature, abrupt
changes in meteorological conditions, and water-table
depth. Initiation of the melt season varied by as many as
28 days (earliest start WY day 5 183, latest start WY
day5 211) in the observed data and 40 days in modeled
streamflow results (earliest start WY day 5 184, latest
start WY day 5 224). Similarly, the number of days
with a diurnal melt signal ranged from 13 to 54 days in
observed data and 6 to 38 days in modeled DPT results.
Depending on the timing of melt-affected days during
the melt season, the process controls that determined
DPTs such as energy input to the snow, its depth, and
melt recharge into the soil may vary, thus resulting in
interseasonal variations in DPTs.
Interseasonal variations also occurred in seasonal
shifts in DPTs. Seasonal DPT shift, defined as the dif-
ference in daily peak time between the first third and the
last third of the melt season, averaged 2.4 h (range of
1–5 h) in the observed data, 3.6 h (range of 1–9.8 h) in the
modeled data, and 3.1 h (range of 0.8–6.3 h) in Q1%
modeled results, among the years of study. The average
peak hour difference between the first and last days of
the melt season for the three streamflow series was
found to be 4.1, 5.5, and 4.1 h, respectively. This differ-
ence ranged from 10 (early season) to 1 h (late season) in
observed data, 12 to 1 h in modeled results, and 11 to 1h
in Q1% modeled results.
2) INFLUENCE OF PHYSICAL CONTROLS ON
INTERSEASONAL PEAK TIME AND ITS
VARIATIONS
Analyses of the relative role of each process control
on average DPT delay for the entire simulation period
[see section 4a(2)(v)] demonstrated that subsurface
translation played the most dominant role on the aver-
age DPT, followed by translation through snow.
However, a closer look revealed that delay times caused
by snow translation, subsurface flow, and river trans-
lation processes at a seasonal scale varied by as much as
0.5–3.2, 0.9–6.4, and 0.1–0.9 h, respectively. The wide
range in delay time caused by each individual process
raises the possibility of different processes dominating in
different years. Further analyses revealed that the snow
translation process was dominant in delaying DPTs in
wetter years while subsurface flowwas dominant in drier
years. For example, average delay times caused by snow
translation, subsurface flow, and stream channel trans-
lation time were 2.8, 1.9, and 0.3 h, respectively, in the
wettest fiveWYs during the simulation period, while the
corresponding delay times in the driest five WYs were
1.2, 5.8, and 0.4 h, respectively. The delay in DPTs from
snow translation process was more prominent in early
melting period. The average delay times due to snow
translation, subsurface flow, and stream channel trans-
lation in the five wettest years during the first third of the
melt season were 4.1, 2.4, and 0.4 h, respectively. The
delay from subsurface flow in dry years tended to be
2240 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
prominent throughout the melt period. For example,
average delays due to subsurface flow processes in the
five driest years were 6.5 and 4.6 h during the first and
last third of the melt period, while the delays due to
snow translation for the corresponding periods were
only 2.0 and 0.9 h. Overall, the average translation time
through snow showed a high correlation with snowfall
amount (r5 0.80). This was not surprising, as years with
greater snowfall amounts tended to generate snowpacks
with greater depths, leading to longer melt translation
times. In addition, more snowfall resulted in a higher
groundwater table near the end of melt season, which in
turn reduced the delay attributable to subsurface flow
processes. As a result, variation of average delay time
caused by subsurface flow processes had a negative
correlation with annual snowfall (r520.46). This weak
correlation revealed that other confounding factors such
as the multimodal behavior of melt flux and change in
net storage and losses [as discussed in section 4a(2)(iii)]
may also contribute to variations in delay time between
years. Since increased snowfall can lead to a larger DPT
delay from the snow translation process but a smaller
DPT delay from subsurface flow, these two translation
processes may compensate each other. This results in
seasonally moderate average DPT delays in wetter
years. The correlation between seasonally averaged
DPT and snowfall amount is only 0.19, indicating that
the average DPT delay may not show a clear relation-
ship to snow depth andmelt recharge volume. However,
the shift in DPT during each melt season (i.e., the dif-
ference in DPT between beginning and end of the melt
season) was still larger in years with high snowfall
amounts because of both larger decreases in snow
translation and subsurface flow time between the begin-
ning and end of each melt season. Average DPT shift for
each year, calculated as the difference in the last third and
first third of themelt season, had a correlation of 0.42with
the snowfall amount. Our analysis indicates that wet
years tended to have larger DPT shift within the melt
season. While the process of translation of meltwater
through snow was a more dominant control on DPTs in
wetter years than in drier years, the influence was more
important in the beginning of themelt period. In contrast,
subsurface flow processes were a more important control
in drier years and maintained this influence on DPTs
throughout the melt season in these years.
5. Summary and conclusions
This study explored the day-to-day intra- and inter-
seasonal variation in the timing of peak daily streamflow
(DPT) in a snow-dominated watershed. We quantita-
tively evaluated the role of hydrologic process controls
on DPT and its variations. Results indicate that the
physics-based integrated model, which consisted of a
snowmelt model coupled to a hydrologic model, was
able to reasonably capture both DPT and its seasonal
variations, with the exception of the first few days in the
melt season in drier years. For days when the model
could capture the melt-affected signal shown in ob-
served data, 80% of variation in observed DPT could be
captured by the model. A process-unmixing approach
was then used to evaluate the relative influence of pro-
cess controls in determining DPT and its variation dur-
ing the melt season. Results show that subsurface flow
was the most dominant influence on DPT delays in this
catchment. The influence of meltwater translation
through the snowpack was a close second. Translation
time in the river channel had negligible contributions to
both peak hour and its variations because of the small
spatial area assessed in this study. Though subsurface
flow was assumed to have minimal effect on DPT in
previous studies (e.g., Lundquist et al. 2005), the results
presented here indicate that for basins with groundwater
flow as the dominant control in the streamflow diurnal
signal (such as RME), the effect of subsurface could not
be neglected.
The relative influence of process controls on DPT
varies during the melt season. Intraseasonally, delay in
peak time from translation of meltwater through snow
was generally larger early in the melt period while sub-
surface flow played a much more prominent role later in
the melt period when most of the snow had ablated. The
average shift in DPT to earlier in the day in the later part
of themelt season wasmostly contributed by the process
of meltwater translation through the snowpack, with
contributions from subsurface flow being secondary.
Interseasonally, translation of meltwater through snow
is the most dominant process in delaying DPT in wet
years while subsurface flow plays a more dominant role
in drier years. Overall, the contribution of meltwater
translation through snow and the subsurface on DPT
delay show a high positive correlation and a moderate
negative correlation with seasonal snow amount, re-
spectively. Average DPT delay due to translation
through snow is larger in wet years because of larger
snow depth. At the same time, the contribution of
translation through subsurface on DPT delay is smaller
in wet years because of increased effective subsurface
conductivity. While of only marginal influence in this
watershed, translation time through stream channels can
be expected to be more important in larger watersheds
with longer river reaches. Our results identify the dominant
process controls on DPTs at both intra- and interseasonal
scales and could be used to prioritize measurement strate-
gies for determination andmonitoring the timing of peak
AUGUST 2016 CHEN ET AL . 2241
daily streamflow. The results suggest that one should be
cautious while analyzing DPT delays, as they are the
product of interactions between several processes con-
trolled by the complex physiography and variable weather
conditions typically encountered in mountain regions.
The physically based coupled modeling system pre-
sented here is ideally suited for process-unmixing ex-
periments to isolate the role of individual processes on
daily peak time. The methodology can be used in other
watersheds and in conjunction with other process-
explicit models. The presented analyses also highlight
that diurnal streamflow data may carry important in-
formation regarding the hydrologic partitioning in the
watershed and can further aid in model diagnosis and
validation. For example, overall increase in observed
daily streamflow magnitude (before the seasonal peak)
even as the daily melt peak varied contrapositively (see
section 3a) indicates that groundwater contribution to
streamflow during the melt season was significant in the
RME watershed. Similarly, the inability of the model to
capture early diurnal signals in dry, cool years [see sec-
tion 2c(2)(ii)(A)] while it overestimated the ground-
water recharge indicates errors in simulated runoff
during early melt season and highlight the need to in-
corporate the effects of changes in hydraulic conduc-
tivity vis-à-vis soil temperature.
It is important to be aware that the magnitude of DPT
delays and the relative contribution of each process
presented in this paper are specific to this watershed and
may vary elsewhere. The relative controls on meltwater
translation through snowpack, the subsurface, and in the
stream may vary between watersheds and can be influ-
enced by a range of factors, including hydrologic parti-
tioning of melt between surface and subsurface flow,
changes in streambed hydraulic conductivity triggered
by temperature variations, dynamics of watershed hy-
drologic connectivity and preferential pathways in the
snowpack, and anthropogenic activities (Gribovszki et al
2006; Lundquist and Cayan 2002; Marks et al 1998;
McNamara et al 2005; Morgenschweis 1995). The
modeling experiment conducted here does not account
for uncertainty. Further studies focused on providing de-
tailed maps of subsurface properties, evolution of pref-
erential pathways in snowpack, and relative contributions
of surface and subsurface discharge that could be used to
refine our results and provide an estimate of, and ulti-
mately help reduce, the uncertainty.
Acknowledgments. Datasets used in the paper are
publicly available from the Northwest Watershed Re-
search Center, USDA anonymous ftp site (ftp://ftp.nwrc.
ars.usda.gov/public/RME_25yr_database). The data and
analysis presented in this paper were funded in part by
NSF CAREER award (EAR 1454983) and USDA-ARS
CRIS Snow and Hydrologic Processes in the Intermoun-
tain West (5362-13610-008-00D). We thank Jeff Dozier
and Jessica Lundquist for providing constructive com-
ments that greatly improved this manuscript.
REFERENCES
Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop
evapotranspiration: Guidelines for computing crop water
requirements. FAO Irrigation and Drainage Paper 56, 300
pp. [Available online at www.fao.org/docrep/X0490E/
X0490E00.htm.]
Ambach, W., M. Blumthaler, and P. Kirchlechner, 1981: Applica-
tion of the gravity flow theory to the percolation of melt water
through firn. J. Glaciol., 27 (95), 67–75.
Anderson, E. A., 1976: A point energy andmass balancemodel of a
snow cover. NOAA Tech. Rep. NWS 19, 150 pp. [Available
online at http://amazon.nws.noaa.gov/articles/HRL_Pubs_PDF_
May12_2009/HRL_PUBS_51-100/81_A_POINT_ENERGY_
AND_MASS.pdf.]
Bain, M. B., J. T. Finn, and H. E. Booke, 1988: Streamflow regu-
lation and fish community structure. Ecology, 69, 382–392,
doi:10.2307/1940436.
Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Po-
tential impacts of a warming climate on water availability in
snow-dominated regions. Nature, 438, 303–309, doi:10.1038/
nature04141.
Barry, R., M. Prévost, J. Stein, and A. P. Plamondon, 1990: Ap-
plication of a snow cover energy and mass balance model in a
balsam fir forest. Water Resour. Res., 26, 1079–1092,
doi:10.1029/WR026i005p01079.
Bengtsson, L., 1982: Groundwater and meltwater in the snowmelt
induced runoff. Hydrol. Sci. J., 27, 147–158, doi:10.1080/
02626668209491097.
Burt, T. P., and P. J. Williams, 1976: Hydraulic conductivity in
frozen soils. Earth Surf. Processes, 1, 349–360, doi:10.1002/
esp.3290010404.
Caine, N., 1992: Modulation of the diurnal streamflow response by
the seasonal snowcover of an alpine basin. J. Hydrol., 137,
245–260, doi:10.1016/0022-1694(92)90059-5.
Caprio, F. M., 1966: Pattern of plant development in the western
United States. Montana Agricultural Experiment Station
Bulletin 607, 42 pp.
Carroll, T., D. Cline, C. Olheiser, A. Rost, A. Nilsson, G. Fall,
C. Bovitz, and L. Li, 2006: NOAA’s national snow analyses.
Proc. 74th Annual Western Snow Conf., Las Cruces, NM,
Western Snow Conference, 31–43. [Available online at http://
www.westernsnowconference.org/node/837.]
Chen, X., M. Kumar, and B. L. McGlynn, 2015: Variations in
streamflow response to large hurricane-season storms in a
southeastern U.S. watershed. J. Hydrometeor., 16, 55–69,
doi:10.1175/JHM-D-14-0044.1.
Colbeck, S. C., 1975: A theory for water flow through a layered
snowpack. Water Resour. Res., 11, 261–266, doi:10.1029/
WR011i002p00261.
——, and G. Davidson, 1973: Water percolation through homo-
geneous snow. IAHS Publ., 107, 242–257.
DeWalle, D. R., and A. Rango, 2008: Principles of Snow Hydrol-
ogy. Cambridge University Press, 428 pp.
Dunne, T., and R. D. Black, 1971: Runoff processes during snowmelt.
Water Resour. Res., 7, 1160–1172, doi:10.1029/WR007i005p01160.
2242 JOURNAL OF HYDROMETEOROLOGY VOLUME 17
——,A.G. Price, and S. C. Colbeck, 1976: The generation of runoff
from subarctic snowpacks. Water Resour. Res., 12, 677–685,
doi:10.1029/WR012i004p00677.
Flerchinger, G. N., 2000: The simultaneous heat and water (SHAW)
model: Technical documentation. Tech. Rep. NWRC 2000-09, 40
pp. [Available online at http://www.ars.usda.gov/SP2UserFiles/
Place/20520000/ShawDocumentation.pdf.]
——, K. R. Cooley, and D. R. Ralston, 1992: Groundwater re-
sponse to snowmelt in a mountainous watershed. J. Hydrol.,
133, 293–311, doi:10.1016/0022-1694(92)90260-3.
——,M. S. Seyfried, and S. P. Hardegree, 2006: Using soil freezing
characteristics to model multi-season soil water dynamics.
Vadose Zone J., 5, 1143–1153, doi:10.2136/vzj2006.0025.
Garen, D. C., and D. Marks, 2005: Spatially distributed energy
balance snowmelt modelling in a mountainous river basin:
Estimation of meteorological inputs and verification of model
results. J. Hydrol., 315, 126–153, doi:10.1016/j.jhydrol.2005.03.026.
Gottardi,G., andM.Venutelli, 1993:A control-volumefinite-element
model for two-dimensional overland flow. Adv. Water Resour.,
16, 277–284, doi:10.1016/0309-1708(93)90019-C.
Graf, W. L., 1999: Dam nation: A geographic census of American
dams and their large-scale hydrologic impacts. Water Resour.
Res., 35, 1305–1311, doi:10.1029/1999WR900016.
Grant, L., M. Seyfried, and J. McNamara, 2004: Spatial variation
and temporal stability of soil water in a snow-dominated,
mountain catchment. Hydrol. Processes, 18, 3493–3511,
doi:10.1002/hyp.5798.
Gray, D. M., P. G. Landine, and R. J. Granger, 1985: Simulating
infiltration into frozen prairie soils in streamflowmodels.Can.
J. Earth Sci., 22, 464–472.Gribovszki, Z., P. Kalicz, and M. Kucsara, 2006: Streamflow
characteristics of two forested catchments in Sopron Hills.
Acta Silvatica et Lignaria Hung., 2, 81–92.
——, ——, J. Szilágyi, and M. Kucsara, 2008: Riparian zone
evapotranspiration estimation from diurnal groundwater level
fluctuations. J.Hydrol., 349, 6–17, doi:10.1016/j.jhydrol.2007.10.049.
——, J. Szilágyi, and P. Kalicz, 2010: Diurnal fluctuations in shal-
low groundwater levels and streamflow rates and their
interpretation—A review. J. Hydrol., 385, 371–383,
doi:10.1016/j.jhydrol.2010.02.001.
Grover, N. C., and A. W. Harrington, 1943: Stream Flow: Mea-
surements, Records and Their Uses. Wiley, 363 pp.
Hanson, C. L., D. Marks, and S. S. Van Vactor, 2001: Long-term
climate database, Reynolds Creek Experimental Watershed,
Idaho, United States. Water Resour. Res., 37, 2839–2841,
doi:10.1029/2001WR000417.
Horiguchi, K., and R. D. Miller, 1983: Hydraulic conductivity
functions of frozen materials. Proc. 4th Int. Conf. on Perma-
frost, Fairbanks, AK, University of Alaska Fairbanks, 504–508.
Johnson, B., B. Malama, W. Barrash, and A. N. Flores, 2013:
Recognizing and modeling variable drawdown due to evapo-
transpiration in a semiarid riparian zone considering local
differences in vegetation and distance from a river source.
Water Resour. Res., 49, 1030–1039, doi:10.1002/wrcr.20122.
Jordan, P., 1983a:Meltwatermovement in a deep snowpack: 1. Field
observations. Water Resour. Res., 19, 971–978, doi:10.1029/
WR019i004p00971.
——, 1983b: Meltwater movement in a deep snowpack: 2. Simu-
lation model. Water Resour. Res., 19, 979–985, doi:10.1029/
WR019i004p00979.
Kobayashi, D., 1986: Separation of a snowmelt hydrograph by
stream conductance. J. Hydrol., 84, 157–165, doi:10.1016/
0022-1694(86)90049-1.
——, and H. Motoyama, 1985: Effect of snow cover on time lag of
runoff from a watershed. Ann. Glaciol., 6, 123–125.
Kumar, M., 2009: Toward a hydrologic modeling system. Ph.D.
thesis, The Pennsylvania State University, 274 pp.
——, and C. J. Duffy, 2015: Exploring the role of domain parti-
tioning on efficiency of parallel distributed hydrologic model
simulations. J. Hydrogeol. Hydrol. Eng., 4 (1), doi:10.4172/
2325-9647.1000119.
——, ——, and K. M. Salvage, 2009: A second-order accurate, fi-
nite volume–based, integrated hydrologic modeling (FIHM)
framework for simulation of surface and subsurface flow.
Vadose Zone J., 8, 873–890, doi:10.2136/vzj2009.0014.
——, D. Marks, J. Dozier, M. Reba, and A. Winstral, 2013: Eval-
uation of distributed hydrologic impacts of temperature-index
and energy-based snowmodels.Adv.Water Resour., 56, 77–89,
doi:10.1016/j.advwatres.2013.03.006.
Link, T., and D. Marks, 1999: Distributed simulation of snowcover
mass- and energy-balance in the boreal forest. Hydrol. Pro-
cesses, 13, 2439–2452, doi:10.1002/(SICI)1099-1085(199910)13:14/
15,2439::AID-HYP866.3.0.CO;2-1.
Loheide, S. P., II, 2008: A method for estimating subdaily evapo-
transpiration of shallow groundwater using diurnal water table
fluctuations. Ecohydrology, 1, 59–66, doi:10.1002/eco.7.
——, and J. D. Lundquist, 2009: Snowmelt-induced diel fluxes
through the hyporheic zone. Water Resour. Res., 45, W07404,
doi:10.1029/2008WR007329.
Lowry, C. S., J. S. Deems, I. I. Loheide, P. Steven, and J. D.
Lundquist, 2010: Linking snowmelt-derived fluxes and
groundwater flow in a high elevation meadow system, Sierra
Nevada Mountains, California. Hydrol. Processes, 24, 2821–
2833, doi:10.1002/hyp.7714.
Lundquist, J. D., and D. R. Cayan, 2002: Seasonal and spatial
patterns in diurnal cycles in streamflow in the western
United States. J. Hydrometeor., 3, 591–603, doi:10.1175/
1525-7541(2002)003,0591:SASPID.2.0.CO;2.
——, and M. D. Dettinger, 2005: How snowpack heterogeneity
affects diurnal streamflow timing. Water Resour. Res., 41,
W05007, doi:10.1029/2004WR003649.
——, D. R. Cayan, and M. D. Dettinger, 2004: Spring onset
in the Sierra Nevada: When is snowmelt independent of
elevation? J. Hydrometeor., 5, 327–342, doi:10.1175/
1525-7541(2004)005,0327:SOITSN.2.0.CO;2.
——, M. D. Dettinger, and D. R. Cayan, 2005: Snow-fed
streamflow timing at different basin scales: Case study of
the Tuolumne River above Hetch Hetchy, Yosemite,
California. Water Resour. Res., 41, W07005, doi:10.1029/
2004WR003933.
Marks, D., 2001: Introduction to special section: Reynolds Creek
Experimental Watershed. Water Resour. Res., 37, 2817,
doi:10.1029/2001WR000941.
——, and A. Winstral, 2001: Comparison of snow deposition, the
snow cover energy balance, and snowmelt at two sites in a
semiarid mountain basin. J. Hydrometeor., 2, 213–227,
doi:10.1175/1525-7541(2001)002,0213:COSDTS.2.0.CO;2.
——, J. Kimball, D. Tingey, and T. Link, 1998: The sensitivity
of snowmelt processes to climate conditions and forest
cover during rain-on-snow: A case study of the 1996 Pa-
cific Northwest flood. Hydrol. Processes, 12, 1569–1587,
doi:10.1002/(SICI)1099-1085(199808/09)12:10/11,1569::
AID-HYP682.3.0.CO;2-L.
——, J. Domingo, D. Susong, T. Link, and D. Garen, 1999:
A spatially distributed energy balance snowmelt model
for application in mountain basins. Hydrol. Processes, 13,
AUGUST 2016 CHEN ET AL . 2243
1935–1959, doi:10.1002/(SICI)1099-1085(199909)13:12/13,1935::
AID-HYP868.3.0.CO;2-C.
——, A.Winstral, andM. Seyfried, 2002: Simulation of terrain and
forest shelter effects on patterns of snow deposition, snowmelt
and runoff over a semi-arid mountain catchment. Hydrol.
Processes, 16, 3605–3626, doi:10.1002/hyp.1237.
Marsh, P., and J. W. Pomeroy, 1996: Meltwater fluxes at
an arctic forest-tundra site. Hydrol. Processes, 10, 1383–1400, doi:10.1002/(SICI)1099-1085(199610)10:10,1383::
AID-HYP468.3.0.CO;2-W.
——, and M. K. Woo, 1985: Meltwater movement in natural het-
erogeneous snow covers. Water Resour. Res., 21, 1710–1716,doi:10.1029/WR021i011p01710.
Maulé, C., and J. Stein, 1990: Hydrologic flow path definition and
partitioning of spring meltwater.Water Resour. Res., 26, 2959–2970, doi:10.1029/WR026i012p02959.
McNamara, J. P., D. Chandler, M. Seyfried, and S. Achet, 2005:
Soil moisture states, lateral flow, and streamflow generation
in a semi-arid, snowmelt-driven catchment.Hydrol. Processes,
19, 4023–4038, doi:10.1002/hyp.5869.
Morgenschweis, G., 1995: Kurzzeitige Vorhersage der Wasser-
entnahme aus einem Flussgebiet. Vortragsmanusskript zur 8
Wissenschaft Tagung Hydrologie und wasserwirtschaft zum
Thema Verfügbarkeit von Wasser, Bochum, Germany, Ruhr-
Universität Bochum, 16 pp.
Neumann, D. W., B. Rajagopalan, and E. A. Zagona, 2003:
Regression model for daily maximum stream temper-
ature. J. Environ. Eng., 129, 667–674, doi:10.1061/
(ASCE)0733-9372(2003)129:7(667).
Pfeffer, W. T., T. H. Illangasekare, andM. F. Meier, 1990: Analysis
andmodeling of melt-water refreezing in dry snow. J. Glaciol.,
36 (123), 238–246.
Poff, N. L., J. D. Allan, M. B. Bain, J. R. Karr, K. L. Prestegaard,
B. D. Richter, R. E. Sparks, and J. C. Stromberg, 1997: The
natural flow regime. BioScience, 47, 769–784, doi:10.2307/
1313099.
Qu, Y., and C. J. Duffy, 2007: A semidiscrete finite volume for-
mulation for multiprocess watershed simulation. Water Re-
sour. Res., 43, W08419, doi:10.1029/2006WR005752.
Quinton, W. L., and P. Marsh, 1998: The influence of mineral
earth hummocks on subsurface drainage in the continuous
permafrost zone. Permafrost Periglacial Processes, 9, 213–
228, doi:10.1002/(SICI)1099-1530(199807/09)9:3,213::AID-
PPP285.3.0.CO;2-E.
——, andD.M. Gray, 2003: Subsurface drainage from organic soils
in permafrost terrain: The major factors to be represented in a
runoff model. Proc. of the Eighth Int. Conf. on Permafrost,
Davos, Switzerland, International Permafrost Association,
917–922. [Available online at http://research.iarc.uaf.edu/
NICOP/DVD/ICOP%202003%20Permafrost/Pdf/Chapter_
161.pdf.]
Reba, M. L., D. Marks, M. Seyfried, A. Winstral, M. Kumar,
and G. Flerchinger, 2011a: A long-term data set for hy-
drologic modeling in a snow-dominated mountain catch-
ment. Water Resour. Res., 47, W07702, doi:10.1029/
2010WR010030.
——, ——, A. Winstral, T. Link, and M. Kumar, 2011b: Sensitivity
of the snowcover in a mountain basin to variations in climate.
Hydrol. Processes, 25, 3312–3321, doi:10.1002/hyp.8155.
Refsgaard, J. C., 1997: Parameterisation, calibration and validation
of distributed hydrological models. J. Hydrol., 198, 69–97,
doi:10.1016/S0022-1694(96)03329-X.
Richards, L. A., 1931: Capillary conduction of liquids through
porous mediums. J. Appl. Phys., 1, 318–333, doi:10.1063/
1.1745010.
Rutter, A. J., K. A. Kershaw, P. C. Robins, and A. J. Morton, 1971: A
predictive model of rainfall interception in forests, 1. Derivation
of the model from observations in a plantation of Corsican pine.
Agric. Meteor., 9, 367–384, doi:10.1016/0002-1571(71)90034-3.
Shi, Y., K. J. Davis, F. Zhang, and C. J. Duffy, 2014: Evaluation of
the parameter sensitivities of a coupled land surface hydro-
logic model at a critical zone observatory. J. Hydrometeor., 15,
279–299, doi:10.1175/JHM-D-12-0177.1.
Soylu, M. E., J. D. Lenters, E. Istanbulluoglu, and S. P. Loheide,
2012: On evapotranspiration and shallow groundwater fluc-
tuations: A Fourier-based improvement to theWhite method.
Water Resour. Res., 48, W06506, doi:10.1029/2011WR010964.
Strelkoff, T., 1970: Numerical solution of Saint-Venant equations.
J. Hydraul. Div., 96 (1), 223–252.
Tobin, C., B. Schaefli, L. Nicótina, S. Simoni, G. Barrenetxea,
R. Smith, M. Parlange, and A. Rinaldo, 2013: Improving the
degree-day method for sub-daily melt simulations with
physically-based diurnal variations. Adv. Water Resour., 55,
149–164, doi:10.1016/j.advwatres.2012.08.008.
Van Genuchten, M. T., 1980: A closed-form equation for
predicting the hydraulic conductivity of unsaturated soils.
Soil Sci. Soc. Amer. J., 44, 892–898, doi:10.2136/
sssaj1980.03615995004400050002x.
Wang, R., M. Kumar, andD.Marks, 2013: Anomalous trend in soil
evaporation in a semi-arid, snow-dominated watershed. Adv.
Water Resour., 57, 32–40, doi:10.1016/j.advwatres.2013.03.004.
Watanabe, K., and M. Flury, 2008: Capillary bundle model of hy-
draulic conductivity for frozen soil. Water Resour. Res., 44,W12402, doi:10.1029/2008WR007012.
Wildenschild, D., and K. H. Jensen, 1999: Laboratory in-
vestigations of effective flow behavior in unsaturated hetero-
geneous sands. Water Resour. Res., 35, 17–27, doi:10.1029/98WR01958.
Winstral, A., and D. Marks, 2002: Simulating wind fields and snow
redistribution using terrain-based parameters to model snow
accumulation and melt over a semi-arid mountain catchment.
Hydrol. Processes, 16, 3585–3603, doi:10.1002/hyp.1238.
——, ——, and R. Gurney, 2009: An efficient method for distrib-
uting wind speeds over heterogeneous terrain. Hydrol. Pro-
cesses, 23, 2526–2535, doi:10.1002/hyp.7141.Yeh, T. C. J., and D. J. Harvey, 1990: Effective unsaturated hy-
draulic conductivity of layered sands. Water Resour. Res., 26,
1271–1279, doi:10.1029/WR026i006p01271.
Yu, X., C. Duffy, D. C. Baldwin, and H. Lin, 2014: The role of
macropores and multi-resolution soil survey datasets for dis-
tributed surface–subsurface flow modeling. J. Hydrol., 516,
97–106, doi:10.1016/j.jhydrol.2014.02.055.
2244 JOURNAL OF HYDROMETEOROLOGY VOLUME 17