Draft Manuscript for Hydrological Processes
July 8, 2010
Controls of streamflow pathways in small catchments across the snowrain transition in the Southern Sierra Nevada, California
Fengjing Liu
Sierra Nevada Research Institute & School of Engineering, University of California, Merced, CA
Carolyn Hunsaker
Pacific Southwest Research Station, USDA Forest Service, Fresno, CA
Roger Bales
Sierra Nevada Research Institute & School of Engineering, University of California, Merced, CA
Corresponding Address†
Fengjing Liu
Sierra Nevada Research Institute & School of Engineering
University of California, Merced
5200 N. Lake Road
Merced, CA 95343
Phone: 209/205-8564
Email: fliu@ucmerced.edu
Abstract. Pathways of streamflow were determined using geochemical tracers for water years
2004-2007 at the Kings River Experimental Watersheds (KREW) in the Southern Sierra Nevada.
Investigated are four rain-dominated catchments at Providence (P301, P303, P304 and D102)
and four snow-dominated catchments at Bull (B201, B203, B204 and T003). Results of
diagnostic tools of mixing models indicate that Ca2+, Mg2+, K+ and Cl- behaved conservatively
for mixing and three endmembers contributed to streamflow in those catchments. Using
endmember mixing analysis (EMMA), the endmembers were determined to be near-surface
runoff, rainstorm runoff and baseflow, and relative endmember contributions evaluated by
recreating streamflow chemistry using fractional contributions and ionic concentrations of these
endmembers. Near-surface runoff contributed >50% of streamflow on average at all catchments
except P304 and T003. The mean contribution of baseflow was >60% at P304 and T003.
Rainstorm runoff contributed <6% on average. The fraction of snow versus rain did not exert a
major control on the pathways of streamflow in these catchments. The flow contributions
(expressed as discharge) of near-surface runoff and baseflow were linearly correlated with
streamflow discharge, with a slope ranging from 0.53 to 0.83 (R2 = 0.92-0.99, n = 100-200) and
from 0.20 to 0.46 (R2 = 0.91-0.97), respectively. These linear regression models can be used to
constrain results of hydrologic models in future studies and to assess changes in hydrologic
regime with future forest management.
Key words: Water chemistry, flow paths, snow/rain transition, Southern Sierra Nevada
Running Title: Streamflow pathways in catchments of snow-rain transition
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INTRODUCTION
Information on streamflow pathways is critical to understand how streamflow responds to
the declining trend of snow relative to rainfall in the mountains of the western United States
[Knowles et al., 2006; Mote et al., 2005; Stewart et al., 2004] and to evaluate potential changes
in stream water quantity and quality associated with a forest treatment [Brown et al., 2005]. The
explicit pathways of streamflow must be known for understanding the role of forest vegetation in
hydrology [Hunsaker et al., in review]. Streamflow pathways have been well studied for both
rain-dominated [e.g., McDonnell et al., 1990] and snow-dominated forests [e.g., McNamara et
al., 2005]. Notably lacking from these studies, however, is a direct comparison of how snow- and
rain-dominated catchments differ in streamflow pathways in the same region with similar
geology, vegetation and soils.
Mechanisms of runoff generation in semiarid, mountain forested catchments where
annual runoff is dominated by snowmelt, a multi-week event, may be fundamentally different
from that in humid regions where frequent short-term rainfall events dominate [Wilcox et al.,
1997]. In humid, forested catchments such as found in the eastern United States, runoff is
generated primarily from old waters through macropore flow [e.g., McDonnell et al., 1990;
Hooper and Shoemaker, 1986]. A few studies from an 870-m2 ponderosa pine hillslope at Los
Alamos have indicated that both lateral subsurface flow and overland flow are important flow
processes that control snowmelt runoff at hillslope scales in semiarid environments [Wilcox et al.,
1997; Newman et al., 1998; Newman et al., 2004]. However, the importance of these processes at
catchment scales in semiarid regions with a seasonal snow cover has received little attention
[McNamara et al., 2005].
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Eight catchments across the snow-rain transition in the southern Sierra Nevada were
selected to determine the endmembers contributing to streamflow and to quantity their
contributions with varying climates from water years 2004 to 2007. The objectives of the study
reported here were to examine the controls of streamflow pathways, by topography versus snowrain proportion, to conceptualize streamflow generation and to develop predictive models of
endmember contributions in those catchments. Three questions were specifically addressed: (i)
what were the end-members contributing to stream flow, (ii) how did the end-member
contributions vary over those catchments across snow-rain transition, and (iii) what conceptual
and predictive models of streamflow generation for snow-rain transition in the Southern Sierra
Nevada do these results suggest?
METHODS
Research Area
This study was conducted in eight forested catchments that make up the Kings River
Experimental Watersheds (KREW), a watershed-level, integrated ecosystem project for longterm research on nested headwater streams in the southern Sierra Nevada (Figure 1). KREW is
operated by the Forest Service’s Pacific Southwest Research Station. The catchments are located
in two groups of four at the Providence site and Bull site within the Sierra National Forest,
northeast of Fresno, California (Figure 1). The four catchments at the Providence site range in
size from 0.49 to 1.32 km2 and in elevation from 1479 to 2113 m, while the four catchments at
the Bull site range in size from 0.53 to 2.28 km2 and in elevation from 2055 to 2490 m (Table 1).
Precipitation was dominated by snow in the high-elevation Bull catchments, with a
varying fraction of 75-90% of annual precipitation from water years 2004 to 2007, and by
3
rainfall in the lower-elevation Providence catchments, with up to 80% as rainfall during the same
water years [Hunsaker et al., 2010]. Mean air temperature was 7.8 and 6.8 oC from 2004 to 2007
at the lower Providence and upper Bull meteorological stations, respectively. Soils are well
drained, mixed, frigid Dystric Xeropsamment, formed from decomposed granite [Dahlgren et al.,
1997], including Shaver and Gerle-Cagwin soils at Providence and colder, Cagwin soils at Bull
[Sierra National Forest, 1983]. Litter depth and depth to bedrock vary across the study area, but
all soils have similar texture and water percolation rate [North et al., 2002]. The Providence
catchments are largely mixed conifer forest, with some chaparral, barren and meadow [Hunsaker
et al., 2010]. The Bull catchments also are mainly mixed conifer forests, with a higher proportion
of red fir at higher elevations [North et al., 2005].
The study area and its vicinity are made up of granitic, metamorphic, and volcanic rocks,
with some of glacial materials. Clay mineralogy is dominated by hydroxyl-Al interlayered
vermiculite and gibbsite, as a result of weathering of feldspar and plagioclase under intense
leaching environment [Dahlgren et al., 1997]. The weathering environment is very effective at
removing Si released by weathering in spite of the cold soil temperatures, resulting in Sidepleted minerals.
Sample collection and analysis
Stream water samples were collected biweekly at the outlets of eight catchments from fall
2003 to fall 2007 (Figure 1). Samples were either grabbed by hand or collected by automated
ISCO samplers. The ISCO samplers were triggered when stream flow exceeds a certain value
and provide samples several hours apart during storm events.
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Soil water was collected from Prenart samplers at two depths, 13 and 26 cm. Each pair of
samplers was placed symmetrically at 2, 4, and 6 m away from a tree understory and one in the
open at each depth. Prenart samplers were deployed at all Providence catchments. Ionic
concentrations of those samples were evaluated in detail in Hunsaker et al. [in preparation].
Results of composite samples were presented in this paper.
Snowmelt was collected using plastic sampling bottles placed at four meteorological
stations (Figure 1). Each bottle has a funnel to gather snow and to allow meltwater flowing into
the bottle. Bottles were placed before a significant storm came and collected soon after snow
melted. Snowmelt was also collected at each Prenart sampler location. But Hunsaker et al. [in
preparation] showed that chemical compositions of those snowmelt samples did not exert a
significant control on stream water chemistry and thus chemical data of those samples were not
included in this study.
Samples were also collected in 2008 and 2009 from piezometers, a spring and
groundwater wells in several locations (Figure 1). Groundwater was collected from drinking
wells at Glenn Meadow, Dinkey Creek, PG & E local office and Blue Canyon Work Center, 2 to
3 times from August 2008 and October 2009. A sample collected from a tank near Dinkey Creek
was actually from a nearby well. Samples were taken once in October 2009 from a spring at
B201 and two 1.5 m depth piezometers at B201 and P301, respectively.
Samples collected from wells, spring and piezometers in 2008-2009 were analyzed for
major cations (Ca2+, Mg2+, Na+, K+) and anions (Cl-, NO3-, SO42-) using Dionex 2000 Ion
Chromatograph (IC) at the Environmental Lab of the University of California, Merced.
Analytical precision (1 standard deviation) for all ions was less than 1% and detection limit less
than 1 eq L-1. All other samples were analyzed for major cations and anions using IC at Pacific
5
Southwest Research Station, Riverside, CA. Precision is also less than 1% of ionic
concentrations. Acid neutralizing capacity (ANC) was calculated as the difference between the
total concentrations of cations and anions, all in eq L-1 and annotated as ANC-CB, where CB
refers to charge balance.
Endmember mixing analysis and diagnostic tools of mixing models
Contributions of endmembers to streamflow were determined using tracer-based
endmember mixing analysis (EMMA) in combination with the diagnostic tools of mixing models
(DTMM), following Liu et al. [2008]. Conservative tracers and the number of endmembers that
contribute to streamflow were identified from stream water chemical data using the diagnostic
tools of mixing models described in Hooper [2003]. The key procedure in this method is to
project stream water chemistry ( X̂ * ) using eigenvectors (V, n  n, where n is the number of
solutes) extracted from the correlation coefficients of stream water chemistry data (X*, including
all available solutes) without using any information from endmembers:
Xˆ *  X *V T (VV T ) 1V
(1)
Conservative tracers and the number of endmembers were determined by examining the
distribution of residuals (the difference between projected and measured values) over the
measured values for each solute. For example, if the distribution is a random pattern using twodimensional eigenvectors, a two-dimensional (2-D) mixing space, i.e., three end-members, is
needed and the solutes that show a random pattern for their residual distributions are deemed to
behave conservatively.
EMMA was then used with the determined conservative tracers to identify endmembers
and quantify the contributions of endmembers to streamflow following Christophersen and
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Hooper [1992] and Liu et al. [2004]. A principal component analysis (PCA) was performed to
extract eigenvectors using a correlation matrix of conservative tracers (not the original ionic
concentrations) that were determined using DTMM above. Stream water chemical data were
orthogonally projected using the eigenvectors by
U  X 1 V1
*
T
(2)
where U is orthogonally projected (U-space projected) data matrix (nm), in which n represents
the number of samples and m the number of mixing subspaces determined above. X1* is
standardized tracer concentrations in stream flow (zero mean and unit standard deviation), with a
dimension of np, where p is the number of conservative tracers used to extract the eigenvectors
V1. V1 has a dimension of m  p, truncated from p  p eigenvector matrix extracted using p
conservative tracers. Tracer concentrations in endmembers were also standardized using the
mean and standard deviation of stream water samples and projected using the same eigenvectors
V1. The first two PCA scores were used to solve for endmember contributions, a procedure
mathematically the same as two-tracers for three-component mixing model.
Three criteria were used to identify eligible endmembers from potential ones, following
Liu et al. [2008]. First, eligible endmembers must form a convex polygon (e.g., a triangle in the
case of three endmembers) to bound all stream water samples. Second, the distance of all eligible
endmembers between original compositions and U-space orthogonal projections should be
reasonably short for all tracers used in the analysis. Third, stream water chemistry must be well
recreated for conservative tracers using the results of EMMA to ensure right results for right
reasons. The original chemical compositions of endmembers were assumed to be in the same
space as stream water (S-space, where S stands for stream flow). U-space orthogonal projections
7
of endmembers and the distance between S-space and U-space were calculated following
Christophersen and Hooper [1992]:
d j  b j  b j
(3)
b j  b jV1 (V1V1 ) 1V1
T
T
(4)
where dj means the Euclidean distance of endmember b for tracer j between original composition
(bj) and U-space projection (bj*) calculated by equation 4 using the eigenvector V1 extracted from
conservative tracers. The distance was expressed as percent by dividing distance by the original
chemical composition. The shorter the distance for all conservative tracers the better fit of an
endmember to the mixing space.
RESULTS
Hydrology and meteorology
Annual precipitation measured at four meteorological stations along an elevation gradient
from 1,750 to 2,463 m was essentially the same (Figure 2a). Annual precipitation was 94-96,
174-182, 193-203, and 75-77 cm in 2004, 2005, 2006 and 2007, respectively. Precipitation
primarily occurred from December to March, as seen from a sharp increase of cumulative daily
precipitation (Figure 2a). A little precipitation occurred after April each year before the wet
season starts again in fall.
Snow started accumulating in November or December and attained a maximum depth in
early spring at all stations (Figure 2b). The maximum depth occurred at Upper Bull
meteorological station, with 266, 380, 397, and 210 cm on March 1 in 2004, March 24 in 2005,
April 5 in 2006 and February 28 in 2007, respectively. The maximum depth at other stations was
less than 70% of those at the Upper Bull station. After then, snow depth declined almost
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monotonically as snow started melting. Snow became depleted 2-3 months after the maximum
accumulation at the Upper Bull, but 4-6 weeks earlier at other stations.
After the maximum snow accumulation, stream runoff increased rapidly at all catchments,
particularly in relatively wet years 2005 and 2006 (Figure 2c). After snow depletion, cumulative
runoff increased slightly with time. In this study streamflow was termed as baseflow after snow
depletion. The annual (total) runoff was much higher in 2005 and 2006 than 2004 and 2007 at all
catchments. The annual runoff also varied significantly from catchments, consistently higher at
B203 and B204 and lower at P303 and D102. For example, annual runoff was 38 and 12 cm at
B203 and P303 in 2004 and 130 and 60 cm in 2005 at the same catchments, respectively.
Ionic concentrations
Mean ionic concentrations of Ca2+, Mg2+, Na+ and K+ in stream water were significantly
higher at Providence than at Bull catchments (Table 2). Mean concentrations of Ca2+ were
greater than 200 eq L-1 at Providence catchments, with 1 > 50 eq L-1, while those were less
than 160 eq L-1 at Bull catchments, with 1 < 35 eq L-1. Mean Cl- concentrations were slightly
higher at Providence than at Bull catchments, but to the contrary SO42- concentrations were
slightly lower.
The temporal variation of ionic concentrations generally followed the opposite pattern of
stream water, with lower concentrations at higher flows during snowmelt and higher
concentrations at low flows during baseflow, as demonstrated by Ca2+, K+ and Cl- in Figure 3.
However, isolated peaks of high ionic concentrations, particularly those of K+ and Cl-, occurred
following a transient increase in stream flow during late summer and fall or even in winter
(Figure 3). Such isolated peaks in stream flow were caused by rainstorms [Hunsaker et al., in
9
preparation]. Ionic concentrations were lowest at P301 and highest at P304 among Providence
catchments and were similar over the water years 2004 to 2007 (Figure 3). Among Bull
catchments, ionic concentrations were lowest at B203 and highest at T003.
Mean ionic concentrations in snowmelt were much lower than in stream water at both
Providence and Bull catchments, but those in soil water were higher than in stream water for all
ions except Na+ and SO42- (Table 2). The mean concentration of Ca2+ in snowmelt was 27 eq L1
, about 10% of that in stream water at Providence catchments, while that of soil water was 398
eq L-1, at least 20% higher than that in stream water. The mean concentration of Na+ in soil
water was 16 eq L-1, twice that in snowmelt, but much lower than that in stream water (45-171
eq L-1). Ionic concentrations in soil water varied significantly over time and locations, with 1
values close to or greater than mean values (Table 2).
Mean ionic concentrations in shallow subsurface water, including those in soil
piezometers and a spring, were greater than those in snowmelt but lower than those in stream
water (Table 2). For instance, the mean concentration of Ca2+ was 81 eq L-1 in subsurface water
at a meadow piezometer at B201, three times that in snowmelt, but about 30% of that in stream
water. Mean ionic concentrations in groundwater wells varied significantly, e.g., with Ca2+
concentration of 426 eq L-1 in a well at Dinkey Creek Ranger Station and 1124 eq L-1 in a
well at Blue Canyon Work Center (Table 2). Due to the lack of well logs, it was assumed that the
ionic concentrations were a reflection of well depth and residence times of groundwater, with
lower concentrations from groundwater developed at shallower aquifers and higher
concentrations from groundwater developed at deeper aquifers.
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Endmember mixing analysis (EMMA)
Conservative tracers and number of endmembers
Stream water chemical data were combined into two single data sets for the Providence
and Bull catchments in diagnostic tools of mixing model to determine conservative tracers and
number of endmembers. The distribution of the residuals between measured and projected values
over the measured ionic concentrations indicated that a two-dimensional (2-D) mixing space was
needed for conservative mixing of stream water chemistry for both Providence and Bull
catchments (Figure 4). Ca2+, Mg2+, K+, Cl- and acid neutralizing capacity (ANC) were found to
be conservative in streams of both sites. The R2 values of the residual distributions significantly
decreased from 1-D to 2-D for Ca2+, K+, Cl- and ANC. For example, the R2 value of Cldecreased from 0.71 to 0.18 from 1-D to 2-D at Providence and from 0.58 to 0.08 for catchments
at Bull. The distribution of SO42- was patterned in 2-D with a linear relationship between
residuals and the measured ionic concentrations (R2 = 0.31 at Providence and 0.54 at Bull). Even
though the R2 values in 2-D were much lower for Na+ than for SO42-, 0.25 and 0.13 at
Providence and Bull, respectively, the pattern of the Na+ residual distributions and the residual
magnitudes did not change much from 1-D to 2-D, indicating that Na+, similar to SO42-, did not
behave completely conservative upon mixing. It is thus deemed that concentrations of Ca2+,
Mg2+, K+, Cl- and ANC in stream water were primarily caused by a mixing of three endmembers.
The same analysis was also performed for each individual catchment using their own
ionic concentrations of stream water (data not shown). Conservative tracers and the number of
endmembers of all individual catchments were consistent with the results above. Note that NO3and PO43- were not included in above analysis because their concentrations were below the
analytical detection limits for a considerable portion of the samples. DTMM requires that all
11
samples have measured values for all species included in the analysis. Note also that the slopes
of linear regression for the distributions of residuals over the measured values were the same as
R2 values in magnitude, as the eigenvectors used to project stream water chemistry were
extracted using stream water chemistry standardized to be zero mean and unit standard deviation.
Identification of endmembers
Mixing diagrams were constructed using the first two PCA projections with which
eigenvectors were extracted from the correlations of four conservative tracers, Ca2+, Mg2+, K+
and Cl- in stream water at both Providence and Bull catchments (Figure 5), following
Christophersen and Hooper [1992], Liu et al. [2004], and Liu et al. [2008]. All potential
endmembers were also projected using the same eigenvectors as for stream water. ANC was not
used in the analysis because it was calculated as the difference between total cationic and anionic
concentrations and its precision and accuracy could not be evaluated.
Most of stream water samples at both Providence and Bull catchments were scattered in
the mixing diagrams along an axis formed by potential endmembers from snowmelt collected at
meteorological stations and groundwater near Dinkey Creek (Figure 5). The samples near
snowmelt were collected during the snowmelt period, while the samples near groundwater at
Dinkey Creek and beyond were collected at low flows in late summer and early fall before
precipitation starts. Therefore, snowmelt runoff and baseflow were two major endmembers
contributing to stream flow. The samples scattered to the lower-right of the axis were collected in
late fall and early winter. The ionic concentrations of those samples were much higher (Figure 3)
and significantly influenced by rainstorms [Hunsaker et al., in preparation]. The third
endmember was thus rainstorm runoff.
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Snowmelt runoff could be characterized by ionic concentrations in snowmelt as of Liu et
al. [2004]. Snowmelt was also well positioned as a vertex to form a triangle to bound most, if not
all, of stream water samples at Bull catchments (Figure 5). However, the S- and U-space
distances of ionic concentrations in snowmelt were somewhat too large, particularly at
Providence catchments (Table 3). The distance was greater than 48% for all tracers except K+ in
snowmelt at Providence. To the contrary, the distance was less than 10% for all tracers in
shallow subsurface water at B201 piezometer. This subsurface water was plotted closely to
snowmelt at Providence catchments (Figure 5), indicating that chemical signature in snowmelt
actually entering into streams has been slightly modified by soils. It is suggested that snowmelt
was delivered as near-surface runoff rather than overland flow at Providence catchments, which
is consistent with Liu et al. [2008] for Vales Caldera, New Mexico. Therefore, near-surface
runoff was used as an endmember for Providence catchments and characterized by subsurface
water at B201 piezometer. Note that B201 piezometer is located in Bull, not Providence. The
chemical signature of B201 piezometer was borrowed for Providence catchments and it did not
mean water at Bull catchments flows to Providence catchments. Even though the distance of
subsurface water at B201 piezometer was much shorter than snowmelt for Bull catchments
(Table 3), subsurface water at B201 piezometer was amid the stream water sample cloud and
thus violated one of the three criteria for being a vertex of the triangle. Snowmelt was still used
to characterize near-surface runoff for Bull catchments.
A sample of stream water collected in late summer and fall with highest ionic
concentrations was selected to characterize rainstorm runoff, namely the one collected on
October 5, 2006 at P304 for Providence catchments and on October 17, 2004 at B201 for Bull
catchments. The U- and S-space distance was less than 5 and 10% for those two samples,
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respectively (Table 3).
Baseflow was characterized by stream water samples collected during low flows,
following Liu et al. [2008]. Several samples from different catchments were geometrically
qualified as a candidate, but the one with shortest distance between U- and S-space was selected.
The stream water sample collected at P304 on July 28, 2004 was selected for Providence
catchments, as its distance was < 5% for all tracers (Table 3). The stream water sample collected
at T003 on August 8, 2007 was selected for Bull catchments, as its distance was < 10% for all
tracers except Cl-.
Endmember contributions
The fractional contributions of near-surface runoff were much higher during the
snowmelt period than the baseflow period and consistently higher at P301 and lower at P304 at
Providence (Figure 6). The mean contribution of 2004-2007 was 0.65 (0.14) for P301 and 0.27
(0.17) for P304 (Table 4). The fractional contributions of rainstorm runoff varied from 0.2 to
1.0 during late summer and early fall, but near zero during the other times of year (Figure 6),
with a mean fraction < 0.06 for all catchments at Providence (Table 4). The fractional
contributions of baseflow basically counterbalance those of near-surface runoff, with lowest
mean of 0.32 (0.13) at P301 and greatest mean of 0.67 (0.20) at P304 (Table 4).
The seasonal variation of fractional contributions of near-surface runoff at Bull
catchments was similar to those at Providence catchments, with higher values during snowmelt
and lower values during baseflow (Figure 6). The fractional contributions were consistently
highest at B203, with a mean of 0.72 (0.14) from 2004 to 2007, and lowest at T003, with a
mean of 0.32 (0.16) (Table 4). The mean contributions of rainstorm runoff were < 0.04, slightly
14
lower than those at Providence catchments. Similar to the Providence catchments, the fractional
contributions of baseflow almost counterbalance those of near-surface runoff, with highest mean
of 0.66 (0.16) at T003 and lowest mean of 0.25 (0.09) at B203 (Table 4).
Correlation of endmember contributions with topography
Mean near-surface runoff and baseflow normalized by catchment area (mm day-1)
increased slightly with mean drainage elevation (Figure 7), but the trends were not significant at
 of 0.05 (p > 0.05). The near-surface runoff decreased with average slopes of catchments and
again the relationship was not significant at  of 0.05 (p > 0.05). The rainstorm runoff was not
correlated with average elevations and slopes at all. The similar results were obtained for mean
flow contributions (L s-1) of near-surface runoff and baseflow, but not shown here.
Correlation of endmember contributions with stream flow
Contributions of near-surface runoff and baseflow were linearly correlated with stream
discharge at both Providence and Bull catchments (Figures 8 and 9). The R2 values were 0.920.99 and 0.91-0.97 (p < 0.001) for near-surface runoff and baseflow, respectively. The slope
varied from 0.53 to 0.83 for near-surface runoff and from 0.20 to 0.46 for baseflow. The
intercept was all negative for near-surface runoff, with a magnitude < 7, and all positive for
baseflow, with a value also < 7. There were a few outliers for baseflow in some of the
catchments (Figure 9). These outliers mainly occurred on three days, December 31 in 2005,
January 2 in 2006, and February 28 in 2006. It was presumably a result of analytical errors on
ionic concentrations rather than instrumental errors on stream discharge, as there were no
obvious outliers for near-surface runoff.
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DISCUSSION
Evaluation of the selected endmembers
The number of endmembers and conservative tracers were well determined by combining
stream water chemical data into a Providence and Bull catchment set (Figure 4), suggesting that
those four catchments within each set have the same endmembers with similar chemical
characteristics based on Hooper [2003]. Indeed, for example, the distance of baseflow
endmember at P304 is all less than 5% for all conservative tracers when eigenvectors from four
catchments together were used (Table 3) and the geometrical position of this endmember in the
mixing diagram is apt as a vertex for a triangle to bound all stream water samples (Figure 5).
Thus, baseflow of P304 was eligible to represent and characterize baseflow for the other three
catchments at Providence. Likewise, baseflow of T003 is also appropriate for all Bull catchments.
This result is also supported by approximately uniform distribution of soils and vegetation across
catchments within Providence and Bull, respectively.
The selected endmembers between the Providence and Bull catchments have the same
physical meanings but were characterized differently in chemistry. Near-surface runoff was
characterized by a shallow spring for the Providence catchments and by snowmelt for the Bull
catchments. The ionic concentrations were much higher in the shallow spring at B201 than in
snowmelt (Table 2). In addition to endmember distance (Table 3), this selection is justified by
the analysis of ionic concentration-stream discharge relationship. Liu et al. [in preparation] find
out that ionic concentrations in stream flow were almost invariant during high flows during
snowmelt and the envelopes were significantly higher at the Providence than at Bull catchments,
but rather similar within the Providence and Bull catchments. This result indicates that ionic
16
concentrations in near-surface runoff are higher in the Providence than Bull catchments.
Likewise, ionic concentrations in low flows were much higher at Providence than Bull
catchments (Figure 3), also showing the difference in ionic concentrations in baseflow between
the Providence and Bull catchments.
The selection of endmembers for both the Providence and Bull catchments were also
quantitatively evaluated using fractional contributions of EMMA and ionic concentrations of
endmembers (Figure 10), following Christophersen and Hooper [1992] and Liu et al. [2004].
This recreation is not recurring because the fractional contributions of EMMA were determined
by correlations of ionic concentrations [Liu et al., 2008]. The concentrations of Ca2+ and Mg2+
were very well recreated for both Providence and Bull catchments, with a slope near 1.0 and R 2
greater than 0.92 between measured and recreated values. The concentrations of K+ and Cl- were
also very well recreated for Bull catchments, but R2 values were much lower for Providence
catchments. The lower R2 values of K+ and Cl- at Providence catchments were caused by a
number of outliers (Figure 10). ANC was reasonably well recreated for both Providence and Bull
catchments, with a slope of 1.0 or close to 1.0 and R2 > 0.88, even though it was not used in
EMMA. Recreation of Na+ was also reasonably good, with R2 of 0.53 and 0.71 for Providence
and Bull catchments, respectively. Na+ did not behave completely conservatively (Figure 4) and
thus an ideal recreation was not expected. Nonetheless, the recreation of ANC and Na+ enhanced
the confidence of the endmember selection.
Processes controlling streamflow generation
Stream flow is very responsive to snowmelt and rainstorm events in both Providence and
Bull catchments, as shown by the hydrographs (Figure 3). Among the eight catchments, three
17
different dominant mechanisms of streamflow generation were identified based on the relative
contribution of near-surface runoff versus baseflow. The first is near-surface-runoff dominated
mechanism, with a fractional contribution of near-surface runoff > 0.5 year round except during
brief dry periods in late summer and fall. This mechanism is represented by streamflow at P301
and B203 (Figure 6; Table 4). The second one is a baseflow-dominated mechanism, with a
fractional contribution of baseflow > 0.5, as of P304 and T003. The third one is characterized by
approximately equal contributions of near-surface runoff and baseflow, such as P303 and B201.
Streamflow generation is apparently not determined by the proportion of snow and rain,
as both near-surface-runoff and baseflow-dominated mechanisms exist at rain-dominated
Providence and snow-dominated Bull. The mean contribution of both near-surface runoff and
baseflow is also not strongly correlated with elevation (Figure 7), again demonstrating that the
proportion of snow and rain does not exert a major control on the streamflow pathways. The
spatial variation of streamflow pathways over eight catchments is presumably controlled by soil
depth and the topography of bedrock, as described by Freer et al. [1997]. In the catchments of
Freer et al. [1997] where soils are deeper and bedrock has more and deeper hollows,
contribution of baseflow is higher. Unfortunately, however, information on soil depth and
topography of bedrock is not yet available in our catchments.
The contribution of baseflow is also very responsive to rainfall and snowmelt inputs in
these catchments (Figure 9), indicating that baseflow was not from deep bedrock groundwater.
Deep groundwater such as sampled in the Blue Canyon well is not a contributor to streamflow in
these catchments (Figure 5). Lateral subsurface flow has been well depicted as a major
subsurface pathway in stream flow generation in semiarid, forested catchments [Brent et al.,
1997; 1998; 2004; Wilcox et al., 1997; McNamara et al., 2005]. Baseflow of these catchments
18
essentially matches lateral subsurface flow stored near the interface of lower soil horizons and
bedrock. Infiltration of snowmelt and rainwater to the lower soil horizons occurred through
preferential pathways and bypassed the shallow soil horizons [Sandvig and Phillips, 2006].
Supporting this argument is that soil water was not a contributing endmember and the ionic
concentrations in soil water were significantly different from those in stream water, shallow
subsurface flow of piezometer and springs (Table 2).
Implications for Forest Management
Hydrologic models such as Distributed Hydrology, Soil and Vegetation Model (DHSVM)
have often been used to simulate forest treatment effects on water balance of a catchment,
[Waichler et al., 2005]. Common problems encountered with hydrologic models have been the
incapability of incorporating groundwater into the model and the notable errors in recreating
streamflow hydrograph. For example, low flows were overpredicted while high flows were
underpredicted at the H. J. Andrews Experimental Forest using DHSVM [Waichler et al., 2005].
To overcome this problem in an ongoing application of DHSVM to our catchments, the simple,
linear-regression models of this study can be used to predict near-surface runoff and baseflow
using streamflow discharge as a predictor, as the samples of this study cover diverse climates and
annual precipitation (Figure 2). These simple models can also be used in watershed hydrology
and forest treatment projects in two ways. First, they can be used to calibrate hydrologic
parameters associated with routing algorithm of a hydrologic model or validate the results of a
hydrologic model in predicting streamflow pathways. Second, they can be used as a benchmark
to evaluate changes in streamflow regimes with a forest treatment if a similar study is conducted
after the treatment. Any changes in the slopes of the relationship between near-surface runoff
19
and baseflow and streamflow after the treatment is completed could suggest changes in flow
regime.
CONCLUSIONS
Streamflow is dominated by a combination of near-surface runoff and baseflow in small,
forested catchments in the snow-rain transition in the Southern Sierra Nevada. Both near-surface
runoff and baseflow are very responsive to snowmelt and rainstorm events and are strongly
linearly correlated with streamflow discharge. Baseflow is primarily formed by relatively rapid
infiltration of snowmelt and rainwater to the interface of lower soil horizons and bedrock through
preferential pathways and delivered to streams as lateral subsurface flow. The residence times of
baseflow are relatively short, but vary over catchments, likely determined by soil depth and the
topography of bedrock. With changes in snow-rain proportions, systematic changes in
streamflow pathways are not expected in the southern Sierra Nevada catchments, although the
timing of fractional contributions of near-surface runoff relative to baseflow will be certainly
altered at each individual catchment. The linear-regression models can be used to constrain
results of hydrologic models in future studies and to assess changes in hydrologic regime with
future forest management.
ACKNOWLEDGEMENTS
Authors appreciate sample collection of T. Whitaker and other staff at Pacific Southwest
Research Station, USDA Forest Service, Fresno, California. Funding is provided by Forest
Service and NSF grants EAR-0725097 and BES-0610112 to the University of California,
Merced.
20
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23
Figure Captions
Figure 1. Study area, showing locations of eight catchments, stream gauges (stream sampling
sites), meteorological stations (sampling sites of snow), wells and springs.
Figure 2. Hydrometeological data from water years 2004 to 2007 for (a) cumulative daily
precipitation at four meteorological stations, (b) daily snow depth, with peak and snow
depletion dates at the Upper Bull station indicated, and (c) cumulative daily runoff at
eight catchments.
Figure 3. Concentrations of selected ions (Ca2+, K+, Cl-) from water year 2004 to 2007 in stream
water at eight catchments at Providence and Bull, along with streamflow discharge.
Figure 4. Distribution of residuals in 1-D and 2-D mixing spaces with measured ionic
concentrations in stream flow for all solutes used in diagnostic tools of mixing models;
See text for the calculation of residuals.
Figure 5. Mixing diagrams using the first 2 U-space projections, along with selected
endmembers and the triangle they form, for catchments at (a) Providence and (b) Bull.
Note the arrow indicates the change in the distribution of stream water samples from
snowmelt to baseflow period. The inset figures show mixing diagrams at all scales. For
clarity of the figures, the errors are not shown for soil water and Dinkey Creek well in the
enlarged figures.
Figure 6. Fractional contributions of endmembers to streamflow from water year 2004 to 2007 at
Providence and Bull catchments.
Figure 7. Variation of mean endmember contributions with mean drainage elevation and slope at
Providence and Bull catchments, averaged across water years 2004 to 2007.
24
Figure 8. Correlation between contribution of near-surface runoff and streamflow discharge at
each catchment.
Figure 9. Correlation between contribution of baseflow and streamflow discharge at each
catchment.
Figure 10. Recreation of ionic concentrations in stream water based on the fractional
contributions of EMMA and ionic concentrations in endmembers. Note that ANC and
Na+ were not used in EMMA.
25