Supporting Information
Climate-induced shift in hydrologic regime alters basal resource dynamics in a wilderness
river ecosystem
Appendix S1: Methods used to reconstruct and predict the hydrology of Big Creek
1
2
Methods for reconstructing a contemporary hydrograph for Big Creek
Although the Taylor Wilderness Field Station in the Big Creek catchment has served as a
3
hub for natural sciences research within the Frank Church River of No Return Wilderness since
4
1970, no consistent stream discharge observations were recorded between 1958 and 2008 (Fig.
5
S1). However, a United States Geological Survey (USGS) gauging station on Big Creek
6
downstream of Cabin Creek (#13310000) collected daily discharge data from 1944 to 1958. To
7
reconstruct mainstem water discharge between 1958 and 2008, a suite of models were
8
constructed and tested, including spatially-distributed process models and statistical techniques
9
(Olson, 2010). Specifically, we used multiple linear regression (MLR) to reconstruct Big
10
Creek’s discharge, a method that has successfully reconstructed flows at other locations where
11
gauges were discontinued (e.g. Nawaz & Khan, 2006). We used the MLR technique to compare
12
discharge that was measured between 1944 and 1958 at the decommissioned gauge with
13
discharges from active gauges in three reference rivers (Table S1). These three nearby,
14
contemporaneously gauged rivers within the Salmon River catchment have similar climatic and
15
lithologic setting to Big Creek. We developed multiple candidate models by including the active
16
gauges individually or in combination. The fit of each candidate model was then evaluated with
17
root mean square error (RMSE), which was calculated by comparing the modelled hydrograph to
18
the hydrograph that was measured between 1944 and 1958.
19
As part of new investigations of topographic controls on tributary flow patterns (i.e. Olson,
20
2010), a down-looking water-level sensor (OTT Radar Level Sensor) was installed on a bridge at
21
the Taylor Wilderness Field Station in April, 2008 (Fig. S1). This new gauge located on Big
22
Creek, immediately upstream of the confluence with Cliff Creek, provides contemporary
23
discharge data that was not used in model construction but validated the reconstructed
24
hydrograph. The sensor continuously averaged and recorded the height of the water surface (i.e.,
25
stage) beneath the bridge over 10 minute intervals. Over two years, multiple high and low-flow
26
discharge measurements (done with Acoustic Doppler Current Profiler instruments and Acoustic
27
Doppler Velocimeter) were completed on Big Creek and used to construct a stage-discharge
28
relationship (following Rantz, 1982). This new sensor at the Taylor Wilderness Field Station has
29
measured discharge on Big Creek mainstem since April 15, 2008.
30
The MLR that included all three reference rivers provided the best fitting model for
31
reconstructing Big Creek’s hydrograph (RMSE = 2.79 cms, p < 0.0001, r2 = 0.98; Table S2).
32
After correcting for the additional catchment area at the newly installed gauging station,
33
modelled flows were independently compared against recent discharge data from the Taylor
34
Wilderness Field Station and found to be equally accurate (RMSE = 4.82 cms, p < 0.0001, r2 =
35
0.98). Thus, we validated the stationarity assumption of the MLR relationship over the study
36
period.
37
Development and validation of the VIC predictive hydrologic model
38
We predicted Big Creek hydrographs over our study period under various temperature
39
scenarios (+0º, +1º, +2º and +3ºC) using the variable infiltration capacity (VIC) model (Liang et
40
al., 1994; Liang, Wood & Lettenmaier, 1996; Fig. 1), a distributed macroscale energy and water
41
balance model that can simulate hydrographs under various scenarios. We have previously used
42
the model to simulate streamflow at a larger spatial scale in the overall Salmon River catchment
43
(see Tang et al., 2012). Streamflow was simulated by adding together surface runoff and
44
baseflow, which were generated daily at a spatial scale of 1/16th degree. Surface runoff and
45
baseflow were modelled using three soil layers of varying thickness: 10cm for layer 1, 30cm for
46
layer 2 and 100cm for layer 3. Surface runoff was generated in the upper two layers according to
47
a variable infiltration curve that compared the rate at which water penetrates into the layers
48
against the rate that water was delivered to the surface via precipitation or snowmelt. Baseflow
49
was derived from throughflow in the bottom layer (Todini, 1996). To account for downstream
50
transport time and simulate hydrographs, the VIC model was coupled with a hydrologic routing
51
model (Lohmann, Nolte-Holube & Raschke 1996; Lohmann et. al., 1998). The static inputs of
52
the VIC model included these soil layer thicknesses, regional topography, vegetation data and
53
soil properties. The only time-varying input was the meteorological data.
54
Input data for the VIC model in the Big Creek catchment— Input data for the VIC model
55
were derived from multiple sources. Meteorological data for Big Creek included air
56
temperature, precipitation and wind. Temperature and precipitation were obtained from the Land
57
Surface Hydrology Group at University of Washington
58
(http://www.hydro.washington.edu/surfacewatergroup/data.php) based on National Climatic
59
Data Center Cooperative Observer station data (Maurer et al., 2002). Daily surface wind speeds
60
were obtained from the NCEP/NCAR reanalysis project (Kalnay et al., 1996). The raw soil
61
characteristics data were taken from the State Soil Geographic Database (STATSGO) maintained
62
by the Earth System Science Center (Abdulla et al., 1996). The soil properties datasets included
63
field capacity, wilting point, saturated hydraulic conductivity, soil types and porosity. The VIC
64
model allowed different types of vegetation and land cover (Liang et al., 1994). The raw land
65
cover characterization was obtained from the Land Data Assimilation Scheme (LDAS) based on
66
the University of Maryland global vegetation classifications (Hansen & Reed, 2000). Vegetation
67
parameters such as architectural resistance, minimum stomatal resistance, albedo, roughness
68
length, zero-plane displacement, rooting depth and fraction were specified for each individual
69
vegetation class.
70
Calibration of VIC model—Model calibration was performed by comparing VIC’s simulated
71
streamflow to observed streamflow at the USGS Big Creek gauge (#13310000) for 1944-1958
72
(the only period of record for this gauge). We used a 9-year subset (1944-1952) for the
73
calibration process and a 5-year period (1953-1958) for validation.
74
The variable infiltration capacity curve and baseflow curve were the two governing curves of
75
the VIC model. The infiltration parameter (binf) and maximum infiltration capacity defined the
76
shape of the variable infiltration capacity curve. The shape of the baseflow curve was defined by
77
four parameters: (a) the maximum baseflow that could occur from the third soil layer (Dsmax; in
78
mm/day); (b) the fraction of Dsmax where non-linear (rapidly increasing) baseflow began; (c) the
79
fraction of the maximum soil moisture (of the lowest soil layer) where non-linear baseflow
80
occurred; and (d) the depth of the second soil layer (d2; Liang et al., 1994; Wood et al., 1997).
81
Sensitivity analysis showed that binf and d2 were the key parameters for model calibration in this
82
study. We iteratively adjusted these parameters to maximize the model fit to the calibration
83
dataset, as measured using the Nash-Sutcliffe coefficient that was calculated on a monthly basis
84
(Ef) (Nash & Sutcliffe, 1970) and the widely used r2 value (coefficient of determination).
85
Calibration results— The r2 value between the VIC +0ºC scenario and the gauged hydrology
86
data in the validation set was 0.81 and the Ef value was 0.75, which was above the threshold
87
typically considered a good model fit (i.e. Ef ≥ 0.7). However, the modelled and observed
88
peakflows did differ in May, June, or July for some years (Fig. S2). This is likely due to the
89
input precipitation data not accurately representing high elevation snow accumulation. There are
90
very few meteorological or snowpack telemetry (SNOTEL) stations in this mountainous
91
wilderness setting and most are located in lower-elevation valleys (Kunkel & Pierce 2010),
92
which could lead to under-prediction of both liquid and solid precipitation and snow
93
accumulation. Although there are discrepancies between the modelled and observed
94
hydrographs, the VIC model exhibited a good model fit, as evidenced by the strong correlation
95
between the timing of flow metrics from 1990 to 2009 (Figs. S2 and S3) and the high Ef value.
96
Modelled temperature changes— Multiple model runs were completed using uniformly
97
applied temperature increases to explore how discharge would have been different if, keeping all
98
other parameters equal, air temperatures had been higher during the study period. Beyond the
99
initial baseline scenario (+0ºC), we ran three stepwise increases in temperature: +1º, +2º and
100
+3ºC (Fig. 1), which encapsulated the range of temperature increases projected for this region by
101
ca. 2080 (Mote & Salathe 2010). We do not currently have forecasted meteorological data for
102
the years 2009 to 2050; therefore, hydrologic output from the +1, +2 and +3ºC scenarios were
103
limited to water years 1990 to 2009. Because they were included in the best-fitting regression
104
equations that related biofilm biomass and hydrology (see Fig. 3), we used the simulated
105
discharges to calculate the timing of peakflow, median flow, 95% percentile flow and centre of
106
mass for each water year and temperature scenario (Fig. 2).
107
108
References
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109
hydrologic model to estimate the water balance of the Arkansas Red River basin. Journal of
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Geophysical Research-Atmospheres, 101, 7449-7459.
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Hansen M.C. & Reed B. (2000) A comparison of the IGBP DISCover and University of
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1365-1373.
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K.C., Ropelewski C., Wang J., Leetmaa A., Reynolds R., Jenne R. & Joseph D. (1996) The
117
NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society,
118
77, 437-471.
119
120
Kunkel M.L. & Pierce J.L. (2010) Reconstructing snowmelt in Idaho's watershed using historic
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121
Liang X., Lettenmaier D.P., Wood E.F. & Burges S.J. (1994) A simple hydrologically based
122
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123
Geophysical Research-Atmospheres, 99, 14415-14428.
124
Liang X., Wood E.F. & Lettenmaier D.P. (1996) Surface soil moisture parameterization of the
125
VIC-2L model: evaluation and modification. Global and Planetary Change, 13, 195-206.
126
Lohmann D., Nolte-Holube R. & Raschke E. (1996) A large-scale horizontal routing model to be
127
coupled to land surface parametrization schemes. Tellus 48, 708-721.
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Lohmann D., Raschke E., Nijssen B. & Lettenmaier D.P. (1998) Regional scale hydrology: I.
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Formulation of the VIC-2L model coupled to a routing model. Hydrological Sciences
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Journal, 43, 131-141.
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Maurer E.P., Wood A.W., Adam J.C., Lettenmaier D.P. & Nijssen B. (2002) A long-term
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hydrologically based dataset of land surface fluxes and states for the conterminous United
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States. Journal of Climate, 15, 3237-3251.
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135
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Mote P.W. & Salathe E.P. (2010) Future climate in the Pacific Northwest. Climatic Change, 102,
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Olson N.F. (2010) Hydrology of Big Creek, Idaho: spatial and temporal heterogeneity of runoff
141
in a snow-dominated wilderness mountain watershed. MSc Thesis. Idaho State University,
142
Pocatello.
143
144
Rantz S.E. (1982) Measurement and computation of streamflow: volume 2, computation of
discharge. United States Geological Survey Water Supply Paper 2175.
145
Tang C., Crosby B.T., Wheaton J.M. & Piechota T.C. (2012) Assessing streamflow sensitivity to
146
temperature increases in the Salmon River Basin, Idaho. Global and Planetary Change, 88-
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89, 32-44.
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Todini, E. (1996) The ARNO rainfall-runoff model. Journal of Hydrology, 175, 339-382.
149
Wood E.F., Lettenmaier D., Liang X., Nijssen B. & Wetzel S.W. (1997) Hydrological modeling
150
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Table S1: Gauging stations used to reconstruct discharges in Big Creek, Idaho. The first three
stations were used in a multiple linear regression against the fourth (see Table S2). The
reconstructed flow record was then validated by comparing modelled flows against observed
discharges at the fifth site. The decommissioned Big Creek gauge (#13310000) was located just
downstream of Cabin Creek (see Fig. S1).
USGS station designation
Station
ID
Catchment
area
(km2)
Johnson Creek at Yellow Pine, ID
13313000
561.2
Mean
catchment
elevation
(m)
2174.1
Salmon R. at Salmon, ID
13302500
9738.4
Salmon R. at Whitebird, ID
13317000
Big Creek nr Big Creek, ID
Big Creek at Taylor Bridge, ID
Distance to
Big Creek
gauge (km)
Start
date
End date
54
9/1/1928
present
2255.0
76
10/1/1912
present
35094.3
2053.7
137
9/1/1910
present
13310000
1181.1
2117.1
0
9/5/1944
10/31/1958
NA
1444.1
2100.0
7.8
4/15/2008
present
Table S2: Root mean square error (RMSE) for four candidate models that reconstructed the
contemporary discharge in Big Creek, Idaho. The multiple regression model, which was based
on all three gauges, provided the lowest RMSE and was the best fitting model.
Multiple regression model
RMSE
(m3 s -1)
2.79
Johnson Creek at Yellow Pine, ID
3.98
Salmon R. at Salmon, ID
6.19
Salmon R. at Whitebird, ID
103.7
Candidate model name
Fig. S1: Locations of the 6 study streams (Cave, Cliff, Cougar, Goat, Pioneer and Rush Creeks)
in the lower Big Creek catchment, Idaho. Study streams indicated by shading. The site of the
decommissioned USGS gauge (#13310000) downstream of Cabin Creek is represented by ‘A’
and the recently installed gauge at the Taylor Wilderness Field Station is represented by ‘B.’
Inset shows the Big Creek catchment in Idaho.
Direction
of flow
Fig. S2: Comparison of the VIC-generated hydrograph for the T+0ºC scenario and the
contemporary hydrograph reconstructed using a multiple linear regression (MLR) model.
Although the VIC +0ºC scenario underestimated the magnitude of peakflows in water years 2008
and 2009, the hydrograph exhibited good fit in all other years.
250
T+0
Contemporary
3
-1
Discharge (m s )
200
150
100
50
0
'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10
Calendar date
Fig. S3: Linear regressions comparing the timing of flow metrics (peakflow, median flow and
centre of mass) for the contemporary hydrograph and the VIC +0ºC scenario. The high radj2
indicates that the +0ºC scenario was an effective surrogate for the contemporary hydrographs
during the sampling period (water year 1990 to 2009). Timing of peakflow and median flow
VIC T+0 Julian date of median flow
(d)
VIC T+0 Julian date of peakflow
(d)
were the best predictors of biofilm biomass (see Results). CT is centre of mass.
170
p < 0.0001
radj2 = 0.74
160
150
140
130
120
130 140 150 160 170
Contemporary Julian date of peakflow
(d)
170
160
p < 0.0001
radj2 = 0.87
150
140
130
120
120
130
140
150
160
VIC T+0 Julian date of CT
(d)
Contemporary Julian date of median flow
(d)
160
150
p < 0.0001
radj2 = 0.84
140
130
120
110
100
100 110 120 130 140 150
Contemporary Julian date of CT
(d)