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Supplemental Materials
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3
Methods
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Comparison of SWE Simulations with Observations
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To evaluate the performance of the VIC snow model under historic conditions, we
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performed a limited set of point simulations for comparison with SnoTel observations.
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VIC simulations at 1/16º spatial resolution using the Livneh et al (2013) forcing data
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were made for the period 1987-2005 to provide overlap with the SnoTel observations.
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The simulations were run for grid cells that contained SnoTel sites as well as for the eight
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surrounding grid cells. Sub-grid cell elevation heterogeneity was accounted for through
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the use of up to five elevation bands within each grid cell. Using our simulations, we
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binned simulated SWE and observed SWE by 300 m elevation increments corresponding
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to the elevation of the SnoTel site, ranging from 500 m to 3500 m, and the average over
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each was computed.
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Comparison of Simulated to Observed SWE
The VIC model has been widely used to represent hydrological conditions and to
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study future hydrological change (e.g. Nijssen et al 2001). Mote et al (2005) examined
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long-term trends in April 1 SWE and found that the fraction of negative trends was nearly
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identical for observations and VIC output (75% for snow course observations, 73% for
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VIC). Mao et al (2015) compared elevation profiles of VIC-simulated SWE and
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observations over California and found good agreement.
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Past comparisons of VIC-modeled SWE have been over limited parts of our
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domain, and the Mote et al. (2005) comparisons are now somewhat out of date (through
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1997). Our hydrologic simulations using historical gridded data (described in Section 2.3)
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addressed this by comparing VIC-simulated SWE to observations of SWE from SnoTel
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stations, shown in Online Resource 1, temporally averaged over 1987-2005. Although the
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range of values differs between observed and simulated SWE, with simulated SWE often
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exhibiting a larger range particularly around 2600 m, the mean, 10th and 90th percentile
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values are in relatively good agreement. This is particularly the case for middle elevations
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(2000 m to 3000 m). The White Mountains are the only range that shows little agreement
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between observed and simulated SWE, particularly in the 10th and 90th percentile values.
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This likely is due in part to the small number of available observations for this region (the
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area of the White Mountains is substantially smaller than that of the other ranges,
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corresponding to a much smaller number of SnoTel stations, e.g. less than ten versus
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hundreds). Additionally, some of the lack of agreement in the White Mountains may be
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due to the large interannual variability in simulated SWE relative to observed.
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Calculation of Dead Fuel Moisture (DFM)
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Daily minimum and maximum temperature, relative humidity and precipitation were
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used to derive EMC and calculate 100-hr and 1000-hr DFM as detailed in Section 2.4.
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We derived minimum and maximum daily relative humidity (RH) from specific humidity
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(calculated internally by VIC) by assuming that specific humidity is approximately equal
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to the mixing ratio (w). We then computed minimum and maximum daily RH as 𝑅𝑅 =
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100 × 𝑅/𝑅𝑅 , where 𝑅𝑅 = 0.622 × 𝑅𝑅 /𝑅, 𝑅𝑅 is the saturation vapor pressure
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calculated with the minimum and maximum daily temperatures and p is the atmospheric
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pressure. We accounted for precipitation duration using an empirical transform that
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translates daily precipitation amount to precipitation duration (Holden and Jolly 2011)
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and constrained precipitation duration not to exceed 8 hours.
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FIGURES
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Online Resource 1: Comparison of simulated and observed April 1 SWE (1987 – 2005)
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Online Resource 2: Selected Global Climate Models from CMIP5
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Online Resource 3: Average winter (November through March) historical temperature
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and projections for increases in temperature in western US mountain ranges
Online Resource 4: Total winter (Nov-March) precipitation projections for mountain
ranges and lowland regions from CMIP5
Online Resource 5: Projected losses in April 1 SWE storage (in km3) for five mountain
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ranges, averaged across the ten GCMs. The maximum and minimum losses
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denote the largest and smallest SWE storage losses projected by the ten selected
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GCMs.
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Online Resource 6: Change in mean simulated April 1 SWE between historical and future
periods for all GCMs. Changes in grey are not statistically significant.
Online Resource 7: Shifts in hydrologic model grid cell classifications based on winter
temperature
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Online Resource 8: Comparison of projected soil moisture changes between GCMs
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Online Resource 9: Total spring (March-May) precipitation projections for mountain
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ranges and lowland Regions from CMIP5
Online Resource 10: Average summer (JJAS) 1000-hr DFM for
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a) mountain ranges
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b) lowland regions
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Online Resource 11: Number of models projecting positive changes in 1000-hour DFM
minus number of models projecting negative changes
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Online Resource 12: Areas of mountain ranges and lowland regions in domain
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References
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Nijssen, B, O’Donnell GM, Lettenmaier DP, Lohmann D, Wood, EF (2001) Predicting
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the Discharge of Global Rivers. J Climate 14: 3307-3323. doi: 10.1175/1520-
99
0442(2001)014<3307:PTDOGR>2.0.CO;2
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Holden ZA and Jolly WM (2011) Modeling topographic influences on fuel moisture and
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fire danger in complex terrain to improve wildland fire management decision
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support. Forest Ecology and Management 262, 2133-2141. doi:
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10.1016/j.foreco.2011.08.002.
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Tables
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Table 1: Selected Global Climate Models from CMIP5
Global Climate Model
Model Source
BCC-CSM1-1-M
Beijing Climate Center- Meteorological
Administration, China
CanESM2
Canadian Centre for Climate Modeling and
Analysis
CCSM4
National Center of Atmospheric Research,
US
CNRM-CM5
National Centre of Meteorological
Research, France
CSIRO-Mk3-6-0
Commonwealth Scientific and Industrial
Research Organization/Queensland Climate
Change Center of Excellence, Australia
HadGEM2-CC
Met office Hadley Center, United Kingdom
HadGEM2-ES
Met Office Hadley Center, United
Kingdom
IPSL-CM5A
Institut Pierre Simon Laplace, France
MIROC5
Atmosphere and Ocean Research Institute,
University of Tokyo; Japan Agency for
Marine-Earth Science and Technology
NorESM1-M
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Norwegian Climate Center, Norway
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Table 2: Average winter (November through March) historical temperature and
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projections for increases in temperature in western US mountain ranges
Mountain
Range
Sierra
Nevada
Cascades
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113
Historical
(19701999)
C
RCP 4.5
20102039
Increase
(C)
RCP 4.5
20402069
Increase
(C)
RCP 4.5
20702099
Increase
(C)
RCP 8.5
20102039
Increase
(C)
RCP 8.5
20402069
Increase
(C)
RCP 8.5
20702099
Increase
(C)
-1.06
+1.05
+2.14
+2.79
+1.33
+2.72
+4.55
-1.76
+1.06
+2.13
+2.81
+1.32
+2.68
+4.48
Northern
-6.15
Rockies
Southern
-6.20
Rockies
White
-0.78
Mountains
+1.23
+2.50
+3.24
+1.52
+3.16
+5.31
+1.34
+2.60
+3.30
+1.58
+3.34
+5.61
+1.22
+2.29
+2.90
+1.41
+2.97
+4.96
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Table 3: Projected losses in April 1 SWE storage (in km3) for five mountain ranges,
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averaged across the ten GCMs. The maximum and minimum losses denote the largest
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and smallest SWE storage losses projected by the ten selected GCMs.
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Mountain
Historical
Range
Sierra
Nevada
17.33
Cascades
58.41
Northern 103.89
Rockies
Southern 32.18
Rockies
0.24
Whites
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119
Average [km3 ]
Minimum [km3 ]
Maximum [km3 ]
2020s
2050s
2080s
2020s
2050s
2080s
2020s
2050s
2080s
RCP 4.5 3.55
RCP 8.5 4.07
RCP 4.5 11.62
RCP 8.5 12.58
RCP 4.5 9.94
RCP 8.5 10.8
RCP 4.5 2.01
RCP 8.5 1.94
RCP 4.5 0.11
RCP 8.5 0.11
7.06
7.87
19.6
24.08
19.21
26.22
5.76
7.6
0.18
0.2
8.04
11.27
25.87
37.46
28.25
50.17
7.35
13.6
0.21
0.23
0.98
2.08
7.36
4.86
1.45
3.3
0.21
0.16
0
0.03
5.18
5.54
7.62
12.57
11.49
13.86
2.5
4.31
0.12
0.15
5.4
9.86
18.98
25.3
16.34
32.96
2.36
8.64
0.15
0.18
6.1
5.74
17.4
19.11
15.96
17.31
6.71
4.24
0.2
0.16
9.5
10.47
26.5
36.08
27.76
38.8
10.65
12.42
0.25
0.25
10.53
13.37
35.72
47.3
40.2
65.73
12.28
22.76
0.27
0.29
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Table 4: Areas of mountain ranges and lowland regions in domain
Area (x 1,000 km2)
Region
Mountain Ranges
Sierra Nevada
53.7
Cascades
101
Northern Rockies
304
Southern Rockies
175
White Mountains
4.82
Lowland Regions
121
Coastal North
83.7
Coastal South
154
Northwest Interior
289
Lower Colorado
308
Great Basin
287
Missouri
562