Supplementary Material for
Snowpack and Runoff Response to Climate
Change in Owens Valley and Mono Lake
Watersheds
Mariza Costa-Cabral, Sujoy B. Roy, Edwin P. Maurer, William B. Mills, and Limin Chen
Table of Contents
0. Regional map and topographic map of the Owens Lake and Mono Lake
watersheds ............................................................................................................... 2
1. Brief review of literature: Observed hydrologic and ecologic changes associated
with climate change in the Western U.S. .................................................................. 4
2. Observed climatology and sensitivity of runoff to climate in the main runoffproducing areas of the watersheds of Owens Lake and Mono Lake in the
historical period 1950-1999 ...................................................................................... 6
3. The 16 global climate models studied ....................................................................... 10
4. Statistical downscaling of GCM projections of temperature and precipitation ........... 11
5. VIC Model calibration and verification ....................................................................... 15
6. Relationship between mean annual runoff and mean annual precipitation for each
individual GCM ....................................................................................................... 23
Literature References .................................................................................................... 30
1
0. REGIONAL MAP AND TOPOGRAPHIC MAP
OF THE OWENS LAKE AND MONO LAKE
WATERSHEDS
Figure 0-1: The watersheds of Owens Lake and Mono Lake in the Eastern Sierra Nevada, and the Los Angeles
Aqueducts.
2
Figure 0-2: Topography of the Owens Lake and Mono Lake watersheds. Map produced by Tetra Tech, Inc.
The runoff-producing areas on the eastern slopes were modeled and are depicted in Figure 5-1.
3
1. BRIEF REVIEW OF LITERATURE:
OBSERVED HYDROLOGIC AND ECOLOGIC
CHANGES ASSOCIATED WITH CLIMATE
CHANGE IN THE
WESTERN U.S.
Climate change impact studies on snow-melt dominated watersheds show some consistency in the types of
projected impacts (Adam el at., 2009). Past studies in the Western US have identified some of the types of
changes that are expected to occur, although these may not occur consistently across all regions. Typical
changes include earlier snowmelt, lower snowpack accumulation, changes in the quantity and timing of winter
precipitation, and greater extremes of precipitation events and droughts. These studies have been based on the
instrumental record from the latter part of the 20th Century (e.g., Cayan, 1996; Groisman et al., 2004; Hamlet
et al., 2005; Knowles et al., 2006; Mote et al., 2005) and on global climate model (GCM) projections for the
21st Century (e.g., Cayan et al., 2008; Barnett et al., 2005, 2008; Maurer, 2007b,c).
Research on recent hydrologic and ecological changes associated with climate in the North American West
and Sierra Nevada has documented the following observations:

A shift in snowmelt timing toward earlier dates (by 5 to 30 days) (Gleick et al, 1987; Lettenmaier and Gan,
1990; Roos, 1987, 1991; Cayan et al., 2001; Wahl, 1992; Aguado et al., 1992; Pupacko, 1993; Dettinger
and Cayan, 1995; Johnson et al., 1999; Brown, 2000; Dettinger et al., 2004b; Stewart et al., 2005; Mote et
al., 2005; Regonda et al., 2005; Knowles et al., 2006; Maurer et al., 2007c; Coats, 2010);

A shift from snowfall to rainfall (ACPI, 2004; Hamlet et al., 2005; Regonda et al., 2005; Knowles et al.,
2006);
4

A decline in snow water content in many high-elevation locations including the northern Sierra Nevada,
but a rising trend at the highest elevations in the southern Sierra Nevada (Johnson et al., 1999; Mote, 2003;
Groisman et al., 2004; Mote et al., 2005; Regonda et al., 2005; Knowles et al., 2006);

The attribution of the observed decline in western U.S. winter snowpack to climate change forced by
anthropogenic greenhouse gases and aerosols (Pierce et al., 2008; see also Moser et al., 2009).

Earlier onset of spring (Cayan et al., 2001; Wolkovich et al., 2012);

The impacts of the above changes on water supply in California and throughout the West (Hamlet et al.,
2005; Mote et al., 2005; Barnett et al., 2008);

Increasing risk of wildfire (Westerling et al., 2006; Running, 2006; Brown et al., 2004). In 2007, the
catastrophic Angora Fire in the Tahoe basin provided a stunning illustration of what can occur when
increased fuel availability, associated with legacy overstocked stands, combines with dry terrestrial
moisture conditions to increase fire hazard.
Parallel observations have been made in other high-elevation environments in the U.S. (e.g. in New England,
in studies by Hodgkins et al., 2003 and Hodgkins and Dudley, 2006; and in the American Cordillera, as
reported in Pederson et al., 2011) and elsewhere (e.g. in the Pyrenees, in the study by López-Moreno and
García-Ruiz, 2004).
5
2. OBSERVED CLIMATOLOGY AND
SENSITIVITY OF RUNOFF TO CLIMATE IN
THE MAIN RUNOFF-PRODUCING AREAS
OF THE WATERSHEDS OF
OWENS LAKE
MONO LAKE IN THE HISTORICAL
PERIOD 1950-1999
AND
To represent observed climatology we use the observation-based 1950-2005 distributed time series of
temperature and precipitation developed by Maurer et al. (2002). This observational dataset, which has
1/8˚ (approx. 12 km) spatial resolution and daily temporal resolution, was used as the basis for statistical
downscaling of GCM projections (1950-1999), and also for the calibration and verification of the VIC
hydrologic model (1950-2005).
Figure 2-1 displays the mean annual temperature and precipitation values observed in water years
1951-1999, averaged over the Owens-Mono watersheds’ runoff producing area, i.e., the eastern Sierra
slopes (depicted in the section on model calibration). For example, the year labeled as 1951 represents
the period from October 1, 1950, through September 30, 1951. Total annual precipitation in these 49
years varied from 302 mm (1977) to 1064 mm (1969), i.e., by a factor of 3.5. The range in mean annual
temperature was 0.29°C to 3.0°C. There is a weak negative correlation (R2=0.12) between the two
variables, warmer years appearing less likely to be wet.
6
The total annual runoff observed in water years 1951-2005 is displayed in Figure 2-2. Annual runoff had
a mean value of 318 mm (0.475 MAF) and covered a wide range, from 155 mm (in 1977, the lowest
precipitation year) to 628 mm (in 1969, the highest precipitation year), corresponding to a factor of 4.
Figure 2-1: Mean annual temperature and precipitation values observed in 1951-1999 (i.e., from water
year 1950-51 through 1998-99), averaged over the eastern Sierra slopes of the Owens-Mono
watersheds. These are gridded data (1/8° resolution for the entire watershed area based on
observations at point locations. Data source: Maurer et al. (2002).
7
Figure 2-2: Mean annual unimpaired runoff observed in water years 1951-2005, averaged over the
eastern Sierra slopes of the Owens-Mono watersheds. The 55-year average is 318 mm (0.475 MAF) per
year. Data source: Los Angeles Department of Water and Power.
Figure 2-3: Observed water-year runoff and precipitation in the eastern Sierra slopes of the Owens-Mono
watersheds, in 1951-1999. Each of the 49 points corresponds to a water year. The best-fit straight line is
shown, with a slope of 0.574 and an intercept of -13 mm. The linear correlation is high (R2=0.887).
8
Figure 2-4: Observed annual runoff in the plotted against mean annual temperature, in the eastern
Sierra slopes of the Owens-Mono watersheds, in 1951-1999. Each of the 49 plotted points corresponds
to a water year. The linear correlation is low (R2=0.1).
9
3. THE 16 GLOBAL CLIMATE MODELS
STUDIED
Table 3-1 lists the sixteen global climate models (GCMs) studied, the corresponding modeling group, and
the primary literature reference for each model.
Table 3-1: The sixteen global climate models
Modeling Group, Country
IPCC Model I.D.
Primary Reference
1.
Bjerknes Centre for Climate Research
BCCR-BCM2.0
(Furevik et al., 2003)
2.
Canadian Centre for Climate Modeling & Analysis
CGCM3.1 (T47)
(Flato and Boer,
2001)
3.
Météo-France / Centre National de Recherches Météorologiques, France
CNRM-CM3
(Salas-Mélia et al.,
2005)
4.
CSIRO Atmospheric Research, Australia
CSIRO-Mk3.0
(Gordon et al., 2002)
5.
US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory, USA
GFDL-CM2.0
(Delworth et al.,
2006)
6.
US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory, USA
GFDL-CM2.1
(Delworth et al.,
2006)
7.
NASA / Goddard Institute for Space Studies, USA
GISS-ER
(Russell et al., 2000)
8.
Institute for Numerical Mathematics, Russia
INM-CM3.0
(Diansky and
Volodin, 2002)
9.
Institut Pierre Simon Laplace, France
IPSL-CM4
(IPSL, 2005)
10.
Center for Climate System Research (The University of Tokyo), National Institute for Environmental
Studies, and Frontier Research Center for Global Change (JAMSTEC), Japan
MIROC3.2
(medres)
(K-1 model
developers, 2004)
11.
Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA
ECHO-G
(Legutke and Voss,
1999)
12.
Max Planck Institute for Meteorology, Germany
ECHAM5/MPIOM
(Jungclaus et al.,
2006)
13.
Meteorological Research Institute, Japan
MRI-CGCM2.3.2
(Yukimoto et al.,
2001)
14.
National Center for Atmospheric Research, USA
PCM
(Washington et al.,
2000)
15.
National Center for Atmospheric Research, USA
CCSM3
(Collins et al., 2006)
16.
Hadley Centre for Climate Prediction and Research / Met Office, UK
UKMO-HadCM3
(Gordon et al., 2000)
A description of the statistically downscaled projected datasets from these 16 models, under emissions
scenarios B1 and A2 is given in Maurer et al. (2007a).
10
4. STATISTICAL DOWNSCALING OF GCM
PROJECTIONS OF TEMPERATURE AND
PRECIPITATION
While large scale (200-500 km) patterns of precipitation and temperature simulated by state-of-the-art
GCMs can be realistic, even the best models display biases on regional scales that are large enough to
confound studies of the hydrologic impacts of climate change. Many different techniques have been
employed to process the raw GCM output to retain the large scale signal of the evolving climate
simulated by the GCM while reproducing historical climate patterns on the landscape at local scales, an
essential characteristic for meaningful hydrologic analysis (Wood et al., 2004).
For this study, we employ the BCSD (Bias Correction and Statistical Downscaling) technique, originally
developed by Wood et al. (2002) for using global model forecast output for long-range streamflow
forecasting. This technique was later adapted for use in studies examining the hydrologic impacts of
climate change (Hayhoe et al., 2004; Maurer and Duffy, 2005; Payne et al., 2004; van Rheenen et al.,
2004). BCSD is an empirical statistical technique in which the monthly precipitation and temperature
output from a GCM are downscaled. The technique uses a quantile-based mapping (Panofsky and Brier,
1968) of the probability density functions for the monthly GCM precipitation and temperature onto those
of grided observed data, spatially aggregated to the GCM scale. This same mapping is applied to future
GCM projections. This allows the mean and variability of a GCM to evolve in accordance with the GCM
simulation, while matching all statistical moments between the GCM and observations for the base
period. This technique has compared favorably to different statistical and dynamic downscaling technique
(Wood et al., 2004; Maurer and Hidalgo, 2008) in the context of hydrologic impact studies. In particular,
the BCSD method, using monthly GCM output as in the current study, was shown to outperform
11
dynamically downscaled RCM output for simulating observed monthly streamflows across the Pacific
Northwest. The method is computationally efficient and has thus been applied to studies downscaling
multiple, extended GCM simulations for hydrologic and other impact studies (Cayan et al., 2009; Hayhoe
et al., 2004; Maurer and Duffy, 2005; Maurer, 2007). Recently, 112 150-year GCM projections were
downscaled over much of North America using the BCSD method (Maurer et al., 2007), demonstrating
its applicability to downscaling large ensembles of GCM simulations.
The BCSD technique assumes that the relationship between the predictor variables (GCM-scale
precipitation and temperature) and the predictands (1/8, or 12 km, precipitation and temperature) is
stationary. However, some of the anticipated changes will be accompanied by non-linear feedbacks,
which cannot be represented in a statistical scheme based on past observations, when the feedbacks did
not exist at a particular location. For example, as warmer temperatures cause the snow line to retreat to
higher elevations, the ground that is exposed has a much lower albedo than the snowpack, thus absorbing
more incident solar radiation. This effect produces higher temperatures in the vicinity of the retreating
snow line, enhancing further melt as a positive feedback. For a region where the observational record
always has snow cover, this sort of effect would not be captured in any statistical relationship.
12
Figure 4-1: Projections by the 16 GCMs for future time horizons, and observations-based historical
values, for period-mean precipitation and temperature. While the individual GCMs are not identified
(given the large number of points), the horizon and emissions scenario is indicated by color.
13
B1
A2
Figure 4-2: Projected rise in monthly temperature for the end of century period (2070-2099) under the
B1 and A2 emissions scenarios, for each GCM and their average. On average, the rise in September
temperatures is the largest, and has the greatest degree of agreement among models.
14
5. VIC MODEL CALIBRATION AND
VERIFICATION
The Variable Infiltration Capacity (VIC) hydrologic model (Liang et al., 1994, 1996; Nijssen et al., 1997,
2001) is a macro-scale hydrologic model that balances both surface energy and water fluxes over a grid
mesh, at resolutions that can range from a fraction of a degree to several degrees latitude by longitude.
One distinguishing characteristic of the VIC model is its use of a subgrid parameterization of the effects
of spatial variability in soils, topography, and vegetation that allows it to represent the observed nonlinear
soil moisture dependence of the partitioning of precipitation into direct runoff and infiltration. The VIC
model also features a nonlinear mechanism for simulating slow (baseflow) runoff response, and explicit
treatment of vegetation effects on the surface energy balance. VIC includes a physically-based
representation of snow dynamics (Cherkauer et al., 2003; Cherkauer and Lettenmaier, 2003).
VIC has been successfully applied to watersheds worldwide for many years (Nijssen et al., 1997; Wood et
al., 1997), and has been extensively used in climate change studies in California and the Western U.S.
(e.g., Cayan et al., 2008; Hayhoe et al., 2004; Maurer and Duffy, 2005; Maurer, 2007; Payne et al., 2004;
van Rheenen et al., 2004).
In our study, the soil characteristics used were taken from gridded 1/8° data sets developed as part of the
Land Data Assimilation Scheme (Mitchell et al., 1999) project, which were in turn derived from the 1-km
resolution data set produced by Pennsylvania State University (Miller and White, 1998) using US
Department of Agriculture State Soil Geographic (STATSGO) Database. Soil parameters, including the
depth of each of the three soil layers, binf, Ds, and Dsmax, were used in model calibration (see
http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Documentation/Calibration.shtml).
Land
cover characterization was based on the University of Maryland global vegetation classifications
described by Hansen et al. (2000), which has a spatial resolution of 1 km, with the hydrologically
15
important characteristic of leaf area index (LAI) derived from Myneni et al. (1997). Details on the uses
and adaptations of these datasets for incorporation in the VIC model are in Maurer et al. (2002).
VIC has been successfully applied from the global scale to river basin scale (e.g., Abdulla et al., 1996;
Maurer et al., 2001, 2002; Nijssen et al. 1997, 2001), for studies of land surface climate teleconnections
(e.g. Zhu et al., 2005), climate change assessments (Payne et al., 2004; van Rheenen et al., 2004;
Christensen et al., 2004; Maurer and Duffy, 2005; Maurer et al., 2007b; Elsner et al., 2010; Hamlet et al.,
2010), and other purposes.
In our study, the VIC model was calibrated separately for each subregion (or “calibration element”) of the
eastern Sierra slopes of Owens Valley and Mono Lake watersheds marked in color in Figure 5-1.
Calibration was based in the observational period 1950-1979 and validation was based in the
observational sub-period 1980-2005. The grid used for meteorological forcings to the model is shown in
Figure 5-2. When all calibration elements are combined, we obtain results for the entire colored area.
These results are shown in Figures 5-3 and Figure 5-4.
In addition to the soil parameters used in calibration (see above), parameters related to snow were
important for our calibrations. The depth of precipitation within a pixel (1.8˚, see Figure 5-2) was
assumed to increase linearly with elevation. The rate of linear increase with elevation was calibrated
separately for each calibration element. The rate of decline in snow albedo in time was also used in
calibration, affecting the energy balance and snowmelt, so as to match the snowmelt hydrograph in each
calibration element.
16
Figure 5-1: Topographic and hydrographic map of the Mono-Owens watersheds showing ten subregions of the watershed contributing the most runoff. These colored regions amount to an estimated
2,118 km2. The gage numbers indicated in the figure are given for location purposes only. The
streamflow values from summary stations representing unimpaired flows were used, rather than
measurements taken at these gages alone. Streamflow data were provided by the Los Angeles
Department of Water and Power and are gratefully acknowledged.
17
Figure 5-2: Map of the Mono-Owens watersheds showing the regular grid used for climatological input
to the VIC hydrologic model. This grid has resolution 1/8° which, at this latitude, corresponds to a grid
cell size of approximately 157 km2.
18
Figure 5-3: Simulated versus observed annual runoff in the simulation area shown in Figure 1, in 19502005. Each of the plotted points corresponds to a year. The correlation is high, R2=0.986.
Figure 5-4: Simulated and observed mean monthly runoff in the simulation area shown in Figure 1, in
1950-2005. In those areas where reliable observed unimpaired streamflows are not available, the
observed streamflows were replaced by the simulated values to create this figure. This includes subregions 2a and 7b as well as portions of sub-regions 4, 5 and 6.
19
Calibration and Verification Periods:
Calibration Period: 1950-1979 (30 years)
Verification Period: 1980-2005 (26 years)
Figure 5-5: Simulated and observed hydrographs (monthly, for 1950-2005), for the entire simulation
area (shown in color in Figure 5-1).
20
Figure 5-6: Simulated snow water equivalent (monthly), 1950-2005, for the entire simulation area.
Figure 5-7: Simulated and observed hydrographs with simulated snow water equivalent superimposed
(monthly, for 1950-2005), for the entire simulation area.
21
Figure 5-8: Cumulative runoff, observed and simulated (monthly, for 1950-2005), for the entire
simulation area.
22
6. RELATIONSHIP BETWEEN MEAN
ANNUAL RUNOFF AND MEAN ANNUAL
PRECIPITATION FOR EACH INDIVIDUAL
GCM
In our manuscript’s Figure 5e we plot the period mean runoff plotted against period mean precipitation,
for all GCMs, with the 30-year period and emissions scenario (B1 or A2) indicated by color; plus the
mean simulations for the 49-year observed period 1951-1999 (in yellow).
In figure 6-1, we decompose that Figure 5e into one plot per GCM. There are now only 7 points in each
plot. We also add (at the top) the plot for the observational period that served as the basis for the
statistical downscaling of the projections (1951-1999). Because the period is only 50 years in length, we
used 10-year (instead of 30-year) time periods – see the top figure panel.
The slope (0.871) and intercept (179) obtained by least-squares linear regression using all data points in
Figure 5e, cannot be rejected for any of the subsets in Figure 6-1.
To provide insight into Figure 6 of the manuscript, in Figure 6-2 we plot results for a single GCM run,
CNRM (A2) for water years 1951-2099 (149 years). In this figure, each gray point corresponds to a
simulated water year. The years composing the wettest decade (the 2020s) are marked in green, and the
years of the driest decade (the 2060s) are in orange. The 30-year year means are plotted in black. There
are only five black points (the number of 30-year periods) thus we also plotted the 10-year means (15
points, in blue).
A year belonging to the wet decade will tend to plot above the regression line of all years, while a year
with a similar precipitation total but that belongs to the dry decade will tend to plot below the regression
23
line. This is explained by antecedent moisture conditions. See also the lower-right inset of Figure 6 in the
manuscript.
In Figure 6-2, the regression slope for the 10-year means is 0.8903, similar to the slope in Figure 5e
(0.8906, rounded to 0.891 in equation 5). The regression slope for the individual years is lower, 0.723.
We calculated the regression slope (and intercept) for the individual years for all model runs, and plotted
the results in Figure 6-3.
24
25
26
Figure 6-1: Simulated period-mean runoff plotted against projected period-mean precipitation for the
historical period (observed and simulated by VIC, top panel) and for each individual GCM for future time
horizons.
27
Figure 6-2: Analogous to Figure 6 of the manuscript, but for a single GCM run, CNRM (A2). See text for
details and interpretation.
28
Scenario B1
Scenario A2
Figure 6-3: Linear regression parameters a and b in y = a∙x – b, where x and y are mean annual
precipitation and runoff, respectively. Figure 6-2 shows details for the case of CNRM (A2). Each color
corresponds to a GCM, as indicated in the legend. Parameter values are plotted for: 1) the entire
simulation period (water years 1951-2099), 2) the 59-year historical period 1951-2009, 3) the 30-year
21st-century period (2010-2039), 4) the 30-year mid-21st century period (2040-2069), and 5) the 30-year
late 21st century period (2070-2099). The range of variability among models is least when the entire
period (1951-2099) is considered because the sample size is larger. The mean of model values for each
time period is shown by the solid black line. The values of a* and b* are indicated by the dashed black
lines are the parameters of Equation 5 which represents the 30-year means rather than annual values,
and are distinctly higher than any values of a and b (respectively) for any of the models in any of the
time periods.
29
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