- American Meteorological Society

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Climate Change Impacts on the Water Balance of the Colorado Headwaters:
High-Resolution Regional Climate Model Simulations
ROY RASMUSSEN, KYOKO IKEDA, CHANGHAI LIU, DAVID GOCHIS, AND MARTYN CLARK
National Center for Atmospheric Research,* Boulder, Colorado
AIGUO DAI
National Center for Atmospheric Research,* Boulder, Colorado, and University at Albany, State
University of New York, Albany, New York
ETHAN GUTMANN, JIMY DUDHIA, FEI CHEN, MIKE BARLAGE, AND DAVID YATES
National Center for Atmospheric Research,* Boulder, Colorado
GUO ZHANG
National Center for Atmospheric Research,* Boulder, Colorado, and Chinese Academy of Meteorological
Sciences, Beijing, China
(Manuscript received 16 July 2013, in final form 9 January 2014)
ABSTRACT
A high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question:
How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters?
This question is examined using a climate sensitivity approach in which eight years of current climate is
compared to a future climate created by modifying the current climate signal with perturbation from the
NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for
greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry
(SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux
evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the
Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing
aridity in the future despite a positive increase of precipitation. Runoff decreased by an average of 6%,
reflecting the increased aridity.
Precipitation increased in the future winter by 12%, but decreased in the summer as a result of increased low-level inhibition to convection. The fraction of precipitation that fell as snow decreased from
0.83 in the current climate to 0.74 in the future. Future snowpack did not change significantly until
January. From January to March the snowpack increased above ;3000 m MSL and decreased below that
level. Snowpack decreased at all elevations in the future from April to July. The peak snowpack and
runoff over the headwaters occurred 2–3 weeks earlier in the future simulation, in agreement with previous studies.
1. Introduction
* The National Center for Atmospheric Research is sponsored
by the National Science Foundation.
Corresponding author address: Roy Rasmussen, National Center
for Atmospheric Research, Research Applications Laboratory,
P.O. Box 3000, Boulder, CO 80307-3000.
E-mail: [email protected]
DOI: 10.1175/JHM-D-13-0118.1
Ó 2014 American Meteorological Society
Hydrologic impacts of climate change in snowmeltdominated river basins are characterized by more rain
and less snow and earlier initiation of snowmelt-driven
streamflow (e.g., Barnett et al. 2005; Hamlet et al. 2005;
Mote et al. 2005; Regonda et al. 2005; Maurer 2007;
Barnett et al. 2008; Pierce et al. 2008; Raisanen 2008;
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Adam et al. 2009; Hidalgo et al. 2009; Stewart 2009;
Rasmussen et al. 2011). These climate change impacts
can be considered predictable, at least to some extent,
because of growing confidence that temperatures will
increase throughout much of the globe in the coming
decades (e.g., Meehl et al. 2007).
The relatively high confidence that we have in the
impacts of climate change on snow hydrology may not
translate to other aspects of the regional water balance.
This leads us to a key question in studies of the global
and regional water cycle: how will long-term changes in
climate impact the partitioning of annual precipitation
between evapotranspiration (ET) and runoff ? On a global
scale, higher temperatures lead to increased ocean and
land surface evaporation and plant transpiration, while
increased atmospheric water vapor may lead to clouds
with higher water content and more intense and longerlasting precipitation (Trenberth et al. 2003; Sheffield and
Wood 2008). This suggests that both precipitation and
evaporation are likely to increase (e.g., Seager et al.
2012), although changes at regional and local scales are
much less straightforward. Because runoff is the difference between precipitation and evaporation over the
long term (in which changes in soil moisture storage are
small), the relative rate of change of each of these processes is critical in determining whether runoff, and
therefore water resources, will increase or decrease in
a future warmer and moister climate.
There is a great deal of uncertainty in estimates of
changes in runoff over the Colorado River basin, a basin
that extends from Arizona in the southwest to Colorado
in the northeast. Several studies suggest that there will
be increased aridity in the southwestern United States,
characterized by an increase in the ratio of evaporation to
precipitation (Seager et al. 2007) and reductions in spring
snowpack and late spring and summer soil moisture
(Cayan et al. 2010). Christensen and Lettenmaier (2007)
showed that the Fourth Assessment Report (AR4) climate projections expect an overall increase in winter
precipitation and a decrease in summer precipitation.
Downscaling these projections and using them as input to
the Variable Infiltration Capacity (VIC) hydrologic
model gives reductions in runoff ranging from 0 to 211%
(Christensen and Lettenmaier 2007). Alexander et al.
(2013) also found a decrease in summertime rainfall over
much of Colorado from the North American Regional
Climate Change Assessment Program (NARCCAP) future climate projection. Regional climate model sensitivity studies illustrate an increase in winter precipitation
(Rasmussen et al. 2011) and a decrease in the fraction of
precipitation falling as snow (Wi et al. 2012). Vano et al.
(2012) demonstrate that the change in runoff in the
Colorado River basin depends strongly on which land
VOLUME 15
surface or hydrologic model is used—a point we will
return to later in the paper.
Much of the water for this region, however, comes
from a relatively small high-elevation region in Colorado that stores water in the form of winter snowpack.
This snowpack melts in the spring to feed the major
rivers in the region and is the primary source of water for
the Colorado River basin. This study will focus on the
current and expected future climate changes to this
critical region.
Confidence in the changes in regional estimates of the
water cycle from current-generation global climate models
suffers from the relatively poor representation of the
physical processes related to the water cycle (Trenberth
et al. 2003). These processes can be relatively small scale
and difficult to parameterize, such as orographic precipitation (Rasmussen et al. 2011), atmospheric convection, and plant transpiration, as compared to the
globally well-mixed concentration of CO2 driving temperature changes. For instance, the complex terrain of
the Colorado Headwaters region is represented in the
National Center for Atmospheric Research Community
Climate System Model, version 3 (CCSM3) as a large
mound with a peak elevation of around 2300 m located
near the Colorado–Utah northern border, which is as
much as ;1000 m below typical peak elevations in the
headwaters region. Snowfall estimates in this region,
based on this coarse-resolution climate model, suffer
from both poor spatial representation and a low bias in
amount (Rasmussen et al. 2011). Such biases in precipitation location and amount can occur as a result of
the inadequate representation of important processes
and interactions with terrain, leading to a large degree of
uncertainty.
While some of the biases in climate model precipitation
could be efficiently resolved using statistical downscaling,
such methods require assumptions of stationarity in the
spatiotemporal distribution of precipitation. These sorts
of assumptions can prevent statistical downscaling from
producing the proper changes in the distribution of precipitation in a future climate, and the methods themselves
often introduce artifacts even in current climate (Maraun
2013). Dynamic downscaling (Gutmann et al. 2012; Pierce
et al. 2013), in contrast, using high-resolution models (with
4-km grid spacing and smaller) is typically able to simulate many of the dominant storm vertical motions that
drive the precipitation processes in complex terrain
(Ikeda et al. 2010), as well as explicitly simulate convection without the need of a convective parameterization (Weisman et al. 1997). As a result, many of the
important hydroclimatic processes and their possible
changes in a future climate regime are more accurately represented in high-resolution models. The use
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FIG. 1. (a) Model domain and elevation. Black dots are SNOTEL observation sites. Red box indicates the headwaters region of
Colorado over which the analysis of this study focuses. Denver, Colorado (DEN), is indicated on the map as a reference. (b) Topography
over the headwaters region inside the red box in (a). Red stars indicate locations of AmeriFlux sites. (c) Photographs of (top) the allweather weighing precipitation gauge and (bottom) snow pillow at the Brooklyn Lake SNOTEL site in southern Wyoming.
of high-resolution models limits the number of future
climate models and scenarios that can be addressed, but
the process understanding gained from high-resolution
models provides important insight into the problem.
While many global models now include microphysical
parameterizations, poor estimates of vertical velocity in
complex terrain and the boundary layer in those models
requires a series of ad hoc estimates of vertical velocity
(e.g., based on terrain complexity and surface roughness) to provide meaningful precipitation simulations.
By contrast, the high-resolution dynamical downscaling technique of Rasmussen et al. (2011) and Ikeda
et al. (2010) using the Weather Research and Forecasting (WRF) (Skamarock et al. 2005) climate model
at 6-km grid spacing or less is able to accurately estimate
vertical motions driven by topography and, as a result, the
seasonal snowfall and snowpack over the Colorado
Headwaters region within 5% of Snowpack Telemetry
(SNOTEL) measurements (Serreze et al. 1999, 2001).
The current paper builds on the foundation of these
two studies to examine the water balance in the Colorado Headwaters region by conducting a continuous
8-yr simulation following the procedures in Ikeda et al.
(2010) and Rasmussen et al. (2011). Eight continuous
years are chosen to 1) more accurately spin up the hydrological states of the model, such as soil moisture;
2) obtain full annual cycles; and 3) include more years to
address the reliability of the results with respect to interannual variability. The change in the water balance in
the future is estimated using the pseudo global warming
(PGW) approach (Sch€
ar et al. 1996; Sato et al. 2007; Hara
et al. 2008; Kawase et al. 2009; Rasmussen et al. 2011).
The model setup and domain are described in section 2.
Section 3 presents comparisons of current climate simulation results to observations. Section 4 discusses the
change in water balance components in the future climate
simulation. A summary of water balance analysis is given
in section 5, followed by a discussion in section 6. Section 7
summarizes the conclusions of this study.
2. WRF simulations and analysis approach
a. Analysis domain
This study focuses on the Colorado Headwaters region (Fig. 1, red outline), the primary water source for
eight major western U.S. rivers. These include the Colorado, Yampa, Gunnison, and San Juan Rivers, which
flow to the west, and the Rio Grande, South Platte,
Republican, and Arkansas Rivers, which flow to the
east. Water from these rivers is relied on for a variety of
activities in the western United States, such as irrigation, drinking water, hydropower generation, power
plant cooling, recreation, and ecosystem maintenance.
As discussed in Miller and Yates (2005), the demand
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TABLE 1. WRF model physics options used in this study. The convective parameterization was activated only for the 12-km and
36-km simulations.
Parameterization schemes
References
Land surface
WRF physics
Noah LSM, version 3.2, with upgraded snow physics
Microphysics
Planetary boundary layer (PBL)
Longwave and shortwave radiation
Convective parameterization
Thompson mixed phase
Yonsei University PBL
Community Atmosphere Model, version 3 (CAM3)
Betts–Miller–Janjic
Chen and Dudhia (2001)
Barlage et al. (2010)
Thompson et al. (2008)
Hong et al. (2006)
Collins et al. (2006)
Janjic (1994)
for irrigation and drinking water is expected to dramatically increase in the twenty-first century, and, as a
result, water managers and scientists have focused on
how the amount and allocation of water between precipitation, evaporation, groundwater, and runoff may
change in a future warmer climate. Thus, it is vitally important to understand likely changes to the water cycle in
this region under future climate change. While a number
of studies have focused on the future water balance in the
Colorado River basin (e.g., Milly et al. 2005; Hoerling
and Eischeid 2007; Christensen and Lettenmaier 2007;
Seager et al. 2007; Hoerling et al. 2009; Vano et al. 2012),
relatively few have specifically studied the balance in
the Colorado Headwaters region, and even fewer have
studied it at high resolution.
b. WRF configuration and historical simulations
Rasmussen et al. (2011) conducted high-resolution simulations of the water balance over the Colorado Headwaters for four winter seasons (from 1 October to 1 May)
with the regional WRF model. They were able to correctly
capture the amount and spatial distribution of the snowfall
and snowpack over the Colorado Headwaters region as
validated against SNOTEL observations of precipitation
and snow water equivalent (SWE). They showed a 12%–
15% increase in snowfall in a future climate using the
PGW approach. Simulations were conducted for one full
water year (1 October 2007–30 September 2008) to evaluate changes in the regional water balance in a future
climate, but it was noted that the soil moisture was not
correctly spun up at the beginning of the water year in the
PGW experiments and, consequently, that simulated
runoff change projections were likely in error. We address
this issue directly in the study presented here.
The WRF model is configured in the same general
manner as in Rasmussen et al. (2011) in terms of domain
and model physics, except that the snow physics in the
Noah land surface model (LSM) has been improved by
the work of Barlage et al. (2010) through modifications
to snow roughness length and albedo and surface exchange reduction for stable boundary layers; see Table 1
for details on the physics options used. The model’s full
domain is shown in Fig. 1a, and the red rectangle shows
the headwaters domain (Fig. 1b) with the SNOTEL
stations indicated by black dots. A typical SNOTEL site
is shown in Fig. 1c. Note that the headwaters domain
shown here is for analysis purposes only. The initial and
3-hourly lateral boundary conditions for the model run
were taken from the North American Regional Reanalysis (NARR) (Mesinger et al. 2006) and applied to
the model domain shown in Fig. 1a. Convective parameterization was used for the 12- and 36-km simulations, but not for the 4-km simulation.
This paper improves the accuracy of potential changes
in the regional water balance by simulating the snowfall,
snowpack, soil moisture, and runoff over the Colorado
Headwaters region for eight continuous years starting
on 1 October 2000. Continuous simulation, as opposed
to the seasonal time-slice approach used previously,
provides more realistic cycling of soil moisture, allowing
for the proper evolution of the soil moisture and temperature fields. In the previous simulations (Rasmussen
et al. 2011) the soil moisture and temperature states
were initialized from the NARR in both the current and
future climate simulations in each time slice period (i.e.,
1 October–1 May). As such, the future soils were already
both too wet and too warm at the beginning of the time
slice period which, when forced by the warmer future
climate, resulted in spurious drainage of soil water
throughout the winter at many of the headwaters locations. By allowing the model to run continuously and
discarding the entire first year of model simulation, the
new simulations correct this artifact, allowing for a more
accurate estimate of the seasonality of the water balance
for both the current and future climate.
c. Pseudo-global-warming climate change approach
The future water balance is examined by applying the
PGW approach described in Rasmussen et al. (2011).
The technique imposes a mean monthly climate-modeldetermined perturbation to the NARR initial and
boundary conditions to simulate the future climate.
The climate model used to calculate the perturbations
was the NCAR CCSM3 model under the A1B scenario.
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The monthly averaged climate change perturbation was
calculated as the difference between the 10-yr monthly
average temperature, water vapor, winds, and geopotential height from 2045 to 2055 and 1995 to 2005.
These perturbations were applied to the NARR data
for initial conditions and at the model boundary every 3 h for the 8 yr of the current climate to simulate
the future climate. The same increase of CO2 in the
CCSM3 model was also applied to the WRF model.
Further details of the PGW approach are described in
Rasmussen et al. (2011).
This procedure preserves the mean monthly changes
in dynamics, temperature, moisture, and winds, but not
the submonthly perturbations due to individual storms.
Thus, storm track changes are not explicitly simulated.
One of the reasons why this approach was adopted was
because most global climate models agree on the sign
and magnitude of the temperature and moisture perturbation (Solomon et al. 2007), but the changes in the
water cycle are much more uncertain, suggesting that
the simulation of storm track changes and other components of the water cycle are not handled in a consistent manner in the current generation of global models.
In any case, the current approach provides a reasonable
estimate of the changes in the water cycle assuming that
the storm tracks in the future climate are the same as the
current reanalysis climate. Summer storms, as will be
seen later, are not so constrained.
Q 5 P 2 ET .
The water cycle is examined over the Colorado
Headwaters region (Fig. 1b). The land surface components of the water cycle to be discussed include precipitation, evapotranspiration, runoff, and soil moisture.
In this assessment, runoff from each land-surface model
grid point is aggregated over our study region and is not
transported laterally, meaning there is no accounting for
the horizontal movement of water. Additionally, the
calculated ‘‘total’’ runoff from each model grid cell is the
sum of water that does not infiltrate (i.e., surface runoff)
and that which drains out the bottom of the soil column
(underground or subsurface runoff). Thus, the total
runoff represents the flux of water that is not taken up by
evapotranspiration or stored as soil moisture.
The water mass budget over a basin or region can be
written as
(1)
where dS/dt is change in the storage of water in and
above the ground (S) over time, P is precipitation, ET is
evapotranspiration, and Q is runoff. If multiple annual
(2)
The question posed by this paper is how the terms in this
equation change on a mean annual basis over the Colorado Headwater region as a result of climate change. In
that case, we can rewrite Eq. (2) as
DQ 5 DP 2 DET,
(3)
where the D refers to the difference between future and
current climate for the various terms on an annual basis.
Runoff can decrease by either a decrease in precipitation or an increase of evapotranspiration. If we divide
Eq. (2) by precipitation and rearrange terms, we can
describe the annual mean water balance by the following equation:
1 5 ET/P 1 Q/P ,
d. Analysis approach
dS
5 P 2 ET 2 Q ,
dt
cycles are considered, modeled changes in storage can
be neglected because net changes in the 2-m soil column
moisture storage over the 8-yr simulation period are
orders of magnitude less than the accumulated water
fluxes associated with precipitation, evapotranspiration,
and runoff. Therefore, in this study, we examine the
mean monthly and annual water cycle over seven years
by neglecting the storage term and ignoring the first year
of model spinup period, focusing on the climatological
partitioning of precipitation between evapotranspiration and runoff:
(4)
or under climate change, the following equation can also
be written:
1 5 DET/DP 1 DQ/DP .
(5)
This permits us to describe the water balance in terms of
the ratio of ET and runoff to precipitation or the fractional ET and runoff efficiency. These terms have also
been referred to as the ET and runoff elasticity (Schaake
1990). Thus, an increase in the fractional evapotranspiration will result in a decrease in the runoff efficiency as
more of the precipitation is used for evapotranspiration.
3. Evaluation of the WRF current climate
simulation
Before proceeding with analysis of future change in
water budgets, it is important to assess the realism of
WRF-simulated land surface components of the water
budget by comparing them against long-term observations under current climate conditions. This section
focuses on the evaluation of precipitation, SWE, runoff, and ET.
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FIG. 2. (a) Observed and simulated 8-yr climatological mean precipitation accumulation for a full water-year cycle.
The observed and model data were averaged over 93 SNOTEL sites in the headwaters region. Blue and red vertical
bars represent 61 std dev from the 8-yr climatological mean. (b) Scatterplot comparison of 8-yr climatological
monthly total precipitation between SNOTEL observations and WRF simulation data at SNOTEL sites. (c) Observed and simulated 8-yr climatological average snow water equivalent. (d) Comparison of 8-yr climatological SWE
on the first of each calendar month between SNOTEL and WRF output at all 93 SNOTEL sites. The solid black lines
in (b) and (d) are the one-to-one line, and the solid red lines are the least-squared fit through the data points. Values
in (b) are the correlation coefficients between the WRF simulation and SNOTEL precipitation for the full season,
winter season (October–May), and summer season (June–September), respectively; values in (d) are the correlation
between the WRF simulation and SNOTEL for SWE for the full season and for winter (October–May), respectively.
a. Precipitation
The SNOTEL precipitation gauge and snow pillow
data were used to verify the WRF simulations from
1 October 2000 to 30 September 2008. Ninety-three
SNOTEL sites over the headwaters domain provided
continuous measurements during the simulation period
(black dots in Fig. 1). The sites are typically located at
elevations between 2400 and 3600 m in a forest clearing
(Fig. 1c). The measurement error for the SNOTEL precipitation gauges is on the order of 10%–15% owing to wind
undercatch (Yang et al. 1998; Rasmussen et al. 2012). To
compare the model results to the observations, model values
were obtained by taking the inverse-distance weighted average of the four data points closest to each SNOTEL site.
Figures 2 and 3 compare the seasonal accumulated precipitation and snowpack at SNOTEL sites. Note the good
comparison of the model precipitation accumulation to
that of the SNOTELs on an 8-yr average basis (Fig. 2a)
and for each year (Fig. 3). Particularly remarkable is the
excellent comparison to the standard deviation over the
8 yr (Fig. 2a). The linear fit of monthly averaged WRF
precipitation (Fig. 2b) and snowpack (Fig. 2d) to SNOTEL
observations is close to the one-to-one line and has correlation coefficients greater than 0.8 over an annual cycle.
These results are consistent with the good comparisons
found in the Ikeda et al. (2010) and Rasmussen et al.
(2011) WRF simulations for four water years at 2-km
horizontal grid spacing. In addition, the spatial patterns of
WRF and SNOTEL observations over the 8 yr are very
similar for winter (e.g., Figs. 4a,b showing the spatial
patterns for the 2008 water year).
It should be noted that SNOTEL precipitation is
likely biased slightly low owing to the impact of wind
undercatch of snowfall (Rasmussen et al. 2012), with the
exact amount depending on the wind speed. Because
wind speed data are not generally available at SNOTEL
sites, it is difficult to estimate the exact amount of
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FIG. 3. Time history of observed and simulated precipitation accumulation, averaged over 93 SNOTEL sites, for each
of the eight water years.
undercatch to be expected. In general, SNOTEL sites
are sited in forest clearings, which typically experience
wind speeds less than 2 m s21. This suggests that the undercatch is less than 10% (Rasmussen et al. 2012). The
good comparison of the model-simulated precipitation to
SNOTEL observation in terms of amount and spatial
distribution suggests that precipitation is estimated within
65% of the SNOTEL observations on an annual basis
(Figs. 2a and 3).
A notable result is the good agreement in precipitation amounts and spatial pattern between model and
observations for summer as well (Figs. 4c,d), indicating
that the 4-km WRF simulations are capturing the statistics and general spatial pattern of convective precipitation on a monthly time scale reasonably well. Note
that rain gauge data from the Global Historical Climatological Network (GHCN) (Klein Tank et al. 2002)
have been added to Fig. 4d to show rainfall observations
from low elevation surface sites. The GHCN data were
not used for comparing the wintertime precipitation
distribution because not all gauges compiled in the
GHCN database are appropriate for snowfall measurements (e.g., gauges are not heated nor properly
protected from winds).
Compared with the 4-km WRF data, model simulations at 12- and 36-km grid spacings over the same time
period using a convective parameterization (Betts–
Miller–Janjic scheme, Table 1) show significantly greater
overestimation of precipitation in the summertime over
the headwaters region (Fig. 5), indicating that the 4-km
model runs with explicit convection capture the main
effects of summertime precipitation far better than
coarser-resolution models using convection parameterizations. As will be shown later, this ability is crucial in
capturing the annual water balance of this region and lays
the foundation for conducting future simulations. The
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underestimation of precipitation at 36-km grid spacing
for the winter found in the Rasmussen et al. (2011) study
and mentioned earlier is also shown in Fig. 5c.
b. Snow water equivalent
The simulations of SWE in Rasmussen et al. (2011)
showed a significant low bias in the snowpack amount
and an error in the timing of the onset and complete
melting of the snowpack. The current results using the
updated snow physics in the Noah LSM (Barlage et al.
2010) have significantly improved. Figure 6 illustrates
that WRF and the observations have similar spatial
patterns in SWE both at the time of peak SWE (1 April;
Figs. 6a,b) and near the end of the melt season (1 June;
Figs. 6c,d). Nevertheless, the comparison of the mean
SWE between SNOTEL and WRF simulation illustrates
a lingering underestimate of peak SWE of about 20%
compared with SNOTEL values (Fig. 2c). The model
bias is also presented in Fig. 2d, which shows SWE on
the first day of each calendar month from SNOTEL
observations and WRF simulations at the site locations.
The onset and offset of snowpack is well simulated for
all 8 yr, and the bias has significantly improved, although
there is still a low bias in the maximum snowpack in most
years (not shown). This underestimate in peak SWE is
partially due to continued deficiencies in the Noah LSM
snow treatment, primarily the lack of canopy separation
from the snowpack. This results in too much surface energy being used to sublimate the snowpack in the accumulation phase and melt the pack in the beginning of the
ablation phase.
c. Comparison to observed streamflow
FIG. 4. The 4-km WRF simulation of (a) winter and (c) summer
precipitation amounts and (b) winter and (d) summer observations
from the 2008 water year (1 October 2007 to 30 September 2008).
Winter snowfall observations are from SNOTEL data. SNOTEL
(circles) and GHCN (triangles) datasets are shown for summertime
rainfall. The light gray contours (same for Figs. 6, 9, 11, 12, 14–16)
are elevation contours every 600 m. See Fig. 1 for topography and
absolute elevation contours. The small circles on (a) and (c) mark
the SNOTEL locations.
Figure 7 compares the runoff from the current climate
simulation to naturalized streamflow at the Cameo stream
gauge on the Colorado River near Grand Junction, Colorado. The model streamflow was determined by summing the runoff from all model grid points contained
within watersheds that drain into the Colorado River
upstream of the Cameo stream gauge. The naturalized
streamflow was obtained from the Bureau of Reclamation, U.S. Department of the Interior (www.usbr.gov/lc/
region/g4000/NaturalFlow/current.html). The streamflow
from the headwaters simulation agrees within 5% of the
naturalized streamflow on a mean annual and mean
monthly basis, though it is important to note that this
good agreement at the scale of the upper Colorado River
basin does mask rather significant error structures occurring in smaller headwater basins (not shown).
Analysis of the water budget over smaller headwater
basins is the subject of a forthcoming paper and is
not discussed in detail here. Nevertheless, the reasonable estimation of runoff from WRF (Fig. 7) and
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FIG. 5. The 8-yr average of the model bias (model minus observation) in monthly total precipitation (bars) and accumulative difference
over a full year cycle (blue line with filled circles) from the (a) 4-km, (b) 12-km, and (c) 36-km simulations. Error bars on the monthly bias
are one standard deviation associated with the 8-yr-average model bias, indicating the year-to-year spread in the model biases.
the rather good representation of precipitation (Figs. 2–6)
suggest that the model is doing a credible job of partitioning precipitation into ET and runoff components at
the large river basin scales and over the headwaters
region.
d. Evapotranspiration
High-quality long-term data of surface heat fluxes
within the Colorado Headwaters modeling domain are
available at two AmeriFlux sites: Niwot Ridge, Colorado, and the Glacier Lakes Ecosystem Experiments
Site (GLEES), Wyoming (see Fig. 1b for locations; information on the AmeriFlux network can be found at
http://public.ornl.gov/ameriflux/sop.shtml). The GLEES
site experienced a major spruce bark beetle epidemic in
2008 that reduced evapotranspiration because of stoppage of water and nutrients exchange from tree roots to
the crown. Since these effects are not represented in
the Noah LSM, the GLEES data were not used in
this comparison. Therefore, the 7-yr (1 October 2001 to
1 October 2008) data of latent heat flux obtained from the
Niwot Ridge site are used to evaluate WRF-simulated
evapotranspiration. The Niwot Ridge AmeriFlux site is
located above 3000 m elevation in the Roosevelt National
Forest in the Rocky Mountains of central Colorado. It is
dominated by subalpine forest with a leaf area index of
4.2 m2 m22 and canopy height of 11.4 m. The latent heat
fluxes (LH) were measured using the eddy-covariance
technique and were used to calculate the mass transfer of
water between the surface and the atmosphere by converting the LH directly into millimeters of water equivalent, such that
ET 5 LH/LV
(mm),
(6)
where LH is in watts per square meter and LV is the
latent heat of vaporization (2.48 3 106 J kg21, an averaged value of LV at 08 and 208C). To account for the
uncertainty in this conversion during snow seasons due
to possible presence of water on the snow surface, a
second set of estimated ET is produced using both latent
heat of sublimation (2.83 3 106 J kg21, when the air
temperature is below 08C) and latent heat of vaporization when the air temperature is above 08C. However,
the differences in ET estimated from these two methods
are generally small (Fig. 8).
Figure 8 shows that the seasonal trend, magnitude,
and annual variability in WRF-simulated ET generally
agree with observations. Modeled winter ET agrees with
observations as well, but the model produces higher ET
in late spring and summer. The problem of overestimation
of summer ET in the Noah LSM for the Niwot Ridge site
was previously reported by Kumar et al. (2011) and was
attributed to a somewhat too low canopy resistance calculated by the simple Jarvis scheme in the Noah LSM.
Note that the WRF winter and spring precipitation
amount is higher than observations at this site, which
partially contributes to high ET in WRF. Therefore, a
more reasonable way of evaluating the model is to
compare the evapotranspiration-to-precipitation ratio.
The 7-yr average of the ET-to-P ratio simulated by
WRF for this site is 0.89 (60.11 standard deviation),
which is nearly identical to the observed ratio of 0.90
(60.11 standard deviation, using the latent heat of vaporization) and 0.86 (60.11 standard deviation, using
the latent heat of vaporization and sublimation).
4. Changes in water balance components
a. Precipitation
Figure 9 illustrates winter and spring precipitation
(Figs. 9a–c, winter precipitation hereafter) and summer
and early fall precipitation (Figs. 9d–f, summer precipitation hereafter) for the 7-yr period in the historical
and PGW simulations. The spatial pattern of winter
precipitation (1 October–31 May, Fig. 9a) is closely
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FIG. 6. Snow water equivalent on 1 April 2008 and 1 June 2008
from the (a),(c) WRF simulation with 4-km grid spacing and (b),(d)
SNOTEL observations. Open circles on (d) indicate SNOTEL sites
with SWE amount of zero; that is, all snow on the ground has
melted by 1 June 2008.
associated with topography, with the highest precipitation correlated with the highest elevations. In the future
scenario, precipitation is observed to increase with a
pattern similar to that in the current climate distribution
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at first glance (Fig. 9b). However, the difference plot
between future and current winter precipitation (Fig. 9c)
shows that the precipitation in the future is increasing
nearly everywhere in the domain. This reveals an unexpected result: the positive difference pattern is not
primarily determined by the height of the terrain, as
similar positive differences occur in the valleys and
lower elevations.
The spatial pattern of summer precipitation (1 June–
30 September) is also correlated with topography, with
the highest precipitation amounts associated with the
highest topography in both the current (Fig. 9d) and
future simulations (Fig. 9e). This finding is consistent
with the general diurnal orographic convective regime
inherent to many mountainous regions around the world
[e.g., agrees with satellite observations of Banta and
Schaaf (1987), which show that convection in summer is
preferentially initiated at higher elevations as a result of
the elevated heat source and mountain/valley diurnal
circulations]. The difference between future and current
simulations (Fig. 9f) shows that the precipitation in the
summer decreases in the future, in contrast to the winter
results when precipitation increases over the domain.
An analysis of various convective indices from the simulations was performed for the months of June–August in
order to understand the reason for the decrease in precipitation. The results showed that the future soundings
consistently exhibited a stronger cap for convection than
the current climate soundings. This was quantified by
using the Bmin parameter, which is the maximum negative buoyancy beneath the level of free convection
(LFC) (Trier et al. 2011). Larger negative values of Bmin
indicate more inhibition for convective storm development. Table 2 provides the mean values of Bmin for June–
August at 1500, 1800, and 2100 UTC (late morning into
early afternoon) from the current and future simulations
and the difference. As indicated in the last column, Bmin
has a consistently larger negative value in the future simulations. The spatial distribution of Bmin for these three
months showed negative values throughout the domain.
The reason for the larger inhibition was traced back to the
preferential midlevel heating in the driving global climate
model (CCSM3) for the future climate change simulation.
The total amount of precipitation in the summer is
approximately half that occurring in the winter (cf. Figs.
9a and 9d) and one-third the annual total (not shown)
for both current and future simulations. The average
increase in precipitation over the simulation period in
the PGW experiments is positive, and the pattern of
change closely resembles that of the winter precipitation
change. Precipitation increases at all elevations in winter
(Figs. 10a,h) and decreases at all elevations in summer
(Figs. 10a,i). The maximum mass increase of precipitation
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FIG. 7. (a) Mean accumulated monthly flow volumes for Colorado River at the Cameo stream gauge. ‘‘Reconstructed’’ is based on the naturalized streamflow from the Bureau of Reclamation, U.S. Department of Interior
(available from www.usbr.gov/lc/region/g4000/NaturalFlow/current.html). WRF-Current is based on the 4-km current climate simulation. Values are the average between 2001 and 2008. (b) The 7-yr mean annual flow at Cameo
from the streamflow gauge and the WRF simulation.
over the headwaters domain on an annual basis occurs at
an elevation of 2300 m (Fig. 10g). At this elevation the
increase of precipitation combines with the relatively
large surface area to produce the precipitation maximum.
b. Snow–rain partitioning
Changes in rainfall and snowfall depend on elevation
(Figs. 10 and 11). Consistent with the increase in winter
precipitation presented in Fig. 9c, winter snowfall increases at elevations higher than 2900 m MSL (Figs. 10b,e),
and large increases in rainfall occur in the western part
of the headwaters domain (Fig. 11c) at elevations below
2900 m MSL (Figs. 10c,e). The average surface temperature in regions above 2900 m is less than 08C in the
winter future simulation, allowing for snowfall and snow
accumulation to continue at high elevations.
There is a reduction in wintertime snowfall at low
elevations (yellow regions in Fig. 11a; Figs. 10b,e) associated with the conversion of snow to rain owing to the
rise in freezing levels by ;200 m in the warmer climate
(Rasmussen et al. 2011). Also note that the increases in
wintertime rainfall are somewhat muted at higher elevations (cf the changes in winter precipitation in Fig. 9c
with the changes in rainfall in Fig. 11c as well as Fig.
10c), as most of high-elevation winter precipitation falls
as snow in those regions.
The elevation controls on snow–rain partitioning are
also evident during summer (Figs. 10b, 10c and 11b,d).
In particular, note the decrease in snowfall at higher
elevations (Figs. 10b and 11b), associated with both the
domainwide decrease in summer precipitation (Fig. 9f)
and a greater fraction of the high-elevation precipitation
falling as rain. The decreases in summertime rainfall are
less coherent spatially, where the widespread decreases in
summertime rainfall (consistent with precipitation decreases) are interspersed with small areas of increased
precipitation. The increased rainfall generally occurs at
higher elevations during early summer (June), reflecting
the transition of snow to rain as a result of warmer temperatures during that period (Figs. 10c,f). The most
striking result for rain is the overall decrease in the future
for the summer months (Fig. 10c)—a feature that will
have an important impact on the net change in runoff.
c. Snowpack (SWE)
Snow water equivalent on 1 April is strongly correlated with topography, with higher SWE amounts at the
FIG. 8. Comparison of the 2001–08 climatological average of
monthly evapotranspiration from the 4-km WRF simulation and
measurements at the Niwot Ridge AmeriFlux site (Obs). Vertical
bars are one standard deviation from the 2001–08 average.
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FIG. 9. The 7-yr average of winter precipitation from the 4-km (a) current and (b) future climate simulations: (c) difference (future minus
current). (d)–(f) As in (a)–(c), but for summer precipitation.
highest elevations (Figs. 12a,b and 13). The 1 April
snowpack is found to decrease throughout the headwaters domain in the future (Fig. 12c) except for small
increases in the mountains on the very eastern portion of
the analysis domain (Front Range and Sangre de Cristo
Mountains). These mountains received nearly double
the amount of snowfall in the future scenario as compared to other mountain ranges farther west in the
headwaters region (Figs. 9a–c) and, thus, have sufficient
amounts of snow to offset the enhanced melting in the
warmer climate. Snowpack did not change significantly
at any elevation until January (Fig. 13). From January
through March the snowpack increased above ;3000 m
MSL and decreased below that level. Snowpack decreased at all elevations in the future from April through
July (elevations at which snowpack was present in the
current climate).
d. Soil moisture
The total soil water content (mm) in the 2-m deep
WRF soil column shows maximum soil moisture at the
highest elevations and lowest values at low elevations
(Fig. 14). This reflects the fact that the primary source
of moisture for the soil is from the winter precipitation
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TABLE 2. The 8-yr climatological maximum negative buoyancy
(Bmin, K) averaged over the headwaters domain at 1500, 1800, and
2100 UTC. Values within parentheses indicate one standard deviation from the climatological mean Bmin; Bmin was determined
from levels beneath the LFC or below 3 km AGL when there was
no LFC. For a description of Bmin, see Fig. 10 in Trier et al. (2011).
Bmin
Month
Time
Current
climate
Jun
1500 UTC
1800 UTC
2100 UTC
1500 UTC
1800 UTC
2100 UTC
1500 UTC
1800 UTC
2100 UTC
23.93 (0.41)
22.36 (0.44)
21.87 (0.33)
23.12 (0.37)
21.61 (0.26)
21.40 (0.16)
23.18 (0.51)
21.77 (0.46)
21.52 (0.34)
Jul
Aug
Future
climate
Future
minus
current
24.27 (0.44)
22.68 (0.45)
22.11 (0.35)
23.42 (0.41)
21.84 (0.33)
21.51 (0.21)
23.52 (0.59)
22.03 (0.53)
21.69 (0.40)
20.34 (0.05)
20.31 (0.04)
20.24 (0.05)
20.30 (0.06)
20.22 (0.06)
20.11 (0.05)
20.33 (0.09)
20.26 (0.08)
20.17 (0.07)
and snowpack that also maximizes over the highest
elevations. At these elevations, the ample supply of
water in the form of snowmelt means that evapotranspiration is primarily controlled by the available
energy.
The changes in soil moisture in Fig. 14f show substantial decreases (;10%) in high-elevation soil moisture in the future in summer. This is likely due to four
factors: 1) earlier snowpack melting results in a longer
period for ET to deplete soil moisture over the summer,
2) the warmer temperatures in the future results in higher
ET rates, 3) the reduction of rainfall in the summer at high
elevations, and 4) longer periods of time during the year
with relatively warm soils that allow water to flow freely
through the soil as opposed to being bound in partially
frozen conditions. Moreover, the presence of seasonally
high soil moisture values at high elevations means that this
region has the largest potential for decreases. The reduced
soil moisture has significant implications for ecosystem
changes in this region (Molotch et al. 2009) and for
wildfire frequency (Morton et al. 2013); these aspects are
explored in more detail in section 4g.
e. Evapotranspiration
Figure 15 shows seasonal ET from the current and
future simulations and the differences in the two simulations. The amount of ET in summer is at least a factor
of 2 larger than that in winter as the onset of spring
growth and increased temperatures increase the rate of
ET (Fig. 15). The domain-average amount of summer
ET is quite large, ;400 mm, similar to the total amount
of precipitation in this region (Fig. 9). Thus, the net
runoff [Eq. (2)] is the difference between two large
terms. As a result, small changes in either precipitation
1103
or evapotranspiration can have a disproportionately
large impact on runoff.
The spatial pattern of winter ET shows the highest ET
occurring at midlevel elevations (Fig. 15a). These midelevations combine relatively high precipitation amounts
(Fig. 9a) with an appreciable fraction of wintertime precipitation falling as rain and frequent midwinter melt
events (not shown) and warmer temperatures to drive
higher ET. At higher elevations, temperatures are lower,
the ground is covered with snow for the entire winter, and
the terrain is less vegetated, which leads to less total ET.
In the future climate, there are small increases in winter
ET across the entire headwaters region, with increases
most pronounced at midelevation locations (Fig. 15c).
This enhanced ET region appears to be driven by a cascade of impacts associated with warmer temperatures,
including a longer growing season when temperatures are
sufficiently high to support high levels of photosynthesis
and higher evaporative demand. Additionally, regions
with enhanced ET in the future are also regions in which
winter precipitation is shown to be enhanced (see Fig. 9c),
which provides a source of water for the additional ET.
The spatial pattern of ET in summer shows a strong
correlation of the rate of ET with elevation (Figs. 15d–e).
Figure 15f shows that increases in summertime ET in
the future are restricted to higher elevations where
there is ample meltwater from snow and increases in
the incoming radiation at the surface for ET to have an
unfettered impact.
Despite the large increases in incoming radiation at
the surface during summer, there are large areas of the
domain, particularly in midelevation regions, where
summertime ET does not change a great deal or where
summertime ET actually decreases in the future climate (Fig. 15f). The areas of summertime ET decreases
are generally collocated with areas of lower climatological soil moisture (Fig. 14d), suggesting that decreases in
plant-available water associated with both less wintertime snowfall and earlier snowmelt (although small) are
sufficient to create drought stress and offset the increases
in energy. The potential for increased drought stress on
vegetation at midelevations will clearly impact ecosystem
health and will be examined in more detail in future
studies.
f. Runoff
The spatial pattern of runoff shows that the source
regions for runoff are at elevations above ;2600 m
(Fig. 16a), where there is an extensive snowpack (Fig. 9a).
Summer runoff is restricted to a narrower elevation
range (above ;3200 m, Fig. 16d) than winter runoff, as
the snowpack at midelevations is melted off in spring
(April and May). The summer runoff magnitude is higher
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FIG. 10. Climatological differences between the current and future climate (a) precipitation; (b) snowfall; (c) and rainfall amount for
a full water year (annual), winter (October–May), and summer (June–September) in elevation bands between 1500 and 3900 m (increments of 200 m). The difference is taken as future minus current climate. The values are averaged over the headwaters domain.
Horizontal bars on data points indicate one standard deviation from the climatological mean difference and express a year-to-year
variation in the future minus current differences. The climatological differences in total water equivalent mass of snow and rain in the
elevation bands are shown for (d) a full water year, (e) October–May, and (f) June–September. (g)–(i) Total precipitation mass (i.e., mass
of snow and rain).
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Figure 16f shows strong reductions in summertime runoff
at high elevations that occur as a result of depleted latespring snowpack owing to earlier melting in a warmer
climate (Figs. 10b, 13), the decrease in summer rainfall
(Fig. 9f), and drier soil (Fig. 14f) that enhances the infiltration of rainfall into soil layers.
g. Summary of seasonal changes in water balance
components
FIG. 11. The 7-yr average difference of snowfall amount between
the 4-km future and current simulations for (a) winter and (b) summer
months. (c),(d) As in (a),(b), but for rainfall.
than in the winter period at high elevations as a result of
the deep snow available at these elevations. In the future,
there is an increase in runoff during winter (Fig. 16c),
which occurs as a result of more of the precipitation
falling as rain during winter and earlier snowmelt.
Figure 17 illustrates the seasonal cycles of precipitation, evapotranspiration, runoff, and potential evaporation (PE) for the current and future periods. The monthly
changes in precipitation owing to climate change are
dominated by increases during winter (through May) and
then decreases during summer (Fig. 17b). The increase of
winter precipitation is approximately four times the decrease of precipitation in the summer, resulting in a net
increase in precipitation of 31 mm yr21 (Fig. 17b). Offsetting the precipitation increases, monthly ET is higher
in nearly all months in the future climate scenario with
ET increases most pronounced during late spring and
early summer (Fig. 17d). ET actually decreases slightly in
August and September in the future because of the reduction in future precipitation during this period and the
depletion of soil moisture. The annual increase in ET is
37 mm yr21 (Fig. 17d), resulting in a net annual decrease
in runoff of 6 mm yr21 (Fig. 17f).
The annual cycle of runoff from the historical period
shows a rapid increase from April to May as a result of the
snowmelt cycle (Fig. 17e). Very little snowmelt occurs after
30 June and consequently, the contribution of snowmelt to
runoff decreases to near zero. After 30 June, most of the
runoff is driven by rainfall, which is significant during July–
August (;40 mm month21, Fig. 17a). Runoff, however,
remains small (,5 mm month21) because nearly all of the
rainfall in summer in the model evaporates because of
high temperatures, solar radiation levels, and relatively
low humidity (Fig. 17e).
In the future simulation, runoff increases more rapidly
between March and April than the current simulation,
owing to the early onset of snowmelt in the future
warmer climate and snow melting to rain before reaching the ground. As a result of the earlier melt and the
increased ET from April through June, the amount of
meltwater available for runoff in the summer is reduced;
therefore, runoff is decreased in the summer in a future
climate. The net effect over the annual cycle is a slight
net decrease in runoff due to the enhanced ET from
April to June, the reduction of rain in July and August,
and the earlier onset of runoff in the spring.
Current and future average values of SWE on the first
of the month and of monthly-mean soil moisture are
shown in Fig. 18. Differences in SWE shown in Fig. 18b
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FIG. 12. (a) Current and (b) future SWE on 1 April averaged over 7 yr (2001–08) from the 4-km simulations. (c) The differences between
future and current SWE on 1 April.
illustrate a dramatic reduction in springtime SWE values
across the headwaters region, indicating an earlier onset
of snowmelt. For all months, domain-average SWE in
the PGW simulation are less than those in the current
climate simulation.
Changes in monthly values of soil moisture exhibit a
more differential response to warming where late winter
and spring months possess higher soil moisture values,
while summer and autumn months have decreased soil
moisture. The increase in March–May soil moisture is
likely the result of increased rain versus snow in the late
winter and of increased snowmelt in the early spring.
Figure 17 synthesizes the interplay among water input
(precipitation), atmospheric demand (potential evaporation), and actual evapotranspiration. PE essentially
indicates a demand for evaporation for given atmosphere and soil conditions. It is important to assess the
variations in PE under future climate and its relation to
actual evaporation. PE is calculated in the Noah LSM
by a Penman-based energy balance approach that includes a stability-dependent aerodynamic resistance
(Mahrt and Ek 1984; Chen and Dudhia 2001). Increased summertime shortwave radiation, surface air
temperature, and wind speed, along with decreasing
humidity, leads to a noticeable increase in summer PE
in PGW (;8% for June–August, Fig. 17h). In the future climate, even though higher winter precipitation
and earlier spring snowmelt (Figs. 17a,b) resulted in
more late-spring soil water storage (April and May,
Fig. 18b), the combination of higher summer PE (Fig. 17h)
and less summer precipitation leads to higher late-spring
and early summer ET (Fig. 17d) and drier soil in summer
(Fig. 18b).
This differential behavior equates to an amplification
of the annual cycle of soil moisture in the headwaters
region. This amplified seasonal cycle, particularly the
extended periods of decreased soil moisture, are likely to
have secondary ecosystem impacts such as increased
warm-season drought stress on vegetation and changes in
biogeochemical cycles as a result of altered levels of soil
water that regulate numerous biogeochemical exchanges
in mountain ecosystems. In particular, the substantial
increases in water deficit (PE 2 P) under future climate
from April to August (Figs. 17a,g) outweigh the enhanced
winter–spring precipitation, which amplifies soil aridity,
ecosystem stress, and fire activity (assuming other fire
triggers remain the same) in this water-limited region.
Morton et al. (2013) reported a strong positive correlation
between PE and burned area for the Rockies. Therefore,
despite an increase in high-elevation winter precipitation
in future climate, enhanced spring and summer water
deficit (PE 2 P) and drier summer soil may increase
summer wildfire activity or fire potential in this region.
5. Water balance analysis
The water balance equation [Eq. (2)] can now be analyzed quantitatively. The results are shown in Fig. 19 in
which the mean value and standard deviation (number
in parentheses) of the annual values is given.
The results for the 4-km current climate simulation show
that the 7-yr average annual precipitation is ;534 mm
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FIG. 13. Climatological differences (future minus current climate) in first-of-the-month SWE (mm) in various elevation bands between
1500 and 3900 m (increments of 200 m). Vertical bars indicate one standard deviation from the climatological mean difference.
and evapotranspiration is ;430 mm (Fig. 19). Runoff is
only 104 mm in this case with a runoff fraction of 0.19.
Thus, evapotranspiration is depleting much of the
precipitation. The ET spatial analysis (Fig. 15) shows
that much of the high values of ET in winter are located
at midelevations of the region where sufficient vegetation combines with warmer temperatures and the
presence of precipitation and snowpack to optimize
ET. During the summer, ET maximizes at the highest
elevations.
In the future, annual ET increases by ;37 mm, while
annual precipitation increases by slightly less, 31 mm. As
a result, runoff is reduced by approximately 6 mm. Thus,
while both precipitation and ET increase in the pseudoglobal-warming future climate, the ET increases more
than the precipitation increases, resulting in a slight reduction in runoff. Considering the magnitude of the
total annual precipitation and ET, the change in runoff is
only a 1% change in either quantity. The larger increase
in ET in the future compared to precipitation reduces
the runoff fraction to 0.17.
A key factor reducing the runoff efficiency is the reduction of precipitation over the summer months due to
an increase of convective inhibition. Otherwise, the increase in ET and precipitation would have been nearly
the same, resulting in no change to the runoff.
6. Discussion
The 8-yr current climate simulations provide a highresolution depiction of the water cycle in the Colorado
Headwaters region for the period between 2000 and
2008. The results show a region dominated by the annual
cycle of snow accumulation during the winter, melt in
the spring, and runoff during the spring and summer.
The results show that more than 80% of the annual
precipitation evaporates. This means that runoff in this
region is very sensitive to any changes in precipitation or
evapotranspiration. For instance, a decrease in precipitation or increase of evapotranspiration of 10% would
reduce runoff by 40%, assuming that the water balance
stays the same. In reality, evapotranspiration also changes
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FIG. 14. As in Fig. 9, but for soil moisture.
when precipitation changes, so the actual change in precipitation needs to be considered as well. These results
show the critical importance of understanding future
changes in both precipitation and evapotranspiration in
this region by either climate change or land surface disturbance such as beetle kill or forest fires.
The PGW simulation shows that winter precipitation
changes from 83% snow in the current climate to only
74% snow in the future. The annual precipitation, however, increases by 6% owing to the warmer climate, and
as a result, snowfall actually increases during the core
winter months of December–February at elevations
above ;3000 m. Snowpack begins melting 2–3 weeks
earlier than current and also disappears 2–3 weeks earlier. The combination of these factors results in the onset
of runoff being shifted 2–3 weeks earlier and also being
more intense in March and April than in the current climate. Runoff decreases by June and throughout the rest
of the summer to values less than current as a result of the
earlier start, the increase in ET depleting moisture (longer and more intense), and the reduction in rainfall in the
summer. Thus, the seasonality of runoff is predicted to be
significantly altered in agreement with previous studies
(Barnett et al. 2005; Regonda et al. 2005; Christensen and
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FIG. 15. As in Fig. 9, but for evapotranspiration.
Lettenmaier 2007; Maurer 2007; Barnett et al. 2008;
Pierce et al. 2008; Raisanen 2008; Adam et al. 2009;
Hidalgo et al. 2009; Stewart 2009; Rasmussen et al. 2011).
The annual amount of runoff is predicted to decrease
slightly under this optimistic (in terms of precipitation)
climate change scenario.
Changes in runoff depend on seasonal changes in the
primary components of the regional water balance, how
changes in individual water balance components vary
spatially, and the extent to which ET across different areas
of the landscape is energy limited or water limited (Koster
and Suarez 1999; Christensen et al. 2008; Gao et al. 2011).
Evapotranspiration is energy limited in areas where there
is ample supply of water with respect to the available
energy: in these areas, increases in available energy in
a warmer climate yield increases in ET. However, in many
areas across the landscape, ET is limited by the amount of
plant-available water during the growing season, which is
controlled by changes in winter precipitation, rain–snow
partitioning, timing of snowmelt, and summertime precipitation events. Such cold season–warm season interactions create a complex web of interactions—meaning
that changes in partitioning of precipitation between ET
and runoff is far from predictable.
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FIG. 16. As in Fig. 9, but for runoff. Both surface and underground runoff have been added together.
These results provide input into the key issue on the
extent to which changes in snow accumulation and melt,
tempered by changes in summertime precipitation, affect water stress on vegetation during the growing season and the extent to which changes in plant-available
water accentuate or offset changes in available energy.
Previous results in the literature are mixed. In a sitespecific study at Niwot Ridge, Hu et al. (2010) demonstrated the dependence of forest carbon uptake on snow
meltwater and presented observations that show net
ecosystem productivity is lower in years with a longer
growing season (i.e., years characterized by shallower
snowpack and earlier melt). Similarly, Trujillo et al.
(2012) showed a strong inverse relationship between
maximum snow accumulation and vegetation greenness
in the Sierra Nevada in California, with relationships
strongest in water-limited midelevation areas. These results are consistent with modeling studies for the Sierra
Nevada, where Christensen et al. (2008) demonstrated
that annual transpiration at intermediate elevations was
strongly correlated with peak snow depth and transpiration at higher elevations was correlated with growingseason mean temperature [see also Tague et al. (2009),
who demonstrated that higher temperatures are associated
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FIG. 17. (a) The 7-yr climatology of monthly precipitation from the 4-km current and future simulations averaged
over the headwaters domain. (b) Difference in the 7-yr average monthly precipitation (bars) and accumulation of the
differences (dots) between the future and current conditions with vertical lines through the dots showing the one
standard deviation from the 7-yr mean. (c),(d) ET; (e),(f) Q; and (g),(h) PE.
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FIG. 18. (a) The 2001–08 climatology of SWE on the first of the month from the 4-km current and future simulations
averaged over the headwaters domain and (b) mean difference in SWE between the two simulations, with vertical
lines showing 1 std dev from the 7-yr mean difference. (c) Monthly-mean soil moisture averaged over the 7 yr. (d) The
7-yr average in change in the monthly-mean soil moisture between the 4-km future and current climate simulations.
with a reduction in evapotranspiration at lower elevations and an increase in evapotranspiration at higher
elevations]. Note that precipitation in California mostly
falls in the winter–spring period, leading to pronounced cold season–warm season interactions. In
contrast to these results, modeling studies at larger
spatial scales illustrate a positive relationship between
earlier snowmelt and total ET (e.g., Hamlet et al. 2007;
Painter et al. 2010). Evaluating changes in runoff therefore depend critically on the fidelity of model-based ET
estimates. For example, the large-scale multimodel simulations of drought by Wang et al. (2009) illustrated that
FIG. 19. The 7-yr climatological water balance terms over the headwaters domain from the
4-km current and future simulations and their differences. Numbers above the bars represent
the average value of the water balance term (mm) (precipitation, evapotranspiration, or runoff)
over the 7 yr, and in parenthesis 1 std dev of the yearly terms.
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RASMUSSEN ET AL.
the runoff-to-precipitation ratio ranged from 0.09 to 0.31
when averaged over the contiguous United States. Similarly, the study by Vano et al. (2012) examined ET for five
of the most commonly used land surface and hydrology
models and showed ET to vary by 65% over the Colorado River basin over a 25-yr period, depending on the
land surface or hydrology model used. Uncertainty in
model-based ET estimates is due to both inadequacies in
the land surface model representation of ET as well as
inadequacies in model simulations of snow accumulation
and melt, to the extent that model representation of
snow hydrology impacts plant-available water during the
growing season. The estimation of ET in land surface
models and from various measurement systems in nature
are clearly areas needing more research to improve understanding of changes in runoff in a warmer climate.
b. Future climate (PGW)
1) SNOWFALL, SNOWPACK (SWE),
PRECIPITATION, AND RUNOFF CHANGES
The 8-yr, PGW future climate simulation showed the
following list of changes in SWE, precipitation, and runoff:
d
d
7. Conclusions
a. Current climate
The 8-yr simulation of the Colorado Headwaters region by the WRF model at 4-km horizontal grid spacing
driven by NARR revealed the following key points:
d
d
d
d
d
The WRF model compared well to SNOTEL observations of snowfall in both spatial distribution and
amount. Comparison of model snowpack to SNOTEL
snowpack observations also showed good agreement in
onset and melt-out timing, but the snowpack on 1 April
was considerably less than that observed, though improved over the previous simulations by Ikeda et al.
(2010) and Rasmussen et al. (2011).
An integrated assessment of mean annual runoff at the
Cameo stream gauge near Grand Junction, Colorado,
agreed with WRF-model estimate of runoff within 5%.
Comparison to an AmeriFlux site at Niwot Ridge,
Colorado, showed that the modeled ET, especially the
evaporative faction (ET/P) is reasonable. However,
only one site was considered; thus, further ET observations are needed to verify these types of models
owing to the key role that ET plays in the water balance
in this region.
The simulation of runoff gave improved results over
the previous results of Rasmussen et al. (2011) and
Ikeda et al. (2010) in response to proper spinup of
soil moisture with respect to phase and amount and
the improved snowpack modeling in the Noah LSM
(Barlage et al. 2010).
The ET/P for the current climate is 0.81 over the
headwaters region, indicating that water loss in
this region is dominated by evaporative processes
and not runoff, which is only 0.19 of the annual
precipitation.
1113
d
d
d
d
Seven-year-average precipitation during the winter
increased by 12% while summer precipitation decreased
by 8%, resulting in a mean precipitation increase over the
headwaters domain of 6% in the future. The increase in
winter precipitation was associated with the 14% increase
in water vapor content associated with the 28C warming
of the climate (Clausius–Clapeyron equation). The decrease in summer precipitation was associated with increased convective inhibition associated with preferential
midlevel warming from the driving climate model.
Snowpack did not change significantly until January.
From January through March the snowpack increased
above ;3000 m MSL and decreased below that elevation. Snowpack decreased at all elevations in the future
from April through July.
The onset of spring snowmelt and total snowmelt
occurred 2–3 weeks earlier in the future, in agreement
with previous studies.
The 1 April SWE decreased throughout the headwaters
domain except for the Front Range mountains. The
headwaters domain-averaged future decrease in SWE
on 1 April is 23%.
The fraction of precipitation falling as snow decreased
from 0.83 in the current climate to 0.74 in the future.
Runoff peaked 2–3 weeks earlier in the future.
2) FUTURE WATER BALANCE
The current and future climate simulations showed that
a warmer and moister future climate impact water balance
in the headwaters region of Colorado. Key findings for the
future water balance are as follows:
d
d
d
Both precipitation and evapotranspiration increase
under a warmer, moister climate.
Evapotranspiration increased slightly more than precipitation on average, yielding a slightly negative
change in runoff averaged over the 7 yr. This was largely
because of the decrease in summer precipitation, yielding a smaller increase in total precipitation amount than
expected from the increase in moisture.
The uncertainty of the runoff change for the future
climate is 610%, taking into account the likely uncertainty in evapotranspiration and precipitation. This results
in a range of likely values of runoff from 21 to 211 mm,
or a percentage range from 21% to 211%, given the
absolute runoff value near 100 mm. This agrees well with
1114
d
d
d
JOURNAL OF HYDROMETEOROLOGY
the estimate of runoff decrease in a future climate over
the much larger Colorado River basin by Christensen
and Lettenmaier (2007) based on the application of the
VIC hydrological model to AR4 climate model output.
The evaporative fraction (ET/P) increased from 0.81
in the current climate to 0.83 in the future, indicating
a trend toward a dryer climate and reduced runoff
despite an optimistic precipitation scenario.
Runoff in the Colorado Headwaters is extremely
sensitive to changes in both precipitation and evapotranspiration. A decrease of precipitation of 10% in the
future would potentially reduce runoff from the Colorado Headwaters by ;40% [indicating a factor of 4
sensitivity (Schaake 1990)]. Vano et al. (2012) determined a factor of 3 sensitivity for a 10% increase of
precipitation (as in this study) for the Colorado River
basin using the Noah LSM, version 2.7. This suggests that
runoff from the headwaters region may be even more
sensitive to precipitation changes than in the Colorado
River basin. An increase of evapotranspiration of only
10% would similarly reduce runoff by nearly 40%. In
either case, runoff in the Colorado Headwaters is remarkably sensitive to even small changes in precipitation
or evapotranspiration in a future warmer climate or from
other causes of precipitation/evapotranspiration changes
such as bark beetle infestations or forest fires.
A robust finding from this study is the increased aridity
of the soils during the spring/summer period despite the
increase in high-elevation winter precipitation in the
future climate. This will likely lead to increased plant
stress and other ecosystem impacts. Molotch et al.
(2009) have shown strong ecosystem impacts in regions
that are snow-free in the spring that had previously
been snow covered. Moreover, Morton et al. (2013)
reported a strong positive correlation between potential evaporation and burned area for the Rockies in
the United States. Therefore, increasing water deficit
(PE 2 P) and drier soil in summer suggest a greater
likelihood of summer wildfire events in this region.
Acknowledgments. This research was funded by National Science Foundation as part of the NCAR Water
System program. Logistical support and/or data were
provided by the NSF-supported Niwot Ridge LongTerm Ecological Research project and the University
of Colorado Mountain Research Station. The authors
are grateful for the thoughtful comments by two anonymous reviewers that improved this paper.
REFERENCES
Adam, J. C., A. F. Hamlet, and D. P. Lettenmaier, 2009: Implications of global climate change for snowmelt hydrology in
VOLUME 15
the twenty-first century. Hydrol. Processes, 23, 962–972,
doi:10.1002/hyp.7201.
Alexander, M. A., J. D. Scott, K. Mahoney, and J. Barsugli, 2013:
Greenhouse gas–induced changes in summer precipitation
over Colorado in NARCCAP regional climate models.
J. Climate, 26, 8690–8697, doi:10.1175/JCLI-D-13-00088.1.
Banta, R. M., and C. B. Schaaf, 1987: Thunderstorm genesis
zones over the Colorado Rocky Mountains as determined
by traceback of geosynchronous satellite images. Mon. Wea.
Rev., 115, 463–476, doi:10.1175/1520-0493(1987)115,0463:
TGZITC.2.0.CO;2.
Barlage, M., and Coauthors, 2010: Noah land surface model modifications to improve snowpack prediction in the Colorado
Rocky Mountains. J. Geophys. Res., 115, D22101, doi:10.1029/
2009JD013470.
Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential impacts of a warming climate on water availability in
snow-dominated regions. Nature, 438, 303–309, doi:10.1038/
nature04141.
——, and Coauthors, 2008: Human-induced changes in the hydrology of the western United States. Science, 319, 1080–1083,
doi:10.1126/science.1152538.
Cayan, D. R., T. Das, D. W. Pierce, T. P. Barnett, M. Tyree, and
A. Gershunov, 2010: Future dryness in the southwest US and the
hydrology of the early 21st century drought. Proc. Natl. Acad.
Sci. USA, 107, 21 271–21 276, doi:10.1073/pnas.0912391107.
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface
hydrology model with the Penn State–NCAR MM5 modeling
system. Part I: Model implementation and sensitivity. Mon.
Wea. Rev., 129, 569–585, doi:10.1175/1520-0493(2001)129,0569:
CAALSH.2.0.CO;2.
Christensen, N. S., and D. P. Lettenmaier, 2007: A multimodel
ensemble approach to assessment of climate change impacts
on the hydrology and water resources of the Colorado River
basin. Hydrol. Earth Syst. Sci., 11, 1417–1434, doi:10.5194/
hess-11-1417-2007.
——, C. L. Tague, and J. S. Baron, 2008: Spatial patterns of simulated transpiration response to climate variability in a snow
dominated mountain ecosystem. Hydrol. Processes, 22, 3576–
3588, doi:10.1002/hyp.6961.
Collins, W. D., and Coauthors, 2006: The Community Climate
System Model version 3 (CCSM3). J. Climate, 19, 2122–2143,
doi:10.1175/JCLI3761.1.
Gao, Y., J. A. Vano, C. Zhu, and D. P. Lettenmaier, 2011: Evaluating climate change over the Colorado River basin using regional climate models. J. Geophys. Res., 116, D13104, doi:10.1029/
2010JD015278.
Gutmann, E. D., R. M. Rasmussen, C. Liu, K. Ikeda, D. J. Gochis,
M. P. Clark, J. Dudhia, and G. Thompson, 2012: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Climate, 25, 262–281,
doi:10.1175/2011JCLI4109.1.
Hamlet, A. F., P. W. Mote, M. P. Clark, and D. P. Lettenmaier,
2005: Effects of temperature and precipitation variability on
snowpack trends in the western United States. J. Climate, 18,
4545–4561, doi:10.1175/JCLI3538.1.
——, ——, ——, and ——, 2007: Twentieth-century trends in runoff, evapotranspiration, and soil moisture in the western United
States. J. Climate, 20, 1468–1486, doi:10.1175/JCLI4051.1.
Hara, M., T. Yoshikane, H. Kawase, and F. Kimura, 2008: Estimation of the impact of global warming on snow depth in
Japan by the pseudo-global warming method. Hydrol. Res.
Lett., 2, 61–64, doi:10.3178/hrl.2.61.
JUNE 2014
RASMUSSEN ET AL.
Hidalgo, H. G., and Coauthors, 2009: Detection and attribution of streamflow timing changes to climate change in the
western United States. J. Climate, 22, 3838–3855, doi:10.1175/
2009JCLI2470.1.
Hoerling, M., and J. K. Eischeid, 2007: Past peak water in the
southwest. Southwest Hydrol., 6, 18–19. [Available online at
www.swhydro.arizona.edu/archive/V6_N1/feature2.pdf.]
——, D. P. Lettenmaier, D. Cayan, and B. Udall, 2009: Reconciling
projections of Colorado River streamflow. Southwest Hydrol.,
8, 20–21. [Available online at www.swhydro.arizona.edu/
archive/V8_N3/feature2.pdf.]
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion
package with an explicit treatment of entrainment processes.
Mon. Wea. Rev., 134, 2318–2341, doi:10.1175/MWR3199.1.
Hu, J., D. J. P. Moore, S. P. Burns, and R. K. Monson, 2010: Longer
growing seasons lead to less carbon sequestration by a subalpine forest. Global Change Biol., 16, 771–783, doi:10.1111/
j.1365-2486.2009.01967.x.
Ikeda, K., and Coauthors, 2010: Simulation of seasonal snowfall over Colorado. Atmos. Res., 97, 462–477, doi:10.1016/
j.atmosres.2010.04.010.
Janjic, Z. I., 1994: The step-mountain Eta coordinate model: Further developments of the convection, viscous layer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945,
doi:10.1175/1520-0493(1994)122,0927:TSMECM.2.0.CO;2.
Kawase, H., T. Yoshikane, M. Hara, F. Kimura, T. Yasunari,
B. Ailikun, H. Ueda, and T. Inoue, 2009: Intermodel variability of future changes in the Baiu rainband estimated by the
pseudo global warming downscaling method. J. Geophys. Res.,
114, D24110, doi:10.1029/2009JD011803.
Klein Tank, A. M. G., and Coauthors, 2002: Daily dataset of 20thcentury surface air temperature and precipitation series for
the European Climate Assessment. Int. J. Climatol., 22, 1441–
1453, doi:10.1002/joc.773.
Koster, R. D., and M. J. Suarez, 1999: A simple framework for examining the interannual variability of land surface moisture fluxes.
J. Climate, 12, 1911–1917, doi:10.1175/1520-0442(1999)012,1911:
ASFFET.2.0.CO;2.
Kumar, A., F. Chen, D. Niyogi, J. G. Alfieri, K. Mitchell, and M. Ek,
2011: Evaluation of a photosynthesis-based canopy resistance
formulation in the Noah land surface model. Bound.-Layer
Meteor., 138, 263–284, doi:10.1007/s10546-010-9559-z.
Mahrt, L., and M. Ek, 1984: The influence of atmospheric stability
on potential evaporation. J. Climate Appl. Meteor., 23, 222–234,
doi:10.1175/1520-0450(1984)023,0222:TIOASO.2.0.CO;2.
Maraun, D., 2013: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue. J. Climate, 26, 2137–
2143, doi:10.1175/JCLI-D-12-00821.1.
Maurer, E. P., 2007: Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two
emissions scenarios. Climatic Change, 82, 309–325, doi:10.1007/
s10584-006-9180-9.
Meehl, G. A., and Coauthors, 2007: Global climate projections.
Climate Change 2007: The Physical Science Basis, S. Solomon
et al., Eds., Cambridge University Press, 747–845.
Mesinger, F., and Coauthors, 2006: North American Regional
Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi:10.1175/
BAMS-87-3-343.
Miller, K., and D. Yates, 2005: Climate change and water resources: A primer for municipal water providers. Awwa
Research Foundation/UCAR Doc., 83 pp. [Available online at http://waterinstitute.ufl.edu/WorkingGroups/downloads/
WRF%20Climate%20Change%20DocumentsSHARE/Project%
1115
202973%20-%20Climate%20Change%20and%20Water%
20Resources.pdf.]
Milly, P. C. D., K. A. Dunne, and A. V. Vecchia, 2005: Global
pattern of trends in streamflow and water availability in a changing climate. Nature, 438, 347–350, doi:10.1038/nature04312.
Molotch, N. P., P. D. Brooks, S. P. Burns, M. Litvak, R. K. Monson,
J. R. McConnell, and K. Musselman, 2009: Ecohydrological
controls on snowmelt partitioning in mixed-conifer sub-alpine
forests. Ecohydrology, 2, 129–142, doi:10.1002/eco.48.
Morton, D. C., G. J. Collatz, D. Wang, J. T. Randerson, L. Giglio,
and Y. Chen, 2013: Satellite-based assessment of climate
controls on US burned area. Biogeosciences, 10, 247–260,
doi:10.5194/bg-10-247-2013.
Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier,
2005: Declining mountain snowpack in western North
America. Bull. Amer. Meteor. Soc., 86, 39–45, doi:10.1175/
BAMS-86-1-39.
Painter, T. H., J. S. Deems, J. Belnap, A. F. Hamlet, C. C. Landry,
and B. Udall, 2010: Response of Colorado River runoff to dust
radiative forcing in snow. Proc. Natl. Acad. Sci. USA, 107,
17 125–17 130, doi:10.1073/pnas.0913139107.
Pierce, D. W., and Coauthors, 2008: Attribution of declining
western U. S. snowpack to human effects. J. Climate, 21, 6425–
6444, doi:10.1175/2008JCLI2405.1.
——, and Coauthors, 2013: Probabilistic estimates of future
changes in California temperature and precipitation using
statistical and dynamical downscaling. Climate Dyn., 40, 839–
856, doi:10.1007/s00382-012-1337-9.
Raisanen, J., 2008: Warmer climate: Less or more snow? Climate
Dyn., 30, 307–319, doi:10.1007/s00382-007-0289-y.
Rasmussen, R. M., and Coauthors, 2011: High-resolution coupled
climate runoff simulations of seasonal snowfall over Colorado:
A process study of current and warmer climate. J. Climate, 24,
3015–3048, doi:10.1175/2010JCLI3985.1.
——, and Coauthors, 2012: How well are we measuring snow? The
NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer.
Meteor. Soc., 93, 811–829, doi:10.1175/BAMS-D-11-00052.1.
Regonda, S. K., B. Rajagopalan, M. Clark, and J. Pitlick, 2005:
Seasonal cycle shifts in hydroclimatology over the western
United States. J. Climate, 18, 372–384, doi:10.1175/JCLI-3272.1.
Sato, T., F. Kimura, and A. Kitoh, 2007: Projection of global
warming onto regional precipitation over Mongolia using
a regional climate model. J. Hydrol., 333, 144–154, doi:10.1016/
j.jhydrol.2006.07.023.
Schaake, J. C., 1990: From climate to flow. Climate Change and U.S.
Water Resources, P. E. Waggoner, Ed., John Wiley, 177–206.
Sch€ar, C., C. Frie, D. L€
uthi, and H. C. Davies, 1996: Surrogate
climate-change scenarios for regional climate models. Geophys. Res. Lett., 23, 669–672, doi:10.1029/96GL00265.
Seager, R. M., and Coauthors, 2007: Model projections of an imminent transition to a more arid climate in southwestern North
America. Science, 316, 1181–1184, doi:10.1126/science.1139601.
——, N. Naik, and L. Vogel, 2012: Does global warming cause
intensified interannual hydroclimate variability? J. Climate,
25, 3355–3372, doi:10.1175/JCLI-D-11-00363.1.
Serreze, M. C., M. P. Clark, R. L. Armstrong, D. A. McGinnis, and
R. S. Pulwarty, 1999: Characteristics of the western United
States snowpack from snowpack telemetry (SNOTEL) data.
Water Resour. Res., 35, 2145–2160, doi:10.1029/1999WR900090.
——, ——, and A. Frei, 2001: Characteristics of large snowfall
events in the montane western United States as examined
using snowpack telemetry (SNOTEL) data. Water Resour.
Res., 37, 675–688, doi:10.1029/2000WR900307.
1116
JOURNAL OF HYDROMETEOROLOGY
Sheffield, J., and E. F. Wood, 2008: Projected changes in drought
occurrence under future global warming from multi-model,
multi-scenario, IPCC AR4 simulations. Climate Dyn., 31, 79–
105, doi:10.1007/s00382-007-0340-z.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M.
Barker, W. Wang, and J. G. Powers, 2005: A description of the
Advanced Research WRF version 2. NCAR Tech. Note
NCAR/TN-4681STR, 88 pp. [Available online at www.mmm.
ucar.edu/wrf/users/docs/arw_v2.pdf.]
Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt,
M. Tignor, and H. L. Miller Jr., Eds., 2007: Climate Change 2007:
The Physical Science Basis. Cambridge University Press, 996 pp.
Stewart, I. T., 2009: Changes in snowpack and snowmelt runoff for key
mountain regions. Hydrol. Processes, 23, 78–94, doi:10.1002/
hyp.7128.
Tague, C., K. Heyn, and L. Christensen, 2009: Topographic controls on spatial patterns of conifer transpiration and net primary productivity under climate warming in mountain
ecosystems. Ecohydrology, 2, 541–554, doi:10.1002/eco.88.
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008:
Explicit forecasts of winter precipitation using an improved
bulk microphysics scheme. Part II: Implementation of a new
snow parameterization. Mon. Wea. Rev., 136, 5095–5115,
doi:10.1175/2008MWR2387.1.
Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons,
2003: The changing character of precipitation. Bull. Amer.
Meteor. Soc., 84, 1205–1217, doi:10.1175/BAMS-84-9-1205.
Trier, S. B., M. A. LeMone, F. Chen, and K. W. Manning, 2011:
Effects of surface heat and moisture exchange on ARW-WRF
VOLUME 15
warm-season precipitation forecasts over the central
United States. Wea. Forecasting, 26, 3–25, doi:10.1175/
2010WAF2222426.1.
Trujillo, E., N. P. Molotch, M. L. Goulden, A. E. Kelly, and R. C.
Bales, 2012: Elevation-dependent influence of snow accumulation on forest greening. Nat. Geosci., 5, 705–709, doi:10.1038/
ngeo1571.
Vano, J. A., T. Das, and D. P. Lettenmaier, 2012: Hydrologic sensitivities of Colorado River runoff to changes in precipitation
and temperature. J. Hydrometeor., 13, 932–949, doi:10.1175/
JHM-D-11-069.1.
Wang, A. H., T. J. Bohn, S. P. Mahanama, R. D. Koster, and D. P.
Lettenmaier, 2009: Multimodel ensemble reconstruction of
drought over the continental United States. J. Climate, 22,
2694–2712, doi:10.1175/2008JCLI2586.1.
Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The
resolution dependence of explicitly modeled convective
systems. Mon. Wea. Rev., 125, 527–548, doi:10.1175/
1520-0493(1997)125,0527:TRDOEM.2.0.CO;2.
Wi, S., F. Domingues, M. Durcik, J. Valdes, H. F. Diaz, and C. L.
Castro, 2012: Climate change projection of snowfall in the
Colorado River basin using dynamical downscaling. Water
Resour. Res., 48, W05504, doi:10.1029/2011WR010674.
Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bates,
T. Pangburn, and C. L. Hanson, 1998: Accuracy of NWS 8’’
standard nonrecording precipitation gauge: Results and
application of WMO intercomparison. J. Atmos. Oceanic
Technol., 15, 54–68, doi:10.1175/1520-0426(1998)015,0054:
AONSNP.2.0.CO;2.