Snowmelt Runoff Modeling in the Upper Colorado River Basin
using MODIS Fractional Snow Cover
Christopher J. Crawford
Oak Ridge Associated Universities
Cryospheric Sciences Laboratory (Code 615)
NASA/GSFC
Dorothy K. Hall
Cryospheric Sciences Laboratory (Code 615)
NASA/GSFC
Nicolo E. DiGirolamo
Science Systems & Applications Inc.
George A. Riggs
Science Systems & Applications Inc.
James L. Foster
Emeritus, Hydrological Sciences Laboratory (Code 617)
NASA/GSFC
C.J. Crawford, MtnClim 2014, Midway, Utah
1. Problem Statement
2. Historical Context / Study Region
3. Study Region Climatology
4. Research Objective #1
5. Research Objective #2
6. MODIS Collection 6 Fractional Snow Cover
7. Snowmelt Runoff Model (SRM) Overview
8. SRM Results
9. Concluding Thoughts
Photo: D.K. Hall / NASA
C.J. Crawford, MtnClim 2014, Midway, Utah
Northern Hemisphere Spring Snow Cover Reductions
INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 27: 739–748 (2007)
Published online 10 October 2006 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/joc.1426
Trends in snow ablation over North America
GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L19504, doi:10.1029/2012GL053387, 2012
Jamie L. Dyera * and Thomas L. Moteb
a
Department of Geosciences, Mississippi State University, P.O. Box 5448, Mississippi State, MS 39762-5448
b Department of Geography, University of Georgia
Spring snow cover extent reductions in the 2008–2012 period
exceeding climate model projections
Abstract:
C. Derksen1 and R. Brown2
A substantial decrease in snow cover extent (SCE) and snow depth over North America has been observed over the
creates distinct challenges to the determination of 1960–2000
trends in period. One explanation for the changes in North American snow cover is a change in the frequency and/or
intensityofof snow ablation. This study uses a gridded dataset of United States and Canadian surface observations from 1960
Arctic snow depth or snow water equivalent, analysis
to examine patterns of snow ablation over North America. An ablation event is defined as an interdiurnal snow
multiple datasets has shown that we now havetoa2000
good
depth change exceeding a critical value. Results show a significant positive trend in the frequency of ablation events during
understanding of the uncertainty in snow cover extent (SCE)
(p < 0.05) and a significant negative trend in May (p < 0.05), indicating an earlier onset of ablation. This pattern
time series across the Arctic, particularly in springMarch
[Brown
is consistent for ablation of varying intensity. Surface energy budget components and air mass frequencies are examined
et al., 2010]. The longest available satellite-derived time
in relation to the observed trends in snow ablation. Changes in March ablation frequency were shown to be dominated
series
of SCE are5,the
weekly NOAA
produced
The
Cryosphere,
219–229,
2011 snow charts by
increases in the sensible heat flux. A higher frequency of dry moderate instead of moist polar air masses during high
from manual analysis of primarily optical satellite imagery
www.the-cryosphere.net/5/219/2011/
ablation years may explain the increase in sensible heat flux and ablation over the study period. Copyright  2006 Royal
[Robinson et al., 1993]. This dataset is widely used within
Meteorological Society
doi:10.5194/tc-5-219-2011
Received 30 July 2012; revised 6 September 2012; accepted 9 September 2012; published 10 October 2012.
[1] Analysis of Northern Hemisphere spring terrestrial
snow cover extent (SCE) from the NOAA snow chart
Climate Data Record (CDR) for the April to June period
(when snow cover is mainly located over the Arctic) has
revealed statistically significant reductions in May and June
SCE. Successive records for the lowest June SCE have been
set each year for Eurasia since 2008, and in 3 of the past
5 years for North America. The rate of loss of June snow
cover extent between 1979 and 2011 (!17.8% decade!1)
is greater than the loss of September sea ice extent the climate community and has been subject to detailed
Author(s)in2011.
CC Attribution
3.0 Wang
License.
(!10.6% decade!1) over the same period. Analysis of Cou- ©evaluation
the spring
period [e.g.,
et al.,
2005;
KEY WORDS snow; ablation; North America
pled Model Intercomparison Project Phase 5 (CMIP5) model Brown et al., 2007, 2010; Frei and Lee, 2010].
Received
series11 January 2006; Revised 14 August 2006; Accepted 20 August 2006
output shows the marked reductions in June SCE observed
[4] Here we utilize the NOAA snow chart time
since 2005 fall below the zone of model consensus defined by (1967–2012) to (1) report the latest trends in snow cover
+/!1 standard deviation from the multi-model ensemble during the spring period when snow cover is confined
INTRODUCTION
1999; Brown, 2000). Variations in snow depth over
mean. Citation: Derksen, C., and R. Brown (2012), Spring snow largely to the Arctic, and temperature induced albedo feedthe same time period also exhibit decreasing trends,
cover extent reductions in the 2008–2012 period exceeding climate backs are strongest across high latitudes [Groisman et al.,
High-latitude environments within North America have particularly throughout the central Canadian Prairies and
model projections, Geophys. Res. Lett., 39, L19504, doi:10.1029/ 1994; Déry and Brown, 2007] and (2) place these trends in
been shown to have high seasonal and interannual Great Plains region of the United States (Dyer and
2012GL053387.
the context of recent CMIP5 climate model simulations.
variability in snow cover during the winter season over Mote, 2006). Possible causes for the decrease in spring
1
2
R. D. Brown and D. A. Robinson
the past decades (Groisman et al., 1994; Hughes and snow depth and snow extent in the latter part of the
2.
Data
Sets
1. Introduction
1 Climate Processes Section, Climate Research Division, Environment Canada @ Ouranos, 550 Sherbrooke
West, include decreasing snowfall (i.e. from a
Robinson, 1996; Frei and Robinson, 1999; Frei et al., 20th St.
Century
[5]Floor,
We focus
on theQC,
recently
NOAA1999).
snow chart
[2] Reliable information is needed on ongoing and future 19th
Brown (2000) demonstrated that North American general decrease in precipitation, or from an increased
Montréal,
H3Areleased
1B9, Canada
climate data record (CDR) [see Brown and Robinson,
2011] extent (SCE) during the fall and early winter
changes in terrestrial snow cover for a wide range of geo- 2 Department
snow
cover
of Geography, Rutgers University,
54 Global
Joyce
Kilmer Avenue, Piscataway, NJ 08854-8054, fraction
USA of precipitation falling as rain, e.g. Akinremi
physical applications, to advise policy and decision makers, maintained and housed at the Rutgers University
(November–January)
has increased since the early part and McGinn, 2000; Zhang et al., 2000; Groisman and
Snow Lab. The dataset was acquired (from http://climate.
and inform impact and adaptation activities. Of particular Received:
5 November 2010 – Published in The
Cryosphere
Discuss.:
November
2010 from Frei Easterling, 1994), a shorter snow accumulation period
of the
20th century.
This 24
agrees
with results
importance is the timing of snow melt in spring, due to the rutgers.edu/snowcover/) following the update ofetcontinental
al.
(1999),
who
showed
a
general
pattern
of increased (Dye, 2002), or an increase in frequency and/or intensity
Revised:
22
February
2011
–
Accepted:
27
February
2011
–
Published:
16
March
2011
climatological, hydrological, and ecosystem impacts of this monthly SCE through June 2012. The CDR combines the
SCE in the fall over the same period; however, Frei et al. of winter thaws. Although there is evidence supporting
original
190
km
resolution
NOAA
snow
charts
(1967–1999)
seasonal transition of the land surface [Callaghan et al.,
also showed a decreasing pattern of spring SCE, the relationship between changes in snowfall and snow
Interactive
2011]. Contemporary variability and trends in Arctic snow [Robinson et al., 1993] with the 24 km resolution(1999)
indicating
a shift of1theIntroduction
snow season to begin and end duration with decreasing spring snow depth and snow
Abstract.
An
update
is
provided
of
Northern
Hemisphere
Multi-Sensor
(IMS)
snow
product
(1999-present)
described
cover are occurring in the context of amplified increases in
Snow
in Ramsay
andApril)
updated
by cover
Helfrich
etearlier.
al.(SCE)
[2007].
spring[1998]
(March,
snow
extent
overdepth records, which provide additional extent, little attention has been paid to changes in
observed high latitude temperature relative to other regions (NH)
information
beyond Reliable
SCE, indicate
decreasing
SCE and
Production
of the
CDRincorporating
resulted in minor
modifications
[Serreze et al., 2009] with climate model scenarios showing the
1922–2010
period
the new
climate data
frequency
and/orinintensity of winter and spring
information
on spatial
and the
temporal
variability
to the IMS product, primarily by increasing the volume
snow extent
across
much
of
North
America
in
March
and ablation.
this warming is expected to continue [Vavrus et al., 2012]. record
(CDR) version of the NOAA weekly SCE dataset,
continental
and
hemispheric
snow
cover
extent
(SCE)
is imin mountainous areas. The NOAA snow chart April,
data record
particularly in the central Canadian Prairies (Dyer
[3] Monitoring Arctic snow cover is complicated by a lack with
Snow
melt
is known
annual
95%
confidence
intervals
estimated
from
reportant
for
climate
monitoring
(e.g.
Arndt
et
al.,
2010),
cli-to be influenced by the radiative
SCE 2006). This augments the conclusion that
of surface observations, and the high degree of spatial vari- has been found to agree well with other independent
and Mote,
balance
(Groisman
et al., 1994), and variations in air
gression
analysis
and
intercomparison
of
multiple
datasets.
mate
model
evaluation
(e.g.
Foster
et
al.,
1996;
Frei
et
al.,
ability relative to other snow covered regions [Liston, 2004] datasets during the Arctic spring melt period [Brown
North et
American
snow cover is decreasing in extent and temperature (Karl etal.,
al., 1993; Brown and Goodison,
uncertainty analysis indicates a 95% confidence
interval
2010].
2005; which
Roesch,
2006;
Brown
and Frei, 2007) and cryospheredue to pronounced topographic and vegetative controls on The
depth earlier
in the spring,
is of
critical
importance
1996),
as
well
as
surface
energy fluxes (Leathers et al.,
Hemisphere
spring
snow cover
[6] Northern
spring SCE
of ±5–10%
overterrestrial
the pre-satellite
period
wind-induced snow catchment and redistribution. In turn, in NH
climate
feedback
studies
(e.g. Hall
Qu, 2006; Fernanbecause
of the influence
of the
timing and
magnitude
of and
2004; Dyer and Mote, 2002; Kuusisto, 1986); therefore,
extent
(SCE)
anomalies
(excluding
Greenland)
for
April,
this heterogeneity introduces uncertainty into gridded snow and ±3–5% over the satellite era. The multi-dataset
analdes et
2009;hydrologic
Flanner etsystems.
al., 2011).with
Previously
published
spring snow
melt runoff
to al.,
regional
a shallower
winter snow cover, associated changes
May, and June (when snow cover is confined largely to the
datasets, including satellite-derived time series [i.e., Takala ysis
shows larger uncertainties monitoring spring
SCE overto satellite
According
snow
cover
data
over
North
estimates
of
Northern
Hemisphere
(NH)
monthly
SCE
used
in
surface
albedo
and
thermal properties of the snowpack
Arctic)
were
calculated
for
the
1967–2012
period
to
inveset al., 2011], conventional analyses [i.e., Brasnett, 1999], Eurasia (EUR) than North America (NA) due
to
the
more
America,
SCE
has
been
decreasing
through
the
1980s
for
evaluating
climate
models
and
monitoring
variability
and
could lead to an increased
sensitivity to any or all of
and reanalysis products [i.e., Dee et al., 2011]. While this tigate trends and variability during the satellite era. July and
The Cryosphere
Northern Hemisphere spring snow cover variability and change
over 1922–2010 including an assessment of uncertainty
C.J. Crawford, MtnClim 2014, Midway, Utah
Western North America Mountain Snowpack and
Streamflow Declines
476
JOURNAL OF HYDROMETEOROLOGY
VOLUME 6
Trends and Variability in Snowmelt Runoff in the Western United States
GREGORY J. MCCABE
U.S. Geological Survey, Denver, Colorado
MARTYN P. CLARK
University of Colorado, Boulder, Colorado
(Manuscript received 2 September 2004, in final form 5 January 2005)
ABSTRACT
The timing of snowmelt runoff (SMR) for 84 rivers in the western United States is examined to understand the character of SMR variability and the climate processes that may be driving changes in SMR
1136 indicate that the timing of SMR for many
JOU
RNA
F CLIM
A T E States has shifted to
timing. Results
rivers
inLtheOwestern
United
earlier in the snowmelt season. This shift occurred as a step change during the mid-1980s in conjunction with
a step increase in spring and early-summer atmospheric pressures and temperatures over the western
United States. The cause of the step change has not yet been determined.
!
1. Introduction
!
!
VOLUME 18
Changes toward Earlier Streamflow
Timing across
Western
NorthDettinger
America
summer temperatures
(Aguado
et al. 1992;
and Cayan 1995; Regonda et al. 2005; Stewart et al.
T. STEWART
Snowmelt runoff (SMR) is an important source of 2004)IRIS
and possibly global warming (Regonda et al.
Scripps Institution
Oceanography,
Jolla, California
water for much of the western United States (McCabe
2005; of
Stewart
et al. La
2004).
and Wolock 1999; Stewart et al. 2004), and for snowAlthough a number of studies have identified a shift
DANIEL R. CAYAN
melt-dominated basins in the western United States to earlier SMR for many rivers in the western United
Scripps
Institution
and U.S. Geological Survey, La Jolla, California
spring/summer runoff can account for
from
50% oftoOceanography,
States, these
studies have depended on trend analyses
80% of total annual runoff (Serreze et al. 1999; Stewart to identify these changes (Stewart et al. 2004). Trend
MICHAEL D. DETTINGER
et al. 2004). One of the expected hydrologic effects of analyses are unable to determine if a trend is gradual or
U.S. Geological Survey, and Scripps Institution of Oceanography, La Jolla, California
global warming is a shift in the timing of
SMR to earlier a step change. Often the interpretation of trend analysis
in the year (Gleick 1987; McCabe and Ayers 1988; is that the change identified is gradual. Since changes in
(Manuscript received 2 January 2004, in final form 9 July 2004)
Gleick and Adams 2000; Mote et al. 2005; Regonda et SMR timing have been identified by linear trends, there
GEOPHYSICAL
RESEARCH
LETTERS,
doi:10.1029/2007GL031022,
ABSTRACT
al. 2005). Consistent
with this
hypothesis
a numberVOL.
of 34,
is aL16402,
tendency
to attribute these changes2007
to global warmClick
in
spring
snowpack
Herestudies have identified declines
ing
because
of
large correlations
linear
trends
The
highly
variable
timing
of
streamflow
in
snowmelt-dominated
basins acrossbetween
western North
America
is
for
consequence,
and indicator,
of climate
Changes in the
timing
of snowmeltto important
earlier SMR
for many
in SMR
timingfluctuations.
and the increasing
trend
in global
temFull(Mote et al. 2005) and a shift an
derived streamflow from 1948 to 2002 were investigated in a network of 302 western North America gauges
Article
rivers in the western United States
(Aguado
et al. of1992;
often
difficult
to determine
by examining
the center
mass forperature.
flow, springIt
pulse
onsetisdates,
and seasonal
fractional the
flowsphysical
through
trend and principal
component
Statisticaldriving
analysis of
the streamflow
measures
with Pacific
Wahl 1992; Pupacko 1993; Dettinger
and Cayan
1995;analyses.
processes
linear
trends timing
because
so many
variclimate indicators identified local and key large-scale processes that govern the regionally coherent parts of
Rajagopalan and Lall 1995; Cayan
et al. 2001;
Regonda
ables with
linear trends
will correlate
highly with one
the changes and
their relative
importance.
Significance
of
trends
toward
earlier
snowmelt
runoff,
Columbia
and
and regionally
trends toward
of springtime
snowmelt
and streamflow
et al. 2005; Stewart et al. 2004).Widespread
The observed
shift coherent
to another.
To earlier
betteronsets
understand
the
climate
processes
have taken place across most of western North America, affecting an area that is much larger than previearlier SMR
is especially
noticeable
for basins inUnited
the driving
Missouri
Basin
headwaters,
western
States
the changes
to fractions
earlier of
SMR
timing
in theearlier
westously recognized. These timing changes
have resulted
in increasing
annual
flow occurring
northwestern United States (Mote
et al.year
2005;
Regonda
in the water
by 1–4
weeks. The immediate
(or proximal)
for the spatially
coherent parts of
the
ern United
States itforcings
is important
to appropriately
char1
1
2
year-to-year
fluctuations
longer-term trends of streamflow timing have been higher winter and spring
Johnnie
N.2005;
Moore,
Joel
T. 2004).
Harper,
and
Markalso
C.and
Greenwood
et al.
Stewart
et al.
These
studies
have
acterize
the
changes
as
gradual
trends
or
as
step
temperatures. Although these temperature changes are partly controlled by the decadal-scale Pacific cliindicated
that revised
the primary
driving
force
the
shiftpublished
in changes
mate
mode
[Pacific
decadal
oscillation
(PDO)],
a separate
significant
part2002).
of the variance is associated
(McCabe
and
Wolock
Received
15 June 2007;
9 July 2007;
accepted
17of
July
2007;
21
August
2007. and
with ainspringtime
trend that spans the PDO phases.
SMR timing has been increases
spring warming
and earlyThe objectives of this study are to 1) characterize the
[1] We assess changes in runoff timing over the last 55 years [2005] found negative linear trends in several measures of
changes in the timing of SMR, 2) identify when changes
at 21 gages unaffected by human influences, in the spring runoff timing for the period 1948 – 2003. For examin SMR timing have occurred, and 3) determine what
headwaters of the Columbia-Missouri Rivers. Linear ple, spring runoff in streams located in the headwaters of the
climate processes
are concurrent
likely driving
the
observed
little
change
in the
annual
1. address:
Introduction
Corresponding
author
Dr. Gregory McCabe,
USGS, Columbia
regression
models and
tests
for significance
that control
andbeen
Missouri
Rivers shifted
earlier
bytotal
about
6 – dischanges in thecharge
timing
of SMR
the western
United
(Wahl
1992; in
Aguado
et al. Stewart
1992;
Dettinger
Denverdiscoveries’’
Federal Center,ofMSmany
412, Denver,
CO
80225. with a
for ‘‘false
tests,
combined
19
days
for
the
55
year
period
of
record.
et al. and
The
loss
of
mountain
snow
accumulation
and
reducCayan 1995) and only spotty evidence for decreasing
E-mail: gmccabe@usgs.gov
States. and McCabe
conceptual
runoff response model, were used to examine the [2005]
and Clark [2005] attributed their
tions in snowmelt-derived water supply are among the
April–June streamflow volumes (Wahl 1992). Climatic
detailed structure of spring
runoffconsequences
timing. Weexpected
concludefrom
thatclimate
calculated
toward
runoff
primary
warming.trends
factors
affectearlier
the timing
andmostly
amounttooftemperadischarge in
Thus
it is with
somesignificant
concern that
a shift toward
earlier river
only about one third of the
gages
exhibit
trends
ture increase
ratherbasins,
than introducing
precipitation
trends.spatial
The and
recent
significant
temposnowmelt-derived
starting
in the
late Working
© 2005
Meteorological
Society
with time
butAmerican
over half
ofspring
the gages
tested showstreamflow,
significant
report
from
Group
of as
theregional
International
Panel
ral variability
as Iwell
coherence
(seeon
Cayan
1940s, Therefore,
has been observed
several
across
et al.(IPCC)
2001). The
regional coherence
in the observed
relationships with discharge.
runoff for
timing
is regions
Climate
Change
summarized
these findings
by
western North America (Roos 1987, 1991; Wahl 1992; changes for snowmelt-dominated basins strongly sugmore significantly correlated
with annual discharge than stating that in western North America earlier stream flows
Aguado et al. 1992; Pupacko 1993; Dettinger and gests that climate changes and, in particular, temperawith time. This result differs
from
previous
studies
of Stewart
runoff et imply
peak
water accumulation has shifted forward by
Cayan
1995;
Cayan et
al. 2001;
al. 2004;
Re-snow
ture, are playing a dominant role, as moderate temperain the JHM428
western USA that
equate
trends
to a during
aboutthetwo
1950
[Lemke
al., 2007]. snowmeltSuch
gonda
et al.linear
2005).time
Spring
streamflow
lastweeks
ture since
changes
mostly
affectetmidelevation
five decades
shifted so
that the
April–July
response to global warming.
Ourhasresults
imply
thatmajor
changes
in snowpack
snowmelt
runoff could
have(i.e.,
dominated and
basins
most susceptible
to melting
streamflow
peak
arrives Rockies
one or more
weeksimplications
ear- Mote 2003a).
predicting future snowmelt
runoff in
thenow
northern
major
for water resource management and
lier, resulting
in decliningcontrolling
fractions of spring
and early of Concurrent
with the For
observed
snowmelt
and streamwill require linking climate
mechanisms
sustainability
river ecosystems.
example,
predictions
river discharge. Importantly, as yet, there has flow timing trends, the average annual temperature
precipitation, rather thansummer
projecting
response to simple of major changes in snowpack timing in the Sierra Nevada
have increased 1°–2°C since the 1940s over the (north)
linear increases in temperature. Citation: Moore, J. N., J. T. have led the California
Department of Water Resources to
western part of North America, especially during the
C.J. Crawford, MtnClim 2014, Midway, Utah
Western Wyoming Study Region / Wind River Range
MODIS Terra January 18, 2014
Photo: A.E. Putnam
Wind River Range Glaciers
C.J. Crawford, MtnClim 2014, Midway, Utah
Krimmel (2002)
Historical Context for Snowmelt Runoff Modeling
C.J. Crawford, MtnClim 2014, Midway, Utah
C.J. Crawford, MtnClim 2014, Midway, Utah
~1216 sqkm2
~259 sqkm2
C.J. Crawford, MtnClim 2014, Midway, Utah
Research Objective #1: Evaluate historical trends in
discharge timing and amount, and decompose discharge
variability in the time domain for adjacent watersheds
with and without diversions
C.J. Crawford, MtnClim 2014, Midway, Utah
C.J. Crawford, MtnClim 2014, Midway, Utah
Pine Creek Watersheds
Moore et al. (2007), GRL
C.J. Crawford, MtnClim 2014, Midway, Utah
Green River at Warren
Bridge Watersheds
Moore et al. (2007), GRL
Interannual and Decadal Variability
Pine Creek
C.J. Crawford, MtnClim 2014, Midway, Utah
Green River
Ghil et al. 2002, Reviews of Geophysics
Ault and St. George 2010, J. Climate
C.J. Crawford, MtnClim 2014, Midway, Utah
Research Objective #2: Model snowmelt runoff using meteorological observations, snow-water-equivalent measurements, and
MODIS fractional snow cover during 2000-2012
MODIS Collection 6 Fractional Snow Cover
(MOD10A1F)
CGF Daily FSC Map Example
http://modis-snow-ice.gsfc.nasa.gov/
Crawford (2014), Hydrological Processes
C.J. Crawford, MtnClim 2014, Midway, Utah
C.J. Crawford, MtnClim 2014, Midway, Utah
Snowmelt Runoff Model (SRM)
Overview / Inputs
“conceptual semi-distributed hydrological model with
physical basis”
Martinec and Rango 1979, NASA Pub. 2216
Rango and Martinec 1995, WRB
Martinec et al. 2007, SRM Users Man.
C.J. Crawford, MtnClim 2014, Midway, Utah
SRM Structure
Q = average daily discharge [m3s-1]
c = runoff coefficient expressing the losses as a ratio (runoff
precipitation), with cS referring to snowmelt and cR to rain
a = degree-day factor [cm oC-1d-1] indicating the snowmelt depth
resulting from 1 degree-day
T = number of degree-days [oC d]
ΔT = lapse rate adjustment when extrapolating temperature to the
average hypsometric elevation of the basin or zone [oC d]
S = ratio of the snow covered area to the total area
P = precipitation contributing to runoff [cm]. A temperature threshold,
TCRIT, determines whether this contribution is rainfall and
immediate. If precipitation is determined by TCRIT to be new
snow, it is kept on storage
A = area of the basin or zone [km2]
k = recession coefficient indicating the decline of discharge in a period
without snowmelt or rainfall:
k = Qm+1/Qm (m, m + 1 are the sequence of days during a true
recession flow period).
n = sequence of days in discharge computation period
Qn+1 = [CSn * an (Tn + ΔTn) Sn + CRn * Pn] A * 10000/86400 * (1-kn+1) + Qn kn+1
C.J. Crawford, MtnClim 2014, Midway, Utah
Green River
Pine Creek
SRM Simulations
C.J. Crawford, MtnClim 2014, Midway, Utah
Green River
Pine Creek
SRM Simulations
C.J. Crawford, MtnClim 2014, Midway, Utah
Green River
Pine Creek
SRM Simulations
Green River
Pine Creek
SRM Simulations
C.J. Crawford, MtnClim 2014, Midway, Utah
Interannual SRM Performance
C.J. Crawford, MtnClim 2014, Midway, Utah
C.J. Crawford, MtnClim 2014, Midway, Utah
Concluding Thoughts
1. The trend towards earlier snowmelt runoff is modest along
northern periphery watersheds of the Upper Colorado River
Basin
2. Below average discharge is evident across percentiles and
watersheds since the late 1980s
3. SRM can skillfully simulate snowmelt runoff at daily and
seasonal timescales, although diversions and spatial aggregation
underpin model accuracy
The Way Forward
C.J. Crawford, MtnClim 2014, Midway, Utah
1. Transform SRM into an operational environment and move
towards an ensemble approach
2. Identify ways propagate uncertainty through model inputs and
parameterization