a geological framework for interpreting effects of climate ... · a geological framework for...
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Gordon E. Grant
USDA Forest Service PNW Research Station
C. Naomi Tague
Bren School
University of California Santa Barbara
A geological framework for interpreting effects of climate
warming on streamflow in Western U.S. watersheds: what can the past
tell us about the future?
Mohammad SafeeqSarah Lewis
College of Earth, Ocean & Atmospheric Science
Oregon State University
(Nolin and Daly, 2006)
Snow at risk in a warming climate
22% Oregon Cascades
12% Washington Cascades
61% Olympic Range
<3% Pacific Northwest study area
Red = rain instead of snow in the winter
2
average annual for Pacific Northwest
Change in SWE(Snow Water Equivalence)
2020
A1B applied
2040
A1B applied
(Sproles, in prep; OSU PhD thesis)
3
How might
climate change
play out in
landscapes with
strong geological
contrasts?
Tague & Grant, 2009
But…it’s not just
about snow
A. High Cascade(920-2035m)
0
2
4
6
8
10
B. Western Cascade(410-1630m)
Me
an
un
it d
isch
arg
e (
mm
/da
y)
0
2
4
6
8
10
12
14
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
wetter
winters
earlier and lower
snowmelt peak
decreased
summer flow
wetter
winters
minimal
snowmelt earlier summer
drought
Modeling scenarios: current climate; 1.5°C/1.5°C warming
Tague & Grant, 2009
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Historical Trends from Cascade Streams
0
50
100
150
10/1/01 12/1/01 2/1/02 4/1/02 6/1/02 8/1/02 10/1/02
Dis
ch
arg
e (
m3/s
)
Temporal
Centroid (Tc)
(Jefferson, 2006; Jefferson et al., 2008)
Between 1948 and 2006 for Clear Lake:
Temporal centroid - 14 days earlier
Autumn minimum discharge - 1.4 cms lower
Au
gu
st
me
an
dis
ch
arg
e
Can we develop a
more rigorous
analytical
framework to link
changing climate
and underlying
drainage
efficiency?
Tague & Grant, 2009
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Simple model (from Tague and Grant, 2009)
Qt – streamflow at time t (in days)
Qo – streamflow at beginning of
recession
k – recession constant
( ) ( )ktoeQtQ −=
Treating recharge as a single event, we develop a model for summer baseflow:
Qr – summer streamflow
k - drainage efficiency
tr - days between snowmelt (tpk) and time of interest (tsummer)
pk15-day- snowmelt input (peak reduction in a watershed areal
mean of a 15 day running average
Qr = pkl 5−daye−k tr( )
pk15-day
tr
k
(Tague & Grant, 2009)
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Summer flow sensitivity to changes in
snowmelt dynamics (first derivatives)
∂Qr
∂ pk15day( )= e
−k tr( )
∂Qr∂ tr( )
= pk15day ∗ ke−k tr( )
Magnitude
(pk15-day)
Timing
(tr)
Both contain
k, drainage
efficiency
(Tague & Grant, 2009)
(Tague & Grant, 2009)
unit c
hange in
daily
stre
am
flow
(mm
/day)
sensitive
Not sensitive
Magnitude
deep/slow shallow/fast
short
long
7
(Tague & Grant, 2009)
unit c
hange in
daily
stre
am
flow
(mm
/day)
sensitive
Not sensitive
deep/slow shallow/fast
short
long
Timing
Can we use this
framework to
interpret historical
trends in
streamflow in the
western US?
Tague & Grant, 2009
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Interpreting historical streamflow
trends across western US
• Use conceptual framework to identify key
metrics
– Fraction of snow, k
• Extract these metrics from unregulated basins
with long-term streamflow records across
western US
• Use metrics to classify basins into six populations
• Examine streamflow trends in these six classes
and compare to predictions from framework
Rain
Slow
Mixed
Slow
Snow
Slow
Rain
Fast
Mixed
Fast
Snow
Fast
Snowmelt dominated
Climate / Precipitation
(snow fraction)
Drainage
Efficiency
(k)
Rain dominated
Fast draining
(high k)
Slow draining
(low k)
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Classification
of Study Basins
Source: Safeeq et al., in review
Low k (slow draining)
High k (fast draining)
Rain Mixed Snow
Ensemble Hydrographs
Source: Safeeq et al., in review
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Historical Trends in Monthly Streamflow (1950–2010)
Low k (slow draining)
High k (fast draining)
Rain Mixed Snow
Source: Safeeq et al., in review
Historical Trends in Monthly Streamflow (1950–2010)
Low k (slow draining)
High k (fast draining)
Rain Mixed Snow
For Rain-Dominated Basins (Snow <10%)
• Greatest declines in fall and
winter
•Due to change in ppt
• Small difference
between low and
high k
Source: Safeeq et al., in review
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Historical Trends in Monthly Streamflow (1950–2010)
Low k (slow draining)
High k (fast draining)
Rain Mixed Snow
For Snow-Dominated Basins:(Snow >45%)
• Greatest declines in late
spring and summer
• Diminished
snowpack
and/or earlier
snowmelt
• Greater late
summer decline
in low k basinsSource: Safeeq et al., in review
Historical Trends in Monthly Streamflow (1950–2010)
Low k (slow draining)
High k (fast draining)
Rain Mixed Snow
For mixed basins: (10 to 45% snow)
• Overall trend
declining but
variable
• Most sensitive
to rain/snow
threshold
• Slightly
greater
decline in
summer in
low k basins
Source: Safeeq et al., in review
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Historical Trends in Summer Runoff Ratio (1950–2010)L
ow
k (
slo
w d
rain
ing
)H
igh
k (
fas
t d
rain
ing
)
Rain Mixed Snow
(Qsummer / Annual Precipitation)
Source: Safeeq et al., in review
Historical Trends in Summer Runoff Ratio (1950–2010)
Lo
w k
(s
low
dra
inin
g)
Hig
h k
(fa
st
dra
inin
g)
Rain Mixed Snow
Trends in
mixed and
snow basins
are negative
(Qsummer / Annual Precipitation)
Source: Safeeq et al., in review
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Historical Trends in Summer Runoff Ratio (1950–2010)L
ow
k (
slo
w d
rain
ing
)H
igh
k (
fas
t d
rain
ing
)
Rain Mixed Snow
Slopes steepen
with ↓k and ↑snow
1) change in type
of ppt?
2)more ET in
summer?
(Qsummer / Annual Precipitation)
Source: Safeeq et al., in review
Approaches to predicting streamflow
sensitivity to climate change
“Top-down“ Approach :
GCM with greenhouse forcing
Downscalling/regionalization
Hydrologic Model
Future Projection /
Sensitivity
Streamflow sensitivity
at landscape scale
Regionalization of Metrics
Identify “Key” Metrics
Conceptual Model
“Bottom-up” Approach:
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Streamflow sensitivity to unit change of recharge
Sensitivity =
Streamflow sensitivity to change in timing of recharge
Sensitivity =∂Qr∂ tr( )
= pk15day ∗ ke−k tr( )
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Take home messages…
• Historical streamflow trends generally
support the theoretical framework
• It’s not just about the snow
– Streamflow is declining in fall and winter in
rain-dominated basins
– For snow-dominated basins, greatest declines in
late spring and summer; decline higher in low k
basins
– In mixed basins, overall trend is declining, but
quite variable from month to month
• We can now classify
and map sensitivity
at the landscape
scale
• Considering the full
palette of hydro-
geological processes
is essential to
predicting
streamflow response
to climate changeTague & Grant, 2009
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Extra Slides
www.fsl.orst.edu/wpg
July
validation
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Sept
validation
Sensitivity (discharge)
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Comparison of model performance in predicting seasonal
accumulated streamflow between 1950-2006 (n=51)
1/120 degree simulation
show better
performance during
summer on the expense
of spring, particularly in
groundwater dominated
watersheds. Both
simulations agrees well
with declining
streamflow trends,
except during winter &
spring where simulated
decreasing trends are
lower than the
observed.
Comparison of model performance for the different
runoff percentiles between 1950-2006 (n=51)
1. Poor model
performance in
predicting lower
percentile in
groundwater
dominated
watersheds
2. Opposite is true for
higher percentile
flow: poor model
performance in
surfaceflow
dominated
watersheds
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Comparison of model performance for the CT and
low and High flow pulse counts between 1950-2006
(n=51)
1. Both observed and
simulated
streamflow show
decreasing trend in
high pulse count and
increasing trend in
low pulse count.
2. High pulse counts
are over predicted in
groundwater
dominated
watersheds and low
pulse counts are
under-predicted in
surface flow
dominated
watersheds
Lookout Creek, Western Cascade Geology
Clear Lake, High Cascade Geology
Source: WPG 2012; unpublished data
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Sensitivity
Overall sensitivity (timing & magnitude)
July September
Low: Log (Sensitivity) <-4.0
Medium: -4.0< Log (Sensitivity)<-2.0
High: Log (Sensitivity)>-2.0
Sensitivity class:
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Timing (A) and Magnitude (B) of recharge
(precipitation)
–estimated using gridded precipitation data (1915-2006) (courtesy USFS & CIG)
Timing (C) and Magnitude (D) of recharge (snowmelt)
–estimated using VIC simulated snowmelt data (1915-2006) (courtesy USFS, USBR
& CIG)
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Predict and map sensitivity
of streamflows to climate
change in OR and WA
• Create map of sensitivity
where we don’t have streamflow
– Focus on 5th field watersheds
• Develop correlation between watershed
characteristics and key hydrologic metrics
– Derive k from:
• aquifer and soil permeability and relief (OR)
• soil permeability, BFI (from USGS) and relief (WA)
– Extract timing and magnitude of recharge from VIC
modeled data (tr and tpk)
Selected watersheds to determine k
217 unregulated basins (Falcone et al., 2010)
Drainage area:
1.5-8080 mi2
Time period for analysis:
1950-2010
Method for determining k:
modified from Vogel and Kroll
(1992) to exclude the
snowmelt period
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Recession
constant, k
OR: estimated from
aquifer and soil
permeability and relief
WA: estimated from
USGS BFI and relief
Snowmelt
estimated using
VIC simulated
snowmelt data
(1915-2006)
(courtesy USFS,
USBR & CIG)
Precipitation
estimated using
gridded
precipitation data
(1915-2006)
(courtesy USFS &
CIG)
Recharge
(i) Magnitude
(mm/day)
(ii) Timing (day of
water year)
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Snow fraction:
rain vs. snow