1

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

4

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

5

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)

6

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

8

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)

9

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

10

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

11

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

12

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

13

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:

14

Streamflow sensitivity to unit change of recharge

Sensitivity =

Streamflow sensitivity to change in timing of recharge

Sensitivity =∂Qr∂ tr( )

= pk15day ∗ ke−k tr( )

15

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

16

Extra Slides

www.fsl.orst.edu/wpg

July

validation

17

Sept

validation

Sensitivity (discharge)

18

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

19

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

20

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:

21

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)

22

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

23

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)

24

Snow fraction:

rain vs. snow