dr. alan f. hamlet climate impacts group dept. of civil and environmental engineering

Dr. Alan F. Hamlet

• Climate Impacts Group• Dept. of Civil and Environmental Engineering

University of Washington

Effects of Observed Climate Variability and Climate Change on Flooding in the Pacific Northwest

Climate Impacts Group Research Team

Lara Whitely BinderPablo CarrascoJeff DeemsMarketa McGuire ElsnerAlan F. HamletCarrie LeeSe-Yeun LeeDennis P. LettenmaierJeremy LittellGuillaume MaugerNate MantuaEd MilesKristian MickelsonPhilip W. MoteRob NorheimErin RogersEric SalathéAmy SnoverIngrid TohverAndy Wood

http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_chap1_intro_final.pdf

Land surface hydrology and modeling (Elsner et al. 2010; Hamlet and Lettenmaier 2005; Hamlet et al. 2005, 2007; Leung et al. 1999; Mote et al. 2005; Painter et al. 2010)

Integrated hydrological and water resources modeling (Hamlet and Lettenmaier 1999a; Miles et al. 2000; Payne et al. 2004; Vano et al. 2010)

Water resources planning and management (Hamlet 2003; Lee et al. 2009, 2011a,b; Miles et al. 2000; Payne et al. 2004; )

Flooding and assessment of hydrologic extremes (Hamlet and Lettenmaier 2007; Mantua et al. 2010; Lee et al. 2009, 2011a,b)

Impacts of climate variability and climate change on hydrology and water resources (Hamlet and Lettenmaier 1999a; Hamlet and Lettenmaier 1999b; Miles et al. 2000; Mote et al. 2003; Adam et al. 2009; Elsner et al. 2010)

Sustainable water resources management and climate change adaptation strategies (Miles et al. 2000; Hamlet 2003; Snover et al. 2003; Slaughter et al. 2010; Whitely Binder et al. 2010; Hamlet 2010 )

Overview of Research Interests:

Long-lead climate and streamflow forecasts and related water resources applications (Hamlet and Lettenmaier 1999b; Hamlet et al. 2002; Voisin et al. 2006; Lee et al. 2011b)

Modeling of freshwater, and estuarine ecosystems (Crozier et al. 2008; Wenger et al. 2010, 2011; Mantua et al. 2010)

Forest hydrology and impacts to terrestrial ecosystems (Littell et al. 2010)

Paleoclimatic precipitation and streamflow reconstruction (Lutz et al. 2012)

Climate impacts on hydropower and energy systems (Hamlet and Lettenmaier 1999a, Hamlet et al. 2002; Voisin et al. 2006; Hamlet et al. 2010)

Climate services (Gamble et al. 2002; Snover et al. 2003)

Research Interests (cont):

Land surface hydrology and modeling (Elsner et al. 2010; Hamlet and Lettenmaier 2005; Hamlet et al. 2005, 2007; Leung et al. 1999; Mote et al. 2005; Painter et al. 2010)

Integrated hydrological and water resources modeling (Hamlet and Lettenmaier 1999a; Miles et al. 2000; Payne et al. 2004; Vano et al. 2010)

Water resources planning and management (Hamlet 2003; Lee et al. 2009, 2011a,b; Miles et al. 2000; Payne et al. 2004; )

Flooding and assessment of hydrologic extremes (Hamlet and Lettenmaier 2007; Mantua et al. 2010; Lee et al. 2009, 2011a,b)

Impacts of climate variability and climate change on hydrology and water resources (Hamlet and Lettenmaier 1999a; Hamlet and Lettenmaier 1999b; Miles et al. 2000; Mote et al. 2003; Adam et al. 2009; Elsner et al. 2010)

Sustainable water resources management and climate change adaptation strategies (Miles et al. 2000; Hamlet 2003; Snover et al. 2003; Slaughter et al. 2010; Whitely Binder et al. 2010; Hamlet 2010 )

Overview of Research Interests:

The Myth of Stationarity:

1) Climate Risks are stationary in time.

2) Observed streamflow records are the best estimate of future variability.

3) Systems and operational paradigms that are robust to past variability are robust to future variability.

Image Credit: National Snow and Ice Data Center, W. O. Field, B. F. Molniahttp://nsidc.org/data/glacier_photo/special_high_res.html

Aug, 13, 1941 Aug, 31, 2004

The Myth of Stationarity Meets the Death of Stationarity

Muir Glacier in Alaska

Why a Focus on Hydrologic Extremes?

Many human and natural systems are quite robust under “normal” conditions, but have the potential to be profoundly impacted by hydrologic extreme events.

Public Safety and Economic Impacts

Damage to Infrastructure

http://www.nps.gov/mora/parknews/upload/flooddamagev3.pdf

Nisqually River at Sunshine Point (Nov, 2006)

Sediment Transport and Mudslides

Nuts and Bolts:

Traditional Methods for Estimating Hydrologic

Extremes

Step 1: Select Extreme Event from Each Historical Year

1 12 23 34 45 56 67 78 89 1001111221331441551661771881992102212322432542652762872983093203313423533640

5000

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50000

Snohomish Basin WY 1999: peak flow = 47420 cfs

Stre

amflo

w (c

fs)

Day of the Water Year (1 = Oct 1)

Step 2: Rank Extreme Events for All Years and Estimate Quantiles

0.99

0.96

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Median Annual Flood

Stre

amflo

w (c

fs)

Probability of Exceedance

1999

Step 3: Fit a Probability Distribution to the Data

Examples of Commonly Used Probability Distributions:

• Extreme Value Type 1 (EV 1)

• Log Normal (LN)

• Log Pearson

• Generalized Extreme Value (GEV)

For climate change experiments, GEV is a good choice since the true nature of the future probability distributions is essentially unknown. However it turns out that the choice of distribution is not very critical in terms of the evaluating the sensitivity to warming and/or precipitation change.

Step 4: Estimate Extremes Associated with Return Intervals

Site Name Ret. Int. Flow (cfs)SNOMO : 20 68660SNOMO : 50 81332SNOMO : 100 91145

Note that any return interval can be estimated. E.g. one could provide an estimate of the “5000 year flood”, albeit with large uncertainty.

Historical Perspectives:

Changing Flood Risk in the 20th Century

References:

Neiman, P.J., L.J. Schick, F.M. Ralph, M. Hughes, and G.A. Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers.

J. of Hydrometeorology, 12, 1337–1358

Hamlet A.F., D.P. Lettenmaier, 2007: Effects of 20th century warming and climatevariability on flood risk in the western U.S. Water Resour Res,

43:W06427.doi:10.1029/2006WR005099

Observed Characteristics of Extreme Precipitation Events

Evidence of Changing Flood Statistics

Role of Atmospheric Rivers in Flooding (Nov 7, 2006)

Neiman, P.J., L.J. Schick, F.M. Ralph, M. Hughes, and G.A. Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers. J. of Hydrometeorology, 12, 1337–1358

Neiman, P.J., L.J. Schick, F.M. Ralph, M. Hughes, and G.A. Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers. J. of Hydrometeorology, 12, 1337–1358

Role of Atmospheric Rivers in Flooding (Oct 20, 2003)

Modeling Studies of Changing 20th Century Flood Risk in the West

Snow Model

Schematic of VIC Hydrologic Model• Sophisticated, fully distributed,

physically based hydrologic model• Widely used globally in climate

change applications• 1/8th Degree Resolution (~50 km2)

General Model Schematic

Avg WY Date of Flooding VIC

Avg

WY

Dat

e of

Flo

odin

g O

BS

Ln (X

100 /

Xm

ean)

OB

S

Ln (X100 / Xmean) VIC

Evaluating the Hydrologic Model Simulations in the Context of Reproducing Flood Characteristics

Red = PNW, Blue = CA, Green = Colo, Black = GB

Zp

X10

0 GEV

floo

d/m

ean

flood

Red = VICBlue = OBS

5-yr

20-yr

10-yr

50-yr

100-yr

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

oct nov dec jan feb mar apr may jun jul aug sepLi

near

Tre

nd (

Deg

. C p

er c

entu

ry)

CA

CRB

GBAS

PNW

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

oct nov dec jan feb mar apr may jun jul aug sep

Line

ar T

rend

(Deg

. C p

er c

entu

ry)

CA

CRB

GBAS

PNWTmin

Tmax

PNW

CACRB

GB

Regionally Averaged Temperature Trends Over the Western U.S. 1916-2003

Tem

pera

ture

Historic temperature trend

in each calendar month

1915 2003

Detrended Temperature Driving Data for Flood Risk Experiments

“Pivot 2003” Data Set

“Pivot 1915” Data Set

DJF

Avg

Tem

p (C

)

Simulated Changes in the 20-year Flood Associated with 20th Century Warming

X20 2003 / X20 1915

X20 2003 / X20 1915

Freezing Level

SnowSnow

Schematic of a Cool Climate Flood

Precipitation Produces Snow

Precipitation Produces Snow

Precipitation Produces Runoff

Snow Melt

Freezing Level

SnowSnow

Schematic of a Warm Climate Flood

Pre

cipi

tatio

n P

rodu

ces

Sno

w

Pre

cipi

tatio

n P

rodu

ces

Sno

w

Precipitation Produces Runoff

Snow Melt

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mal

ies

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ativ

e to

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PNWCACRBGB

Regionally Averaged Cool Season Precipitation Anomalies

PRECIP

ftp://ftp.hydro.washington.edu/pub/jhamman/PNWCSC_2011/Poster2%20final.pdf

20-year Flood for “1973-2003” Compared to “1916-2003” for a Constant Late 20th Century Temperature Regime

X20 ’73-’03 / X20 ’16-’03

DJF

Avg

Tem

p (C

)

X20 ’73-’03 / X20 ’16-’03

Hypotheses Regarding 21st Century Flooding Impacts

Rain Dominant Basins:Potential increases in flooding due to increased precipitation intensity, but no significant change from warming alone.

Mixed Rain and Snow Basins Along the Coast:Strong increases in flooding due to warming and increased precipitation intensity (both effects increase flood risk)

Inland Snowmelt Dominant Basins:Relatively small overall changes because effects of warming (decreased risks) and increased precipitation intensity (increased risks) are in the opposite directions.

T P

T P

T P

Effects of ENSO and PDO on Flood Risk

DJF

Avg

Tem

p (C

)

DJF

Avg

Tem

p (C

)

DJF

Avg

Tem

p (C

)

X100 nENSO / X100 2003 X100 cENSO / X100 2003X100 wENSO / X100 2003

X100 nENSO / X100 2003 X100 cENSO / X100 2003X100 wENSO / X100 2003

DJF

Avg

Tem

p (C

)

DJF

Avg

Tem

p (C

)

DJF

Avg

Tem

p (C

)

X100 nPDO / X100 2003 X100 cPDO / X100 2003X100 wPDO / X100 2003

X100 nPDO / X100 2003 X100 cPDO / X100 2003X100 wPDO / X100 2003

Scenarios of Flood Risk in the 21th Century

Consensus Forecasts of Temperature and Precipitation Changes from IPCC AR4 GCMs

Mote, P.W. and E. P. Salathe Jr., 2010: Future climate in the Pacific Northwest, Climatic Change, DOI: 10.1007/s10584-010-9848-z

21st Century Climate Impacts for the Pacific Northwest Region

Seasonal Precipitation Changes for the Pacific Northwest

Mote, P.W. and E. P. Salathe Jr., 2010: Future climate in the Pacific Northwest, Climatic Change, DOI: 10.1007/s10584-010-9848-z

http://www.hydro.washington.edu/2860/

• Smaller basins down to ~500 km2

• Monthly and daily streamflow time series

• Assessment of hydrologic extremes

(e.g. Q100 and 7Q10)

Columbia Basin Climate Change Scenarios Project

297 Sites

Available PNW Scenarios

2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099

Downscaling ApproachA1B

Emissions Scenario

B1 Emissions Scenario

Hybrid Delta

hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t47 pcm1 miroc_3.2 ipsl_cm4 hadgem1

2020s 10 9

2040s 10 9

2080s 10 9

Transient BCSD

hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t47 pcm1

1950-2098+ 7 7

Delta Method

composite of 10

2020s 1 12040s 1 12080s 1 1

Hybrid Downscaling Method

• Performed for each VIC grid cell:

Hist. DailyTimeseries

Hist. MonthlyTimeseries

Historic Monthly CDF

Bias CorrectedFuture

Monthly CDF

Projected DailyTimeseries

1916-2006

1970-1999

30 yr window

1916-2006

1916-2006“Base Case”

Spatial Variability of Temperature and Precipitation Changes

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 290

10

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hist

future

Dai

ly P

reci

pita

tion

(mm

)

Day of Month

Monthly to Daily Precipitation ScalingSeaTac. Feb, 1996, hypothetical 30% Increase

Snow Model

Schematic of VIC Hydrologic Model• Sophisticated, fully distributed,

physically based hydrologic model• Widely used globally in climate

change applications• 1/16th Degree Resolution

(~5km x 6km or ~ 3mi x 4mi)

General Model Schematic

Watershed Classifications:Transformation From Snow to Rain

Map: Rob Norheim

Flood Analysis: What’s In? What’s Out?Issue Affecting Analysis Yes No

Based on explicit daily time step simulations of streamflow?

Yes

Changing freezing elevation?

Yes

Rain on snow captured? YesIncreases/decreases in storm intensity?

Yes (monthly statistics only)

Changes in tails of probability distributions affecting extreme daily precipitation ?

No

Changes in size and sequencing of storms?

No

Changes in small scale thunder storms?

No

Includes water management effects?

No

Simulate Daily Time Step Streamflow Scenarios

Associated with Changesin Climate

Fit Probability Distributions To Estimate Flood and Low Flow

Risks

Compare FloodRisks to Those in the 20th Century

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HISTECHAM5_2040

SNOMO

Stre

amflo

w (c

fs)

Probability of Exceedance

2040s Changes in Flood RiskSnohomish at Monroe

A1B B1

Historical10 Member Ensemble Using the Hybrid Delta Downscaling Approach

Changes in High Flows

Q100 values are projected to systematically increase in many

areas of the PNW due to increasing precipitation and

rising snowlines.

http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_chap7_extremes_final.pdf

Relationship Between Change in Q100 and Winter Temp

Changes in Q100 at Small Spatial Scales

Improving Flood Risk Projections Using High Resolution Regional

Climate Models

Overview of Key Science Questions:

Will daily precipitation statistics at smaller spatial scales change differently in response to global climate change than monthly precipitation statistics at large spatial scales?

Will the nature of extreme storms (such as atmospheric rivers) change in response to global climate change?

Will different areas of the PNW experience substantially different changes in extreme precipitation and flood statistics (e.g. the west slopes of the Cascades vs. the east slopes)?

Will the seasonal timing of flood events change?

Regional Climate Modeling at CIG WRF Model (NOAH LSM) 36 to 12 km

ECHAM5 forcing CCSM3 forcing (A1B and A2 scenarios)

HadRM 25 km HadCM3 forcing

Snohomish River Near Monroe, WA

Downscaling

• WRF output is first regridded to 1/16th degree• Then, for each VIC grid cell:

WRF Daily Downscaling Method

WRF DailyTime Series

WRFDaily CDF

Historical VIC Daily CDF

Bias Corrected Daily Time Series

1970-19991970-19992040-2069

1970-19992040-2069

1970-1999

The storm size distribution and time series behavior of the simulations comes directly from the daily WRF simulations.

Preliminary Results for the ECHAM5 A1B Sceario for the 2050s.

Chehalis River at PorterD

aily

Pea

k Fl

ow (c

fs)

Dai

ly P

eak

Flow

(cfs

)

Date of Peak Flow (1 = Oct 1) Probability of Exceedence

ECHAM5 2050 A1B ECHAM5 2050 A1B

Sauk River near Sauk

ECHAM5 2050 A1B ECHAM5 2050 A1B

Dai

ly P

eak

Flow

(cfs

)

Dai

ly P

eak

Flow

(cfs

)

Date of Peak Flow (1 = Oct 1) Probability of Exceedence

Boise River at Boise

ECHAM5 2050 A1B ECHAM5 2050 A1B

Dai

ly P

eak

Flow

(cfs

)

Dai

ly P

eak

Flow

(cfs

)

Date of Peak Flow (1 = Oct 1) Probability of Exceedence

Columbia River at The DallesD

aily

Pea

k Fl

ow (c

fs)

Dai

ly P

eak

Flow

(cfs

)

Date of Peak Flow (1 = Oct 1) Probability of Exceedence

ECHAM5 2050 A1B ECHAM5 2050 A1B

Changing Nature of Extreme Storms?

2030-11-27

2056-11-20

Conclusions:

• Our initial exploration of changing flood risk in the PNW using statistical downscaling points to increasing flood risk in most areas of the region due to projected regional warming and increases in cool season precipitation.

• Regional climate models offer more physically based assessment tools for understanding the potential changes in nature of extreme storms (such as atmospheric rivers), the timing of flooding, and a potentially improved picture of the spatial variations in changing hydrologic extremes across the region.

• Initial results suggest more extreme storms in the early fall and general increases in flood intensity will accompany global climate change in the PNW. In particular, many sites show distinct shifts towards flooding earlier in the water year due to the combination of changes in snowpack and earlier storms.