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Effects of Observed Climate Variability and Climate Change on Flooding i n the Pacific Northwest. Dr. Alan F. Hamlet Climate Impacts Group Dept. of Civil and Environmental Engineering University of Washington. Columbia Basin Climate Change Scenarios Project Research Team - PowerPoint PPT Presentation
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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
Columbia Basin Climate Change Scenarios Project 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
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 hydrologic 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)
http://www.abbegeomorphology.com/?p=69
Impacts to Geomorphology
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
10000
15000
20000
25000
30000
35000
40000
45000
50000
Snohomish Basin WY 1999: peak flow = 47420 cfs
Str
eam
flow
(cf
s)
Day of the Water Year (1 = Oct 1)
Step 2: Rank Extreme Events for All Years and Estimate Quantiles
0.99
0.96
0.93
0.89
0.86
0.83
0.80
0.76
0.73
0.70
0.66
0.63
0.60
0.57
0.53
0.50
0.47
0.43
0.40
0.37
0.34
0.30
0.27
0.24
0.20
0.17
0.14
0.11
0.07
0.04
0.01
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
Median Annual Flood
Str
eam
flow
(cf
s)
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 climate variability 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
Simulate Daily Time Step Streamflow from
1916-2003
Fit GEV Probability Distributions To Estimate Flood Risks
Analyze Changes in Flood RisksAssociated with
Observed Warming, Precipitation Change, etc.
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-0.50
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1.50
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2.50
3.00
oct nov dec jan feb mar apr may jun jul aug sepL
inea
r T
ren
d (
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
Lin
ear
Tre
nd
(D
eg. C
per
cen
tury
)
CA
CRB
GBAS
PNWTmin
Tmax
PNW
CA
CRB
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)
X20 2003 / X20 1915
X20 2003 / X20 1915
Simulated Changes in the 20-year Flood Associated with 20th Century Warming for a Constant Precipitation Regime
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
-3
-2
-1
0
1
2
3
4
1916
1920
1924
1928
1932
1936
1940
1944
1948
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
Std
An
om
alie
s R
elat
ive
to 1
961-
1990
PNW
CA
CRB
GB
Regionally Averaged Cool Season Precipitation Anomalies
PRECIP
ftp://ftp.hydro.washington.edu/pub/jhamman/PNWCSC_2011/Poster2%20final.pdf
X20 ’73-’03 / X20 ’16-’03
DJF
Avg
Tem
p (
C)
X20 ’73-’03 / X20 ’16-’03
20-year Flood for “1973-2003” Compared to “1916-2003” for a Constant Late 20th Century Temperature Regime
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(End of 21st Century for the A2 Scenario)
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 Delta 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”
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 GEV Probability Distributions To Estimate Flood Risks
Compare FloodRisks to Those in the 20th Century
0.99
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0.91
0.86
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0.77
0.73
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0.34
0.29
0.25
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0.16
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0.07
0.03
0
20000
40000
60000
80000
100000
120000
HIST
ECHAM5_2040
SNOMO
Str
eam
flow
(cf
s)
Probability of Exceedance
2040s Changes in Flood RiskSnohomish at Monroe
A1B B1
Historical
10 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
1970-1999
1970-19992040-2069
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 F
low
(cf
s)
Dai
ly P
eak
Flo
w (
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
Flo
w (
cfs)
Dai
ly P
eak
Flo
w (
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
Flo
w (
cfs)
Dai
ly P
eak
Flo
w (
cfs)
Date of Peak Flow (1 = Oct 1) Probability of Exceedence
Columbia River at The DallesD
aily
Pea
k F
low
(cf
s)
Dai
ly P
eak
Flo
w (
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.
Extras
Avg WY Date of Flooding VIC
Avg
WY
Dat
e o
f F
loo
din
g O
BS
Ln
(X
100
/ X
mea
n)
O
BS
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
X1
00 G
EV
flo
od
/mea
n f
loo
dRed = VIC
Blue = OBS
5-yr
20-yr
10-yr
50-yr
100-yr
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
20
30
40
50
60
70
80
90
100
hist
future
Dai
ly P
reci
pita
tion
(mm
)
Day of Month
Monthly to Daily Precipitation Scaling
SeaTac. Feb, 1996, hypothetical 30% Increase