Effects of Observed Climate Variability and Climate Change on Flooding in 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 Lara Whitely Binder Pablo Carrasco Jeff Deems Marketa McGuire Elsner Alan F. Hamlet Carrie Lee Se-Yeun Lee Dennis P. Lettenmaier Jeremy Littell Guillaume Mauger Nate Mantua Ed Miles Kristian Mickelson Philip W. Mote Rob Norheim Erin Rogers Eric Salathé Amy Snover Ingrid Tohver Andy Wood
Overview of Research Interests: • 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 )
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.
The Myth of Stationarity Meets the Death of Stationarity Muir Glacier in Alaska Aug, 13, 1941 Aug, 31, 2004 Image Credit: National Snow and Ice Data Center, W. O. Field, B. F. Molnia http://nsidc.org/data/glacier_photo/special_high_res.html
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.
Damage to Infrastructure Nisqually River at Sunshine Point (Nov, 2006) http://www.nps.gov/mora/parknews/upload/flooddamagev3.pdf
Impacts to Geomorphology http://www.abbegeomorphology.com/?p=69
Nuts and Bolts: Traditional Methods for Estimating Hydrologic Extremes
Step 1: Select Extreme Event from Each Historical Year Streamflow (cfs) Day of the Water Year (1 = Oct 1)
Step 2: Rank Extreme Events for All Years and Estimate Quantiles 1999 Streamflow (cfs) Probability of Exceedance
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 68660 SNOMO : 50 81332 SNOMO : 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
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
Role of Atmospheric Rivers in Flooding (Oct 20, 2003) 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
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 Snow Model
Simulate Daily Time Step Streamflowfrom 1916-2003 Fit GEV Probability Distributions To Estimate Flood Risks Analyze Changes in Flood Risks Associated with Observed Warming, Precipitation Change, etc.
Regionally Averaged Temperature Trends Over the Western U.S. 1916-2003 Tmax PNW GB Tmin CA CRB
Detrended Temperature Driving Data for Flood Risk Experiments “Pivot 2003” Data Set Temperature Historic temperature trend in each calendar month “Pivot 1915” Data Set 2003 1915
Simulated Changes in the 20-year Flood Associated with 20th Century Warming for a Constant Precipitation Regime DJF Avg Temp (C) X20 2003 / X20 1915 X20 2003 / X20 1915
Schematic of a Cool Climate Flood Precipitation Produces Runoff Precipitation Produces Snow Precipitation Produces Snow Snow Snow Freezing Level Snow Melt
Schematic of a Warm Climate Flood Precipitation Produces Runoff Precipitation Produces Snow Precipitation Produces Snow Snow Snow Snow Melt Freezing Level
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 DJF Avg Temp (C) X20 ’73-’03 / X20 ’16-’03 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
X100 wENSO / X100 2003 X100 nENSO / X100 2003 X100 cENSO / X100 2003 DJF Avg Temp (C) DJF Avg Temp (C) DJF Avg Temp (C) X100 wENSO / X100 2003 X100 nENSO / X100 2003 X100 cENSO / X100 2003
X100 wPDO / X100 2003 X100 nPDO / X100 2003 X100 cPDO / X100 2003 DJF Avg Temp (C) DJF Avg Temp (C) DJF Avg Temp (C) X100 wPDO / X100 2003 X100 nPDO / X100 2003 X100 cPDO / X100 2003
Consensus Forecasts of Temperature and Precipitation Changes from IPCC AR4 GCMs (End of 21st Century for the A2 Scenario)
21st Century Climate Impacts for the Pacific Northwest Region Mote, P.W. and E. P. Salathe Jr., 2010: Future climate in the Pacific Northwest, Climatic Change, DOI: 10.1007/s10584-010-9848-z
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
Columbia Basin Climate Change Scenarios Project 297 Sites • Smaller basins down to • ~500 km2 • Monthly and daily streamflow time series • Assessment of hydrologic extremes • (e.g. Q100 and 7Q10)
Available PNW Scenarios 2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099
Hybrid Delta Downscaling Method • Performed for each VIC grid cell: Bias Corrected Future Monthly CDF Hist. Daily Timeseries 30 yr window 1916-2006 Projected Daily Timeseries Historic Monthly CDF Hist. Monthly Timeseries 1916-2006 1970-1999 1916-2006 “Base Case”
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 Snow Model
Watershed Classifications: Transformation From Snow to Rain Map: Rob Norheim
Simulate Daily Time Step Streamflow Scenarios Associated with Changes in Climate Fit GEV Probability Distributions To Estimate Flood Risks Compare Flood Risks to Those in the 20th Century
SNOMO Streamflow (cfs) Probability of Exceedance
2040s Changes in Flood RiskSnohomish at Monroe A1B B1 10 Member Ensemble Using the Hybrid Delta Downscaling Approach Historical