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Alan F. Hamlet JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering University of Washington Quantifying the Effects of Climate Variability and Change on Hydrologic Extremes in the Pacific Northwest
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CBCCSP 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 http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_ chap1_intro_final.pdf
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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.
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Image Credit: National Snow and Ice Data Center, W. O. Field, B. F. Molnia http://nsidc.org/data/glacier_photo/special_high_res.html Aug, 13, 1941Aug, 31, 2004 The Myth of Stationarity Meets the Death of Stationarity Muir Glacier in Alaska
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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.
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Floods
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Drought Evacuated Reservoir During the 2001 PNW Drought
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Wildfire
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Low Flow and Temperature Impacts to Fish Temperature/ Disease Related Fish Kill in the Klamath River in 2002
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Dissolved Gas Management Tailrace below Bonneville Dam
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Dam Safety Aftermath of the Johnstown Flood 1889
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Dilution Flows for Industrial Pollutants
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Stormwater Management
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Sediment Transport and Mudslides
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Nuts and Bolts: Traditional Methods for Estimating Hydrologic Extremes
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Step 1: Select Extreme Event from Each Historical Year Streamflow (cfs) Day of the Water Year (1 = Oct 1)
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Step 2: Rank Extreme Events for All Years and Estimate Quantiles Streamflow (cfs) Probability of Exceedance 1999
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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.
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Step 4: Estimate Extremes Associated with Return Intervals Site NameRet. 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”.
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Step 5 (Optional) : Regionalize the Results In order to avoid the inherent “noise” that comes with using imperfect site specific data, a common approach is to “regionalize” the results. The idea is to pool as many sites as possible that have common hydroclimatic features (e.g. sites in western WA), and express the flood statistics as a simple ratio to the mean annual flood (MAF) averaged over many different basins. E.g. Q 100 = 2.7 * MAF This approach is used by Ecology in providing estimates of extreme events for the Dam Safety Program, for example.
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Low flow analysis is essentially the same except we select the extreme low flow event from each year. 7Q10, for example, extracts the lowest 7- day running mean flow from each historical year, fits a probability distribution to the sequence of extremes, and selects the 90% exceedance value (i.e. a 10% probability of being at or below this extreme value)
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Historical Perspectives: Changing Flood Risk in the 20 th Century
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References: Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review) Hamlet AF, Lettenmaier DP (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
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Observed Characteristics of Extreme Precipitation Events
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Evidence of Changing Flood Statistics
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Role of Atmospheric Rivers in Flooding (Nov 7, 2006) Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review)
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Role of Atmospheric Rivers in Flooding (Oct 20, 2003)
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Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review)
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Modeling Studies of Changing 20 th Century Flood Risk in the West
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Snow Model Schematic of VIC Hydrologic Model Sophisticated, fully distributed, physically based hydrologic model Widely used globally in climate change applications 1/16 Degree Resolution (~5km x 6km or ~ 3mi x 4mi) General Model Schematic
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Avg WY Date of Flooding VIC Avg WY Date of Flooding OBS Ln (X 100 / X mean ) OBS Ln (X 100 / X mean ) VIC Evaluating the Hydrologic Model Simulations in the Context of Reproducing Flood Characteristics Red = PNW, Blue = CA, Green = Colo, Black = GB
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Zp X 100 GEV flood/mean flood Red = VIC Blue = OBS 5-yr 20-yr 10-yr 50-yr 100-yr
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Tmin Tmax PNW CA CRB GB Regionally Averaged Temperature Trends Over the Western U.S. 1916-2003
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Temperature 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
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X 20 2003 / X 20 1915 DJF Avg Temp (C) Simulated Changes in the 20-year Flood Associated with 20 th Century Warming X 20 2003 / X 20 1915
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Freezing Level Snow Schematic of a Cool Climate Flood Precipitation Produces Snow Precipitation Produces Snow Precipitation Produces Runoff Snow Melt
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Freezing Level Snow Schematic of a Warm Climate Flood Precipitation Produces Snow Precipitation Produces Snow Precipitation Produces Runoff Snow Melt
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Regionally Averaged Cool Season Precipitation Anomalies PRECIP
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DJF Avg Temp (C) 20-year Flood for “1973-2003” Compared to “1916-2003” for a Constant Late 20 th Century Temperature Regime X 20 ’73-’03 / X 20 ’16-’03
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Summary of Flooding Impacts Rain Dominant Basins: 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 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 typically in the opposite directions.
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Effects of ENSO and PDO on Flood Risk
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DJF Avg Temp (C) X 100 nENSO / X 100 2003X 100 cENSO / X 100 2003X 100 wENSO / X 100 2003 X 100 nENSO / X 100 2003X 100 cENSO / X 100 2003X 100 wENSO / X 100 2003
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DJF Avg Temp (C) X 100 nPDO / X 100 2003X 100 cPDO / X 100 2003X 100 wPDO / X 100 2003 X 100 nPDO / X 100 2003X 100 cPDO / X 100 2003X 100 wPDO / X 100 2003
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Scenarios of Flood Risk in the 21 th Century
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Mote, P.W. and E. P. Salathe Jr., 2009: Future climate in the Pacific Northwest 21 st Century Climate Impacts for the Pacific Northwest Region
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Seasonal Precipitation Changes for the Pacific Northwest http://cses.washington.edu/db/pdf/wacciach1scenarios642.pdf
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HumanHealth Human Health Agriculture/Economics Salmon Forest Resources CoastsEnergy Infrastructure Water Resources A comprehensive climate change impacts assessment for Washington State Adaptation
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The Columbia Basin Climate Change Scenarios Project This 3-year research project was designed to provide a comprehensive suite of 21 st century hydroclimatological scenarios for the Columbia River basin and coastal drainages in OR and WA. Collaborative Partners: WA State Dept. of Ecology (via HB 2860) Bonneville Power Administration Northwest Power and Conservation Council Oregon Water Resources Department BC Ministry of the Environment 297 Streamflow Sites
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Smaller basins down to ~500 km 2 Monthly and daily streamflow time series Assessment of hydrologic extremes (e.g. Q100 and 7Q10) Columbia Basin Climate Change Scenarios Project 297 Sites
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Available PNW Scenarios 2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099 Downscaling Approach A1B Emissions Scenario B1 Emissions Scenario Hybrid Delta hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t4 7 pcm1 miroc_3.2 ipsl_cm4 hadgem1 2020s109 2040s109 2080s109 Transient BCSD hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t4 7 pcm1 1950- 2098+ 77 Delta Method composite of 10 2020s11 2040s11 2080s11
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Spatial Variability of Temperature and Precipitation Changes
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Hybrid Downscaling Method Performed for each VIC grid cell: Hist. Daily Timeseries Hist. Monthly Timeseries Historic Monthly CDF Bias Corrected Future Monthly CDF Projected Daily Timeseries 1916-2006 1970-1999 30 yr window 1916-2006 “Base Case”
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Daily Precipitation (mm) Day of Month Monthly to Daily Precipitation Scaling SeaTac. Feb, 1996, hypothetical 30% Increase
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Snow Model Schematic of VIC Hydrologic Model Sophisticated, fully distributed, physically based hydrologic model Widely used globally in climate change applications 1/16 Degree Resolution (~5km x 6km or ~ 3mi x 4mi) General Model Schematic
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Blue—Observed Naturalized Flow Red—Bias Adjusted VIC Simulations Evaluation of Historical VIC Simulations for the Skagit River at Ross Dam Streamflow (cfs)
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Flood Analysis: What’s In? What’s Out? Issue Affecting AnalysisYesNo Based on explicit daily time step simulations of streamflow? Yes Changing freezing elevation? Yes Rain on snow captured?Yes Increases/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
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Low Flow Analysis: What’s In? What’s Out? Issue Affecting AnalysisYesNo Based on explicit daily time step simulations of streamflow? Yes Effects of changing snowmelt and soil moisture dynamics? Yes Effects of changing evaporation? Yes, but some potential factors omitted (e.g. changes in cloudiness) Changes in sequencing or duration of drought? No Includes shallow ground water? No, but typically captures relevant affects to low flows anyway (well correlated) Includes deep groundwater?No Includes effects of glaciers?No Includes water management effects? No
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Simulate Daily Time Step Streamflow Scenarios Associated with Changes in Climate Fit Probability Distributions To Estimate Flood and Low Flow Risks Compare Flood Risks to Those in the 20 th Century
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http://www.hydro.washington.edu/2860/
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SNOMO Streamflow (cfs) Probability of Exceedance
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2040s Changes in Flood Risk Snohomish at Monroe A1BB1 Historical 10 Member Ensemble Using the Hybrid Delta Downscaling Approach
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A1BB1 2040s Changes in 7Q10 Snohomish at Monroe Historical 10 Member Ensemble Using the Hybrid Delta Downscaling Approach
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Chehalis at Grand Mound
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Relationship Between Change in Q100 and Winter Temp
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Changes in High Flows Q 100 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
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7Q10 values are projected to systematically decline in many areas due to loss of snowpack and projected dryer summers Changes in Low Flows http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP _chap7_extremes_final.pdf
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Current and Future Research Additional VIC calibration to improve simulations, and comparison with DHSVM models (proposed) Estimate the effects of reservoir management (in progress) Incorporate more realistic effects to extreme precipitation from regional scale climate models (in progress) Incorporate the effects of sea level rise and high flows on inundation using hydrodynamic modeling (proposed)
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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
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Extreme Precipitation Change from 1970-2000 to 2030-2060 in the percentage of total precipitation occurring when daily precipitation exceeds the 20 th century 95 th percentile larger increase on windward slopes of Cascades, Columbia basin small increase or decrease along Cascade crest
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Snohomish River Near Monroe, WA
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Some Implications for Policy Response to Changing Flood Risk
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Scenarios not forecasts! The current projections are an initial attempt to provide quantitative estimates of the magnitude and direction of changing hydrologic extremes across the PNW, but there are many missing pieces: More fully integrated modeling studies and summary products are needed to better support many policy and design decisions. Reducing the cost and increasing the frequency of updates will help keep key products and data sets current.
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Chehalis at Grand Mound
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Flood Inundation
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Nicholls, R. J. and Cazenave, A. (2010) Sea-Level Rise and Its Impact on Coastal Zones. Science 328, 1517-1520 Changing Sea Level Rise Projections
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We need to move forward now with the best available information. We almost certainly will not have all of the data and projections that we would like to have before we have to make difficult decisions that materially affect future outcomes. Identifying “No Regrets” strategies may be the best approach for coping with these realities.
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An example of one idea that has been put forward for land use planning is the adoption of the “200 year flood” (0.5% probability of occurrence each year) as the standard for the FEMA flood insurance program and land use regulations. One can argue that this is a “no regrets” strategy since flood impacts have been mounting over time due to ongoing development within the current Q100 boundaries, and flood risks are projected to increase substantially in many areas. Are there specific geographic areas where such a policy would make more sense than others?
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1/16 th Degree Changes in Natural Flood Risk
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10 Digit HUC Changes in Natural Flood Risk
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