* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download The 2002 Version of SAMS - Civil, Environmental and Architectural
Global warming wikipedia , lookup
Climate change denial wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate change feedback wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate engineering wikipedia , lookup
Solar radiation management wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate governance wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Effects of global warming wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change in the United States wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Climate change and poverty wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate, Growth and Drought Threat to Colorado River Water Supply Balaji Rajagopalan Department of Civil, Environmental and Architectural Engineering And Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado Boulder, CO Presentation to KOWACO February 3, 2009 Collaborators Kenneth Nowak - CEAE / CADSWES Edith Zagona - CADSWES James Prairie - USBR, Boulder Ben Harding - AMEC, Boulder Marty Hoerling Joe Barsugli Brad Udall Andrea Ray - NOAA CIRES/WWA/NOAA CIRES/WWA/NOAA NOAA A Water Resources Management Perspective Inter-decadal Decision Analysis: Risk + Values T • Facility Planning i m – Reservoir, Treatment Plant Size e • Policy + Regulatory Framework H o r i z o n Climate – Flood Frequency, Water Rights, 7Q10 flow • Operational Analysis – Reservoir Operation, Flood/Drought Preparation • Emergency Management – Flood Warning, Drought Response Data: Historical, Paleo, Scale, Models Hours Weather Modeling Framework What Drives Year to Year Variability in regional Hydrology? (Floods, Droughts etc.) Hydroclimate Predictions – Scenario Generation (Nonlinear Time Series Tools, Watershed Modeling) Decision Support System (Evaluate decision strategies Under uncertainty) Diagnosis Forecast Application Resources • http://cadswes.colorado.edu/publications (PhD thesis) Regonda, 2006 Prairie, 2006 Grantz, 2006 Stochastic Streamflow Simulation • http://animas.colorado.edu/~prairie/ • http://animas.colorado.edu/~nowakkc/ •[email protected] •[email protected] Colorado River Basin Overview 7 States, 2 Nations Fastest Growing Part of the U.S. Over 1,450 miles in length Basin makes up about 8% of total U.S. lands Highly variable Natural Flow which averages 15 MAF 60 MAF of total storage Source:Reclamation 1 acre-foot = 325,000 gals, 1 maf = 325 * 109 gals 1 maf = 1.23 km3 = 1.23*109 m3 Upper Basin: CO, UT, WY, NM Lower Basin: AZ, CA, NV 4x Annual Flow 50 MAF in Powell + Mead Irrigates 3.5 million acres Serves 30 million people Very Complicated Legal Environment Denver, Albuquerque, Phoenix, Tucson, Las Vegas, Los Angeles, San Diego all use CRB water DOI Reclamation Operates Mead/Powell When Will Lake Mead Go Dry? Barnett & Pierce, Water Resources Research, 2008 Water Budget Analysis One 50 maf reservoir, increasing UB demands (13.5 in 2008 ->14.1 Maf/yr in 2030, 15.1 maf /yr inflows, current starting contents Linear Climate Change Reduction in Flows w/ some natural variability Results With Linear 20% Reduction in mean flows Over 50 years 10% Chance Live Storage Gone by 2013 50% Chance Live Storage Gone by 2021 50% Chance Loss of Power by 2017 Is that so? Colorado River Demand - Supply 20 Total Colorado River Use 9-year moving average. 18 NF Lees Ferry 9-year moving average 16 12 10 8 6 4 2 Calnder Year 19 98 20 02 20 06 19 94 19 90 19 86 19 82 19 78 19 74 19 70 19 66 19 62 19 58 19 54 19 50 19 46 19 42 19 38 19 34 19 30 19 26 19 22 19 18 0 19 14 Annual Flow (MAF) 14 Declining Lakes Mead and Powell 30 25 Lake Mead Volume in Millions of Acrefeet 1935-2008 20 15 10 120 Foot drop 13 maf lost Current: ~48%, 12 maf 5 0 30 Lake Powell Volume in Millions of Acrefeet 1963-2008 25 5 Years of 10 maf/yr 66% of average flows Worst drought in historic record 20 15 10 5 75 Foot Drop (Max 140) 10.5 maf lost Current: ~56%, 14.5 maf 1963 1965 1966 1968 1969 1971 1972 1974 1975 1977 1978 1980 1981 1983 1984 1986 1987 1989 1990 1992 1993 1995 1996 1998 1999 2001 2002 2004 2005 2007 0 New York Times Sunday Magazine, October 21, 2007 Dropping Lake Mead Lake Mead’s Delta Circa 1999 Lake Powell – June 29, 2002 ~2004 ~1999 Lake Powell – December 23, 2003 Source: USGS, Reclamation Recent conditions in the Colorado River Basin Paleo Context Below normal flows into Lake Powell 2000-2004 62%, 59%, 25%, 51%, 51%, respectively 2002 at 25% lowest inflow recorded since completion of Glen Canyon Dam Colorado River at Lees Ferry, AZ Some relief in 2005 105% of normal inflows Not in 2006 ! 73% of normal inflows 2007 at 68% of Normal inflows 2008 at 111% of Normal inflows 5 year running average observed record Woodhouse et al. 2006 Stockton and Jacoby, 1976 Hirschboeck and Meko, 2005 Hildalgo et al. 2002 Past Flow Summary Paleo reconstructions indicate th century one of the most wettest 20 Long dry spells are not uncommon 20-25% changes in the mean flow Significant interannual/interdecadal variability Rich variety of wet/dry spell sequences All the reconstructions agree greatly on the ‘state’ (wet or dry) information How will the future differ? More important, What is the water supply risk under changing climate? Future Climate The Fundamental Problem with Climate Change For Water Management All water resource planning based on the idea of “climate stationarity” – climate of the future will look like the climate of the past. Reservoir sizing Flood Control Curves System Yields Water Demands Urban Runoff Amounts This will be less and less true as we move forward. Existing Yields now not as certain given both supply and demand changes New water projects have an additional and new element of uncertainty. Science, February 1, 2008 Stuff and IPCC 2007 AR4 Projections Wet get wetter and dry get drier… Southwest Likely to get drier Models Precip and Temp Biases Models show consistent errors (biases) Western North America is too cold and too wet Weather models show biases, too Can be corrected A Large Number of Studies Point to a Drying American Southwest Milly et al., 2005 Seager et a.l, 2007 IPCC WG1, IPCC WG2, 2007 National Academy Study, 2007 IPCC Water Report, 2008 CCSP SAP 4.3, 2008 “From 2040 to 2060, anticipated water flows from rainfall in much of the West are likely to approach a 20 percent decrease in the average from 1901 to 1970, and are likely to be much lower in places like the fast-growing Southwest.” ~ May 28, 2008, New York Times Progression of Data and Models in studies about the influence of climate change on streamflows in the Colorado River Basin 1. Climate Change Data Source 3. Water Supply Operations Model 2. Flow Generation Technique General CirculationModel Regression Temperature Precipitation Hypothetical Scenarios OR Hydrology Models: NWSRFS VIC PRMS Streamflow CRSS CRMM Reservoir storage Hydroelectric power UB Releases Stuff and m Study Climate Change Technique (Scenario/GC M) Flow Generation Technique (Regression equation/Hydrologic model) Runoff Results Operations Model Used [results?] Notes Stockton and Boggess, 1979 Scenario Regression: Langbein's 1949 US Historical Runoff- TemperaturePrecipitation Relationships +2C and -10% Precip = ~ -33% reduction in Lees Ferry Flow Results are for the warmer/drier and warmer/wetter scenarios. Revelle and Waggoner, 1983 Scenario Regression on Upper Basin Historical Temperature and Precipitation +2C and -10% Precip= -40% reduction in Lee Ferry Flow +2C only = -29% runoff, Nash and Gleick, 1991 and 1993 Scenario and GCM NWSRFS Hydrology model runoff derived from 5 temperature & precipitation Scenarios and 3 GCMs using doubled CO2 equilibrium runs. +2C and -10% Precip = ~ -20% reduction in Lee Ferry Flow Used USBR CRSS Model for operations impacts. Many runoff results from different scenarios and subbasins ranging from decreases of 33% to increases of 19%. Christensen et al., 2004 GCM UW VIC Hydrology model runoff derived from temperature & precipitation from NCAR GCM using Business as Usual Emissions. +2C and -3% Precip at 2100 = -17% reduction in total basin runoff Created and used operations model, CRMM. Used single GCM known not to be very temperature sensitive to CO2 increases. Hoerling and Eischeid, 2006 GCM Regression on PDSI developed from 18 AR4 GCMs and 42 runs using Business as Usual Emissions. +2.8C and ~0% Precip at 2035-2060 = -45% reduction in Lee Fee Flow Christensen and Lettenmaier, 2006 GCM UW VIC Hydrology Model runoff using temperature & precipitation from 11 AR4 GCMs with 2 emissions scenarios. +4.4C and -2% Precip at 2070-2099 = -11% reduction in total basin runoff Also used CRMM operations model. Other results available, increased winter precipitation buffers reduction in runoff. -10% Precip only = -11% runoff. Green = 2010-2039 Blue = 2040-2069 Red = 2070-2099 120 110 100 90 80 -40% to +30% Runoff changes in 2070-2099 ~80% 70 Up = Increase Down = Decrease 2C to 6 C 60 Triangle size proportional to runoff changes: ~115% Precip Change in % CRB Runoff From C&L Precipitation, Temperatures and Runoff in 2070-2099 0 1 2 3 Temp Increase in C 4 5 6 Colorado River Climate Change Studies over the Years Early Studies – Scenarios, About 1980 Mid Studies, First Global Climate Model Use, 1990s Nash and Gleick, 1991, 1993 McCabe and Wolock, 1999 (NAST) IPCC, 2001 More Recent Studies, Since 2004 Stockton and Boggess, 1979 Revelle and Waggoner, 1983* Milly et al.,2005, “Global Patterns of trends in runoff” Christensen and Lettenmaier, 2004, 2006 Hoerling and Eischeid, 2006, “Past Peak Water?” Seager et al, 2007, “Imminent Transition to more arid climate state..” IPCC, 2007 (Regional Assessments) Barnett and Pierce, 2008, “When will Lake Mead Go Dry?” National Research Council Colorado River Report, 2007 •Almost all the water is generated from a small region of the basin at very high altitude •GCM projections for the high altitude regions are uncertain Future Flow Summary Future projections of Climate/Hydrology in the basin based on current knowledge suggest Increase in temperature with less uncertainty Decrease in streamflow with large uncertainty Uncertain about the summer rainfall (which forms a reasonable amount of flow) Unreliable on the sequence of wet/dry (which is key for system risk/reliability) The best information that can be used is the projected mean flow Water Supply System Risk Estimation Streamflow Scenarios Conditioned on climate change projections Water Supply Model Management + Demand growth alternatives System Risk Estimates For each year Streamflow Simulation •Paleo •Observations •Need to Combine Need to Combine Paleo and Observed flows for stochastic simulation •Colorado River System has enormous storage of approx 60MAF ~ 4 times the average annual flow - consequently, • wet and dry sequences are crucial for system risk/reliability assessment •Streamflow generation tool that can generate flow scenarios in the basin that are realistic in •wet and dry spell sequences •Magnitude •Paleo reconstructions are •Good at providing ‘state’ (wet or dry) information •Poor with the magnitude information •Observations are reliable with the magnitude •Need for combining all the available information Observed Annual average flow (15MAF) is used to define wet/dry state. Proposed Framework Prairie et al. (2008, WRR) Nonhomogeneous Markov Chain Model on the observed & Paleo data Natural Climate Variability Generate system state ( St ) Generate flow conditionally (K-NN resampling of historical flow) f ( xt St , St 1 , xt 1 ) 10000 Simulations Each 50-year long 2008-2057 Superimpose Climate Change trend (10% and 20%) Climate Change window = 2h +1 Discrete 3h kernel K ( x) (1 x 2 ) for x 1 2 (1 4h ) function Source: Rajagopalan et al., 1996 h Nonhomogenous Markov model with Kernel smoothing (Rajagopalan et al., 1996) Transition Probability (TP) for each year are obtained using a discrete Kernel Estimator t t dwi K hdw i 1 Pdw (t ) nd t tdi K i 1 hdw ndw Pdd (t ) 1 Pdw (t ) h determined with LSCV LSCV (h) 1 n [1 Pˆti (t i )] 2 n i 1 2 state, lag 1 model was chosen ‘wet (1)’ if flow above annual median of observed record; ‘dry (0)’ otherwise. AIC used for order selection (order 1 chosen) Transition Probabilities Simulation •Re-sample a block of years (as desired for planning – say 50 year) •Using the TP for each year generate a ‘state’ (St) •Conditionally Re-sample a streamflow magnitude from the observed flow •Identify K-nearest neighbors from the observations to the ‘feature vector’ (St , St-1 and xt ) •Re-sample one of the neighbor – i.e., one of the years, say year j •Flow of year j+1 is the simulated flow, Xt+1 Generate flow conditionally (K-NN resampling of historical flow) f ( xt St , St 1 , xt 1 ) Drought and Surplus Statistics flow Surplus Length Drought Length Surplus volume Threshold (e.g., median) time Drought Deficit Drought/Surplus Statistics K-NN-1 bootstrap Of observed flow Paleo + Obs Red Paleo stat Blue Observed stat Storage Statistics 60 System Risk •Streamflow Simulation • System Water Balance Model •Management Alternatives (Reservoir Operation + Demand Growth) Lees Ferry, AZ gauge Demarcates Upper and Lower Basin 90% of the entire basin flow passes through this gauge Well maintained gauge Annual Average flow is about 15MaF Sizeable flow occurs between Lake Powell and Mead ~ 750KaF/year Small but useful flow below Mead also comes in to the system ~ 250KaF/year UC CRSS stream gauges LC CRSS stream gauges Water Balance Model Storage in any year is computed as: Storage = Previous Storage + Inflow - ET- Demand •Upper and Lower Colorado Basin demand = 13.5 MAF/yr • Total Active Storage in the system 60 MAF reservoir • Initial storage of 30 MAF (i.e., current reservoir content) • Inflow values are natural flows at Lee’s Ferry, AZ + Intervening flows between Powell and Mead and below Mead • ET computed using Lake Area – Lake volume relationship and an average ET coefficient of 0.436 •Transmission Losses ~6% of Releases Combined Area-volume Relationship ET Calculation ET (MaF) 2 1.5 1 0.5 0 0 10 20 30 40 Storage (MaF) 50 ET coefficients/month (Max and Min) 0.5 and 0.16 at Powell 0.85 and 0.33 at Mead Average ET coefficient : 0.436 ET = Area * Average coefficient * 12 60 70 Management and Demand Growth Combinations A. B C. D. E. F. G. The interim EIS operational policies employed with demand growing based on the upper basin depletion schedule. with the demand fixed at the 2008 level ~ 13.5MaF Same as A but with larger delivery shortages Same as C but with a 50% reduced upper basin depletion schedule. Same as A with full initial storage. Same as A but post 2026 policy that establishes new shortage action thresholds and volumes. Demand fixed at 2008 level and post 2026 new shortage action. All the reservoir operation policies take effect from 2026 INTERIM EIS INTERIM PLUS NEW THRESHOLD Res. Storage (%) Shortage (kaf) Res. Storage (%) Shortage (% of current demand) Res. Storage (%) Shortage (% of current demand) 36 333 36 5 50 5 30 417 30 6 40 6 23 500 23 7 30 7 20 8 Flow and Demand Trends applied to the simulations Red – demand trend 13.5MAF – 14.1MAF by 2030 Blue – mean flow trend 15MAF – 12MAF By 2057 -0.06MAF/year Under 20% - reduction Flow trend with sample simulation 37.2% of simulations > 15MAF 22.3% of simulations > 17MAF 34.7% of simulations > 15MAF 18.8% of simulations > 17MAF PDF of generated streamflows (boxplots) PDF of observed flow (red) AR-1 NHMM Natural Climate Variability Climate Change – 20% reduction Climate Change – 10% reduction When Will Lake Mead Go Dry? Water Resources Research, 2008 Water Budget Analysis Results With Linear 20% Reduction in mean flows Over 50 years 10% Chance Live Storage Gone by 2013 50% Chance Live Storage Gone by 2021 50% Chance Loss of Power by 2017 Problems One 50 maf reservoir, increasing UB demands (13.5 in 2008 ->14.1 maf/yr in 2030, 15.1 maf /yr inflows, current starting contents Linear Climate Change Reduction in Flows w/ some natural variability 1.7 maf/year fixed evaporation plus bank storage Missing 850 kaf/yr inflows Forgotten / Ignored Issues System is on a knife-edge, even with existing flows Normal climate variability can push us over the edge without climate change Probability of at least one drying – Barnett and Pierce (2008) Yellow – AR-1 (Barnett and Pierce, 2008) Red – Scenario I Green – Scenario II Blue – Scenario II Probability of drying in a given year Climate Change – 20% reduction Shortage Statistics Shortage Frequency Shortage Volume (MaF) Climate Change – 10% reduction Shortage Statistics Shortage Frequency Shortage Volume (MaF) Sensitivity to Initial Demand Climate Change – 20% reduction Initial Demand – 12.7MaF Actual Average Consumption In the recent decade Initial Demand – 13.5MaF Sensitivity to Initial Demand Climate Change – 10% reduction Initial Demand – 12.7MaF Actual Average Consumption In the recent decade Initial Demand – 13.5MaF Climate Change – 20% reduction Shortage Statistics Shortage Frequency Shortage Volume (MaF) Sensitivity to Initial Demand Climate Change – 20% reduction Shortage Volume Initial Demand ~13.5MaF Initial Demand ~12.7MaF Summary Interim Guidelines (EIS) are pretty robust Until 20206 these guidelines are as good as any in reducing risk Water supply risk (i.e., risk of drying) is small (< 5%) in the near term ~2026, for any climate variability (good news) Risk increases dramatically by about 7 times in the three decades thereafter (bad news) Risk increase is highly nonlinear There is flexibility in the system that can be exploited to mitigate risk. Considered alternatives provide ideas Smart operating policies and demand growth strategies need to be instilled Demand profiles are not rigid Delayed action can be too little too late Risk of various subsystems need to be assessed via the basin wide decision model (CRSS)