* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Understanding Climate Variability : Implications for Water Resources
Citizens' Climate Lobby wikipedia , lookup
Climate change feedback wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate engineering wikipedia , lookup
Climate governance wikipedia , lookup
General circulation model wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Solar radiation management wikipedia , lookup
Effects of global warming wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change in the United States wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Climate change and poverty wikipedia , lookup
Water Resources Systems and Management CVEN 5393 Lecture 1 Outline • State of Global Water Resources – Global watery cycle – Water availability, demand projections under climate change • Water Resources Management Perspective – Time Scales (hours to decades) – Weather to decadal climate variability • Integrated Framework • Colorado River Water Resources Management - example – Under climate change • Optimization • World Water Resources Development Report 4th edition (2012). Volume 2, first chapter (co-authored by the instructors) – links provided on the class page Regional Renewable Water Supply Estimates Per Capita Water Usage and Requirement • Agriculture is the largest Water user • With projected population growth this will increase significantly – adding to global water stress Global Water Availability Population Under Water Shortage Global Physical and Economic Water Scarcity Projected Per capita water Availability in 2050 • • • • • • • • Water Resources Engineering 21st century Era of big building is over! Increasing population – Rapid urbanization • Deteriorating rural conditions • Better opportunities in cities • 1 billion now living in city slums, world wide Municipal services for urban poor are negligible or non-existent Limited water availability Environmental Impacts Acute water shortages – Rapid increase in demand with insufficient capital to develop – Climate variability Lack of sanitation – Rapid increase in generated waste – Negligible treatment – Result: disease, environmental degradation Vulnerability to natural hazards and disasters – Earthquakes, floods, hurricanes, wildfires, drought, landslides – Lack of resources to plan for and mitigate effects 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 Climate Variability • Daily • Annual • Diurnal cycle • Seasonal cycle • Inter-annual to Interdecadal • Ocean-atmosphere coupled modes (ENSO, NAO, PDO) • Centennial • Millenial • Thermohaline circulation • Milankovich cycle (earth’s orbital and precision) Proposed Integrated Framework What Drives Year to Year Variability in regional Hydrology? (Floods, Droughts etc.) Diagnosis Hydroclimate Predictions – Scenario Generation (Nonlinear Time Series Tools, Watershed Modeling) Application Decision Support System (Evaluate decision strategies Under uncertainty) 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 90 19 94 19 86 19 82 19 78 19 70 19 74 19 66 19 62 19 58 19 54 19 46 19 50 19 42 19 38 19 34 19 26 19 30 19 18 19 22 0 19 14 Annual Flow (MAF) 14 Forecast Colorado River Basin Overview 1 acre-foot = 325,000 gals, 1 maf = 325 * 1 maf = 1.23 km3 = 1.23*109 m3 109 gals 7 States, 2 Nations Upper Basin: CO, UT, WY, NM Lower Basin: AZ, CA, NV 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 4x Annual Flow 50 MAF in Powell + Mead Irrigates 3.5 million acres Serves 30 million people Very Complicated Legal Environment ‘Law of the River’ Denver, Albuquerque, Phoenix, Tucson, Las Vegas, Los Angeles, San Diego all use CRB water DOI Reclamation Operates Mead/Powell Scale Matters Runoff Efficiency (How much Precip actually runs off) Varies Greatly from ~5% (Dirty Devil) to > 40% (Upper Mainstem) You can’t model the basin at large scales and expect accurate results GCMs (e.g. Milly, Seager) and H&E 2006 may get the right answer, but miss important topographical effects % of Total Runoff 14.4% 16.1% 9.9% 2.4% 24.9% 6.3% 14.1% 11.8% Most runoff comes from small part of the basin > 9000 feet Very Little of the Runoff Comes from Below 9000’ (16% Runoff, 87% of Area) 84% of Total Runoff Comes from 13% of the Basin Area – all above 9000’ Basin Area and Runoff By Elevation 20% Elevation % Total Runoff 9000-10,000 25% 10,000-11,000 27% 11,000-12000 22% % 12,000-13,000 11% Sums 9-13 84% Below 9000 16% 18% 16% 14% 12% % Total Area "Productivity" 6.3% 3.9 4.3% 6.2 10.4 Total2.1%Runoff 0.5% 20.4 13.2% 87% 0.2 Runoff 10% 8% Basin Area 6% 4% 2% 0% 0 2000 4000 6000 Runoff as % of Total 8000 10000 Area as % of Upper Basin Total 12000 14000 Current State 20 18 Annual Flow (MaF) 16 14 12 10 8 6 4 Total Colorado River use 9-year moving average 2 NF Lees Ferry 9-year moving average 0 1914 1924 1934 1944 1954 1964 1974 1984 1994 Year 30 25 20 15 10 5 0 Lake Mead Volume in Millions of Acrefeet 1935-2008 2004 Recent conditions in the 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 Woodhouse et al., WRR, 2007 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 2009 at 88% and 2010 at 72.5% 5 year running average Decadal Variability! Paleo Perspective Reconstruction of Colorado River at Lees Ferry streamflow, 7622005, with 10-year running mean • • • • Six 10-year periods before 1900 with reconstructed mean flow lower than 12 MaF (lowest: 1146-1155) 1905-1930 one of the three wettest ~25-year periods in 1200 years Mid-1100s: 57-year period with mean flow of ~13 MaF Century-scale non-stationarity: 100-year mean varies from 13.9 to 15.4 MaF * Slide courtesy of Jeff Lukas, NOAA/WWA Wavelet Power Spectrum of Lees Ferry Flow Features of interest 1) decadal (active past 30 years) 2) Low frequency (more persistent) Wavelet Spectra of Temp and Precip data • Are there features in the T and P data that may be linked to the 2 scales of interest found in the Lees Ferry spectrum? Temperature Precipitation Low frequency feature similar to that of the flow data Decadal scale feature similar to that of the flow data Winter and Summer Precipitation Changes at 2100 – High Emissions Hatching Indicates Areas of Strong Model Agreement Summer 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 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 Clearly, need to combine paleo + observed + projection to generate plausible flow scenarios System Risk •Streamflow Simulation •Prairie et al. (2008) WRR • System Water Balance Model •Management Alternatives (Reservoir Operation + Demand Growth) Rajagopalan et al. (2009), WRR Proposed Framework for flow generation 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 Water Balance Model: Our version Climate Change -20% LF flows over 50 years Lees Ferry Natural Flow (15.0) + Intervening flows (0.8) Upper Basin Consumptive Use (4.5+) Evaporation (varies with stage; 1.4 avg declining to 1.1) LB Consumptive Use + MX Delivery + losses (9.6) “Bank Storage is near long-term equilibrium’ Initial Net Inflow = +0.4 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 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 Management and Demand Growth Combinations Alternative Demand Shortage Policy Initial Storage A 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries per EIS depletion schedule 333 KaF DS when S < 36%, 417 KaF DS when S < 30% and 500 KaF DS when S <23% 30 MAF B 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries per EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% DS when S < 23% 30 MAF C 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% DS when S < 23% D 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% d DS when S < 23% E 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 50%, 6% DS when S < 40%, 7% d DS when S < 30% and 8 % DS when S < 20% 30 MAF 60 MAF* 30 MAF Table 1 Descriptions of alternatives considered in this study. (LB = Lower Basin, MX = Mexico, UB = Upper Basin, DS = Delivery Shortage and S = Storage). Per EIS depletion schedule the total deliveries are projected to be 13.9 MaF by 2026 and 14.4 MaF by 2057. * One alternative with full initial storage (E) illustrates the effects of a full system. Natural Climate Variability Rajagopalan et al. 2009, WRR Climate Change – 10% reduction Climate Change – 20% reduction Shortage Volume Under Climate Change 10% Reduction 20% Reduction Summary 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 Water supply risk occurs well before any ‘abrupt’ climate change – even under modest changes Nonlinear response Complimentary Question In Rajagopalan et al. (2009) what is the risk for water supply under climate change? can management mitigate? What is the probability distribution of 'optimal yield' from the flow scenarios (climate change) given the system capacity and constraints ? ensemble of streamflow sequences PDF of yield, storage mean & std dev provide policy makers with estimates of risk/reliability of various growth targets 12.7, 13.5, 15.0 MaF Systems Approach - Optimization Optimal Yield Y = Yield (MaF) Spillt= Overflow (MaF) Qt = Paleo-reconstructed inflow (MaF/yr) K = Reservoir capacity (MaF) St-1 = Previous year storage (MaF) St = Current storage (MaF) Minimum Storage is specified System storage = 60MaF • Average storage is computed for the optimal yield Yopt, as the average of: Why not build more storage? Plot of Optimal yield vs. storage potential larger tub does NOT lead to greater yield optimal yield plateaus at ~15MaF at around 70 MaF Temporal Variability! The cause Outline of Study More realistic approach Two ‘tubs’ representing Lakes Mead and Powell Implementing operational rules and evaporation... Generate PDFs of optimal yields from given storage capacity in the upper and lower basins and ensembles of streamflow sequences Methodology – Constrained Linear Optimization (Two-Basin Model) • Evaporation is included in linear model as coefficient weighted on current year’s storage. To solve for evaporation, rearrange the formula on the right. • Evaporation coefficient is greater for Lower Basin than Upper Basin Climate Change Scenarios – 0 MaF Minimum Storage Natural Flows 10% Reduction 20% Reduction Two basins are let run dry optimal median yield at least 15MAF (projected demand) No Equalization... natural flows optimal median yield decreases by ~1MAF in each climate change scenario optimal yield decreases as streamflow decreases standard deviation increases less yield and more variable Climate Change Scenarios – 24 MaF Minimum Storage Natural Flows 10% Reduction 20% Reduction Two basins with combined 24 MaF Minimum No Equalization... optimal median yield never greater than15MAF (projected demand) optimal yield decreases to scary lows as streamflow decreases standard deviation low minimum storage prevents variable release Reliability Systems Approach • enables a broader perspective of the problem • provide optimal solutions of decision variables • risk/reliability information • enable robust decision making NEW MODEL What do we do?