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 4 th 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 21 st 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 Time HorizonTime Horizon Inter-decadal Hours Weather Climate Decision Analysis: Risk + Values Data: Historical, Paleo, Scale, Models Facility Planning – Reservoir, Treatment Plant Size Policy + Regulatory Framework – Flood Frequency, Water Rights, 7Q10 flow Operational Analysis – Reservoir Operation, Flood/Drought Preparation Emergency Management – Flood Warning, Drought Response

Climate Variability Daily Annual Inter-annual to Inter- decadal Centennial Millenial Diurnal cycle Seasonal cycle Ocean-atmosphere coupled modes (ENSO, NAO, PDO) Thermohaline circulation Milankovich cycle (earth’s orbital and precision)

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) Forecast Diagnosis Application Proposed Integrated Framework

Colorado River Basin Overview 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 1 acre-foot = 325,000 gals, 1 maf = 325 * 10 9 gals 1 maf = 1.23 km 3 = 1.23*10 9 m 3

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 14.4% 16.1% 24.9% 14.1% 6.3% 9.9% 11.8% 2.4% % of Total Runoff

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’ % Total Runoff Basin Area Runoff

Current State

Recent conditions in the Paleo Context Below normal flows into Lake Powell %, 59%, 25%, 51%, 51%, respectively 2002 at 25% lowest inflow recorded since completion of Glen Canyon Dam Some relief in % 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% Decadal Variability! 5 year running average Woodhouse et al., WRR, 2007

Reconstruction of Colorado River at Lees Ferry streamflow, , with 10-year running mean Six 10-year periods before 1900 with reconstructed mean flow lower than 12 MaF (lowest: ) 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 Paleo Perspective * 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 Summer Hatching Indicates Areas of Strong Model Agreement

2C to 6 C -40% to +30% Runoff changes in ~115% ~80% CRB Runoff From C&L Precipitation, Temperatures and Runoff in Triangle size proportional to runoff changes: Up = Increase Down = Decrease Green = Blue = Red =

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

Generate flow conditionally (K-NN resampling of historical flow) Generate system state Nonhomogeneous Markov Chain Model on the observed & Paleo data Proposed Framework for flow generation Prairie et al. (2008, WRR) Superimpose Climate Change trend (10% and 20%) Simulations Each 50-year long Natural Climate Variability Climate Change

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) “Bank Storage is near long-term equilibrium’ LB Consumptive Use + MX Delivery + losses (9.6) Climate Change -20% LF flows over 50 years Initial Net Inflow = +0.4 Water Balance Model: Our version

Combined Area-volume Relationship ET Calculation ET coefficients/month (Max and Min) 0.5 and 0.16 at Powell 0.85 and 0.33 at Mead Average ET coefficient : ET = Area * Average coefficient * Storage (MaF) ET (MaF)

Flow and Demand Trends applied to the simulations Red – demand trend 13.5MAF – 14.1MAF by 2030 Blue – mean flow trend 15MAF – 12MAF By MAF/year Under 20% - reduction

AlternativeDemandShortage 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% 30 MAF 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% 60 MAF* 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 Management and Demand Growth Combinations 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 * One alternative with full initial storage (E) illustrates the effects of a full system.

Natural Climate Variability Climate Change – 20% reduction Climate Change – 10% reduction Rajagopalan et al. 2009, WRR

20% Reduction 10% Reduction Shortage Volume Under Climate Change

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

 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

Y=Yield (MaF) Spill t =Overflow (MaF) Q t =Paleo-reconstructed inflow (MaF/yr) K=Reservoir capacity (MaF) S t-1 =Previous year storage (MaF) S t =Current storage (MaF) Minimum Storage is specified System storage = 60MaF Average storage is computed for the optimal yield Y opt, as the average of: Optimal Yield

 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

 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

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

 Two basins are let run dry  optimal median yield at least 15MAF (projected demand)  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 Natural Flows 10% Reduction 20% Reduction No Equalization...

 Two basins with combined 24 MaF Minimum  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 Natural Flows 10% Reduction 20% Reduction No Equalization...

NEW MODEL Systems Approach enables a broader perspective of the problem provide optimal solutions of decision variables risk/reliability information enable robust decision making

What do we do?