CALVIN: Optimization of California’s Water Supplies 29 November :00am – 4:00 pm 744 P St., Sacramento, CA; Auditorium CALVIN is an optimization model which suggests operations of California’s inter-tied water supply system which would maximize statewide economic benefits to agricultural and urban water users within environmental flow requirements. Morning sessions will focus on technical aspects, with the afternoon sessions on results, policy, and management implications. Project descriptions, details, and results can be found at: Tentative Workshop Agenda 10:00Overview of CALVIN Model (Jay Lund) 10:30Economic Valuation of Water Uses (Richard Howitt, Mimi Jenkins) 11:00 Managing Data and Model Outputs (Ken Kirby) Model engineering and economic outputs and how they are used Data Management and Databases 11:30Model Calibration (Mimi Jenkins) 12:00Limited Foresight and Carryover Storage (Andy Draper) 12:30Lunch 1:30Limitations of Model and Data (Jay Lund) 1:45Results and Implications (Various speakers) Alternatives Examined (Randy Ritzema) Delivery & Economic Performance with Optimized Operations (Randy Ritzema) Willingness-to-Pay for Additional Water (Richard Howitt) Water Transfers and Exchanges (Richard Howitt) 2:30Break 2:45Results and Implications (continued) Economic Values for Facility Expansion (Stacy Tanaka) Economic Costs of Environmental Flows (Stacy Tanaka) Conjunctive Use (Mimi Jenkins) Other operational changes (Mimi Jenkins) 3:30Implications for State Water Policy and Planning (Jay Lund and Richard Howitt) 3:45Continuing Work (Jay Lund) Conclusions 4:00End Agenda

CALVIN Economic-Engineering Optimization for California’s Water Supplies Professor Jay R. Lund Civil & Environmental Engineering, UC Davis Professor Richard E. Howitt Agricultural & Resource Economics, UC Davis Web site: cee.engr.ucdavis.edu/faculty/lund/CALVIN/

Real work done by Dr. Marion W. JenkinsDr. Andrew J. Draper Dr. Kenneth W. KirbyMatthew D. Davis Kristen B. Ward Brad D. Newlin Brian J. Van Lienden Stacy Tanaka Randy Ritzema Guilherme Marques Siwa M. Msangi Pia M. Grimes Jennifer L. Cordua Mark Leu Matthew EllisDr. Arnaud Reynaud

Funded by CALFED Bay Delta Program State of California Resources Agency National Science Foundation US Environmental Protection Agency California Energy Commission US Bureau of Reclamation Lawrence Livermore National Laboratory

Overview 1) California Water Problems 2) What is CALVIN? 3) Modeling Approach and Data 4) What is Optimization? 5) Results 6) Innovations and Uses 7) Major Themes

California Water Problems California: an often dry place with a good climate Wetter winters, very dry summers. Water in north/mountains, demands in south, central and coast. Groundwater is 30-40% of supply. Competition for water: Agriculture, urban, environment

Motivation for Project California’s water system is huge and complex. Water is controversial and economically important. Major changes are being considered. Can we make better sense of this system? –Understanding from data and analysis –Insights from results –Reduce reliance on narrow perspectives How could system management be improved? What is user willingness to pay for additional water and changes in facilities & policies? These are not “back of the envelope” calculations.

What is CALVIN? Entire inter-tied California water system Surface and groundwater systems Economics-driven optimization model Economic Values for Agricultural and Urban Uses Flow Constraints for Environmental Uses Prescribes monthly system operation

Approach a) Develop schematic of sources, facilities, & demands. b) Develop explicit economic values for agricultural & urban water use for 2020 land use and population. c) Identify minimum environmental flows. d) Reconcile estimates of unimpaired inflows, & identify problems therein. e) Develop databases, metadata, and documentation for more transparent and flexible statewide analysis.

Approach (continued) f) Apply economic-engineering optimization to combine this information and suggest: 1) promising capacity expansion & operational solutions, 2) economic value of additional water to users, 3) costs of environmental water use, and 4) economic value of changes in capacities and policies.

Model Schematic Over 1,200 spatial elements 51 Surface reservoirs 28 Ground water reservoirs 24 Agricultural regions 19 Urban demand regions 600+ Conveyance Links

CALVIN’s Demand Coverage

Economic Values for Water Willingness to pay –Agricultural –Urban Operating Costs

Agricultural Water Use Values 0 10,000 20,000 30,000 40,000 50,000 60,000 70, Deliveries (taf) Benefits ($ 000 ) March August June July May April September October 0 1,000 2,000 3, October February January

Urban Water Use Values 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50, Deliveries (taf) Penalty ($000) Winter Summer Spring

Operating Costs Fixed head pumping –Energy costs –Maintenance costs Groundwater recharge basins Wastewater reuse treatment Fixed head hydropower Urban water quality costs

Environmental Flow Constraints Minimum instream flows Rivers and lakes Delta outflows Wildlife refuge deliveries Sources: DWRSIM PROSIM - CVPIA Various local studies

Hydrology Surface & Groundwater historical period: Monthly unimpaired inflows Surface inflows from State & Federal data Groundwater from Federal & local studies Need to reconcile conflicting data!

Policy Constraints All Cases Environmental flows Flood control storage Current Policy Base Case DWRSIM surface operations CVGSM pumping and deliveries Unconstrained economic case Only environment & flood control

Model Inputs Schematic and facility capacities Agricultural water values Environmental Flow Constraints Urban water values Operating costs Hydrology: Surface & ground water Policy Constraints

Data Flow for the CALVIN Model

Database and Interface Tsunami of data for a controversial system –Political need for transparent analysis –Practical need for efficient data management Databases central for modeling & management Metadata and documentation Database & study management software Planning & modeling implications

What is Optimization? Finding the “best” decisions within constraints. “Best” implies performance objective(s). Constraints limit the range and flexibility of decisions. Some constraints are physical. Other constraints are policy. Optimization can identify promising solutions.

Network Optimization – In Words Decisions: Water operations and allocations Best performance: (1) Minimize sum of all costs over all times Subject to some constraints: (2) Conservation of mass (3) Maximum flow limits (4) Minimum flow limits

Network Flow with Gains - Math Minimize: (1) Z =  c ij X ij, X ij is flow from node i to node j Subject to: (2)  X ji =  a ij X ij + b j for all nodes j (3) X ij  u ij for all arcs (4) X ij  l ij for all arcs c ij = economic costs (ag. or urban) b j = external inflows to node j a ij = gains/losses on flows in arc u ij = upper bound on arc l ij = lower bound on arc

Some Results Delivery, Scarcity, and Cost Performance Willingness to pay Water transfers and exchanges Economic Value of Facility Changes Costs of Environmental Flows Conjunctive Use and other Operations

CALVIN’s Innovations 1) Statewide model 2) Groundwater and Surface Water 3) Supply and Demand integration 4) Optimization model 5) Economic perspective and values 6) Data - model management 7) Supply & demand data checking 8) New management options

Themes 1.Economic “scarcity” should be a major indicator for California’s water performance. 2.Water resources, facilities, and demands can be more effective if managed together, especially at regional scales. 3.The range of hydrologic events is important, not just “average” and “drought” years. 4.Newer software, data, and methods allow more transparent and efficient management.

More Information... Web site: cee.engr.ucdavis.edu/faculty/lund/CALVIN/

Water Demand Functions Urban demands are generally demands for direct consumption. Agricultural demands are derived demands that depend on the cost of other inputs and the value of the output. Environmental demands are represented by constraints. The elasticity of demand measures the responsiveness of the quantity demanded to changes in the price ( cost ) of water. An elastic demand has an elasticity greater than one, and is relatively responsive to price changes. Direct statistical estimation of demands is prevented by the absence of price variation in Californian agricultural or urban water

Calvin’s Economic Requirements Calvin needs a stepped “Penalty function” for each delivery node on the schematic. The penalty is the cost of not having a quantity of water and is the inverse of the demand function that measures the value of having water. The continuous economic demand functions are divided into discrete steps for linear solution. The agricultural and urban demand functions must be calibrated so that the marginal value of the observed deliveries is equal to the observed water price.

Data Base for the SWAP Model The Statewide Agricultural Production Model ( SWAP ) models the regional adjustment of profit maximizing farmers allocating water over the range of crops currently grown in the region. The base year for calibrating SWAP is 1992 Base costs, prices, yields and input quantities are largely based on CVPM data. SWAP crop acreages were extrapolated to 2020 levels using forecasts from DWR Bulletin

SWAP Model Regions

Agricultural Crop Descriptions

Agricultural Response to Changes in the Price and Quantity of Water Changes at the Extensive Margin –Changes in the total Area of Irrigated crops –Adjustments in the regional cropping mix. Changes at the Intensive Margin –A change in crop input use per acre –Changes in Water application efficiency due to technology and management.

Efficiency-Cost Trade-offs. Orchards Sacramento Valley

Calibrating Regional Crop Production Functions Regional data available- acres, average yields, input quantities, crop price, input cash cost, resource constraints Optimize a constrained problem to obtain shadow values on binding constraints. Define the form of the production function- in this case, a quadratic function of three inputs. Use maximum entropy methods, marginal and average product conditions to estimate the production function coefficient values. Use the ME coefficient values to define a production model that calibrates to the base year, but is only constrained by the resource constraints.

Linking Annual Cropping Decisions to Monthly Water Use Assume that crops require water in a predetermined monthly pattern based on ET requirements. Farmers can make small water reallocations between months. Any changes in the total applied water due to technology or stress irrigation are allocated are allocated proportionally across months

Water Availability in KAF Willingness to Pay in $/AF July Demand April Demand SWAP-Derived Agricultural Water demands

Agricultural Water Use Values 0 10,000 20,000 30,000 40,000 50,000 60,000 70, Deliveries (taf) Benefits ($ 000 ) March August June July May April September October 0 1,000 2,000 3, October February January

Tomato production-Yolo county Water Land

Calibrated Production Surface for Grapes, Fresno

Urban Demands Residential demands are based on comprehensive survey of estimated price elasticities. The demand function is fitted through the observed 1995 price and quantity demanded with the function slope determined by the elasticity. The 2020 demand are obtained by scaling the 1995 quantities by population growth. Commercial is assumed proportional to the population and is added to the residential demand. Industrial are based on a 1991 survey of shortage induced production losses in 12 counties, and scaled to 2020.

Residential Price Elasticities

Estimating Residential Demands Residential price elasticities 1995 retail water prices 1995 population x pcu = 1995 Demand Curve 1995 demand curve scaled by 2020 population 4 = 2020 Demand Curve

Calibrating Residential Demands to the 2020 Population

Converting Residential demands to Urban Loss Functions Integrate the 2020 residential demand function Add a constant level of 2020 commercial use Find the quantity that drives the demand price ( or loss of not having water) to zero Plot the loss function against monthly delivery.

Residential Loss Functions for 2020

Industrial Values 1991 production losses due to water shortages – 12 counties 1995 industrial use scaled by population to = 2020 Industrial Loss Function

1995 Urban Residential Water Prices in California

Conclusions Water demand functions must reflect rational adjustments to changing scarcity by users Demand functions need to be specified by use, region and month Urban demands can be estimated on a regional basis using published elasticities and base year data Agricultural demand functions can be estimated using regional production data and ET requirements.

Managing Data in CALVIN Model, Database, Documentation, and Post-processing SOFTWARE

CALVIN Physical Outputs Flows across all links EOM Storage Levels at all storage nodes Evaporation at all storage nodes Deliveries to all demand nodes Flow Storage

CALVIN Economic Outputs Scarcities Cost of scarcities Marginal WTP for more water Shadow values on constraints (Lagrange Multiplier) Marginal value of water at each node Post-processed by intersecting deliveries with economic water loss functions for Ag & Urban Produced directly by HEC-PRM Optimization

SWAP Post-Processed Outputs Irrigation efficiencies Crop acreages Crop yields Gross revenue Net revenue Ag annual deliveries by water source from CALVIN SWAP Model

Transparent model assumptions Easily modified model inputs Object oriented data management Documentation of input values = metadata Metadata attached to each piece of data RELATIONAL DATABASE Data Management Design Principles Time series stored in DSS Paired data stored in DSS Model definition file in ASCII Output in DSS HEC-PRM Requirements

CALVIN Data Storage & Software Data Storage Software

Region 3 Schematic

Network Component Listing

Node Properties

Metadata

Summary Metadata is essential Relational databases and software allow modeling of complex systems in more detail than otherwise possible Data management eliminates majority of input file errors (especially related to syntax) More work to be done

Hydrologic and Agricultural Demand Calibration Integrate DWRSIM’s surface hydrology with CVGSM’s groundwater hydrology Reconcile DWR agricultural water demand assumptions with deliveries in the CVPIA PEIS Produce a model consistent with established representations of California’s hydrology and demands Identify data problems and regions than cannot be fully reconciled

Calibration Procedures 1)Impose “Base Case” diversions, deliveries, and operations on CALVIN 2)Adjust agricultural demands to match BC deliveries 3)Adjust agricultural return flows and reuse rates to calibrate groundwater to CVGSM NAA 4)Run CALVIN Base Case and adjust streamflows to match DWRSIM 514a at 15 matching control points 5)Verify scarcity results with similar estimates

Configuration of Physical and Calibration Links

Agricultural demands increased by about 10% (1.9 MAF) Reuse rates reduced in a few regions SW return flows eliminated in much of Tulare Basin GW calibration successful in Sac Valley (top chart) Problems with GW calibration in Tulare Basin, esp. CVPM region 14, 18, 19, and 21 GW Calibration Results Sacramento Valley GW SJ Valley & Tulare Basin GW

Net addition of 38 taf/year calibration flows Biggest monthly imbalances on Sac R. (below Colusa BD) & Feather R. (incl. Yuba+Bear), > +/- 1 MAF Largest annual imbalances on Sac R taf/yr, Lower SJR -434 taf/yr, and in-Delta CU (- 380 taf/yr) Largest net additions of SW in CALVIN Region 1 and 4; net removals in CALVIN Region 2 and 3 SW Calibration Results Sac R. Colusa BD Sac R. Hood In-Delta CU

What we learned Calibration is necessary and long Hydrologic data needs improvement: –more explicit and separate GW & SW hydrologic data –better estimates (methods) for local accretions/depletions Agricultural water use uncertainty is significant Very weak data for modeling Tulare Basin: –conjunctive use operations –groundwater-surface water interactions Large discrepancy in in-Delta CU estimates

A Limited Foresight Model and the Value of Carryover Storage

Perfect Foresight or the Model that Knew too Much Multi-year optimization – too omniscient –Over valuation of existing facilities –Under valuation of new facilities –Excessive carryover storage prior to droughts Single-year optimization – too short sighted How to construct model with limited foresight to: –Reflect imperfect knowledge of probability distribution of inflows –Balance present and future water needs

Sequential Annual Runs Use one year time period (Oct – Sep) 72 consecutive model runs for period-of-analysis Model runs linked by ending/carryover storage Carryover storage value functions limit drawdown System performance sum of 72 year costs excluding carryover storage penalties

Iterative Solution Run HEC-PRM for one year yr=72? BOP Oct = EOP Sep Set yr=1, read initial conditions STOP Define initial carryover storage penalty function START Significant improvement Yes No yr = yr +1 Revise carryover storage penalty function Calculate total penalties Iteration n=1 Iteration n=1? Yes No Iteration n= n + 1 Yes

Model Run Times Run times remain an obstacle for analyzing complex systems Solver times proportional to cube of number of constraints Solver times proportional to number of variables Run time as a function of years of analysis found to be approx. quadratic Limited foresight model exploits rapid increase in run times

Case Studies Three case studies used as proof of concept –Single reservoir operation –Integrated two reservoir operation –Single reservoir operated conjunctively with groundwater Reservoirs operated for water conservation and flood control Objective function to minimize economic cost of shortage associated with d/s agricultural deliveries Network flow solver (HEC-PRM)

Case (a): Application to a Single Reservoir Four separate models developed: –New Don Pedro Reservoir, Tuolumne River –Lake McClure, Merced River –Pine Flat Reservoir, Kings River –Lake Berryesssa, Putah Creek Case studies to provide a framework for the presentation of ideas rather than a realistic operation of each stream-reservoir system. Value functions developed for agricultural deliveries

Valuing Carryover Storage Assume quadratic penalty P = a + bS + cS2 P|S=K = 0 dP/dS is -ve, d2P/dS2 is +ve dP/dS between reasonable limits

Grid Search Simple to implement Ensures global optimum Response surface mapped-out Computationally inefficient Accuracy limited by grid spacing Coarse grid search starting point for more efficient methods

Nelder-Mead Simplex Method Unconstrained minimization of several variables Zero-order search method Global optimum not guaranteed 1: initial simplex 2: simplex expansion 3: simplex reflection 4: simplex reflection (reflection adjusted to stay within feasible region) 5: simplex reflection (reflection adjusted to stay within feasible region) 6: simplex reflection 7: simplex contraction 8: simplex contraction 9: simplex contraction, solution tolerance satisfied optimal penalties Pmin = -9 $/af, Pmax = -148 $/af

New Don Pedro Reservoir Average Annual Shortage Cost as a Function of P min and P max

Lake McClure Average Annual Shortage Cost as a Function of P min and P max

Pine Flat Reservoir Average Annual Shortage Cost as a Function of P min and P max

Lake Berryessa Average Annual Shortage Cost as a Function of P min and P max

New Don Pedro Reservoir Operation under Perfect Foresight

New Don Pedro Reservoir Operation under Limited Foresight

New Don Pedro Reservoir Carryover Storage for Different Levels of Information

Reservoir Operating Rules

Case (b): Integrated Two- Reservoir System

Flood Control & Carryover Storage

Balancing Storage Between Reservoirs in Parallel Objectives: –Equalize refill potential –Minimize EV(spills) –Avoid inefficient conditions

Carryover Storage Penalty Functions

Carryover Storage: New Don Pedro and New Exchequer Reservoirs

Case (c): Conjunctive Use

Groundwater Mining For perfect foresight model groundwater mining prevented by applying constraint to ending groundwater storage For limited foresight model linear penalty attached to end-of-year storage Initial value of penalty set equal to shadow value on groundwater mining constraint from perfect foresight run Penalty iteratively raised until no groundwater mining occurs

Comparison of Groundwater Storage under Perfect and Limited Foresight Models

Conclusions: Perfect Foresight 1.Perfect foresight may significantly distort reservoir operation where reservoirs are used for over-year storage, and where multi-year droughts occur. 2.The impacts of perfect foresight revealed by ëlack of hedging under average hydrologic conditions ëaggressive hedging during the initial years of an extended drought. 3.The perfect foresight models can substantially under estimate shortages and shortage costs 4.As system storage increases the effects of perfect foresight are diminished.

Conclusions: Limited Foresight 1.The limited foresight model ëResults in an economically derived value of carryover storage. ëPrescribes more realistic reservoir operations. ëMore likely to be acceptable to stakeholders ëFacilitates the deduction of operating rules ëCan quantify the over-achievement of perfect foresight models. 2.There exists a wide range of near-optimal carryover storage policies. 3.Differences between the limited and perfect foresight model are minor except prior and during drought conditions.

Conclusions: Conjunctive Use 1.Conjunctive use can substantially improve overall system reliability and reduce total costs 2.Considerable benefits may accrue by explicitly adjusting surface reservoir operations to account for contingent groundwater supplies. 3.The value of surface carryover storage rapidly diminishes with increasing groundwater supplies. 4.Carryover storage rules determined without explicitly accounting for the presence of groundwater storage become economically very inefficient as groundwater supplies increase. 5.Integrated conjunctive use greatly reduces the impact of perfect foresight.

Limitations Major sources of CALVIN’s limitations: Weak or unavailable input data from other sources. Limitations of HEC-PRM network solver. Lack of hydropower, flood control, and recreation benefits.

Major Limitations 1)Surface Hydrology a)Valley floor inflows b)Delta local supplies c)Southern California 2)Groundwater Hydrology a)Weak Tulare Basin data b)Simplified stream-aquifer interaction c) groundwater extensions d)CVGSM data e)Pumping capacities, costs, and use

Major Limitations (continued) 3)Water Demands and Deliveries a)Year-type variation b)Limited water quality c)Limited understanding of agricultural flows d)Base case delivery data from CVGSM e)Representation of local systems 4)Environmental Regulations a)Delta representation b)Other “pre-operated” flow constraints c)Water quality limitations

Major Limitations (continued) 5)Perfect Foresight a)Less important with lots of groundwater storage b)Probably causes 5-10% of S. California benefits c)Need to be careful d)Limited foresight methods under development e)Value of simulation modeling; 6)Excluded Operating Benefits a)Hydropower b)Flood Control c)Recreation

Limitations Implications A.Data problems apply to ANY regional and statewide analysis. B.Use simulation models to refine and test optimization-based solutions. C.“All models are wrong, but some are useful.” G.E.P. Box (Like budget estimates?)

CALVIN Modeling Alternatives Randall Ritzema

How is water allocated now? Water rights and contracts + Operating agreements + Environmental regulations + Water markets (sometimes) = Complex institutional framework Typically analyzed using simulation

Objective, in words: To quantify overall economic performance under flexible operations and allocations, within environmental requirements. Performed by comparing current operations (Base Case) to flexible operations (Unconstrained Case).

Example Network Optimization SWGW URBAN ENVIRONMENTAL AGRICULTURAL Inflow Link, no economic value Link, w/ economic value

Base Case SW GW URBAN ENVIRONMENTAL AGRICULTURAL Constrained flow Unconstrained flow

BC Delivery Constraints SW GW URBAN ENVIRONMENTAL AGRICULTURAL Constrained flow Unconstrained flow DWRSIM Run 514, CVGSM NAA 1997

BC Operations Constraints SW GW URBAN ENVIRONMENTAL AGRICULTURAL Constrained flow Unconstrained flow CVGSM NAA 1997 DWRSIM Run 514

Environmental Constraints SW GW URBAN ENVIRONMENTAL AGRICULTURAL Constrained flow Unconstrained flow Minimum Instream Flows (PROSIM NAA 1997) Refuges (Level 2)

Unconstrained Case SWGW URBAN ENVIRONMENTAL AGRICULTURAL Constraints: Environmental flows Physical capacities Reservoir flood control storage

Model Alternatives Base Case –Basis for Comparison –Goal: mimic simulation planning models Current water allocations Current water operations Current environmental flows

Model Alternatives Base Case Regional Unconstrained –Statewide model is divided into 5 regions –Supplies re-operated and re-allocated within regions. –Base Case inter-regional boundary flows

CALVIN Regions

Model Alternatives Base Case Regional Unconstrained Statewide Unconstrained –Inter-regional boundary constraints removed

Delivery and Economic Performance Comparison of Base Case, Regional Unconstrained, and Statewide Unconstrained Alternatives Randall Ritzema

Urban and Agricultural Deliveries (annual average, in taf)

Delivery Changes to Ag Sector (annual average, in taf)

Delivery Changes to Urban Sector (annual average, in taf)

Agricultural Scarcity Costs (annual average, in $ millions)

Urban Scarcity Costs (annual average, in $ millions)

Total Costs (annual average, in $ billions)

Total Cost by Region (annual average, in $ millions)

The importance of marginal values and spatial equilibrium Marginal changes in water allocation and management must be evaluated using marginal measures of value. Models of spatial equilibrium have conditions which balance the marginal profitability conditions of water between regions and uses In CALVIN, the marginal values are measured in terms of the productive uses of water at each of the demand nodes. Underlying marginal values are production decisions which balance the marginal value productivity of water across crops and months. Optimal CALVIN runs are in spatial equilibrium since water cannot be reallocated without violating a constraint or reducing the overall economic value of California’s water.

The Spatial information in CALVIN CALVIN explicitly defines the constraints and conveyance costs that constrain the movements of water across space. The shadow values measure the marginal cost of constraints. Calvin has the willingness-to-pay explicitly represented for each node. The gainers and losers from trades are clearly identified by location and sector

Practical Impediments to Water Trades Third party economic impacts in the exporting regions Defining the tradeable quantities of water by the consumptive use of applied water Avoiding negative environmental impacts from wheeling traded water or changing the type and location of use.

Using CALVIN to Measure Third party Economic Impacts CALVIN is optimized for a base condition, and an optimal trade condition. Urban benefits from trades are immediately shown by comparing the net benefits at urban nodes. Agricultural effects are measured by “Post- optimality” analysis using SWAP. CALVIN traded water allocations are fed back into SWAP which then estimates the change in regional crop production caused by the trade. Regional income and employment multipliers are used to measure the community effects of the change in production.

Defining Tradeable Water Quantities CALVIN tries to explicitly measure consumptive use and return flows. The net change in consumptive use by region and water use is calculated. CALVIN only proposes trades in which the change in return flows do not violate environmental constraints.

, ,323 -1,400 -1,200 -1, Upp Sac V. Ag Upp Sac V. Urb L.Sac.V.& BD AgL.Sac.V.& BD Urb SJV.& SBay AgSJV.& SBsy Urb Tulare B. Ag Tulare B. Urb SoCal Ag SoCal Urb TOTAL AG TOTAL URB CHANGES IN SCARCITY COSTS ($ M/yr)

Measuring Wheeling Costs and Constraints In calculating the value of water trades, CALVIN accounts for wheeling costs and constraints Constraints on wheeling water often limit trades, CALVIN shows the marginal shadow values of such constraints. In proposing trades, CALVIN not only identifies the buyer and seller, but shows how the water can optimally be wheeled between the buyer and seller in a particular month and for a water year type.

Conclusions on Marginal Value and Trade Water allocations and adjustments occur “on the margin” accordingly, marginal values- not average values- must be used to calculate efficient allocations. CALVIN optimal results suggest potentially valuable water trades that take into account all the costs and constraints on the system. By “post optimality” analysis CALVIN can measure and adjust for the main third party effects of water trades.

Conjunctive Use and Operational Changes Presented by Mimi Jenkins

Overview Role of GW in California Conjunctive Use Operations & Changes in CALVIN Operational Changes Sacramento Valley Conjunctive Use Potential Impacts Some Limitations Implications

GW in California 30-40% of California’s supplies in average years More groundwater use in dry years Total storage capacity = 850 MAF Largely unmanaged

GW in CALVIN GW Resources: 28 GW basins Fixed inflows Economic Drivers: Economic values for water use Operating costs of using water sources Operating Constraints: Ending storage set to Base Case (no additional mining) Pumping capacities Other capacities

Statewide Reliance on GW and CU

Average Monthly % GW Supply

Statewide Groundwater Storage

Operational Changes in CALVIN Quality exchanges in Sac Valley, & between SJ River & Bay Area *Sac Valley CU Operations Regional operation of Bay Area resources Reduced Delta exports with South-of-Delta re-operations (Region 3, 4 and 5) Increased Ag-Ag & Ag-Urban transfers in Tulare Basin to increase CU and eliminate scarcities Mojave River Basin GW banking operations Southern California Ag-Urban & Urban-Urban transfers, increased CU operations

Sacramento Valley Conjunctive Use UnconstrainedBase Case Non-drought Year Drought Year Sacramento R. American R. Groundwater

Sacramento Valley Re-operations Principal Demands: Upper Sacramento Valley Ag (CVPM 1-4) Lower Sacramento Valley Ag (CVPM 6-8) Lower Sacramento Valley Urban (Greater Sac Area)

Sacramento River Diversions

American River Diversions

Groundwater Pumping

Sac Valley CU Impacts & Outcomes Surplus Delta outflow up in drought years, slightly down in non-drought years, seasonal shift uncertain Drought year diversions down taf on Sac R., 228 taf on Amer. R. More flexibility to manage instream flows on Sac and American R. and in Delta Reduced opportunity costs of environmental flows $10 M/yr reduced operating costs (CVPM 7, 8 and Greater Sac Urban) $42 M/yr reduced scarcity costs

Some Limitations of Results Minimum GW pumping for Ag demands Capacity for Folsom S. Canal supply to CVPM 8 Dynamic stream-aquifer interactions Variable head pumping costs Year-type variation in Ag & Urban demands

Implications GW can serve both seasonal & drought demands “Optimized” GW doesn’t necessarily drain basins Economics & markets can help us better employ GW Optimization models can suggest promising conjunctive use solutions Conjunctive use can substantially reduce need dry year diversions from streams Conjunctive use can ease conflicts with environmental requirements and operations

Conclusions and Implications

Conclusions from Results Some qualitative policy conclusions: a) Regional or statewide markets have great potential to reduce water scarcity costs. b) Economically efficient local and regional management reduces demands for imports. c) Environmental flows have economic costs for agricultural, urban, & other activities. d) Economic values exist for expanded facilities.

e) Some scarcity is optimal. f) Economically optimal water reallocations are very limited, but reduce scarcity and scarcity costs considerably. g) Integrated local, regional, and statewide operation of water decreases competition with environmental uses.

Policy and Planning Implications 1)Optimization works and shows promise. 2)Significant new capabilities: a)Statewide and regional analysis b)Economic and engineering analysis c)Explicitly integrated operations d)Transparent operations e)Suggests new management options f)Take the good, but remember the limitations.

Implications (continued) 3)Any regional & statewide analysis needs to: a)Improve current data Central Valley hydrology and demands Agricultural and urban water use Tulare Basin b)Modernize data management (software and institutions) c)Coordinate & extend data

Implications (continued) 4)CALVIN must graduate from the University. a)Most uses and data are outside the University b)We’re happy to help others use this capability c)Models shouldn’t get tenure. 5)CALVIN is just a tool to help: a)Make better sense of a complex system b)Develop ideas for water management 6)Why do modeling? Need analytical ability to provide convincing ideas.

Uses for CALVIN? 1)Integrated statewide supply and demand accounting & data framework 2)Preliminary economic evaluation 3)Long-term statewide water planning 4)Planning & operations studies: Facility expansion, Joint operations, Conjunctive use, & Water transfers 5) Suggest new management options 6) No panacea, but a step along the way.

Continuing Work: Current Climate Change Study Economic costs and benefits of different Delta Export levels Add hydropower and flood control benefits Faster, more flexible solver Reasonable foresight Fixing little things

Improvements in economic water demands Improvements in environmental demands Policy studies (catastrophe response, water transfers, new facilities, conjunctive use, environmental flows, etc....) More detailed regional studies (Bay Area) Continuing Work: Envisioned

Themes 1.Economic “scarcity” should be a major indicator for California’s water performance. 2.Water resources, facilities, and demands can be more effective if managed together, especially at regional scales. 3.The range of hydrologic events is important, not just “average” and “drought” years. 4.Newer methods, data, and software, including optimization, support more transparent and efficient management.

More Information... Web site: cee.engr.ucdavis.edu/faculty/lund/CALVIN/