Adaptive Groundwater Adaptive Groundwater Pricing with Monsoon Pricing with Monsoon

ForecastingForecastingCasey BrownCasey Brown

Division of Engineering and Applied Division of Engineering and Applied SciencesSciences

Harvard UniversityHarvard University

Advisor: Peter RogersAdvisor: Peter Rogers

The Overview:The Overview:Water & Water &

EnvironmentEnvironmentA Reality Check…A Reality Check…

Typical Situations in South AsiaTypical Situations in South Asia

Command Area

Catchment Area

Feeder Channel

Tank

Dead Storage

Bund

Distribution System

Waste Weir

“Waste” flow

Head Regulator

Degraded catchment; Erosion;sparse vegetation

Impeded flow due to weeds, etc.

Insufficient Storage; Siltation;Poor Water Quality; Poor locationof Dead Storage; Poor biodiversity

Damaged Bund and hydraulicstructures

Inadequate downstream flows.

Downstream wells Inadequate recharge; poor water quality

Poor yields and diversificationChemical pesticide and fertilizer use

Poor system maintenanceInsufficient and erratic flowsConflicts in use

Engineers’ View of the World:Environmental Issues in a Typical Small Dam

System

Command Area

Catchment Area

Feeder Channel

Tank

Dead Storage

Bund

Distribution System

Waste Weir

“Waste” flow

Head Regulator

Treat eroding areas of catchmentCoordinate with Watershed Dept.

Environmentally-sustainable disposalof weeds, silt, etc.

Targeted desiltation as per hydrologicanalysis & safe disposal of siltProper location of dead storageExamine fisheries with local speciesImprove ecology (e.g. wetlands)Proper redesign, construction &reconstruction waste mgmtDam Safety (structural & awareness)Reflect d/s flow needs

Downstream wells Observe groundwater levels

Strong IPM and IPNM programPromotion of organic techniques

Environmentally-safe disposalof channel silt

Engineers’ Solution:Environmental Management Plan

Ocean

River Basin Boundary

Irrigation

Navigation

The REAL World River Basin?

IndustryUrban WSS

Precipitation

Agriculture Department

Irrigation Department

Rural Water Supply Department

Urban Water Supply Department

Power Department

Livestock Department

Industry Department

Fisheries Department

Groundwater Department

Surface Water Department

Reservoir

Recreation

Hydropower

Forest Department

Ocean Development/CZM Department

Fishing

Community Use

Wetlands / Environment

Rainfed Agr

Livestock

Forest

Rural WSS

Irrigation

Groundwater

Infiltration / Recharge

Base Flow / Pumping

Groundwater Inflow

Groundwater Outflow

Runoff

Evaporation / Transpiration

Return Flow

Ocean

River Basin Boundary

Irrigation

NavigationNavigation

IndustryIndustryUrban WSSUrban WSS

ReservoirReservoir

RecreationRecreation

HydropowerHydropower

FishingFishingFishing

Community Use

Wetlands / Environment

Community Use

Wetlands / EnvironmentWetlands / Environment

Rainfed AgrRainfed Agr

LivestockLivestock

ForestForest

Rural WSSRural WSS

IrrigationIrrigation

Groundwater

Infiltration / Recharge

Base Flow / Pumping

Groundwater Inflow

Groundwater Outflow

Runoff

Evaporation / Transpiration

Groundwater

Infiltration / Recharge

Base Flow / Pumping

Groundwater Inflow

Groundwater Outflow

Runoff

Evaporation / Transpiration

Return FlowReturn Flow

Agriculture & Livestock(pesticide and fertilizer pollution, livestock fodder, grazing land availability, medicinal

plants, pest/pesticide management, organic cultivation, clean milk production)

Irrigation(access to water,

waterlogging, water quality, siltation/erosion)

Rural Water Supply & Sanitation(access to clean water, pollution of local water

bodies, drinking water quality & testing)

Industry & Power(access to required water, industrial

effluent and sludge management, thermal pollution, industrial disasters)

Environment(water quality/pollution monitoring, instream flow requirements (incl.

community use), wetlands protection, biodiversity conservation, sand

mining)

Fisheries-related (exotic species, access to

resources, disease & pollution)

Navigation(dredging spoil management, spills)

Tourism(waste management, seasonal

demands)

Groundwater-Related(Overexploitation;

Pollution from natural sources – e.g. of As, Fl; and from anthropogenic

sources – e.g. of Nitrates, Pesticides, TDS)

Land & Forest Management(catchment protection for soil and water

conservation, soil degradation, incl. salinization, biodiversity conservation, recharge, water

harvesting, non-point source runoff)

Coastal Zone Management(Saline water intrusion, coastal wetland management, ocean

pollution, coastal hazard management, fisheries-related)

Urban(health benefits from access to clean water and

sanitation, domestic and stormwater runoff treatment/management, sludge management, solid

and hazwaste management)

Climate & Disasters(droughts, floods and other natural

disasters, climate change)

Overall• Competition for water, growing demandsCompetition for water, growing demands• Maximizing water use and economic efficiency Maximizing water use and economic efficiency • Adequacy of policies, institutions, instruments Adequacy of policies, institutions, instruments • Cultural property management, appropriate Cultural property management, appropriate indigenous knowledge useindigenous knowledge use

Dams(siltation, dam safety, downstream releases)

TypicalWater Issues in a

Basin Context

Environmental Problems AboundInefficient Water Use Stagnation

WeedsPollution

Degraded Lands

Solid WasteImpact on Biota

……and overlap with Social and overlap with Social ProblemsProblems

• Decreased availability of water due to overexploitation of groundwater resources; Increased sand mining in the Basin

• Increased pollution of ground and surface waters• Degradation of Tanks and water supply structures• Siltation in tanks and intake channels• Decrease in the yield and quality of agricultural crops

• Prevalence of water borne disease and other health problems

• Labour shifts from agriculture to industrial sector

• Costs involved in repairing tanks and shifting water supply structures

Depletion of Resources (Water; Forests; Sand)

Degradation of Environment (Water; Soil; Crops;

Infrastructure)

Risks to Health & Ecosystems (Water borne diseases; Occupational health;

Bioaccumulation of pollutants)

Distortion on Employment and Income Generation

(Loss of livelihood; Occupational shifts and migrations)

Macro Economic Basin Wide Issues (Costs of Remediation; Shifting, Rehabilitation of

structures, etc.)

Progression of IssuesProgression of Issues

But there is progress…

Watershed DevelopmentWatershed Development

Watershed Management

Livestock Management

Chlorination of RWS

Catchment Protection

Stakeholder ConsultationsStakeholder Consultations

Joint Walkthroughs

Stakeholder Meetings

Who benefits …Who benefits …

and Who will be forgotten?

The Framework?The Framework?

Institutions, Participation & Equity

Hydrologic Engineering/

Economics

Sustainable Basin

Planning & Management

“Do No Harm”: Minimize environmental/social risks“Do Good”: Maximize sustainable environmental/social benefits

Maximize Sustainable Productivity of Water(Net Benefits of Water)

Climate Variability and Climate Variability and PredictionPrediction

Mechanisms/Systems/DSS

The Analysis: The Analysis: Pricing GroundwaterPricing Groundwater

Water Management MechanismWater Management Mechanism Monsoon Forecast DevelopmentMonsoon Forecast Development Case Study: Simulation Analysis – Case Study: Simulation Analysis –

Palar basin, IndiaPalar basin, India

UnregulatedUnregulated GroundwaterGroundwater

(Rogers et al., 2002; Rogers et al., 1998):(Rogers et al., 2002; Rogers et al., 1998): Water resources underpricedWater resources underpriced

external costs exceed private cost, social benefitexternal costs exceed private cost, social benefit Economic arguments for efficient pricing:Economic arguments for efficient pricing:

minimize opportunity costsminimize opportunity costs improve maintenance of systems improve maintenance of systems water conservationwater conservation Equitable distribution of groundwater benefitsEquitable distribution of groundwater benefits

Negative Externalities of no pricingNegative Externalities of no pricing shift to water intensive cropsshift to water intensive crops well deepening and increase in well investmentswell deepening and increase in well investments well failure and abandonmentwell failure and abandonment neglect of traditional irrigation systemsneglect of traditional irrigation systems

Feasibility of pricing electricityFeasibility of pricing electricity Cost of water unrelated to variability of supplyCost of water unrelated to variability of supply

no adjustments during droughtno adjustments during drought

OverviewOverviewMonsoon Forecasting :Monsoon Forecasting :

- Can we use it to improve water resources - Can we use it to improve water resources management in India?management in India?

Obstacles:Obstacles:- uncertain reliability, usefulness- uncertain reliability, usefulness- difficulty communicating forecasts- difficulty communicating forecasts

Hypothesis: Hypothesis: Pricing can communicate expectation of water Pricing can communicate expectation of water availability and increase the productivity of water availability and increase the productivity of water useuse

Groundwater Management Groundwater Management AlternativesAlternatives

No Action – Gisser and Sanchez, No Action – Gisser and Sanchez, 19801980

Free Market Free Market benefits of cooperation (Shah, 1993)benefits of cooperation (Shah, 1993) Monopoly prices and overdraft Monopoly prices and overdraft

(Palanisami, 2002; Shah, 1993)(Palanisami, 2002; Shah, 1993) Usage tax (Bredehoeft and Young, Usage tax (Bredehoeft and Young,

1970)1970) Conjunctive Use (Reichard, 1995)Conjunctive Use (Reichard, 1995)

Forecast-based taxForecast-based tax

This approach:This approach: Set tax according to forecasted monsoon Set tax according to forecasted monsoon

and estimated net benefits of GW useand estimated net benefits of GW use Economic signal for increased extraction Economic signal for increased extraction

in good monsoonsin good monsoons Enhance rechargeEnhance recharge

Economic signal for conservation in weak Economic signal for conservation in weak monsoonsmonsoons

Crop choiceCrop choice Low probability event mitigationLow probability event mitigation

Less expensive GW in unexpected droughtLess expensive GW in unexpected drought

Usage TaxUsage Tax background background Numerous studies of groundwater and pricingNumerous studies of groundwater and pricing

marginal benefit = marginal costmarginal benefit = marginal cost Static models: Static models:

external cost of groundwater use = cost of pumping external cost of groundwater use = cost of pumping from increasing depthfrom increasing depth

Burt, 1964; Brown & Deacon, 1972Burt, 1964; Brown & Deacon, 1972 Dynamic models: Dynamic models:

external cost = discounted value of future returns external cost = discounted value of future returns from optimal groundwater use; buffer valuefrom optimal groundwater use; buffer value

Tsur & Graham-Tomasi, 1990; Bromley, 1995Tsur & Graham-Tomasi, 1990; Bromley, 1995

Estimating Supply Estimating Supply CostsCosts

Marginal social cost based on extraction Marginal social cost based on extraction costs and opportunity cost – Static Modelcosts and opportunity cost – Static Model

E[TMC] = EnC + ExC + E[O(qE[TMC] = EnC + ExC + E[O(qtt)])]

External cost of withdrawalExternal cost of withdrawal

C C = operational cost of pumping= operational cost of pumpingkk = proportionality constant = proportionality constant

1( , ) t

dCNU x q k

dxExC

Estimating Supply CostEstimating Supply Cost

Opportunity CostOpportunity Cost::

X = target groundwater elevation X = target groundwater elevation

x = current elevationx = current elevation

Replacement Cost = unit cost of Replacement Cost = unit cost of inexhaustible source (desal)inexhaustible source (desal)

Replacement Cost, , 1 t

t

x p qO p x q

X

Pricing Strategy-Demand Pricing Strategy-Demand CurveCurve

Marginal benefit based on farmer decision Marginal benefit based on farmer decision modelmodel Maximize net incomeMaximize net income FAO yield equationFAO yield equation

ii = crop type Y = crop type Yii = yield q = yield qii = groundwater applied = groundwater applied

s = rainfall p = groundwater price Ts = rainfall p = groundwater price Tii = land per = land per crop i crop i

rrii = revenue (Willis and Yeh, 1987) = revenue (Willis and Yeh, 1987)

1

max ( )m

i i i i ii

rY q s pq T

Calculation of Efficiency LossesCalculation of Efficiency Losses

Figure 3. Social Cost determination

0

510

15

20

2530

35

4045

50

0 200 400 600 800 1000

Water Demanded (hectare-mm)

$R/h

ecta

re-m

m MC

Above

Normal

Below

Price SelectionPrice Selection Stakeholder meetings (eg., water users association) Stakeholder meetings (eg., water users association)

generate groundwater prices for Above Normal, generate groundwater prices for Above Normal, Normal and Below Normal monsoonsNormal and Below Normal monsoons

For each price, expected net social benefit is For each price, expected net social benefit is calculatedcalculated

Price that maximizes the social net benefit is Price that maximizes the social net benefit is recommended as water pricerecommended as water price

kE NB(p ) max NB( ,s) = max NB( ,s) P(s )E k l ls fp p

p f p f

Price Selection - Price Selection - Above Normal ForecastAbove Normal Forecast

Water Pricing Monsoon

Water Value

Net Income Subsidy

Net Benefit

Forecasted Prob.

Expected Benefit

20 Above 10 10,132 -2,050 7,329 70 6,736

Normal 16 8,251 -1,756 6,654 20

Below 24 5,792 3,200 2,753 10

10 Above 12 10,889 570 10,329 70 8,220

Normal 19 9,969 4,950 5,450 20

Below 26 8,739 14,688 -5538 10

5 Above 13 14,371 2,576 12,064 70 8,235

Normal 20 12,157 9,015 4,245 20

Below 28 10,342 22,333 -10,593 10 (Rs/ha)

Price Selection - Price Selection - Below Normal ForecastBelow Normal Forecast

Water Pricing Monsoon

Water Value

Net Income Subsidy

Total Net

Benefit Forecast Expected Benefit

20 Above 10 9379 -2050 7329 10 5528

Normal 14 8410 -1008 7402 20

Below 17 5953 -1218 4735 70

10 Above 12 11,547 -795 10,977 10 1998

Normal 16 10,400 3114 7286 20

Below 23 9150 9945 -795 70

5 Above 14 14,640 2880 11,760 10 -486

Normal 17 13,260 6660 6600 20

Below 25 11,740 16,000 -4260 70 (Rs/ha)

Decision MapDecision Map

Winter Monsoon Winter Monsoon ForecastForecast

Mean Annual Rainfall = 92.4 cmMean Annual Rainfall = 92.4 cm Winter Monsoon (OND) = 45.4 cm (49%)Winter Monsoon (OND) = 45.4 cm (49%) Low correlation with All India RainfallLow correlation with All India Rainfall Negatively correlated with summer Negatively correlated with summer

monsoonmonsooncc = - 0.20 (1877 – 1976, cc = - 0.38)cc = - 0.20 (1877 – 1976, cc = - 0.38)Table 1. Correlation of Total Annual Rainfall for All India and selected subdivisions.

State All India Tamil

Nadu

Telangana Rayalaseema Coastal Andhra

Pradesh

All India 1

Tamil Nadu 0.152 1

Telangana 0.644 0.0374 1

Rayalaseema 0.513 0.375 0.491 1

Coastal Andhra

Pradesh

0.521 0.293 0.572 0.639 1

Tamil Nadu PrecipitationTamil Nadu Precipitation

Average rainfall by month

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rai

nfal

l (cm

)

ENSO InfluenceENSO Influence

Relative frequency of Tamil Nadu (OND) rainfall

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

-30 -20 -10 0 10 20 30

Rainfall Anomaly (cm)

Warm

Cold

SST Correlations – 6 months SST Correlations – 6 months aheadahead

Forecast DevelopmentForecast Development

Investigated ENSO indices, SSTs and SLPInvestigated ENSO indices, SSTs and SLP Principal Component AnalysisPrincipal Component Analysis Hierarchical model: linear, cubicHierarchical model: linear, cubic Regional predictors from IITMRegional predictors from IITM Selected Predictors:Selected Predictors:

Pacific SSTs: principal component analysisPacific SSTs: principal component analysisof Apr-May-June mean and Jan-June trendof Apr-May-June mean and Jan-June trend

Region: 22.5N to 32.5S, 137.5E to 77.5WRegion: 22.5N to 32.5S, 137.5E to 77.5W

Indian Ocean SLP: trend (MAM-DJF) at Darwin, Indian Ocean SLP: trend (MAM-DJF) at Darwin, Australia and Nouvelle-AgalegaAustralia and Nouvelle-Agalega

Example Forecasted Example Forecasted PDFs PDFs

-50 0 500

0.05

0.1

Estimated Probability Density Functions based on PC model

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

-50 0 500

0.05

0.1

Case StudyCase Study

Kancheepuram District,Kancheepuram District,

Palar River basinPalar River basin

Tamil NaduTamil Nadu

Location & Characteristics of the Palar Basin

TAMIL NADUTAMIL NADU

IRS, IWS, Taramani, Chennai 600 113

Palar Basin

River Basin Map

Boundaries are indicative and may be under dispute

First Multi-Stakeholder Basin Board in South Asia RegionFirst Multi-Stakeholder Basin Board in South Asia Region

Palar Basin Palar Basin CharacteristicsCharacteristics

• Basin Area: 18,300 km2 (10,910 km2 in TN)

• Potential Supply: About 1,500 MCM SW; 2,700 MCM GW

• Current Demands: 2560 MCM (88% Irrigation)

• Industries: 88 large; 22,695 small (30 MLD effluent)

• River flows for 15 days in year!• Storage: 11 reservoirs, 4,900 rainfed tanks and 661 “system” tanks

• About 250,000 wells (mostly dug wells)

• GW: 50% blocks over-exploited; 41% critical/semi-critical

• Population: 5.4 million (62 million in TN)

Palar BasinPalar BasinTypical Issues Typical Issues

Catchment Degradation

Water Pollution(Tanneries, Waste Dumps, Textiles, Other Industries,

Domestic, Fertilizer/Pesticide)

Tank/Canal/Groundwater Irrigated Agriculture

Sand Mining

Tank Degradation

Water Scarcity

Groundwater Management

Agr. Dept/Agr. Engr. DeptWUA/Farmers

Water Resources OrganizationInstitute for Water Studies

TWAD Board

Fisheries Department

Forest Department

Palar Basin Board& Secretariat

Salt-impacted Agricultural Productivity

SimulationSimulation

Farmer Decision Model Farmer Decision Model Four crop choices: cotton, pulses, ragi, Four crop choices: cotton, pulses, ragi,

groundnutgroundnut Water Price and Rainfall ExpectationWater Price and Rainfall Expectation

Pricing ModelPricing Model Maximize expected value: Net Social BenefitMaximize expected value: Net Social Benefit

SimulationSimulation Rabi season (Oct-Nov-Dec)Rabi season (Oct-Nov-Dec) 37 year record – historical rainfall and 37 year record – historical rainfall and

operational forecastsoperational forecasts Single cell aquiferSingle cell aquifer

SimulatiSimulation on

SchemaSchematictic

Seasonal Rainfall Forecast

Inverse DemandModel

Demand Curve:Above

Demand Curve:Below

Demand Curve:Normal

OptimalPrice Algorithm

Farmer DecisionModel

Production Model

Net Social Benefits Groundwater ElevationFarmer Benefit

Actual Rainfall

Average Rainfall or Forecast

Water tariff

Crop Plan

Feedback

Feedforward

Simulation ResultsSimulation Results

ScenarioScenario Fully Adaptive (Full) – forecast-based, with Fully Adaptive (Full) – forecast-based, with

groundwater elevation updatinggroundwater elevation updating Feedforward (Forecast) – forecast-based, no Feedforward (Forecast) – forecast-based, no

groundwater updatinggroundwater updating Static – single price based on long term Static – single price based on long term

averagesaverages MetricsMetrics

NSB – Net Societal BenefitsNSB – Net Societal Benefits Farmer IncomeFarmer Income Groundwater elevation changeGroundwater elevation change

ResultsResults

Price NSB % increase Farmers' NB Water Use GW changeModel ($/ha-mm) ($/ha) to Static ($/ha) mm/ha mFully Adaptive w/ Perfect Forecasts 0.58 177 113.3% 163 204 -0.59Fully Adaptive 0.62 171 112.9% 158 207 -0.73Feedback (No Forecast) 0.62 169 112.7% 158 208 -0.72Feedforward (No Groundwater) 0.30 -1,217 8.40% 206 506 -11.41Feedforward (Perfect, No Groundwater) 0.28 -1,119 15.82% 197 489 -10.94Static 0.28 -1,329 -- 212 518 -11.80

Average values for 37 year simulation

Groundwater ElevationGroundwater Elevation

Net Social BenefitNet Social Benefit

1965 1970 1975 1980 1985 1990 1995-6000

-5000

-4000

-3000

-2000

-1000

0

1000

Year

$/ha

Farmer Net IncomeFarmer Net Income

1965 1970 1975 1980 1985 1990 199560

80

100

120

140

160

180

200

220

240

260$/

ha

Year

Farmers Net Benefits

Recharge - NSBRecharge - NSB

Forecast User EffectForecast User Effect

NSB % increase Farmers' NBForecast Use ($/ha) to Static ($/ha)Water Managers w/ Perfect Forecasts 177 113.3% 163Water Managers 171 112.9% 158Farmers only 171 112.9% 162Farmers only - Perfect Forecasts 174 113.1% 167Farmers and Water Managers 155 111.7% 142Farmers and Water Managers-Perfect 166 112.5% 154Static -1,329 -- 212

Summary of FindingsSummary of Findings

Adaptive Price superior to static price Adaptive Price superior to static price in terms of NSB, GW elevationin terms of NSB, GW elevation

Highest benefits to incorporating Highest benefits to incorporating groundwater opportunity costgroundwater opportunity cost

Use of forecasts by water managers Use of forecasts by water managers increases NSB by 8%increases NSB by 8% Perfect – increase by 16%Perfect – increase by 16%

Conserving GW costs farmers Conserving GW costs farmers Results sensitive to recharge rateResults sensitive to recharge rate Forecast benefits from water manager Forecast benefits from water manager

use = benefits from farmer useuse = benefits from farmer use

ExtensionsExtensions

Spatially distributed, physically-based Spatially distributed, physically-based modelingmodeling

Multisector socioeconomic modeling Multisector socioeconomic modeling to capture non-farm income (General to capture non-farm income (General Equilibrium Model)Equilibrium Model)

Farmer decision making under Farmer decision making under uncertaintyuncertainty

Replacement ValueReplacement Value

Alternate Static PricesAlternate Static Prices

outcomeforecast climatologyoutcomeforecast

E E E F(w,XforecastF(w,X ) forecast

Forecast Value

w = observed climate

Xf = optimized plan based on forecast

Xc = plan based on climatology

PrePosterior Analysis of Forecast Value

Solve with stochastic dynamic programming

My WorkMy Work Investigate the use of Monsoon Forecasting Investigate the use of Monsoon Forecasting

to increase the efficiency of groundwater to increase the efficiency of groundwater pricingpricing

TasksTasks Develop monsoon forecastDevelop monsoon forecast Formulate the application of forecast to GW Formulate the application of forecast to GW

pricingpricing Estimate cost and benefit functions of GW Estimate cost and benefit functions of GW

extractionextraction Evaluate the benefits of the pricing systemEvaluate the benefits of the pricing system

Pre-Posterior analysis – stochastic dynamic Pre-Posterior analysis – stochastic dynamic programmingprogramming

Simulation Simulation

Literature – Forecast Literature – Forecast ValueValue

Value of ForecastsValue of Forecasts ““Umbrella Problem” – Cost-Loss ratioUmbrella Problem” – Cost-Loss ratio

Epstein,1969; Murphy, 1985; Brown et al., 1986Epstein,1969; Murphy, 1985; Brown et al., 1986 Various agricultural studiesVarious agricultural studies

““Economic Value of Weather and Climate Forecasts,” Economic Value of Weather and Climate Forecasts,” Katz and Murphy, 1995Katz and Murphy, 1995

Hydropower operationHydropower operation Hamlet and Lettenmaier, 1999; Hamlet et al., 2001Hamlet and Lettenmaier, 1999; Hamlet et al., 2001

Literature - Literature - ApplicationsApplications

AgricultureAgriculture Hammer et al., 2001; Hansen, 2002Hammer et al., 2001; Hansen, 2002 Shift from passive acceptance of climate variability to active Shift from passive acceptance of climate variability to active

response to climate forecastresponse to climate forecast Benefit only through viable decision optionsBenefit only through viable decision options Appropriate use requires effective communicationAppropriate use requires effective communication

Water ResourcesWater Resources Hartmann et al., 2002; Bates, 2002Hartmann et al., 2002; Bates, 2002 Limitations to forecast applicationsLimitations to forecast applications

limited predictabilitylimited predictability probabilistic forecasts difficult to interpretprobabilistic forecasts difficult to interpret lacking mechanisms for implementationlacking mechanisms for implementation

SummarySummary

Goddard et al., 2001:Goddard et al., 2001:

““Progress in diagnosing, modelling Progress in diagnosing, modelling and predicting seasonal climate and predicting seasonal climate variability represents a major variability represents a major scientific advancement of the 20scientific advancement of the 20thth century; however, progress in the century; however, progress in the effective utilization of forecasts effective utilization of forecasts lagged behind.”lagged behind.”

ENSO InfluenceENSO InfluenceRelative frequency of Tamil Nadu (OND) rainfall

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

-30 -20 -10 0 10 20 30

Rainfall Anomaly (cm)

Warm

Cold

Relative Frequency of Telangana (JJAS) rainfall

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

-30 -20 -10 0 10 20 30 50

Rainfall Anomaly (cm)

Warm

Cool

“New” Approach to Monsoon Forecast

• Investigated:• ENSO Teleconnections• GCM/RCM and statistical methods• Data

– SSTs: NCEP/NCAR reanalysis, 1949 – 1999, 5º grid

– IITM predictors: 1963 – 1999– Tamil Nadu rainfall: 1871 – 1999, area average

HypothesisHypothesis

Minimize efficiency losses by Minimize efficiency losses by choosing the water tariff based on choosing the water tariff based on the monsoon forecastthe monsoon forecast

• Requires:Requires:– Accurate ForecastAccurate Forecast– Accurate Estimate of Demand and Cost Accurate Estimate of Demand and Cost

CurvesCurves

Expected ResultsExpected Results

Forecast: Below Normal rainfallForecast: Below Normal rainfall Price adjusted upwardPrice adjusted upward Encourages crop choices that decrease water Encourages crop choices that decrease water

useuse Failed forecast: above normal rainfall offsets Failed forecast: above normal rainfall offsets

need for groundwaterneed for groundwater Forecast: Above Normal rainfallForecast: Above Normal rainfall

Price adjusted downwardPrice adjusted downward Encourages ambitious crop planningEncourages ambitious crop planning Failed forecast: low priced water helps offset Failed forecast: low priced water helps offset

below normal rainfallbelow normal rainfall Conditional on groundwater table elevationConditional on groundwater table elevation

Alternate ApproachesAlternate Approaches

Optimal combination of climatology and Optimal combination of climatology and forecast (Dirichlet distributions)forecast (Dirichlet distributions) Maximize likelihood function:Maximize likelihood function: Product of the probability of the observed Product of the probability of the observed

categorycategoryn = years k* = observed n = years k* = observed

category category

PPk,tk,t = vector of probabilities for year t = vector of probabilities for year t

Hierarchical ModelHierarchical Model Representation of annual trendRepresentation of annual trend Cubic and Linear fit of principal Cubic and Linear fit of principal componentscomponents

*,1

( ) ( )n

k tt

L w E P

Decision mapDecision map Categorize forecasts based on Categorize forecasts based on

decision outcome:decision outcome:

Forecast Value - Bayesian Forecast Value - Bayesian AnalysisAnalysis

Probabilistic Categorical ForecastProbabilistic Categorical Forecast PC’s of Pacific, Indian SLP trendPC’s of Pacific, Indian SLP trend

Decision: Single Price vs Forecast Decision: Single Price vs Forecast PricePrice

Maximize Expected Net Social Maximize Expected Net Social BenefitsBenefits

Expectation over observation given Expectation over observation given decisiondecision

kE NB(p ) max NB( ,s) = max NB( ,s) P(s )E k l ls fp pp f p f

Water Manager, pre-monsoon

Forecast

Price 1

Plan

Price 3

Price 2

Dry Monsoon

Normal Monsoon

Wet Monsoon

Dry Monsoon

Normal Monsoon

Wet Monsoon

Dry Monsoon

Normal Monsoon

Wet Monsoon

Dry Monsoon

Normal Monsoon

Wet Monsoon

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Benefits

Decision Outcome

Dry

Dry

Mean

Wet

Wet

Mean

Observes forecast

Does not observe forecast

Dry

Wet

Mean

Forecast Value AddedForecast Value Added

Problem: How to categorize Problem: How to categorize forecasts? forecasts?

Simple A, N, B based on highest Simple A, N, B based on highest probability loses informationprobability loses information

Justifiable rationale for groupings?Justifiable rationale for groupings?