Adapting to a water-limited environment: some observations and insights from the MurrayDarling Basin

Variability and uncertainty: implications for water policy impact analysisThilak Mallawaarachchi, David Adamson,Sarah Chambers, Peggy Schrobback and John Quiggin

http://www.uq.edu.au/rsmg

http://www.uq.edu.au/rsmg1Introduction

Drought has increased the concerns by the community of environmental impacts of reduced water availability and the impacts on irrigated agriculture.

Governments have responded with another bout of reforms, targeting the environment and assistance for irrigators to adjust.

The outcome would be an eventual redistribution of water access rights leading to a decline in water availability for irrigators as available supplies are shared amongst all users.

This could entail costs to irrigators with the potential for climate change to exacerbate the costs.

Adaptation is the only option available to irrigators to reduce costs, and the increasing opportunity costs of water, also impacted by an increasing trend in water trade, provide an incentive to conserve water.

Examining how irrigators have adopted to water shortages over the past decade can provide useful insights to policy development, in particular the role of government and the impact of government involvement on the rate of adaptation.

In this presentation, we provide some insights based on an examination of ABS data, ABARE farm survey results, including some case studies and results from model simulations.IntroductionPolicy decisions are made with limited knowledge as they juggle to addressComplexity, instability and variability of natural systems, while trying to meetTransparency, stability and consistency attributes of public policy

The evidence-based approach to public policygood sciencerobust socio-economic analysis to makeInformed decisions on the triple bottom line

Are the decisions well informed? And improve social welfare, or,Does it really matter?http://www.uq.edu.au/rsmgOutlineDecision-making under uncertainty, Variability, risk and uncertaintySources of variability

Addressing uncertainty in economic analysis

Policy analysis for Basin water allocationsRSMG model adaptationsConclusionshttp://www.uq.edu.au/rsmgDecision-making under uncertaintyVariability is inherent in natural systemsKnowledge about natural systems is limitedChallenge in policy is to balance scientific uncertainty and policy reliabilityPeople are concerned with outcomes, not probabilitiesPeople demonstrate learning behaviourHow can we make better decisions under increasing uncertainty, drawing on learning?http://www.uq.edu.au/rsmg4Agriculture predominates as a land useIrrigation uses around 65-70% of waterA Mediterranean climate in the South and sub tropical in the northWater scarcity increased since 2002 with low precipitation and consequent low flows to storages.Inflows to Murray-Darling River System 1892- to 2006Source: Murray-Darling Basin Commission

http://www.uq.edu.au/rsmg5Gross value of irrigated production$ millionSource: Australian Bureau of Statisticshttp://www.uq.edu.au/rsmgUncertainty & variabilityCan we separate uncertainty and variability?

Uncertain outcomes are dependent upon states of nature, which can take a range of values, making us less confident of outcomes.

Then, if we could sort these different states (the state space) into mutually exclusive states that define narrow ranges, we can then have greater confidence about natural variation.

As confidence increases, we can associate certain actions to certain states, and make choices about what to do, and when.

State-contingent production theoryhttp://www.uq.edu.au/rsmgInflows to Murray-Darling River System 1892- to 2006Source: Murray-Darling Basin Commission

http://www.uq.edu.au/rsmg8State-contingent analysisIdentifiable states of nature

State-allocable inputs

State-contingent technologies

State-contingent outputs

DryNormalWetWater (x)Production practices y = f (x,...)CottonWheatRicehttp://www.uq.edu.au/rsmgManaging the Problem of ChangeDecisions are always made subject to information

Learning improves knowledge, and knowledge improves judgement, over time.

The ability to separate management learning from natural variability provides a degree of control in managing change.

Uncertainty provides opportunities to make profits particularly in good states, or to trade-off benefits and costs in different states of nature.Information that allow a distinction between different states is valuable .

http://www.uq.edu.au/rsmgSources of variability in the value of informationVariability a source of natural variation

Uncertainty Gaps in knowledge or understanding

http://www.uq.edu.au/rsmgUncertainty in economic modelsErrors in understanding and applicationModel designParameter uncertaintyModeller subjectivity

Residual errorAlgorithmic and computational errorsIncreases with the complexity and the speed of model developmentMulti-platform developments can check these errorshttp://www.uq.edu.au/rsmgModelling issuesCapturing adaptation dynamicsPerennial sector (ex-ante optimisation)capital fixity and other rigiditiesexistence of a recursive solution

Annual enterprises (ex-post optimisation)convergence to an equilibrium outcomecommodity mix to maximise benefits

Adjustment where water trade infeasiblehttp://www.uq.edu.au/rsmgUncertainty in policy analysisHelp understand the limits of analysis.

Better appreciation of the range of outcomes, including critical variables and their impacts.

Could highlight unintended consequences of policy directions.

Inform research and information needs.http://www.uq.edu.au/rsmgPolicy Impact Analysis of Basin Water AllocationsUnderstanding trade-offs between production and the environmentResponses over water use, commodity outputs, and regional incomeInfluenced by the level of withdrawal and current use patterns

Impacts over different spatial units and economic agents and time frames.

Complex interactions of the water productivity relations and market forces across scales and over time

Identification of critical constraints and use appropriate model/s for the problem in handscalecomplexityTime frames of analysishttp://www.uq.edu.au/rsmgRSMG Model Adaptations for the analysis of Water Allocations for the Basin PlanUnderstanding trade-offs between production and the environmentResponses over water use, commodity outputs, and regional incomeInfluenced by the level of withdrawal and current use patterns

Impacts over different spatial units and economic agents and time frames.

Complex interactions of between water productivity relations and market forces across scales and over time.

Identification of critical constraints and use appropriate model/s for the problem in handscalecomplexityreliability and responsivenesshttp://www.uq.edu.au/rsmg

http://www.uq.edu.au/rsmgThe Task: simulate producers responses to changes in access to irrigationInputshydrological data for 19 catchments114 years of data (1895 -2008); CDL & SDL

Variability analysisCompared flow variability for the full period a& the past 10 years for each catchmentProbabilities for different states of water availabilityVariability volumes associated with each state

http://www.uq.edu.au/rsmgThe Task: simulate producers responses to changes in access to irrigationInputshydrological data for 19 catchments114 years of data (1895 -2008); CDL & SDL

Variability analysisCompared flow variability for the full period a& the past 10 years for each catchmentProbabilities for different states of water availabilityVariability volumes associated with each state20, 50 and 70th percentile (dry, normal and wet)adjusted for catchments with large dams to allow transfers from wet to dry states

http://www.uq.edu.au/rsmgBaselineBaselineNormalDryWetExpected ValueState-ContingentArea irrigated (000 ha)2,0672,2291,3671,8902,012Water use (GL)9,3426,5219,8938,9439,534Surplus ($m)2,5551,5083,5702,6502,118Gross value ($m)10,2386,22414,62910,7538,940http://www.uq.edu.au/rsmgA Plan scenario (37% reduction)BaselineNormalDryWetExpected ValueState-ContingentArea irrigated (000 ha)1,8411,5869601,5251,726Water use (GL)6,5075,7206,8136,4416,814Surplus ($m)2,1841,2172,7872,1721,713Gross value ($m)8,7765,06111,7518,9257,184http://www.uq.edu.au/rsmgSummary comparisonComparisonBaselineBasin PlanAbsolute DifferencePercentageDifferenceSequential solutionArea irrigated (000 ha)2,0121,726-286-14Water use (GL)9,5346,814-2,720-29Surplus ($m)2,1181,713-405-19Gross value ($m)8,9407,184-1,756-20Global solutionArea irrigated (000 ha)1,8211,614-207-11Water use (GL)10,5606,814-3,746-35Surplus ($m)2,3251,954-371-16Gross value ($m)9,1707,725-1,445-16http://www.uq.edu.au/rsmg

http://www.uq.edu.au/rsmgConclusionsModelling is prone to errors of uncertaintyConventional methods based on mean values may distort possible adaptationsThe state-contingent approach provides an improvementSubjective judgements, data errors and knowledge gaps can still be an issueThe weakest link applies and need to be aware of thosehttp://www.uq.edu.au/rsmgConclusions ctd.Does uncertainty matter? Certainly it doesConventional methods based on mean values may distort possible adaptationsMay not signal opportunities for action; and underestimates the costs of actionIncreasing uncertainty is not a bad thingBut not responding to uncertainty means lost opportunities.http://www.uq.edu.au/rsmg