Variability and uncertainty: implications for water policy impact analysis

1. Variability and uncertainty: implications for water policy impact analysis Thilak Mallawaarachchi, David Adamson,Sarah Chambers, Peggy Schrobback and John Quiggin http://www.uq.edu.au/rsmg

2. Introduction • Policy decisions are made with limited knowledge as they juggle to address • Complexity, instability and variability of natural systems, while trying to meet • Transparency, stability and consistency attributes of public policy • The evidence-based approach to public policy • ‘good science’ • ‘robust socio-economic analysis’ to make • Informed decisions on the ‘triple bottom line’ • Are the decisions well informed? And improve social welfare, or, • Does it really matter?

3. Outline • Decision-making under uncertainty, • Variability, risk and uncertainty • Sources of variability • Addressing uncertainty in economic analysis • Policy analysis for Basin water allocations • RSMG model adaptations • Conclusions

4. Decision-making under uncertainty • Variability is inherent in natural systems • Knowledge about natural systems is limited • Challenge in policy is to balance scientific uncertainty and policy reliability • People are concerned with outcomes, not probabilities • People demonstrate learning behaviour • How can we make better decisions under increasing uncertainty, drawing on learning?

5. Inflows to Murray-Darling River System 1892- to 2006 Source: Murray-Darling Basin Commission

6. Gross value of irrigated production$ million Source: Australian Bureau of Statistics

7. Uncertainty & variability Can 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 theory

8. Inflows to Murray-Darling River System 1892- to 2006 Source: Murray-Darling Basin Commission

9. State-contingent analysis • Identifiable states of nature • State-allocable inputs • State-contingent technologies • State-contingent outputs Dry Normal Wet Water (x) Production practices y = f (x,...) Cotton Wheat Rice

10. Managing the ‘Problem of Change’ • Decisions 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 .

11. Sources of variability in the value of information • Variability – a source of natural variation • Uncertainty – Gaps in knowledge or understanding

12. Uncertainty in economic models • Errors in understanding and application • Model design • Parameter uncertainty • Modeller subjectivity • Residual error • Algorithmic and computational errors • Increases with the complexity and the speed of model development • Multi-platform developments can check these errors

13. Modelling issues • Capturing adaptation dynamics • Perennial sector (ex-ante optimisation) • capital fixity and other rigidities • existence of a recursive solution • Annual enterprises (ex-post optimisation) • convergence to an equilibrium outcome • commodity mix to maximise benefits • Adjustment where water trade infeasible

14. Uncertainty in policy analysis • Help 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.

15. Policy Impact Analysis of Basin Water Allocations • Understanding trade-offs between production and the environment • Responses over water use, commodity outputs, and regional income • Influenced 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 hand • scale • complexity • Time frames of analysis

16. RSMG Model Adaptations for the analysis of Water Allocations for the Basin Plan • Understanding trade-offs between production and the environment • Responses over water use, commodity outputs, and regional income • Influenced 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 hand • scale • complexity • reliability and responsiveness

18. The Task: simulate producers’ responses to changes in access to irrigation • Inputs • hydrological data for 19 catchments • 114 years of data (1895 -2008); CDL & SDL • Variability analysis • Compared flow variability for the full period a& the past 10 years for each catchment • Probabilities for different states of water availability • Variability – volumes associated with each state

19. The Task: simulate producers’ responses to changes in access to irrigation • Inputs • hydrological data for 19 catchments • 114 years of data (1895 -2008); CDL & SDL • Variability analysis • Compared flow variability for the full period a& the past 10 years for each catchment • Probabilities for different states of water availability • Variability – volumes associated with each state • 20, 50 and 70th percentile (dry, normal and wet) • adjusted for catchments with large dams to allow transfers from wet to dry states

20. Baseline

21. A Plan scenario (37% reduction)

22. Summary comparison

24. Conclusions • Modelling is prone to errors of uncertainty • Conventional methods based on mean values may distort possible adaptations • The state-contingent approach provides an improvement • Subjective judgements, data errors and knowledge gaps can still be an issue • The weakest link applies and need to be aware of those

25. Conclusions ctd. • Does uncertainty matter? Certainly it does • Conventional methods based on mean values may distort possible adaptations • May not signal opportunities for action; • and underestimates the costs of action • Increasing uncertainty is not a bad thing • But not responding to uncertainty means lost opportunities.