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Evidence Based Policy Making: Water resources case studies D. Rios Insua, RAC J. Cano, A. Udías, URJC Kiombo Jean Marie, U. Agostinho Neto Hocine Fellag,

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Presentation on theme: "Evidence Based Policy Making: Water resources case studies D. Rios Insua, RAC J. Cano, A. Udías, URJC Kiombo Jean Marie, U. Agostinho Neto Hocine Fellag,"— Presentation transcript:

1 Evidence Based Policy Making: Water resources case studies D. Rios Insua, RAC J. Cano, A. Udías, URJC Kiombo Jean Marie, U. Agostinho Neto Hocine Fellag, U. Tizi Ouzou Paris, December’10

2 Agenda Background Case 1: Kwanza river management Case 2: Fair water distribution in Kabylia Common lessons

3 Background

4 Two case studies in water resources management Africa (Angola, Algeria) Spanish Cooperation Agency (us, local uni, one or more local agents) Complex Policy making (public policy decision making) Evidence based?? Policy analytics

5 Background: EBDM A. Smith. Towards an Evidence-Based Society Most of us with rationalist pretensions presumably aspire to live in a society in which decisions about matters of substance with significant potential social or personal implications are taken on the best available evidence, rather than on the basis of irrelevant evidence or no evidence at all. Of course, the nature of what constitutes evidence in any particular instance could be a matter of debate. Evidence-based medicine (JAMA) de-emphasizes intuition, unsystematic clinical experience and pathophysiologic rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research. Evidence- based medicine requires new skills of the physician, including efficient literature searching and the application of formal rules of evidence evaluating the clinical literature

6 Background: EBDM ODI. Evidence-Based Policy Making. What relevance for developing countries? Replacing ideologically driven politics with rational decision making Better utilization of evidence in policy and practice Helps people make well informed decisions about policies, programmes and projects by putting the best available evidence form research at the heart of policy devlopment and implementation Different types of evidence Hierarchy of evidence What evidence is useful to policymakers

7 Background: EBDM ODI. Evidence-Based Policy Making. What relevance for developing countries? More troubled political context… Conflict Political volatility Role of civil society Centralised policy processes, Less open Lack of performance indicators Lack of data (and if existing difficult to get)

8 Case 1: Kwanza river management

9 Background info. The Kwanza river Divided in three parts: Upper Kwanza, Middle Kwanza, Lower Kwanza Length: 960 Km Basin area: 152,570 Km2 Flow average: 825 m3/s

10 Background info. Angola Estimated population: 17600000 inhabitants Human Development Index (2007):0.564, place 143 of 182 Gross Domestic Product per capita (2008): US$ 4961 Population without access to improved water source (2006): 49% Population without access to energy (2005): 13,5 million of persons Electrification rate: 15% between 2000 and 2005 Proportion of renewable energy supply: approximately 1% in 1990 and 1.5% in 2005 Number of hydrographic basins: 47, the most important is the Kwanza river.

11 Background info. Cambambe Capanda

12 Problem formulation. Decision variables Monthly water through Capanda turbines Monthly water through Capanda spillgates Similarly for Cambambe Monthly water for irrigation at Southern Luanda agricultural projects Monthly water for consumption at Luanda

13 Problem formulation. Uncertainties Water inflow to Capanda Evaporated water in Capanda Similarly in Cambambe Water demand for irrigation in Luanda Water demand for human consumption in Luanda

14 Problem formulation. Constraints 1 Energy production constraints Release constraints Water through turbines smaller than the maximum It. Through spillgates Releases should respect navigability minima… Maximum capacity for water treatment Storage constraints Storage at reservoir smaller than maximum Continuity constraints

15 Problem formulation. Objectives Maximize energy production Minimize water releases through both spillgates Minimize water deficit for irrigation Minimize water deficit for human consumption Changed several times

16 Preference modelling. Energy production g 1 (e t ) = 1.005 – 0.991 * exp(-0.012 * e t ) g t (dec, inc) = 0,30g 1 (e t )+0,15g 2 (U 2 Pt )+0,15 g 3 (U 2 Bt )+0,17 g 4 (k t )+0,23g 5 (h t )

17 Uncertainty modelling Unregulated inflows. DLMs Regulated inflows. Regression models, Water travelling time model. Data available for different years… simulate to generate data for same years Evaporation data not available: A model (Visentini)

18 Problem formulation The problem consists of maximising the following expected utility: Ψ(U) = ∫…∫∑[(0,30(1.027 – 1.012 * exp(-0.0056 * e t )) + 0.15(-0.045 + 1.098 * exp(-0.475 * U 2 Pt ))+0.15(-0.045 + 1.098 * exp(-0.475 *U 2 Bt ))+0.17(0.020 + 0.984*exp(- 0.083*k t ))+0.23(-0.007 +1.058 * exp(-1.232 * h t ))]h(i,u)dî B dî p dû urb dû irr Subject to constraints

19 Problem solution. MC Approximation of objective function 1/N ∑[(g t (U 1 Bt, U 2 Bt, U 1 Pt, U 2 Pt, U j irrt, U j urbt, i,d )+ (g t+1 (U 1 Bt+1, U 2 Bt+1, U 1 Pt+1, U 2 Pt+1, U j irr(t+1), U j urb(t+1 ), i,d )+…] Similarly for gradient

20 Problem solution. Number of stages vs Sample size (MATLAB on a standard Laptop) N T 100200300400500 287.75205.61718.70542.80838.95 3359.612030.804269.10218717952.6 41073.39688.8177485552.412382 54334.67187.5151763575665003 61504.411492821811026029911

21 Problem solution. Strategy adopted At a given time: Forecast for next twelve months. (Inflows, Demands, ‘Evaporations’) Optimise/plan for next twelve months. (Solve MEU) Implement optimal alternative obtained this month. Collect data for forecasting model. Move to next time. Variants: A different number of months Add a penalty term to mitigate miopicity

22 Problem solution. An example of a run 18 months planning. At each stage, 12 months. MC size 500.

23 The Kwanza problem Finetuning this DSS Capacity expansion- National energy plan (with A. Simbo)

24 Case 2: Fair water distribution in Kabylia

25 Background info

26 Total area: 2957 km 2 ; 67 municipalities (1380 villages); Total population: 1,100,000; Population over 900 m: 110,000 (90 villages); Annual rainfall: 900 mm; Groundwater: 72%; In 2009: 6,500,559 m 3, up to 80% losses; 9 wells (Bouaid), pumping capacity: 1,040 m 3 h −1; 5 wells (Takhoukht) pumping capacity: 140 m 3 h −1 ; Taksebt reservoir, pumping capacity: 140 m 3 h −1 ; 38 reservoirs (storage capacity: 28,000 m 3 ); 6 intermediate pumping stations 5,400 m 3 h −1 ;

27 Description of the problem Distribute water in a equitable & cost-efficient way. Difficulties to satisfy water demand adequately. Frequent water shortages create political unrest (Optimization of the pump operational schedules & strategic planning) Complex rules in energy fares (daytime & contractual issues). Pumping water uphill creates important engineering problems & high costs due to electricity consumption

28 Solutions to the problem Egalitarian solution (suggested by the water company), inflict same per capita water deficit L k to all users & minimize L k ; Smorodinsky-Kalai, min-max L k ; Utilitarian solution, maximize sum of attained utilities (minimizing sum of L k  minimize average per capita water deficit); Variance solution (not an arbitration solution), minimize standard deviation of L k ; Minimize inter and intravariability.

29 Formulation of the problem OBJ1: Minimize Cost (total electricity pumping consumption); OBJ2: Achieve equitable distribution. CONSTRAINTS: Pumping capacity; Storage capacity; Water flow capacity;

30 Generation of Pareto frontiers

31 Case study Two scenarios of distribution network losses (50% and 80% over-demand). Four infrastructure scenarios.

32 Case study Previously ‘fixed’ demand Demand data unavailable until this project DLM model per village. Hierarchical approach. Censored data Stochastic programming

33 Common lessons

34 Multiple objectives Unclear objectives which evolved Uncertainty Lack of data (simulate, extrapolate, censored) Planning over time Computational compromises No participation First quantitative approaches for our clients Encouraging results Unveil new realities Angola (Capacity expansion, Transmission reliability) Algeria (Thefts, Modify network, Reliability) Policy making Evidence based? Possibly (best available methods, best data available, careful work with experts) DSS Policy analytics

35 Discussion

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