1. Pricing water to respond to climate change in Amman, Jordan David E. Rosenberg david.rosenberg@usu.edu http://www.engr.usu.edu/cee/faculty/derosenberg/ AWRA Specialty Conference Anchorage, Alaska May 4 – 6, 2009

2. 2 Outline Jordan’s water Climate change impacts Deductive model Rates and prices simulated Results & limitations Conclusions Top: Tanker truck refilling rooftop tank Middle: New diversion works near the Dead Sea Bottom: Leaky distribution pipe

3. 3 Jordan Israel / Palestine Amman Ma’an Aqaba Zarka Irbid 0 50 100 km 100 mm/yr 500 900 Jordan Water Overview 5+ mill. people 1,000 Mm 3 /yr consumption 850 Mm 3 /yr supplies Severe groundwater overdraft Red Sea SAUDI ARABIA EGYPT IRAN PAL.

4. Amman, Jordan

5. 5 Amman water system 2.2+ million residents 360,000+ connections 133 Mcm/yr supply 2,700 km 2 service area 5,100 km of mains 1,700+ employees Significant leakage Rationing Rapid population growth Right: Component analysis for 2005

6. 6 Increased temperatures (0 to 4 o C) Reduced precipitation (0 to 20%) Reduced runoff (12 to 70%) Unknown impacts on groundwater Jordan just starting to address Right: Drying mudflat at Burqa, eastern Jordan Climate Change Impacts

7. 7 Deductive Modeling Approach 1.Identify available options 2.Characterize each option 3.Describe interdependencies 4.Quantify shortage events 5.Optimize –Stochastic programming with recourse –Monte Carlo simulations 6.Simulate different rate structures & price schedules

8. 8 Identified 39 potential actions

9. 9 Characterize action costs and effectiveness Above: Drip irrigation store

10. 10 Stochastic optimization with recourse Objective: Minimize expected annual costs 1 st stage decisions: Long-term actions (L i ) Stochastic events: Public water availability (e) 2 nd stage decisions: Short-term actions (S je ) Subject to: –Mass balance; Storage capacity; Rate structure –Upper limits on actions –Interdependencies –Meet water requirements in each event Monte-Carlo samples: parameter variability among households 1 st Stage Event 2 nd Stage = Decision = State where stochastic information acquired

11. 11 Calibrate to household piped water use 500 Monte-Carlo simulated households Adjust occupancy parameter (vacant residences) K-S test significant at 98%

12. 12 Demand response before and after household conservation Note: η = -0.187 ± 0.053 [α = 1%; 10,564 HH; Salman et. al (2008)]

13. 13 Willingness-to-pay to avoid network rationing

14. Rate Structures Simulated Uniform Increasing block rates IRB-shrinking block width Declining block rates Flat / Uniform Linear (quadratic charge) Historical (mixed flat, IBR, quadratic) 59 price schedules

15. Demand response under alternative rate structures and price schedules Existing availability 40% SW reduction 60% SW reduction

16. Distributions of use among customers 16

17. Distributions of use among customers 17

18. Cost Recovery among Rate Structures 18

19. 19 Limitations 1.Initial estimates set upper bounds on water use 2.Risk neutral decision criteria 3.Perfect price information 4.Rising temperatures and reduced precipitation do not otherwise influence residential demand 5.Commensurate reductions in use by other classes Above: “Biader Water” sells RO filtered water in 20-liter jugs

20. 20 Conclusions 1.At low prices, neither prices nor rate structure type significantly influence use 2.IBRs and linear price structures most promising at higher prices 3.Still must double or triple prices! 4.Fairness and equity associated with rate structure choice 5.Need pricing and leak reduction, conservation, plus other actions Above: Store selling rooftop water tanks

21. 21 Further Information Rosenberg et al (2007) Water Resources Research. “Modeling integrated water user decisions in intermittent supply systems.” Rosenberg (2007) ASCE-JWRPM. “Probabilistic estimation of water conservation effectiveness.” Rosenberg et al (2008) Water International. “Intermittent water supplies: challenges and opportunities for residential water users in Jordan.” Rosenberg (submitted) ASCE-JWRPM. Residential water demand under alternative rate structures: a simulation approach

22. 22 ?? Questions ?? David E. Rosenberg david.rosenberg@usu.edu http://www.engr.usu.edu/cee/faculty/derosenberg/ Left: Wadi Musa wastewater treatment plant (near Petra)