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Water Demand Forecasting in Umm Al-Quwain (UAE) Using the IWR-MAIN Specify Forecasting Model

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  • Mohamed Mohamed
  • Aysha Al-Mualla

Abstract

IWR-MAIN software is used in this paper to forecast water demand in the Emirate of Umm Al-Quwain (UAQ), located in the northern part of the United Arab Emirates (UAE), for the next twenty 5 years. Two different databases are used. The first one provides average yearly water consumptions since 1980, while the second provides more detailed monthly water consumptions from 2000. The correlation between three different independent variables and water consumption is studied. These variables are population of UAQ, average temperature, and average rainfall. Results show that population is the most significant variable that affects water consumption in Umm Al-Quwain. Several calibration simulations are performed and each simulation is divided into two periods. In the first period the software “Statistical Package for the Social Sciences” (SPSS) is used to determine the correlation coefficients between the independent variables and actual water consumptions. These coefficients are used in IWR-MAIN over the second period to calculate values of water demand which are compared against actual water consumptions. Model calibration indicates that starting the calibration in 1999 in database one and 2006 in database 2 minimizes differences between actual and simulated water demands. Therefore, these simulations were used as the bases for several forecasting scenarios of water demand in Umm Al-Quwain. Results of one of these scenarios show that 50% increase in water demand is expected by the year 2015 and double of the current demand will be needed before 2025. In another forecasting scenario, it was found that by considering the expected increase in the income level, the water demand will increase by 40% in one decade. A new technique of using IWR-MAIN to separate estimates of metered demand, unmetered demand, and unaccounted water (losses) is also presented in this paper. Finally, results of a fourth scenario indicate that water demand in Umm Al-Quwain will be highly affected by the expected high migration rate due to the anticipated new developments in the emirate. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Mohamed Mohamed & Aysha Al-Mualla, 2010. "Water Demand Forecasting in Umm Al-Quwain (UAE) Using the IWR-MAIN Specify Forecasting Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4093-4120, November.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:14:p:4093-4120
    DOI: 10.1007/s11269-010-9649-1
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    References listed on IDEAS

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    Cited by:

    1. Jean-Daniel Rinaudo, 2015. "Long-Term Water Demand Forecasting," Post-Print hal-01183853, HAL.
    2. Nadjib Drouiche & Noreddine Ghaffour & Mohamed Naceur & Hacene Mahmoudi & Tarik Ouslimane, 2011. "Reasons for the Fast Growing Seawater Desalination Capacity in Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2743-2754, September.
    3. Kolin Loveless & Aamir Farooq & Noreddine Ghaffour, 2013. "Collection of Condensate Water: Global Potential and Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1351-1361, March.
    4. Negin Ashoori & David A. Dzombak & Mitchell J. Small, 2016. "Modeling the Effects of Conservation, Demographics, Price, and Climate on Urban Water Demand in Los Angeles, California," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5247-5262, November.
    5. Jean-Daniel Rinaudo, 2015. "Long-Term Water Demand Forecasting," Post-Print hal-01290178, HAL.

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