Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok

Mukand Babel (), Nisuchcha Maporn and Victor Shinde

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 7, 2049-2062

Abstract: With concerns relating to climate change, and its impacts on water supply, there is an increasing emphasis on water utilities to prepare for the anticipated changes so as to ensure sustainability in supply. Forecasting the water demand, which is done through a variety of techniques using diverse explanatory variables, is the primary requirement for any planning and management measure. However, hitherto, the use of future climatic variables in forecasting the water demand has largely been unexplored. To plug this knowledge gap, this study endeavored to forecast the water demand for the Metropolitan Waterworks Authority (MWA) in Thailand using future climatic and socioeconomic data. Accordingly, downscaled climate data from HadCM3 and extrapolated data of socioeconomic variables was used in the model development, using Artificial Neural Networks (ANN). The water demand was forecasted at two scales: annual and monthly, up to the year 2030, with good prediction accuracy (AAREs: 4.76 and 4.82 % respectively). Sensitivity analysis of the explanatory variables revealed that climatic variables have very little effect on the annual water demand. However, the monthly demand is significantly affected by climatic variables, and subsequently climate change, confirming the notion that climate change is a major constraint in ensuring water security for the future. Because the monthly water demand is used in designing storage components of the supply system, and planning inter-basin transfers if required, the results of this study provide the MWA with a useful reference for designing the water supply plan for the years ahead. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: ANN; Climate change; Climate downscaling; Sensitivity analysis; Thailand; Water demand forecasting (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11269-014-0598-y

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