3 years ago

A Two-stage Approach to Basin-scale Water Demand Prediction

Xiaohong Chen, Yanhui Zheng, Yanhu He, Zhaoli Wang, Kairong Lin, Jie Yang

Abstract

Water demand prediction (WDP) is the basis for water allocation. However, traditional methods in WDP, such as statistical modeling, system dynamics modeling, and the water quota method have a critical disadvantage in that they do not consider any constraints, such as available water resources and ecological water demand. This study proposes a two-stage approach to basin-scale WDP under the constraints of total water use and ecological WD, aiming to flexibly respond to a dynamic environment. The prediction method was divided into two stages: (i) stage 1, which is the prediction of the constrained total WD of the whole basin (T w ) under the constraints of available water resources and total water use quota released by the local government and (ii) stage 2, which is the allocation of T w to its subregions by applying game theory. The WD of each subregion (T s ) was predicted by calculating its weight based on selected indicators that cover regional socio-economic development and water use for different industries. The proposed approach was applied in the Dongjiang River (DjR) basin in South China. According to its constrained total water use quota and ecological WD, T w data were 7.92, 7.3, and 5.96 billion m3 at the precipitation frequencies of 50%, 90%, and 95%, respectively (in stage 1). Industrial WDs in the domestic, primary, secondary, tertiary, and environment sectors are 1.08, 2.26, 2.02, 0.44, and 0.16 billion m3, respectively, in extreme dry years (in stage 2). T w and T s exhibit structures similar to that of observed water use, mainly in the upstream and midstream regions. A larger difference is observed between T s and its total observed water use, owing to some uncertainties in calculating T w . This study provides useful insights into adaptive basin-scale water allocation under climate change and the strict policy of water resource management.

Publisher URL: https://link.springer.com/article/10.1007/s11269-017-1816-1

DOI: 10.1007/s11269-017-1816-1

You might also like
Never Miss Important Research

Researcher is an app designed by academics, for academics. Create a personalised feed in two minutes.
Choose from over 15,000 academics journals covering ten research areas then let Researcher deliver you papers tailored to your interests each day.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.