Tradeoff for water resources allocation based on updated probabilistic assessment of matching degree between water demand and water availability. Academic Article uri icon


  • Water resources allocation is very important for water resources management. However, it is subject to the uncertainty in water availability (WA) or water demand (WD), as well as the pressure exerted by multi-stakeholders. Therefore, we propose a general framework as following: (i) applying Bayes theorem to develop a forecasting model for WA and WD probability distributions; (ii) constructing the matching matrix showing matching degree between WA and WD and assessing the probabilistic behavior of water resources allocation solutions based on the matching matrices; and (iii) performing the trade-off analysis among the solutions under different stakeholders' objectives to meet requirements of multi-stakeholders. Longgang River basin is selected as a case study area to demonstrate the proposed framework. Results show that, the forecast probability distributions of WA and WD may be updated timely with newly introduced data, and reflect their statistical characters well. Furthermore, the matching matrices illustrate the probabilities of the possible outcomes of each allocation solution clearly. From the probabilistic assessment; the results suggest: 21160104 m3 diverted water are required to surely satisfy the current water demands, which is exactly the amount currently diverted for the study area. The proposed framework provides the updated probabilistic assessment for the possible outcomes, contributing to stakeholders to perform the tradeoff with each other. It makes significant contributions to address water allocation issues under uncertainty and is worthy to be applied broadly.

published proceedings

  • Sci Total Environ

author list (cited authors)

  • Li, S., Chen, X., Singh, V. P., Qi, X., & Zhang, L.

citation count

  • 7

complete list of authors

  • Li, Shenlin||Chen, Xiaohong||Singh, VP||Qi, Xinjian||Zhang, Lan

publication date

  • May 2020