Tradeoff for water resources allocation based on updated probabilistic assessment of matching degree between water demand and water availability

• Published in: The Science of the total environment Vol. 716; p. 134923
• Main Authors: Li, Shenlin, Chen, Xiaohong, Singh, V.P., Qi, Xinjian, Zhang, Lan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 10.05.2020
• Subjects: Bayes theorem; Bayesian theory; case studies; probability distribution; resource allocation; stakeholders; Trade-off analysis; uncertainty; water allocation; Water demand; water resources; Water resources allocation; watersheds; Trade-off analysis; Water resources allocation; Water demand; Bayes theorem
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.134923

[Display omitted] •The forecasting of water availability or demand was updated timely.•The probabilities of possible outcomes of every solution can be illustrated clearly.•Trade-off analysis selected different solution for divergent stakeholders’ objectives. 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: 21160×104 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.