Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88731
Title: Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach
Authors: Dai, Chao
Qin, Xiaosheng
Chen, Y.
Guo, H. C.
Keywords: Gini Coefficient
Chance-constrained Programming
Issue Date: 2018
Source: Dai, C., Qin, X. S., Chen, Y., & Guo, H. C. (2018). Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach. Journal of Hydrology, 561, 322-334.
Series/Report no.: Journal of Hydrology
Abstract: A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit () and Gini coefficient () under different significance levels (i.e. ) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.
URI: https://hdl.handle.net/10356/88731
http://hdl.handle.net/10220/44772
ISSN: 0022-1694
DOI: 10.1016/j.jhydrol.2018.04.012
Rights: © 2018 Elsevier B. V.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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