An interval multi-objective programming model for irrigation water allocation under uncertainty

•A multi-objective model under uncertainty for sustainable irrigation allocation is developed.•Interval values of hydrological elements under different frequencies are generated.•Contradictions among benefit, yield and water-saving can be tackled.•Optimal irrigation schemes are obtained and analyzed...

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Bibliographic Details
Published in:Agricultural water management Vol. 196; pp. 24 - 36
Main Authors: Li, Mo, Fu, Qiang, Singh, Vijay P., Liu, Dong
Format: Journal Article
Language:English
Published: Elsevier B.V 31.01.2018
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ISSN:0378-3774, 1873-2283
Online Access:Get full text
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Summary:•A multi-objective model under uncertainty for sustainable irrigation allocation is developed.•Interval values of hydrological elements under different frequencies are generated.•Contradictions among benefit, yield and water-saving can be tackled.•Optimal irrigation schemes are obtained and analyzed under different scenarios. An interval linear multi-objective programming (ILMP) model for irrigation water allocation was developed, considering conflicting objectives and uncertainties. Based on the generation of interval numbers through statistical simulation, the ILMP model was solved using a fuzzy programming method. The model balances contradictions among economic net benefit, crop yield and water-saving in irrigation systems incorporating uncertainties in both objective functions and constraints that are based on the conjunctive use of surface water and groundwater. The model was applied to Hulan River irrigation district, northeast China. Tradeoffs between various crops in different subareas under different frequencies were analyzed, and scenarios with different objectives were considered to evaluate the changing trend of irrigation water allocation.Results indicated that the ILMP model provided effective linkages between revenue/output promotion and water saving, and offers insights into tradeoffs for irrigation water management under uncertainty.
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ISSN:0378-3774
1873-2283
DOI:10.1016/j.agwat.2017.10.016