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    Determination of Irrigation Allocation Policy under Climate Change by Genetic Programming

    Source: Journal of Irrigation and Drainage Engineering:;2015:;Volume ( 141 ):;issue: 004
    Author:
    Parisa-Sadat Ashofteh
    ,
    Omid Bozorg Haddad
    ,
    Habib Akbari-Alashti
    ,
    Miguel A. Mariño
    DOI: 10.1061/(ASCE)IR.1943-4774.0000807
    Publisher: American Society of Civil Engineers
    Abstract: This paper develops and evaluates rule curves of reservoir operation and compares them for baseline and future periods. The rules are calculated by genetic programming (GP). Also, the rules extracted are based on the rate of inflow, storage volume, and downstream irrigation network demand. The objective function used is the minimization of the average of squared monthly relative deficiencies in the allocation of water to irrigation demand. The study focuses on the reservoir system as well as the downstream irrigation network of Aidoghmoush dam in East Azerbaijan, Iran, under baseline conditions (time interval 1987–2000) and climate change conditions (time interval 2026–2039). To investigate the optimal allocation policy, three operational scenarios are considered: (1) development of current rules under baseline conditions; (2) employment of current rules for future conditions; and (3) development of future rules for future conditions. Results show that the current allocation policy (resulting from current optimal rules) should be modified under climatic change conditions. Also, the investigation indicates that the application of a future optimal allocation policy under future conditions relative to current rules under current conditions decreases (improves) the root-mean-square error (RMSE) and mean absolute error (MAE) performance criteria approximately 29 and 30%, respectively. In addition, efficiency indicators in the optimal allocation of reservoir water are calculated under climate change (policy used in the third operational scenario) and compared with its corresponding values in baseline conditions. Results show that under climate change conditions as compared to the baseline period, indexes of reliability, vulnerability, and resiliency, respectively, decrease 50%, increase 6%, and decrease 14%. Awareness of this issue by planners and decision makers can propel them to reduce the volume of network water requirements. This may be realized through changes, e.g., in the cropping pattern and cultivation area.
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      Determination of Irrigation Allocation Policy under Climate Change by Genetic Programming

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    https://yetl.yabesh.ir/yetl1/handle/yetl/71616
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    contributor authorParisa-Sadat Ashofteh
    contributor authorOmid Bozorg Haddad
    contributor authorHabib Akbari-Alashti
    contributor authorMiguel A. Mariño
    date accessioned2017-05-08T22:06:50Z
    date available2017-05-08T22:06:50Z
    date copyrightApril 2015
    date issued2015
    identifier other28978095.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/71616
    description abstractThis paper develops and evaluates rule curves of reservoir operation and compares them for baseline and future periods. The rules are calculated by genetic programming (GP). Also, the rules extracted are based on the rate of inflow, storage volume, and downstream irrigation network demand. The objective function used is the minimization of the average of squared monthly relative deficiencies in the allocation of water to irrigation demand. The study focuses on the reservoir system as well as the downstream irrigation network of Aidoghmoush dam in East Azerbaijan, Iran, under baseline conditions (time interval 1987–2000) and climate change conditions (time interval 2026–2039). To investigate the optimal allocation policy, three operational scenarios are considered: (1) development of current rules under baseline conditions; (2) employment of current rules for future conditions; and (3) development of future rules for future conditions. Results show that the current allocation policy (resulting from current optimal rules) should be modified under climatic change conditions. Also, the investigation indicates that the application of a future optimal allocation policy under future conditions relative to current rules under current conditions decreases (improves) the root-mean-square error (RMSE) and mean absolute error (MAE) performance criteria approximately 29 and 30%, respectively. In addition, efficiency indicators in the optimal allocation of reservoir water are calculated under climate change (policy used in the third operational scenario) and compared with its corresponding values in baseline conditions. Results show that under climate change conditions as compared to the baseline period, indexes of reliability, vulnerability, and resiliency, respectively, decrease 50%, increase 6%, and decrease 14%. Awareness of this issue by planners and decision makers can propel them to reduce the volume of network water requirements. This may be realized through changes, e.g., in the cropping pattern and cultivation area.
    publisherAmerican Society of Civil Engineers
    titleDetermination of Irrigation Allocation Policy under Climate Change by Genetic Programming
    typeJournal Paper
    journal volume141
    journal issue4
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/(ASCE)IR.1943-4774.0000807
    treeJournal of Irrigation and Drainage Engineering:;2015:;Volume ( 141 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian