Determination of Irrigation Allocation Policy under Climate Change by Genetic Programming

• Published in: Journal of irrigation and drainage engineering 2015-04, Vol.141 (4)
• Main Authors: Ashofteh, Parisa-Sadat, Haddad, Omid Bozorg, Akbari-Alashti, Habib, Mariño, Miguel A
• Format: Article
• Language: English
• Subjects: Technical Papers
• ISSN: 0733-9437; 1943-4774
• DOI: 10.1061/(ASCE)IR.1943-4774.0000807

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.