Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty

Youzhi Wang, Shanshan Guo, Qing Yue, Xiaomin Mao and Ping Guo

Agricultural Water Management, 2021, vol. 247, issue C

Abstract: In the agricultural water resources system, the regional yield is hard to be simulated accurately under the impacts of spatial heterogeneities of soil, weather, and crop types. The optimal water allocation schemes may be not in accordance with actual conditions due to neglecting the actual crop growth process. Meanwhile, the uncertainties in the simulation-optimization models are not easy to be addressed. To deal with the above problems, this paper develops a framework of the distributed AquaCrop simulation nonlinear multi-objective dependent-chance programming (distributed AquaCrop NMOFDCP) for irrigation water resources management under uncertainties. This developed model is applied to a case study of irrigation water resources management in the middle reaches of the Heihe River Basin in China. The 134 homogeneous decision-making units (DMUs) are divided to depict the spatial heterogeneities of the study area, and 472 decision variables (irrigation amount) for 134 DMUs are optimized. Moreover, the model deals with uncertainties expressed as fuzzy goals and tradeoffs relationships between objectives of the yield and water productivity, and measures the satisfactory degrees between objectives and their fuzzy goals. Two groups of Pareto solutions corresponding to the maximum satisfactory degree of the yield, and the maximum satisfactory degree of the water productivity are obtained by the parallel genetic algorithm (PGA) method respectively. In addition, water allocation, satisfactory degree of the yield, and satisfactory degree of water productivity are analyzed at the decision-making unit scale, crop scale, and irrigation-district scale separately. Besides the effects of three weather conditions and four soil types on the system’s outputs are conducted. The results show that weather conditions and soil types have obvious effects on the system’s outputs at three analysis scales, and different water allocation patterns at the growth period affect the yield and water productivity.

Keywords: Distributed simulation-optimization model; Distributed AquaCrop modeling; Nonlinear multi-objective dependent-chance programming; Parallel genetic algorithm; Uncertainty (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377421000172
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:247:y:2021:i:c:s0378377421000172

DOI: 10.1016/j.agwat.2021.106752

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().