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

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.

[1]  Sudhindra N. Panda,et al.  Optimization and Simulation Modelling for Managing the Problems of Water Resources , 2013, Water Resources Management.

[2]  Chenglong Zhang,et al.  An Interval Quadratic Fuzzy Dependent-Chance Programming Model for Optimal Irrigation Water Allocation under Uncertainty , 2018 .

[3]  Driss Ouazar,et al.  Simulation-Optimization Modeling for Sustainable Groundwater Development: A Moroccan Coastal Aquifer Case Study , 2011 .

[4]  M. Mohammad Rezapour Tabari,et al.  Conjunctive Use of Surface and Groundwater with Inter-Basin Transfer Approach: Case Study Piranshahr , 2014, Water Resources Management.

[5]  J. Cho,et al.  A river water quality management model for optimising regional wastewater treatment using a genetic algorithm. , 2004, Journal of environmental management.

[6]  M. Singh,et al.  Performance evaluation of AquaCrop model for maize crop in a semi-arid environment , 2012 .

[7]  Amy B. Chan Hilton,et al.  Groundwater Remediation Design under Uncertainty Using Genetic Algorithms , 2005 .

[8]  Ping Guo,et al.  An inexact CVaR two-stage mixed-integer linear programming approach for agricultural water management under uncertainty considering ecological water requirement , 2017, Ecological Indicators.

[9]  Xu Xu,et al.  Regional scale model for simulating soil water flow and solute transport processes- GSWAP , 2011 .

[10]  Liang-Cheng Chang,et al.  Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming , 2009 .

[11]  Zailin Huo,et al.  Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model , 2016 .

[12]  Bhagu R. Chahar,et al.  Analytic elements method and particle swarm optimization based simulation–optimization model for groundwater management , 2011 .

[13]  M. Nikoo,et al.  A game theoretical low impact development optimization model for urban storm water management , 2019 .

[14]  P. Steduto,et al.  Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran , 2011 .

[15]  Ping Guo,et al.  A fuzzy dependent-chance interval multi-objective stochastic expected value programming approach for irrigation water resources management under uncertainty , 2021 .

[16]  Wenzhi Zhao,et al.  Water requirements of maize in the middle Heihe River basin, China. , 2010 .

[17]  Huicheng Zhou,et al.  A fuzzy‐dependent chance multi‐objective programming for water resources planning of a coastal city under fuzzy environment , 2011 .

[18]  J. Yazdi,et al.  A simulation – Optimization models for multi-reservoir hydropower systems design at watershed scale , 2020 .

[19]  Ajay Singh,et al.  An overview of the optimization modelling applications , 2012 .

[20]  Fan Zhang,et al.  A full fuzzy-interval credibility-constrained nonlinear programming approach for irrigation water allocation under uncertainty , 2020 .

[21]  Eufemia Tarantino,et al.  Calibration of the AquaCrop model for winter wheat using MODIS LAI images , 2016 .

[22]  M. Mohammad Rezapour Tabari,et al.  Multi-Objective Optimal Model for Conjunctive Use Management Using SGAs and NSGA-II Models , 2012, Water Resources Management.

[23]  Li He,et al.  Simulation-Based Inexact Rough-Interval Programming for Agricultural Irrigation Management: A Case Study in the Yongxin County, China , 2012, Water Resources Management.

[24]  Li He,et al.  A credibility-based chance-constrained optimization model for integrated agricultural and water resources management: A case study in South Central China , 2016 .

[25]  Ajay Singh,et al.  Simulation–optimization modeling for conjunctive water use management , 2014 .

[26]  Jiang Li,et al.  Modeling crop water consumption and water productivity in the middle reaches of Heihe River Basin , 2016, Comput. Electron. Agric..

[27]  Ping Guo,et al.  An interval multistage joint-probabilistic chance-constrained programming model with left-hand-side randomness for crop area planning under uncertainty , 2017 .

[28]  Shanshan Guo,et al.  A bi-level multi-objective linear fractional programming for water consumption structure optimization based on water shortage risk , 2019, Journal of Cleaner Production.

[29]  Vijay P. Singh,et al.  An efficient irrigation water allocation model under uncertainty , 2016 .

[30]  Zailin Huo,et al.  Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model , 2015 .