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Towards sustainable water management in an arid agricultural region: A multi-level multi-objective stochastic approach

机译：在干旱农业区可持续水资源：多层次多目标随机方法

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Water-shortage crisis is threatening the food production and sustainable development around the world. Especially for arid agricultural regions, it is necessary to plan sustainable agricultural water management strategies for improving water use efficiency. But there are many complexities in it, such as multiple decisionmaking levels, objectives, water users and uncertainties. To effectively tackle these complexities, this study presents a novel optimization-modeling approach consisting of a multi-level multi-objective stochastic programming (MLMOSP) model and weighting quantification method for formulating sustainable water-allocation schemes in arid agricultural regions. The MLMOSP model incorporates multi-level programming, multi-objective programming, and stochastic expectation programming into a general framework. The proposed approach is capable of: 1) quantifying key factors affecting water-allocation systems through weighting quantification methods; 2) describing the main conflicting objectives of each decision-making level, including economic benefits, environment impacts, fairness, effectiveness, and crop yield; 3) considering tradeoffs among conflicting objectives, and 4) reflecting the leader-follower relationship under different scenarios of surface water availability at a regional scale and a monthly temporal resolution. The proposed approach is applied to a real-world case in a typical arid agricultural region of northwest China for verifying its validity. From this real-world case, it is found that: 1) optimization results corresponding to different flow-level scenarios of surface runoff can provide upper-, middle-, and lower-level decision makers with a set of decision alternatives to help identify the most appropriate management strategy; and 2) multiple model comparisons show that the MLMOSP approach can not only give more practical results guaranteeing the achievement of decision-making goals at different decision-making levels, but also help reduce groundwater extraction under different flow level scenarios of surface runoff.
机译：缺水危机正在威胁到世界各地的食品生产和可持续发展。特别是对于干旱农业区域，有必要计划可持续的农业水管理策略，以改善水利用效率。但它有许多复杂性，例如多种决定水平，目标，水用户和不确定性。为了有效地解决这些复杂性，本研究提出了一种新颖的优化建模方法，包括用于在干旱农业地区制定可持续水分配方案的多级多目标随机编程（MLMOSP）模型和加权量化方法。 MLMOSP模型包括多级编程，多目标编程和随机期望编程到一般框架。所提出的方法能够：1）通过加权量化方法量化影响水分配系统的关键因素; 2）描述每个决策水平的主要相互矛盾目标，包括经济利益，环境影响，公平，有效性和作物产量; 3）考虑到相互冲突目标之间的权衡，4）以区域规模和每月时间分辨率反映出不同情景的领导者关系。拟议的方法适用于西北地区典型干旱农业区的真实案例，以验证其有效性。从这个真实的情况来看，发现：1）对应于表面径流的不同流程场景的优化结果可以提供具有一组决策替代方案的高级，中间和下层决策者，以帮助识别最合适的管理策略; 2）多种型号比较表明，MLMOSP方法不仅可以提供更实用的结果，保证了在不同决策水平下实现决策目标，而且还有助于降低地表径流的不同流量方案下的地下水提取。

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