Abstract：
Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators\' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.
摘要：
多重不确定性，如水质过程，受气候变化影响的水流随机性，指标\'相互关系，和社会经济发展给流域的水量和水质管理（WQQ）带来了重大风险。这项研究开发了一种集成的仿真优化建模方法（ISMA），以同时解决多个不确定性。这种方法结合水质分析模拟编程，马尔可夫链，广义似然不确定性估计，和区间两阶段左侧机会约束联合概率规划到一个集成的非线性建模框架。以东江流域下游和三角洲的多个取水工程为例，对所提出的模型进行了验证。结果表明，ISMA有助于预测水质变化趋势，定量分析WQQ之间的相互作用。随着联合概率水平的增加，在严格的水质情景下，系统效益将增加[3.23，5.90]×109元，基于数量和质量的综合水资源短缺将减少[782.24，945.82]×106m3，随着水量分配的增加和污染物产生的减少。与确定性和水量模型相比，它有效地分配水，并量化更多的经济损失和水资源短缺。因此,这项研究对改善流域水质具有重要意义，平衡水质违规的利益和风险，稳定社会经济发展。
