Abstract This research developed an integrated simulation-optimization method (ISOM). This model incorporated eutrophication modeling, water resource allocation and trophic status assessment within a general modeling framework. In ISOM, the simulation effort [i.e. environmental fluid dynamics code (EFDC)] was used to forecast the concentration of water quality variables to evaluate the lake trophic status under various conditions, while the optimization studies were used to identify the optimal water transfer strategies from a number of alternatives. To solve the model, a surrogate-based genetic algorithm (GA) was proposed in which the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the functional relationships between water transfer and lake trophic status. By replacing the EFDC and the corresponding trophic state index (TSI) equations with the surrogates, the computation efficiency could be improved. The developed ISOM was applied to the inter-basin water transfer management of the Niulanjiang-Dianchi Water Transfer Project (NDWTP) to support the eutrophication restoration of Lake Dianchi. Optimal water transfer schemes for three different remediation durations were generated from the model. The results demonstrated that NDWTP could exert a positive influence on the ecology and environment of Lake Dianchi, and that the trophic level for the Lake Dianchi could be effectively mitigated through the adoption of optimal water transfer schemes.

Abstract Phosphorus (P) is viewed as one limiting factor for phytoplankton growth in freshwater lakes. Simple budget models are very efficient for cross-lakes comparisons, while neglecting key distinction between algal P and other forms. Here, a phosphorus budget model was developed to balance between process resolution and cross-system applicability, in which lake total phosphorus (TP) was divided into algal-bound P and other fractions. The model was tested for six lakes on the Yunnan Plateau, China and the Markov Chain Monte Carlo (MCMC) algorithm of Bayesian hierarchical inference was employed for parameters estimation. The model results showed that (a) both algal species composition and P loading are key factors that influence the efficiency of converting phosphorus into algal P; (b) variability of the settling velocity of non-algal P and algal P decreases with increasing TP concentrations, representing a lower capacity for restoration; and (c) settling velocity declined exponentially with the increase of trophic state index, indicating a potential rapid rise of P removal rates during eutrophication restoration. Two conceptual models were then proposed to identify the prior countermeasures for eutrophication restoration in the lakes: (a) for Conceptual Model II, e.g. Lake Lugu, increasing the physical settling of phosphorus should be given priority to; (b) for Conceptual Model I, including the other five lakes, increasing the biological settling of phosphorus should be paid extra attention.

Abstract Sensitivity analysis is a primary approach used in mathematical modeling to identify important factors that control the response dynamics in a model. In this paper, we applied the Morris sensitivity analysis method to identify the important factors governing the dynamics in a complex 3-dimensional water quality model. The water quality model was developed using the Environmental fluid dynamics code (EFDC) to simulate the fate and transport of nutrients and algal dynamics in Lake Dianchi, one of the most polluted large lakes in China. The analysis focused on the response of four water quality constituents, including chlorophyll-a, dissolved oxygen, total nitrogen, and total phosphorus, to 47 parameters and 7 external driving forces. We used Morris sensitivity analysis with different sample sizes and factor perturbation ranges to study the sensitivity with regard to different output metrics of the water quality model, and we analyzed the consistency between different sensitivity scenarios. In addition to the analysis with aggregate outputs, a spatiotemporal variability analysis was performed to understand the spatial heterogeneity and temporal distribution of sensitivities. Our results indicated that it is important to consider multiple characteristics in a sensitivity analysis, and we have identified a robust set of sensitive factors in the water quality model that will be useful for systematic model parameter identification and uncertainty analysis.

To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming (REILP) approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeoff factor ($\alpha$) into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty-based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of $\alpha$ was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demonstrate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.

Abstract Human activities are the main drivers of alterations of regional N cycles. With increasing population and economic development, human-induced N inputs are expected to continue to increase in the future, especially in many regions of developing countries. Because N sources vary substantially at different temporal and spatial scales and stages of economic development, it is of great importance for environmental managers to be able to simulate the dynamics of N inputs to a specific region of interest. Based on the concept of net anthropogenic N inputs (NANI), a quasi-mass-balance method, a system dynamics model simulating regional N inputs (NANI-SD) is developed and presented here for the first time. The NANI-SD model evaluates how much new N from anthropogenic activities is introduced to the whole basin, providing a simple but effective way to examine human influences on regional N cycles. Our application of the NANI-SD model to the Lake Dianchi basin in China shows that human-induced N inputs will continue to increase under current trends of development. Scenarios focused on lowering population growth rate and banning crop production were not effective in achieving long-term reductions in N inputs because their impacts were compensated by the increases in croplands and food imports, respectively. However, adjusting diet patterns and limiting livestock numbers within the basin were shown to be highly effective in controlling regional N inputs without compromising environmental sustainability of food imported regions. There was a significant trade-off between N self-sufficiency and N inputs to the region, posing the issue of “pollution transfer” as the regions of livestock production providing animal products to the Lake Dianchi basin could suffer from locally intensified levels of N pollution introduced while producing those animal N products. The positive relationship between NANI and the proportion of animal-based protein in food indicates that reducing meat consumption could be an effective way of controlling local N inputs without sacrificing food sovereignty. NANI to the basin could also be reduced by recycling N in human and livestock wastes, but its capacity to reduce NANI is limited and projected to diminish with time.

Rapid urbanization and population growth have resulted in worldwide serious water shortage and environmental deterioration. It is then essential for efficient and feasible allocation of scarce water and environment resources to the competing users. Due to inherent uncertainties, decision making for resources allocation is vulnerable to failure. The scheme feasibility can be evaluated by reliability, representing the failure probability. A progressive reliability-oriented multi-objective (PROMO) optimal decision-making procedure is proposed in this study to deal with problems with numerous reliability objectives. Dimensionality of the objectives is reduced by a top-down hierarchical reliability analysis (HRA) process combining optimization with evaluation. Pareto solutions of the reformulated model, representing alternative schemes non-dominated with each other, are generated by a metalmodel-based optimization algorithm. Evaluation and identification of Pareto solutions are conducted by multi-criteria decision analysis (MCDA). The PROMO procedure is demonstrated for a case study on industrial structure transformation under strict constraints of water resources and total environmental emissions amounts in Guangzhou City, South China. The Pareto front reveals tradeoffs between economic returns of the industries and system reliability. For different reliability preference scenarios, the Pareto solutions are ranked and the top-rated one was recommended for implementation. The model results indicate that the PROMO procedure is effective for model solving and scheme selection of uncertainty-based multi-objective decision making.

In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

Abstract In this research, an interval-fuzzy possibilistic programming (IFPP) method was developed by integrating interval parameter programming (IPP), fuzzy possibilistic programming (FPP), and a fuzzy expected value equation within a general optimization framework. The developed IFPP method can not only effectively address uncertainties presented in terms of crisp intervals and fuzzy-boundary intervals in both the objective function and constraints, but it can also improve the traditional fuzzy mathematical programming by choosing the credibility degree of constraints based on the decision maker’s preference and avoiding complicated intermediate models with high computational efficiency. The developed method was applied to identify optimal placements for best management practices (BMPs) to control nutrient pollution in the Baoxianghe River watershed in China, in which a GIS-aided export coefficient model (ECM) was employed to estimate the phosphorus loads from a nonpoint source (NPS). The optimization results showed that the hybrid approach could be used to generate a series of implementation levels for BMPs under multiple credibility levels, ensuring that the NPS phosphorus loads discharged into rivers reduce to an allowable level and considering a proper balance between expected system costs and risks of violating the constraints. Relaxing the sub-basin discharge permits suggests a global discharge permit for the entire watershed, which may allow managers to shift BMP implementation among sub-basins to meet the overall discharge permit at a lower cost.

In this research, a large-scale inexact optimization method was developed for the conjunctive use management of a watershed-lake water distribution system. The modeling framework has the advantages in taking into account the water balance of multi-reservoirs, satisfying the municipal industrial and agricultural water demands in the multi-period context, reflecting the relationship among multi-reservoirs, multiple water related projects, and maintaining the operational rules of certain lake levels. Moreover, such a method can also handle the uncertainty expressed as fuzzy membership functions through integrating the fuzzy credibility chance-constrained programming. The developed method was applied to the conjunctive use management of water resources in the lake Dianchi watershed, China. Cost-effective water allocation schemes for the groundwater project and the water transfer project, and optimal operational rules for the lake and multiple reservoirs were successfully obtained. Also, the annual water balance of the watershed-lake system and the system cost of the conjunctive water use were investigated and analyzed under multiple credibility levels of meeting the water demands for municipal, industrial and agricultural users.

构建了基于正交设计-水质模拟-方案评价的污染物总量控制方案的方法体系,并应用于滇池流域.选择TN、TP作为总量控制指标,将滇池流域划分为6个子区单元,通过设计5水平(0%,5%,10%,15%,20%)6因素的正交试验表,得到25种不同削减方案;耦合EFDC模型模拟得到25种不同削减情景条件下湖泊水质的TN、TP浓度分布,计算各方案综合营养状态指数(TSI),筛选符合中度富营养化标准(60

随着我国有机污染物控制力度不断加大,氮磷营养盐逐步凸显为流域环境质量改善与经济社会发展的重要约束因子.如何以流域氮磷环境承载力为约束,实现经济社会优化发展已成为我国流域环境管理面临的主要难题.传统的环境承载力研究局限于流域内部的自然资源约束和经济社会优化,忽略了流域内外物质交换对承载力的影响,结果往往以牺牲外部可持续性为代价.基于开放系统视角,本研究提出可持续性约束下的流域氮磷环境承载力概念与测度方法,认为流域氮磷环境承载力是指在流域内外氮磷营养盐交换平衡的前提下,流域水环境氮磷含量达标情况下所能承载的最大人口规模.以氮磷污染严重的滇池流域为例,构建了氮磷环境承载力优化模型,得到滇池湖体氮磷达标和流域内外氮磷交换平衡约束下流域的最大人口承载能力为538万人,相对于现状2010年可提升32%;此外,外流域调水、氮磷循环利用率和污水外排等措施对承载力有显著影响.

蒙特卡洛模拟方法是一种根据已有样本数据的特征随机产生试验样本从而扩大样本量的模拟方法,可减少人力物力的浪费,克服数据不足的缺陷。通过采用蒙特卡洛模拟方法扩大生态安全评价指标权重的样本数据量,进行了模拟抽样,获取了最佳抽样调查样本量区间,并应用于湖泊生态安全评价案例。结果表明:蒙特卡洛模拟方法能较好地解决样本量不足的问题;在群决策获得指标权重的过程中,随机选取20~30个抽样样本量的数据很稳定,20~30个样本量是能反映真实决策偏好最小的样本调查区间。

湖滨河口湿地对湖泊的水质与生态健康有重要影响,但其对营养物质的截留效应亦备受争议.因此深入了解营养物质在湿地"水-底泥-植物"中的输移转化过程,明确导致湿地"源-汇"功能转换的主要驱动机制与关键影响因子具有重要意义.本文以磷(P)为对象,分析了水文过程的变异性对湖滨河口湿地的影响,综述了湿地截磷的植物机制、微生物机制及"上覆水-底泥"界面过程,比较了短期和长期机制的差异;识别了湿地截P的重要影响因素及其交互反馈过程,主要包括水文条件、底泥理化性质、离子含量、水生植物种类等;总结了目前已开展的野外监测、室内培养、吸附等温线、统计分析、机理模型等研究方法,提出未来需将湿地水文模型与生态模型相结合,用于识别和评估磷在湿地"水-底泥-植物"系统中的输移关键过程及截留效应.

人类活动净氮输入(NANI)是影响河流氮输出的重要因素,研究两者的响应关系对制定氮污染的削减策略具有重要意义.基于NANI核算模型,评估了海河流域人为氮输入强度,并采用实测数据估算了同期的河流氮输出,最终得到干旱缺水区河流氮输出对NANI的响应特征.结果表明,海河流域2008—2012年均NANI输入强度为13258 kg·km~(-2)·a~1,化肥施用、食品与饲料输入、大气沉降和农作物固氮分别占76%、17%、5%和2%;在空间分布上,黑龙港运东子流域的NANI负荷最高,达到24238 kg·km~(-2)·a~(-1),最小的是永定河子流域,为5320 kg·km~(-2)·a~(-1);海河流域主要河流的氮通量与子流域NANI输入呈现显著正相关关系(p<0.05),NANI变化可解释67%的河流氮通量变化.然而,仅2%的NANI由河流输出,这一比例低于其他地区研究成果,表明缺水地区河流作为氮输出的功能被削弱,河流不是流域氮输出的主要途径.