| 引用本文: | 刘永,蒋青松,梁中耀,吴桢,刘晓钰,冯秋园,邹锐,郭怀成.湖泊富营养化响应与流域优化调控决策的模型研究进展.湖泊科学,2021,33(1):49-63. DOI:10.18307/2021.0103 |
| Liu Yong,Jiang Qingsong,Liang Zhongyao,Wu Zhen,Liu Xiaoyu,Feng Qiuyuan,Zou Rui,Guo Huaicheng.Lake eutrophication responses modeling and watershed management optimization algorithm: A review. J. Lake Sci.2021,33(1):49-63. DOI:10.18307/2021.0103 |
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| 湖泊富营养化响应与流域优化调控决策的模型研究进展 |
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刘永1, 蒋青松1, 梁中耀1, 吴桢1, 刘晓钰1, 冯秋园1, 邹锐1,2, 郭怀成1
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1.北京大学环境科学与工程学院, 国家环境保护河流全物质通量重点实验室, 北京 100871;2.北京英特利为环境科技有限公司, 北京 100191
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| 摘要: |
| 湖泊富营养化是全球水环境领域面临的长期挑战，富营养化响应与流域优化决策模型是制定经济和高效调控方案的关键.然而已有的模型研究综述主要集中于模型开发、案例应用、敏感性分析、不确定性分析等单一方面，而缺少针对非线性响应、生态系统长期演变等最新湖泊治理挑战的研究总结.本文对数据驱动的统计模型、因果驱动的机理模型和决策导向的优化模型进行了综述.其中，统计模型包含经典统计、贝叶斯统计和机器学习模型，常用于建立响应关系、时间序列特征分析以及预报预警；机理模型包含流域的水文与污染物输移模拟以及湖泊的水文、水动力、水质、水生态等过程的模拟，用于不同时空尺度的变化过程模拟，其中复杂机理模型的敏感性分析、参数校验、模型不确定性等需要较高的计算成本；优化模型结合机理模型形成“模拟-优化”体系，在不确定性条件下衍生出随机、区间优化等多种方法，通过并行计算、简化与替代模型可一定程度上解决计算时间成本的瓶颈.本文识别了湖泊治理面临的挑战，包括：①如何定量表征外源输入的非线性叠加和湖泊氮、磷、藻变化的非均匀性？②如何提高优化调控决策和水质目标的关联与精准性？③如何揭示湖泊生态系统的长期变化轨迹与驱动因素？最后，本文针对这些挑战提出研究展望，主要包括：①基于多源数据融合与机器学习算法以提升湖泊的短期水质预测精度；②以生物量为基础的机理模型与行为驱动的个体模型的升尺度或降尺度耦合以表达多种尺度的物质交互过程；③机器学习算法与机理模型的直接耦合或数据同化以降低模拟误差；④时空尺度各异的多介质模拟模型融合以实现精准和动态的优化调控. |
| 关键词: 湖泊富营养化 非线性 统计模型 机理模型 优化模型 模型融合 |
| DOI：10.18307/2021.0103 |
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| 基金项目:国家自然科学基金项目（51779002）资助. |
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| Lake eutrophication responses modeling and watershed management optimization algorithm: A review |
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Liu Yong1, Jiang Qingsong1, Liang Zhongyao1, Wu Zhen1, Liu Xiaoyu1, Feng Qiuyuan1, Zou Rui1,2, Guo Huaicheng1
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1.College of Environmental Sciences and Engineering, State Environmental Protection Key Laboratory of All Materials Flux in Rivers, Peking University, Beijing 100871, P. R. China;2.Beijing Inteliway Co., Ltd, Beijing 100191, P. R. China
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| Abstract: |
| Lake eutrophication is a global challenge. The models of eutrophication response and watershed optimization control decision-making are the key to formulating economic and efficient action plans. However, the existing reviews of eutrophication model mainly focus on the single aspects of model development, case study, sensitivity analysis, uncertainty analysis, simplification and surrogate model. There is a lack of the summary of studies on the coupling of macro and micro aspects, such as the non-linear response, treatment decision and water quality improvement and long-term evolution of ecosystems. Therefore the models and methods of lake eutrophication response simulation and pollution-reduction optimization were summarized and analyzed in this study. The eutrophication models are divided into (a) data-driven statistical models, (b) causal-driven mechanism models, and (c) decision-oriented optimization models. Generalized statistical models including classic statistics, Bayesian statistics, and machine learning are often used on response relationships establishment, time series analysis, and spatio-temporal forecasting and early warning. The mechanism model consists of the hydrological, hydrodynamic, water quality, aquatic ecology, and other processes. It is usually used to simulate the change on different spatial and temporal scales, in conjunction with the watershed models. Among them, sensitivity analysis, parameter verification, and model uncertainty will cause high computing costs. The decision-oriented optimization model combines the mechanism model to form a “simulation-optimization” system, which derives a variety of methods such as stochastic optimization and interval optimization under uncertainty. It can deal with the cost of computing time through parallel calculation, simplification, and surrogate models. The challenges for future lake management were identified, including: (a) integration of the external input and the heterogeneity of the lake's nitrogen, phosphorus and algae; (b) improving the correlation and accuracy of optimal control decisions and water quality targets; and (c) exploring the long-term changes of lake ecosystems trajectory and driven factors. Several research focus were proposed to deal with the challenges, including (a) prediction of lake water quality response based on multivariate data fusion and machine learning algorithms; (b) upscaling or downscaling coupling of mechanism models based on biomass and action-driven agent-based model to express the interaction process of population at multiple scales; (c) machine learning algorithms and mechanism models are directly coupled or data assimilation to reduce simulation errors; and (d) multiple simulation models with different spatial-temporal scales are fused to achieve precise and dynamic optimization. |
| Key words: Lake eutrophication nonlinear statistical model mechanism model optimization model model fusion |
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