千岛湖水域生态系统研究进展:基于野外站长期观测、研究与示范
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1.中国科学院南京地理与湖泊研究所;2.浙江省生态环境监测中心;3.杭州市生态环境局淳安分局

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国家自然科学基金项目(重点项目,国际合作项目)U2340209,42220104010


Research progress on ecosystem of Qiandaohu Reservoir: Based on long-term observation, research and demonstration at Qiandaohu Ecosystem Research Station
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Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences

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    摘要:

    水库作为一种人工湖泊,在我国其数量和控制水量已接近天然湖泊,是我国地表可调控静态水资源的重要组成部分。水库的水文过程、地形地貌及管控利用方式等与天然湖泊有所不同,在生态学上兼具流水生态系统与静水生态系统的部分特征,湖沼学机制有其独特性,设立科学观测站开展水库生态系统长期生态观测、原位试验研究与修复技术开发,对科学支撑水库生态环境安全保障与资源可持续利用具有重要意义。本文以中国科学院南京地理与湖泊研究所千岛湖生态系统研究站(简称“千岛湖生态站”)20年来对千岛湖水环境与水生态系统科学观测、试验研究与技术示范成果分析为例,探讨了大型深水水库生态学研究的成就、热点与难点。千岛湖生态站近5年的水质与生态指标监测表明,千岛湖水质总体稳定在贫-中营养状态,但关键水质断面仍面临水质不稳定、局部水域存在藻类异常增殖等生态环境风险;热分层等水体生态环境指标的分层现象明显,且存在显著的季节变化和年际波动;研究表明,增温、强降水事件、高温干旱等气象水文事件对千岛湖水质和生态影响甚大;流域土地利用变化及水库渔业经营模式等人类活动对千岛湖水质与水生态影响深远;此外,基于千岛湖生态站开发的水库监测、水质与水华预警及库体生态修复等技术对我国类似水源水库的生态环境保护具有较高的示范价值。水源水库的成套水质安全保障技术、光热及水文水动力等水库物理环境变化机制及其生态学效应、大型水库的食物网结构及其生态调控技术、深水水库碳循环与温室气体排放机制,以及水库水环境的数字孪生与AI智慧管控技术等是当前千岛湖水域生态系统的研究重点。

    Abstract:

    Reservoirs, as a type of artificial lake, have their number and controlled water volume in China approaching that of natural lakes, making them an important component of China"s surface-based manageable static water resources. The hydrological processes, topography, and management/utilization patterns of reservoirs differ from those of natural lakes. Ecologically, reservoirs exhibit characteristics of both riverine and lake ecosystems, and their limnological mechanisms are unique. Establishing scientific observation field stations to conduct long-term ecological monitoring, in-situ experimental research, and the development of restoration technologies for reservoirs is of great significance for scientifically supporting the ecological and environmental security of reservoirs and the sustainable use of their resources. Taking the Qiandaohu Ecosystem Research Station of the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (referred to as the "Qiandaohu Ecological Station") as an example, this paper analyzes the achievements, hotspots, and challenges in the ecological research of large deep-water reservoirs based on 20 years of scientific observation, experimental research, and technology demonstration on the water environment and aquatic ecosystem of Qiandaohu Reservoir. Monitoring of water quality and ecological indicators at the Qiandaohu Ecosystem Research Station over the past five years indicates that the overall water quality of Qiandaohu Reservoir remains stable at an oligotrophic to mesotrophic state. However, key water quality monitoring sections still face risks such as unstable water quality and abnormal algal proliferation in local areas. Stratification of ecological and environmental indicators, such as thermal stratification, is evident, with significant seasonal variations and interannual fluctuations. Research shows that meteorological and hydrological events, such as warming, heavy precipitation events, and high-temperature droughts, have substantial impacts on the water quality and ecology of Qiandaohu Reservoir. Human activities, including changes in watershed land use and reservoir fishery management models, also profoundly affect the water quality and aquatic ecology of Qiandaohu Reservoir. Furthermore, technologies developed at the Qiandaohu Ecosystem Research Station, such as reservoir monitoring, water quality and algal bloom early warning, and ecological restoration of the reservoir, offer significant demonstration value for the ecological and environmental protection of similar source water reservoirs in China. Current research priorities for the Qiandaohu Reservoir aquatic ecosystem include integrated technologies for ensuring water quality safety in source water reservoirs, mechanisms of physical environmental changes (e.g., light, heat, and hydrodynamic processes) in reservoirs and their ecological effects, food web structures and ecological regulation technologies in large reservoirs, carbon cycling and greenhouse gas emission mechanisms in deep-water reservoirs, and digital twin and AI-based intelligent management technologies for reservoir water environments.

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  • 收稿日期:2026-02-28
  • 最后修改日期:2026-04-10
  • 录用日期:2026-04-13
  • 在线发布日期: 2026-04-13
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