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应用生态学报 ›› 2022, Vol. 33 ›› Issue (1): 191-200.doi: 10.13287/j.1001-9332.202201.013

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鄱阳湖不同水文连通性子湖水生植被覆盖度对年际水位变化的响应

王欢1,2, 陈文波2*, 何蕾3, 李海峰2   

  1. 1江西农业大学国土资源与环境学院, 南昌 330045;
    2南昌市景观与环境重点实验室, 南昌 330045;
    3江西财经大学旅游与城市管理学院, 南昌 330045
  • 收稿日期:2021-06-24 接受日期:2021-09-06 出版日期:2022-01-15 发布日期:2022-07-15
  • 通讯作者: * E-mail: cwb1974@126.com
  • 作者简介:王 欢, 女, 1998年生, 硕士研究生。主要从事资源遥感研究。E-mail: 1125613457@qq.com
  • 基金资助:
    国家自然科学基金项目(41961036,41901130)和江西省土壤侵蚀与防治重点实验室开放基金项目(9131208013)

Responses of aquatic vegetation coverage to interannual variations of water level in different hydrologically connected sub-lakes of Poyang Lake, China

WANG Huan1,2, CHEN Wen-bo2*, HE Lei3, LI Hai-feng2   

  1. 1College of Land Resource and Environment, Jiangxi Agricultural University, Nanchang 330045, China;
    2Nanchang Key Laboratory of Landscape and Environment, Nanchang 330045, China;
    3College of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330045, China
  • Received:2021-06-24 Accepted:2021-09-06 Online:2022-01-15 Published:2022-07-15

摘要: 水位变化是影响水生植被生长的主控环境因子,研究其对不同水文管控模式子湖的水生植被覆盖度的影响具有重要的现实意义。本研究基于谷歌地球引擎(GEE)遥感云计算平台,以鄱阳湖自由连通子湖蚌湖、局部控制子湖大湖池为研究对象,采用像元二分法估算2000—2019年间水生植被生长期的植被覆盖度并分析其时空分异特征,运用Sen+M-K相结合的方法对其变化趋势进行模拟分析,构建水位波动参数集,分析研究期间水位变化特征,探讨不同水文连通性子湖各水文参数与水生植被覆盖面积的关系。结果表明: 蚌湖的水生植被覆盖度更易受水位变化的影响,大湖池更稳定。在植被覆盖度较低的年份,水生植被呈斑状零星分布,较高的年份则呈环带状分布,且植被覆盖度由湖心至湖岸逐渐升高。蚌湖的水生植被覆盖度更易受水位波动率的影响,而大湖池的水生植被覆盖度更易受17 m特征水位水淹时长的影响。19 m特征水位水淹时长与蚌湖和大湖池的水生植被覆盖度均有很强的负相关性。蚌湖的水生植被变化趋势以稳定不变或轻微改善为主,而大湖池的水生植被变化趋势以稳定不变或显著退化为主。本研究有助于进一步了解不同水文连通性湖泊的水文生态系统动力学的变化及其对生态系统结构和功能的影响,可为湖泊的管理和保护提供参考。

关键词: 水文连通性, 水位变化, 水生植被覆盖度, 谷歌地球引擎

Abstract: The variation of water level is the main environmental factor controlling the growth of aquatic vegetation. It is of significance to understand its influences on aquatic vegetation coverage in sub-lakes under different hydrolo-gical control modes. Taking the free connected sub-lake Bang Lake and locally controlled sub-lake Dahuchi Lake of Poyang Lake as a case and based on remote sensing cloud computing platform of the Google Earth Engine (GEE), we used the pixel binary model to estimate aquatic vegetation coverage from 2000 to 2019, and analyzed the temporal and spatial differentiation characteristics, and the variation trend was simulated by combining the method of Sen+M-K. We analyzed the water level change characteristics during the study period and the relationship between the hydrological parameters and the aquatic vegetation coverage area of sub-lakes with different hydrological connectivity was explored by setting up the water level fluctuation parameters. The results showed that the aquatic vegetation coverage of Bang Lake was more susceptible to water level changes, while Dahuchi Lake was more stable. The aquatic vegetation was patchily and sporadically distributed in the years with low vegetation coverage. In the years with high vegetation coverage, it was distributed in a ring-like pattern, spreading from the center of the lake to the shore. The aquatic vegetation coverage of Bang Lake was more likely influenced by water level fluctuation rate, while the aquatic vegetation coverage of Dahuchi Lake was more likely influenced by the flooding duration of 17 m characteristic water level. The flooding duration of 19 m characteristic water level had a strong negative correlation with the aquatic vegetation coverage of Bang Lake and Dahuchi Lake. The trend of aquatic vegetation in Bang Lake was dominated by stabilization and slight improvement, while that in Dahuchi Lake was dominated by stabilization and significant degradation. Our results could help to further understand the dynamics of water hydrological ecosystem with different hydrological connectivity and provide a reference for lake management and conservation.

Key words: hydrological connectivity, water level change, aquatic vegetation coverage, Google Earth Engine