| 引用本文: | 程俊翔,徐力刚,王青,鄢帮有,万荣荣,姜加虎,游海林.洞庭湖近30a水位时空演变特征及驱动因素分析.湖泊科学,2017,29(4):974-983. DOI:10.18307/2017.0421 |
| CHENG Junxiang,XU Ligang,WANG Qing,YAN Bangyou,WAN Rongrong,JIANG Jiahu,YOU Hailin.Temporal and spatial variations of water level and its driving forces in Lake Dongting over the last three decades. J. Lake Sci.2017,29(4):974-983. DOI:10.18307/2017.0421 |
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| 洞庭湖近30a水位时空演变特征及驱动因素分析 |
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程俊翔1,2, 徐力刚1, 王青1,2, 鄢帮有3, 万荣荣1, 姜加虎1, 游海林1,4
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1.中国科学院南京地理与湖泊研究所中国科学院流域地理学重点实验室, 南京 210008;2.中国科学院大学, 北京 100049;3.江西省山江湖开发治理委员会办公室, 南昌 330046;4.江西省科学院鄱阳湖研究中心, 南昌 330096
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| 摘要: |
| 洞庭湖地处北亚热带季风湿润气候区，水情时空变化尤为明显. 为了探明洞庭湖水位时空演变特征，以洞庭湖6个水位站（城陵矶、鹿角、营田、杨柳潭、南咀、小河咀）、出入湖流量（“三口”总入湖流量、“四水”总入湖流量、城陵矶出湖流量）和长江干流流量（宜昌、螺山）等1985-2014年逐日数据为基础，通过构建泰森多边形计算湖泊水位，运用Morlet小波分析、层次聚类分析和地统计理论研究湖泊水位的周期性变化规律及空间分布格局和自相关性. 研究结果表明：洞庭湖水位变化具有典型的季节性，且年际变化具有28和22 a的多时间尺度特征；水位空间分布格局呈现出小河咀、南咀、杨柳潭（Group 1）以及城陵矶、鹿角、营田（Group 2）两种聚类，且在不同水文季节的空间自相关性依次表现为丰水期 >退水期 >涨水期 >枯水期. 通过建立两类水位在不同水文季节与径流量的多元逐步回归模型揭示了洞庭湖水位时空演变的驱动因素，其中Group 1水位演变主要受长江干流水文情势的影响，Group 2水位演变由出入湖径流量和长江干流径流量共同作用，并随着不同水文季节江湖关系的改变以及湖泊自身水力联系的变化而变化. 研究结果对于科学认识洞庭湖水位的时空演变规律以及湖泊生态系统保护和水资源的规划、管理与调控具有重要意义. |
| 关键词: 水位时空变化 驱动因素 小波分析 聚类分析 地统计 逐步回归模型 洞庭湖 |
| DOI：10.18307/2017.0421 |
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| 基金项目:国家科技支撑计划课题（2014BAC09B02）、国家自然科学基金项目（41371121）和赣鄱英才555工程联合资助. |
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| Temporal and spatial variations of water level and its driving forces in Lake Dongting over the last three decades |
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CHENG Junxiang1,2, XU Ligang1, WANG Qing1,2, YAN Bangyou3, WAN Rongrong1, JIANG Jiahu1, YOU Hailin1,4
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1.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P.R.China;2.University of Chinese Academy of Sciences, Beijing 100049, P.R.China;3.Office of the Mountain-River-Lake Development Committee of Jiangxi Province, Nanchang 330046, P.R.Chian;4.Poyang Lake Research Center, Jiangxi Academy of Sciences, Nanchang 330096, P.R.China
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| Abstract: |
| Located in the subtropical humid monsoon climate zone, Lake Dongting is significantly experiencing hydrological regime changes at temporal and spatial scales. In this paper, the data of water level at six stations (Chenglingji, Lujiao, Yingtian, Yangliutan, Nanzui, Xiaohezui) in Lake Dongting and streamflow of Sankou, Sishui, Chenglingji, Yichang, Luoshan stations were collected to study the temporal and spatial variations of water level in Lake Dongting. Both water level and streamflow data are daily observation from 1985-2014. Water level of the lake was calculated by Thiessen Polygon. Wavelet analysis, cluster analysis and geostatistics were used to reveal temporal and spatial variations of water level in Lake Dongting. Results indicate that the water level shows typical seasonal features, and its annual fluctuation has multiscale of 28 a and 22 a in Lake Dongting. There are two clusters of the spatial distribution pattern in Lake Dongting, one is Xiaohezui, Nanzui, Yangliutan (Group 1), and another is Chenglingji, Lujiao, Yingtian (Group 2). The magnitude of spatial autocorrelation in different periods is wet season > retreating season > rising season > dry season. The driving factors of temporal and spatial variation of water level in Lake Dongting were revealed by multiple stepwise regression model among two kinds of water level and runoff in four seasons. The hydrological regime alteration in Yangtze River is the main factor for Group 1. In different seasons, the driving factors of Group 2 are different,including the discharge of lake and the hydrological regime of Yangtze River. The difference is mainly caused by the relationship between Lake Dongting and Yangtze River as well as the flooded and exposed bottomlands in different seasons. The study is essential for protecting the ecosystem of Lake Dongting and reasonably regulation, management and utilization the water resources. |
| Key words: Temporal and spatial variations of water level driving factors wavelet analysis cluster analysis geostatistics stepwise regression model Lake Dongting |
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