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鄱阳湖水文情势演变原因及对策
引用本文:郭振天,黄峰,郭利丹,吴瑶.鄱阳湖水文情势演变原因及对策[J].长江科学院院报,2021,38(6):27-31.
作者姓名:郭振天  黄峰  郭利丹  吴瑶
作者单位:1.河海大学 水文水资源学院, 南京 210098; 2.河海大学 商学院,南京 211100; 3.江西省鄱阳湖水利枢纽建设办公室,南昌 330046
基金项目:江西省水利厅科技项目(KT201701,KT201538)
摘    要:由于气候变化以及人类活动的影响,鄱阳湖水文情势发生了一定程度的变化,需定量评估不同驱动因子对鄱阳湖水情演变的影响。采用一系列水文指标表征鄱阳湖水文情势,构建BP神经网络模型模拟鄱阳湖水位,通过情境对比分析长江干流流量、鄱阳湖子流域入湖流量以及地形变化对各水文指标变化的贡献率。结果表明:长江干流流量是鄱阳湖7—10月份月平均水位降低的主要驱动因子,贡献率为52%~67%,对年最高极值水位的拉低效应明显,对年最低极值水位起到一定的抬高作用;地形是鄱阳湖12月—翌年3月月平均水位下降的主要驱动因子,贡献率为92%~185%,对年最低极值水位的拉低效应明显。最后对鄱阳湖水资源管理和调控提出一些建议:优化三峡水库运行调度,根据实际情况将三峡水库汛末蓄水时间适当提前;加强鄱阳湖子流域水利工程建设,在三峡蓄水期间增加五河泄流;规范采砂。

关 键 词:水情演变  BP神经网络模型  水文因子  地形因子  贡献率  鄱阳湖  
收稿时间:2020-04-29
修稿时间:2020-07-20

Causes and Countermeasures of Hydrological Regime Evolution in Poyang Lake
GUO Zhen-tian,HUANG Feng,GUO Li-dan,WU Yao.Causes and Countermeasures of Hydrological Regime Evolution in Poyang Lake[J].Journal of Yangtze River Scientific Research Institute,2021,38(6):27-31.
Authors:GUO Zhen-tian  HUANG Feng  GUO Li-dan  WU Yao
Affiliation:1. College of Hydrology and Water Resources,Hohai University, Nanjing 210098, China; 2. Business School, Hohai University, Nanjing 211100, China; 3. Poyang Lake Hydroproject Construction Office of Jiangxi Province, Nanchang 330046, China
Abstract:The hydrologic regime of the Poyang Lake has changed due to the impact of climate change and human activities. The influence of different driving factors on the hydrologic regime evolution of the Poyang Lake needs to be quantitatively evaluated. With a series of hydrological indicators characterizing the hydrological situation of Poyang Lake, a BP neural network model was constructed to simulate the water level of the Poyang Lake. The rates of contribution of flow rate of mainstream Yangtze River, inflow from sub-basins to the Poyang Lake, and topographic changes to the variation of hydrological indicators are studied through scenario analysis. Results manifest that the flow rate of mainstream Yangtze River is the major driving factor for the decline of the average water level of Poyang Lake from July to October, with a contribution rate of 52%-67%; the flow rate of mainstream Yangtze River cut obviously the annual maximum extreme water level while boosted the annual minimum extreme water level. Topography is the main driving factor for the decrease of average water level of the Poyang Lake from December to next March, with a contribution rate of 92% to 185%. Topography also brought down the minimum extreme water level apparently. In addition, some suggestions are put forward for the management and regulation of water resources in the Poyang Lake. The operation of the Three Gorges Reservoir should be optimized, and the water storage of the Three Gorges Reservoir should be appropriately advanced in time according to actual situation. The construction of water conservancy projects in the Poyang Lake sub-basins should be strengthened, and the discharge from five rivers during the impoundment of the Three Gorges Reservoir can be enhanced. Sand mining must also be regulated.
Keywords:hydrologic regime evolution  BP neural network model  hydrologic factor  topographic factor  contribution rate  Poyang Lake  
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