首页|洞庭湖中枯水期水情变化及其驱动因素

洞庭湖中枯水期水情变化及其驱动因素

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为揭示洞庭湖中枯水期水情变化特征及其驱动因素,采用长短期记忆神经网络模拟洞庭湖出湖流量及湖区水位,通过情境模拟开展水情变化归因分析.洞庭湖1992-2019年9-10月出湖流量大幅减少,主要受长江流量降低的影响.洞庭湖中枯水期水位主要呈下降趋势,其中9-10月平均水位在西洞庭湖、南洞庭湖降幅约1 m,在东洞庭湖降幅约2 m.地形变化对中枯水期水位主要起拉低作用,长江和流域四水流量变化在9-10月起拉低作用、在12月至次年3月起抬升作用,其中对东洞庭湖水位的影响相对更为显著.研究结果可为洞庭湖中枯水期水资源管理和湿地保护提供参考.
Water Regime Changes and the Driving Factors of Dongting Lake in the Normal and Dry Seasons
This study investigated the water regime changes and the associated driving factors of Dongting Lake in the normal and dry seasons.The long short-term memory network modeled the outflow and water level of Dongting Lake,and the driving fac-tors of water regime variation were detected using scenario simulation.From 1992 to 2019,the outflow of Dongting Lake in Sep-tember and October decreased dramatically,caused by the reduced flow of the Yangtze River.The water level of Dongting Lake during the normal and dry seasons mainly lowered.The average water level in September and October dropped by 1 m in West and South Dongting Lake,while it dropped by 2 m in East Dongting Lake.Morphologic changes mainly lowered the water level during the normal and dry seasons.Flow changes of the Yangtze River and the four tributaries of the Dongting Lake basin dropped the water level in September and October and elevated the water level from December to March of the following year.The impact of flow changes on the water level of East Dongting Lake was relatively more significant.The results provided implica-tions for water resources management and wetland protection of Dongting Lake in the normal and dry seasons.

hydrologic regimelong short-term memory networkscenario simulationdriving factor analysisDongting Lake

申幸志、黄峰、韩帅、钱湛、姜恒

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湖南省水利水电勘测设计规划研究总院有限公司,湖南长沙 410007

河海大学水文水资源学院,江苏南京 210098

水文情势 长短期记忆神经网络 情境模拟 归因分析 洞庭湖

湖南省水利科技重大项目湖南省水利科技重大项目湖南省重点领域研发计划

XSKJ2021000-03XSKJ2019081-052020SK2129

2024

水文
水利部水文局 水利部水利信息中心

水文

CSTPCD北大核心
影响因子:0.742
ISSN:1000-0852
年,卷(期):2024.44(1)
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