| 引用本文: | 程海云,香天元,唐聪.长江中游城陵矶河段2016—2020年汛期水位非正常波动:影响因子及滤波修正.湖泊科学,2022,34(1):286-295. DOI:10.18307/2022.0123 |
| Cheng Haiyun,Xiang Tianyuan,Tang Cong.Abnormal fluctuation of water level in the Chenglingji reach of the middle Yangtze River during flood seasons, 2016-2020: Influencing factors and filtering correction. J. Lake Sci.2022,34(1):286-295. DOI:10.18307/2022.0123 |
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| 摘要: |
| 长江经济带的人口和经济总量均超过全国的40%，长江中游城市群受地理位置与水文条件的综合影响更易遭受洪涝灾害的威胁.莲花塘水位站作为长江中游城陵矶河段蓄滞洪区启用指标控制站，当三峡水库水位达155.0 m后，如莲花塘站水位达到34.40 m并继续上涨，则城陵矶附近区蓄滞洪区需采取分洪措施.然而受河流形态和江湖关系（长江与洞庭湖）的双重影响，莲花塘水位站水位呈现出"潮汐"式不规则周期性波动变化（俗称"假潮"），给蓄滞洪区启用等工作带来一定决策困扰.为了研究洞庭湖出口长江城陵矶河段高水期洪水位波动现象相关因子和水位滤波方法，本文对莲花塘水位站近5年18个月（2016年6-8月、2017年7-10月、2018年5-8月、2019年6-8月、2020年6-9月）5 min水位自记数据进行分析，首先明确了单日水位波动值D>0.05 m为发生"假潮"现象的判别标准；通过二元Logistic回归分析表明，在监利站高水位（Zj ≥ 34.00 m）期间，当监利至莲花塘水位落差（ΔZjl）大于2.75 m时，"假潮"发生比例为100%，模型预测结果综合百分比校正值为96.4%，表明ΔZjl为发生"假潮"的主要影响因子；然后通过快速傅里叶变换法、局部加权回归算法两种数据滤波方法对水位波动序列进行处理，研究表明，两种方法均适用于不同振幅的波形态数据分析，不受短时间数据丢失影响，不受水位瞬时值的影响，具有较大的应用价值；从误差值的波动情况来看，局部加权回归算法误差值波动更小，更适用于对波动水位数据的处理. |
| 关键词: 洞庭湖 假潮 二元Logistic回归模型 快速傅里叶变换法 局部加权回归算法 滤波 |
| DOI：10.18307/2022.0123 |
| 分类号: |
| 基金项目:湖南省水利科技项目（（2013）243-15）资助. |
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| Abnormal fluctuation of water level in the Chenglingji reach of the middle Yangtze River during flood seasons, 2016-2020: Influencing factors and filtering correction |
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Cheng Haiyun1, Xiang Tianyuan1, Tang Cong2
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1.Water Resources Survey, CWRC, Wuhan 430012, P. R. China;2.Middle Yangtze River Bureau of Hydrology and Water Resources Survey, CWRC, Wuhan 430012, P. R. China
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| Abstract: |
| The Yangtze economic belt accounts for more than 40% of China's total populaton and economy, and the urban agglomeration in the middle reaches of the Yangtze River is more vulnerable to the threat of flood disasters due to the comprehensive influence of geographical location and hydrological conditions. The water level of the Lianhuatang station is served as the index control station of the flood storage and detention area in the middle reaches of the Yangtze River. When the water level of the Three Gorges Reservoir reaches 155.0 m, if the water level of the Lianhuatang station reaches 34.40 m and continues to rise, flood diversion measures should be taken in the flood storage and detention area near Chenglingji. While under the dual influence of the river form and the relationship between the river and the lake (the Yangtze River and Lake Dongting), the water level of the Lianhuatang station presents tidal irregular periodic fluctuation (commonly known as "false tide"), which brings decision-making difficulties to the flood storage and detention area. To study the related factors of flood level fluctuation phenomenon and water level filtering method in the Chenglingji reach of the Yangtze River at the outlet of Lake Dongting, this paper analyzed the self-recorded water level data of Lianhuatang water station in the last 5 years and 18 months (June-August 2016, July-October 2017, May-August 2018, June-August 2019, and June-September 2020). Firstly, the single-day water level fluctuation value D>0.05 m is defined as the criterion for the occurrence of "false tide". And the Binary Logistic regression analysis showed that during the period of high water level (Zj ≥ 34.00 m) at Jianli station, when the water level drop from Jianli to Lianhuatang (ΔZjl) is greater than 2.75 m, the proportion of "false tide" is 100%. The comprehensive correction value of model prediction results was 96.4%, indicating that ΔZjl was the main factor influencing the false tide. Then, two data filtering methods, fast fourier transform and local regression, are used to process the water level fluctuation sequence, which shows that both of the two methods are applicable to the analysis of wave shape data with different amplitudes, and are not affected by short-term data loss and instantaneous water level value, which may have great application value. From the perspective of the fluctuation of the error value, the locally weighted regression algorithm has a smaller fluctuation of the error value and is more suitable for the processing of the fluctuating water level data. |
| Key words: Lake Dongting false tide binary Logistic regression analysis fast fourier transform filter analysis local regression filtering method |
