近60年以来洞庭湖流域水温情势演变
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作者单位:

1.华北电力大学水利与水电工程学院;2.湖南水利水电职业技术学院

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基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目),湖南省水利科技项目


The evolution of surface water temperature in the Dongting Lake Basin over the past 60 years
Author:
Affiliation:

1.College of Water Resources and Hydropower Engineering,North China Electric Power University;2.Hunan Polytechnic of Water Resources and Electric Power

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan),Water Conservancy Science and Technology Project of Hunan Province

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    摘要:

    湖泊水温是表征湖泊生态环境状况的重要指标,影响湖泊中的物理、化学和生物过程。探究湖泊水温情势演变规律对于治理和改善湖泊生态环境具有重要意义。基于长短期记忆(LSTM)神经网络构建了洞庭湖水温重构模型,评估了气温(T)、年积日(DOY)和流量(Q)变量在湖泊水温重构时的性能表现,利用LSTM模型重构了洞庭湖流域长序列水温,分析1960—2020年洞庭湖水温的变化特征,探究不同气象数据集作为模型补充输入对模型模拟水温性能的影响。结果表明:(1)基于LSTM神经网络构建的水温重构模型模拟水温性能良好,DOY和Q作为输入变量均能提升模型性能,DOY对模型性能提升效果更显著。(2)1960—2020年洞庭湖流域年平均水温呈现增温趋势,平均增温速率为0.15℃/10a,水温变暖速率存在空间差异性,洞庭湖湖区的变暖速率高于其主要支流。洞庭湖流域水温变暖速率存在显著的季节差异,春季是水温升温最快的季节,平均增温速率为0.29℃/10a,夏季水温升温速率则较为平缓,平均升温速率为0.04℃/10a。(3)洞庭湖湖区及其主要支流的年平均水温和年平均气温的突变特征较为一致,均在1996年前后发生突变,气温突变是水温突变的重要原因。(4)气象因子和滞后信息作为水温重构模型的补充输入对模型性能均有不同程度的提高,但模型性能提升幅度具有区域差异性,其可能原因为不同区域水温变化的驱动机制以及水气温响应关系存在差异。研究结果对洞庭湖流域水环境保护具有重要意义,可为实测资料短缺湖泊的水温演变研究提供新思路。

    Abstract:

    Lake water temperature is an important indicator to characterize the ecological environment of lakes, affecting the physical, chemical, and biological processes occurring in lakes. Investigating the evolution of lake water temperature is of great significance for the management and improvement of lake ecological environment. A Long Short-Term Memory (LSTM) neural network was used to construct a reconstruction model for the water temperature of Dongting Lake. The performance of variables including air temperature (T), day of year (DOY), and discharge (Q) was evaluated in the reconstruction of lake water temperature. Using the LSTM model, the long-term water temperature series of the Dongting Lake Basin was reconstructed, and the characteristics of water temperature changes from 1960 to 2020 were analyzed. The study also explored the impacts of different meteorological datasets as supplementary inputs on the model's performance in simulating water temperature. The results showed that: (1) The LSTM neural network-based water temperature reconstruction model demonstrated strong performance in simulating water temperature. Incorporating both DOY and Q as input variables enhanced the model’s accuracy, with DOY contributing more significantly to the improvement than Q. (2) From 1960 to 2020, the annual average water temperature in the Dongting Lake Basin showed a warming trend, with an average warming rate of 0.15°C/10a. However, there were spatial differences in the warming rates, with the lake area warming faster than the main tributaries. On a seasonal scale, there were significant seasonal variations in the warming rates, with spring being the season with the fastest warming, at an average rate of 0.29°C/10a, while summer had a more moderate warming rate of 0.04°C/10a. (3) The annual average water temperature and air temperature in the Dongting Lake Basin and its main tributaries exhibited similar abrupt change characteristics, both undergoing abrupt changes around 1996. The abrupt change in air temperature was a significant factor driving the abrupt change in water temperature. (4) Meteorological factors and lagged information, as supplementary inputs to the water temperature reconstruction model, contributed to varying degrees of improvement in model performance. However, the extent of performance enhancement showed regional differences, which may have been due to variations in the driving mechanisms of water temperature changes and the relationship between water and air temperature responses in different regions. The findings of this study are significant for protecting the water environment of the Dongting Lake Basin and offer new insights into the study of water temperature evolution in lakes with limited observational data.

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陶家欣,王远坤,尤扬,赵磊,覃书豪,肖慧芳.近60年以来洞庭湖流域水温情势演变.生态学报,,(). http://dx. doi. org/[doi]

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