Effect of Three Gorges Dam on Poyang Lake water level at daily scale based on machine learning

2021 ◽  
Vol 31 (11) ◽  
pp. 1598-1614
Author(s):  
Sheng Huang ◽  
Jun Xia ◽  
Sidong Zeng ◽  
Yueling Wang ◽  
Dunxian She
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Lake Water ◽  
Poyang Lake ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
Lake Water Level ◽  
Daily Scale

scholarly journals Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1389 ◽  
Author(s):  
Chen Liang ◽  
Hongqing Li ◽  
Mingjun Lei ◽  
and Qingyun Du
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Short Term Memory ◽  
Dongting Lake ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
Short Term ◽  
Term Memory ◽  
Lake Water Level ◽  
Long Short Term Memory

To study the Dongting Lake water level variation and its relationship with the upstream Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory (LSTM) network is used to establish a model that predicts the daily water levels of Dongting Lake. Seven factors are used as the input for the LSTM model and eight years of daily data (from 2003 to 2012) are used to train the model. Then, the model is applied to the test dataset (from 2011 to 2013) for forecasting and is evaluated using the root mean squared error (RMSE) and the coefficient of determination (R2). The test shows the LSTM model has better accuracy compared to the support vector machine (SVM) model. Furthermore, the model is adjusted to simulate the situation where the TGD does not exist to explore the dam’s impact. The experiment shows that the water level of Dongting Lake drops conspicuously every year from September to November during the TGD impounding period, and the water level increases mildly during dry seasons due to TGD replenishment. Additionally, the impact of the TGD results in a water level decline in Dongting Lake during flood peaks and a subsequent lagged rise. This research provides a tool for flood forecasting and offers a reference for TGD water regulation.

scholarly journals Evidences of hydraulic relationships between groundwater and lake water across the large floodplain wetland of Poyang Lake, China

2017 ◽  
Vol 18 (2) ◽  
pp. 698-712 ◽  
Author(s):  
Yunliang Li ◽  
Jing Yao ◽  
Guizhang Zhao ◽  
Qi Zhang
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Poyang Lake ◽  
Spatial And Temporal Variability ◽  
Floodplain Wetland ◽  
Lake Water Level ◽  
Groundwater Dynamics ◽  
Isotope Evidence ◽  
To Receive ◽  
Poyang Lake Wetland

Abstract Hydraulic relationship between wetlands and lakes has become an important topic for the scientific and decision-making communities. Poyang Lake, an open freshwater lake in China, and the extensive floodplain wetland surrounding the lake, plays an important role in protecting the biodiversity of this internationally recognized wetland system. This paper is the first field-based study into an investigation of the groundwater dynamics in the floodplain wetland and the associated hydraulic relationship with the lake using hydrological, hydrochemical and stable isotope evidence, as exemplified by Poyang Lake wetland. Results show that groundwater stores within the floodplain wetland exhibit spatial and temporal variability in terms of the magnitudes of groundwater level variations. Floodplain groundwater fluctuations largely reflect patterns of the precipitation and the lake water level; however, the groundwater dynamics are highly affected by the variations in the lake water level, rather than local precipitation. Floodplain wetland is most likely to receive the lake water during spring and summer and may recharge the lake during periods of low lake water level. Additionally, floodplain groundwater displays similar hydrochemical and environmental isotope signatures to that of the lake at different sampling periods, indicating a close hydraulic relationship between groundwater and the lake throughout the year.

scholarly journals Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China

2016 ◽  
Vol 47 (S1) ◽  
pp. 69-83 ◽  
Author(s):  
Bing Li ◽  
Guishan Yang ◽  
Rongrong Wan ◽  
Xue Dai ◽  
Yanhui Zhang
Keyword(s):  
Water Level ◽  
Random Forests ◽  
Lake Water ◽  
Poyang Lake ◽  
Time Lag ◽  
Water Levels ◽  
Support Vector ◽  
Lake Water Level ◽  
Water Level Variations

Modeling of hydrological time series is essential for sustainable development and management of lake water resources. This study aims to develop an efficient model for forecasting lake water level variations, exemplified by the Poyang Lake (China) case study. A random forests (RF) model was first applied and compared with artificial neural networks, support vector regression, and a linear model. Three scenarios were adopted to investigate the effect of time lag and previous water levels as model inputs for real-time forecasting. Variable importance was then analyzed to evaluate the influence of each predictor for water level variations. Results indicated that the RF model exhibits the best performance for daily forecasting in terms of root mean square error (RMSE) and coefficient of determination (R2). Moreover, the highest accuracy was achieved using discharge series at 4-day-ahead and the average water level over the previous week as model inputs, with an average RMSE of 0.25 m for five stations within the lake. In addition, the previous water level was the most efficient predictor for water level forecasting, followed by discharge from the Yangtze River. Based on the performance of the soft computing methods, RF can be calibrated to provide information or simulation scenarios for water management and decision-making.

scholarly journals Quantitative Assessment of the Influences of Three Gorges Dam on the Water Level of Poyang Lake, China

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1519 ◽  
Author(s):  
Dan Wang ◽  
Shuanghu Zhang ◽  
Guoli Wang ◽  
Qiaoqian Han ◽  
Guoxian Huang ◽  
...  
Keyword(s):  
Water Level ◽  
Yangtze River ◽  
Poyang Lake ◽  
Model Simulation ◽  
Water Storage ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
The Yangtze River ◽  
Middle Reach ◽  
Significant Downward Trend

Lakes are important for global ecological balance and provide rich biological and social resources. However, lake systems are sensitive to climate change and anthropogenic activities. Poyang Lake is an important wetland in the middle reach of the Yangtze River, China and has a complicated interaction with the Yangtze River. In recent years, the water level of Poyang Lake was altered dramatically, in particular showing a significant downward trend after the operation of the Three Gorges Dam (TGD) in 2003, thus seriously affecting the lake wetland ecosystem. The operation of the TGD changed both the hydrological regime and the deeper channel in the middle reach of the Yangtze River, and affected the river–lake system between the Yangtze River and Poyang Lake. This study analyzed the change in the water level of Poyang Lake and quantified the contributions of the TGD operation, from the perspectives of water storage and erosion of the deeper channel in the middle reach of the Yangtze River, through hydrodynamic model simulation. The erosion of the deeper channel indicated a significant decrease in annual water level. However, due to the water storage of the TGD in September and October, the discharge in the Yangtze River sharply decreased and the water level of Poyang Lake was largely affected. Especially in late September, early October, and mid-October, the contributions of water storage of the TGD to the decline in the water level of Poyang Lake respectively reached 68.85%, 59.04%, and 54.88%, indicating that the water storage of the TGD was the main factor in the decrease in water level. The erosion of the deeper channel accelerated the decline of the water level of Poyang Lake and led to about 10% to 20% of the decline of water level in September and October. Due to the combined operation of the TGD and more reservoirs under construction in the upper TGD, the long-term and irreversible influence of the TGD on Poyang Lake should be further explored in the future.

Lake water level modeling using newly developed hybrid data intelligence model

2020 ◽  
Vol 141 (3-4) ◽  
pp. 1285-1300 ◽  
Author(s):  
Zaher Mundher Yaseen ◽  
Shabnam Naghshara ◽  
Sinan Q. Salih ◽  
Sungwon Kim ◽  
Anurag Malik ◽  
...  
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Lake Water Level ◽  
Hybrid Data
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