The Three Georges Dam (TGD) has brought many benefits to the society by periodically changing the water level of its reservoir (TGR). Water discharging regularly takes places in the falling season when the downstream of the Yangtze River is drying. The TGD, the world’s largest hydroelectric project, can greatly mitigate the risk of flood caused by extreme precipitation with the prior discharging policy applied in the preflood season. At the end of flood season, water impounding in the storage season can help resist a drought the next year. However, owing to the difficulty in mining causality, the considerable debate about its environmental and climatic impacts have emerged in much of the empirical and modeling studies. We used causal generative neural networks (CGNN) to construct the linkage of water level–climate–vegetation across the TGD areas with a ten-year daily remotely sensed normalized difference vegetation index (NDVI), gauge-based precipitation, temperature observations, water level and streamflow. By quantifying the causality linkages with a non-linear Granger-causality framework, we find that the 30-days accumulated change of water level of the TGR significantly affects the vegetation growth with a median factor of 31.5% in the 100 km buffer region. The result showed that the vegetation dynamics linked to the water level regulation policy were at the regional scale rather than the local scale. Further, the water level regulation in the flood stage can greatly improve the vegetation growth in the buffer regions of the TGR area. Specifically, the explainable Granger causalities of the 25 km, 50 km, 75 km and 100 km buffer regions were 21.72%, 19.24%, 17.31% and 16.03%, respectively. In the falling and impounding stages, the functionality of the TGR that boosts the vegetation growth were not obvious (ranging from 6.1% to 8.3%). Overall, the results demonstrated that the regional vegetation dynamics were driven not only by the factor of climate variations but also by the TGR operation.

中文翻译：

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探索三个乔治水坝区水位-气候-植被的实证联系

三乔治水坝 (TGD) 通过定期改变其水库 (TGR) 的水位，为社会带来了许多好处。在长江下游干涸的秋季，定期排水。TGD是世界上最大的水电项目，通过在汛前实施的先行排放政策，可以大大降低极端降水造成的洪水风险。汛期末，蓄水期蓄水，可抗旱次年。然而，由于难以挖掘因果关系，在许多实证和建模研究中出现了关于其环境和气候影响的大量争论。我们使用因果生成神经网络 (CGNN) 构建了 TGD 地区的水位-气候-植被与十年每日遥感归一化差异植被指数 (NDVI)、基于仪表的降水、温度观测、水位之间的联系和流量。通过用非线性格兰杰因果框架量化因果关系，我们发现 TGR 水位的 30 天累积变化显着影响植被生长，在 100 公里缓冲区内的中值为 31.5%。结果表明，与水位调控政策相关的植被动态是区域尺度的，而不是局部尺度的。此外，洪水期的水位调节可以极大地促进三峡地区缓冲区内的植被生长。具体来说，25公里、50公里、75公里和100公里缓冲区的可解释Granger因果关系分别为21.72%、19.24%、17.31%和16.03%。在落水和蓄水阶段，TGR促进植被生长的功能不明显（6.1%~8.3%）。总体而言，结果表明区域植被动态不仅受气候变化因素的驱动，还受 TGR 运行的驱动。