Liu Xiangmei, Chen Jiaqi, Zhang Qiwen, Zhang Xi, Wei Ersa, Wang Nuoya, Wang Qingwei, Wang Jiahan, Chen Jiansheng
College of Artificial Intelligence and Automation, Hohai University, Changzhou, 213200, China.
College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
J Environ Manage. 2024 Aug;365:121513. doi: 10.1016/j.jenvman.2024.121513. Epub 2024 Jun 22.
Situated in the north of the Qinghai-Tibet Plateau, the Qaidam Basin experiences limited precipitation and significant evaporation. Despite these conditions, it stands out as one of the most densely distributed lakes in China. The formation of these lakes is controversial: whether the lake water primarily originates from local precipitation or external water sources. To address this issue, this paper explores the recharge sources of lakes in the Qaidam Basin and the circulation patterns of groundwater from a remote sensing perspective. Based on deep learning networks, we optimized the soft object regions of the Object-Contextual Representations Network (OCRNet) and proposed the Remote·Sensing Adaptive-Improved OCRNet (RSA-IOCRNet). Compared with seven other networks, RSA-IOCRNet obtained better experimental results and was used to construct an area sequence of 16 major lakes in the Qaidam Basin. Combined with multi-source data, the comprehensive analysis indicates no significant correlation between climatic factors and lake changes, while an obvious correlation between lakes and groundwater changes in the eastern Qaidam, consisting with the results of the field survey. Deep-circulating groundwater recharges numerous Qaidam lakes through upwelling from fault zones, such as Gasikule Lake and Xiaochaidan Lake. Groundwater in the Qaidam Basin is more depleted in hydrogen-oxygen isotope characteristics than surface water in the basin, but similar to some river water in the endorheic Tibetan Plateau. This indicates that Tibetan seepage water, estimated at approximately 540 billion m/a, is transported through the Qaidam Basin via deep circulation. Moreover, it rises to recharge the groundwater and lakes within this basin through fracture zones, extending to various arid and semi-arid regions such as Taitema Lake. This work provides a new perspective on the impact of deep groundwater on lakes and water circulation in these areas.
柴达木盆地位于青藏高原北部，降水稀少，蒸发量大。尽管如此，它却是中国湖泊分布最为密集的地区之一。这些湖泊的形成存在争议：湖水主要源自当地降水还是外部水源。为解决这一问题，本文从遥感角度探讨柴达木盆地湖泊的补给来源以及地下水的循环模式。基于深度学习网络，我们优化了对象上下文表示网络（OCRNet）的软目标区域，提出了遥感自适应改进OCRNet（RSA - IOCRNet）。与其他七个网络相比，RSA - IOCRNet取得了更好的实验结果，并被用于构建柴达木盆地16个主要湖泊的面积序列。结合多源数据进行综合分析表明，气候因素与湖泊变化之间无显著相关性，而柴达木东部湖泊与地下水变化之间存在明显相关性，这与实地调查结果一致。深部循环地下水通过断层带向上涌流，补给了众多柴达木盆地的湖泊，如尕斯库勒湖和小柴旦湖。柴达木盆地地下水的氢氧同位素特征比盆地地表水贫化，但与青藏高原内流区的一些河水相似。这表明，估计约5400亿立方米/年的藏南渗透水通过深部循环流经柴达木盆地。此外，它通过断裂带上升，补给该盆地内的地下水和湖泊，并延伸至台特玛湖等各种干旱和半干旱地区。这项工作为深部地下水对这些地区湖泊和水循环的影响提供了新的视角。
