Lake Responses and Mechanisms to El Nino on the Tibetan Plateau Using Deep Learning-Based Semantic Segmentation

Numerous lakes across the Tibetan Plateau (TP) serve as crucial indicators of climate change and are significantly influenced by El Nino events. Previous studies of lake response to El Nino events have focused on a limited number of lakes. Despite advances in remote sensing technology, there have been few comprehensive and largescale studies using deep learning, and there are still gaps in understanding the response mechanisms on a larger scale. This study leverages advanced deep-learning techniques to map lake responses, offering unprecedented insights into the large-scale hydrological impacts of El Nino. Our results show that lakes shrink significantly during El Nino events on the TP. Lakes located in the central and southern parts of the TP and small lakes with areas ranging from 1 to 50 km2 (over 60 % of them) exhibited strong responses. The range of lake response to El Nino events varies with their intensity, with stronger El Nino events causing an expansion of the response range along the latitudinal direction. We propose four possible mechanisms for lake response patterns to El Nino from the perspective of lake water sources. Strong shrinkage is primarily caused by decreased precipitation and increased evaporation, with a possible contribution from reduced meltwater. Strong expansion is due to increased precipitation, more glacier and frozen soil meltwater, and reduced evaporation. For slight shrinkage and expansion patterns, the balance of meltwater may offset or even counteract the El Nino signal. The study's results could improve predictions of extreme weather events like droughts and floods in the Third Pole region, enhance water resource management and responsiveness, and offer valuable insights for ecological monitoring and early warning systems development.