Work

Snow-resilient mapping reveals three decades of surface water expansion on the Qinghai–Tibet Plateau

Year: 2025

Type: article

Abstract: As the “Water Tower of Asia”, the Qinghai–Tibet Plateau (QTP) plays a pivotal role in maintaining regional hydrological balance and regulating the global climate system.Its high elevation and extensive snow cover, however, present substantial challenges for accurate surface water detection via remote sensing. This difficulty is primarily due to spectral confusion between water and snow, particularly during the winter and spring months. In response to these challenges, we have developed a novel Snow-Water Separation Detection Method (SWS) that utilizes a combination of multispectral indices to effectively minimize snow interference and enhance the precision of water body identification. Validation of this method shows that 98.87% of the 2,687 evaluated open water samples conformed to classification standards, while misclassifications were exceptionally low, with only 0.02% of the 2,545 terrestrial samples and 0.52% of the 2,203 open water samples incorrectly identified as water. This demonstrates the method’s robust discriminatory capacity. We employed the SWS method on approximately 159,000 Landsat images from 1990 to 2024 across the QTP to construct a high-resolution, long-term dataset of surface water dynamics (QTP-SW). Analysis of this dataset indicates a net increase of 18,053 km 2 in permanent water bodies and 2,296 km 2 in seasonal water bodies over the past 35 years.River surface area also expanded by 1,788 km 2 . These changes are significantly correlated with regional climate trends, underscoring the pronounced impact of climate change on water resources. The SWS method exhibits strong generalizability and is well-suited for application in other cold, high-altitude regions. The publicly available QTP-SW dataset ( https://10.5281/zenodo.15870117 ) offers a valuable resource for studies on lake evolution, cryospheric dynamics, and sustainable water resource management.

Source: Remote Sensing Applications Society and Environment

DOI: 10.1016/j.rsase.2025.101769