High-resolution surface water dataset for the Hindu Kush Himalaya using Vision Transformer and Sentinel-2 imagery

The Hindu Kush Himalaya (HKH), known as the “Water Tower of Asia”, faces mounting challenges from climate change, accelerated glacial retreat, intensified land-use changes, and transboundary water management complexities, leading to significant hydrological transformations that threaten regional water sustainability. To address the urgent need for precise water monitoring, we produced the comprehensive 10-meter resolution surface water dataset (HKH-SWD10m) for the HKH region spanning from 2016 to 2022. The dataset is produced by developing a Vision Transformer-based deep learning network optimized for rapid, automated water extraction. The resulting network achieved exceptional performance metrics on our test dataset, with an Overall Accuracy (OA) of 0.9981, an Intersection over Union (IoU) of 0.9734, and a Kappa of 0.9855. Extensive validation using 15,000 stratified random sampling points demonstrated high accuracy with an OA of 0.9787, a Producer’s Accuracy (PA) of 0.9638, a User’s Accuracy (UA) of 0.9856, and a Kappa of 0.9476. Comparative analysis with the 30-meter resolution Global Surface Water (GSW) dataset revealed that HKH-SWD10m is generally consistent with the GSW product but captures more small water bodies while providing superior boundary delineation precision. Based on the HKH-SWD10m dataset, we analyzed changes of surface water in the HKH over the years 2016–2022. Our interannual analysis (2016–2022) not only corroborates previous hydrological findings but reveals novel sub-regional divergence in surface water trends when analyzed through national and basin-level frameworks, suggesting localized climate impacts. This dataset advances hydrological monitoring capabilities by offering unprecedented spatial-temporal resolution for the HKH, serving as a critical resource for water security assessments, ecosystem management, and climate adaptation strategies. The HKH-SWD10m dataset is publicly available through Zenodo (https://doi.org/10.5281/zenodo.15067176) and National Earth System Science Data Center of China (https://doi.org/10.12041/geodata.551748804486886.ver1.db).