TPLake-MED: A Monthly Extent Dataset for Lakes on the Tibetan Plateau

Abstract. Lakes on the Tibetan Plateau have expanded markedly over recent decades, reflecting complex interactions between the regional water cycle and the cryosphere. Whereas annual datasets capture long-term trends, they often overlook short-term hydrological responses and seasonal transitions that are resolved by monthly observations. Consequently, a systematic understanding of intra-annual lake variability remains limited, largely because most existing datasets are designed for interannual scales, which makes monthly variations and seasonal patterns difficult to characterise. These limitations hinder investigations into the driving mechanisms and complicate assessments of climate-change impacts. To address this gap, we utilised Google Earth Engine (GEE) and the MODIS Surface Reflectance product MOD09A1 (500 m) to construct a monthly vector boundary dataset for lakes larger than 10 km² across the Tibetan Plateau for 2000–2024. Within this dataset, the number of large lakes larger than 50 km² ranged from 142 to 175, and the number of smaller lakes (10–50 km²) varies between 232 and 260 across the study period. A random forest classifier based on spectral indices was developed and validated with 533 balanced water/non-water samples, achieving an overall accuracy of 93.21 % and an F1 score of 0.927. To enhance spatial precision, we implemented a boundary optimisation workflow integrating filtering, morphological operations, and geometric rectification, thereby improving agreement between extracted and actual lake extents. Aggregate lake area on the Plateau increased at 34.91 km² per year, and typically reached its annual maximum in September or October. The relative monthly rate of area change showed higher values in the west, lower in the east, and stronger variability centrally; for individual lakes the maximum monthly relative change reached 28.43 % from 2000 to 2024. In addition, smaller lakes were more sensitive to environmental change than larger lakes. To our knowledge, this is the first monthly resolution vector dataset of Tibetan Plateau lakes that couples multi-temporal classification with morphological optimisation. The dataset provides critical support for climate-change research, ecological conservation, and policy formulation, and is publicly available at https://doi.org/10.12443/BNU.RSEC.TPLake-MED20251028.