TPLake-MED: a monthly extent dataset for lakes on the Tibetan Plateau

Abstract

Lakes on the Qinghai–Tibet Plateau have expanded markedly over recent decades, reflecting complex interactions between the regional water cycle and the cryosphere. Annual datasets capture long-term trends but often overlook short-term hydrological responses and seasonal transitions, which 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, making 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 TPLake-MED, a monthly vector boundary dataset for lakes larger than 10 km2 across the Qinghai–Tibet Plateau for 2000–2024. Within this dataset 142 to 175, and the number of smaller lakes (10–50 km2) ranges from 232 to 260 across the study period. A random forest classifier based on spectral indices was developed and validated using 633 balanced water/non-water sample pixels, achieving a precision of 0.986, recall of 0.875, and F1-score of 0.927 for the water class. We also implemented a boundary optimisation workflow that integrates filtering, morphological operations, and geometric rectification to improve agreement between the extracted and actual lake extents. The total lake area on the plateau increased by 34.91 km2 yr−1, 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 on the central plateau. 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. This is the first monthly resolution vector dataset of Qinghai–Tibet 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/10.5281/zenodo.19205532 (Zhao et al., 2026).