Integrating DEM and machine learning to estimate monthly water level variability on the Qiangtang Plateau from 2000 to 2021

To improve the accuracy and consistency of QP lake water level monitoring, we account for key parameters that affect DEM-derived water level errors, including the accuracy of lake boundary extraction, DEM quality, and the reliability of the area-elevation relationship. XGBoost model is employed to correct these discrepancies, leveraging altimetry-derived water level as a reference for calibration, thereby facilitating more accurate water level estimations. Based on the corrected results, we reconstruct monthly lake water level for QP lakes from 2000 to 2021, revealing the spatiotemporal dynamics of water level.