A XGBOOST ERROR CORRECTION MODEL FOR IMPROVING MONTHLY LAKE WATER LEVEL ESTIMATION ON QIANGTANG PLATEAU

Accurate and consistent monitoring of lake water levels is essential for understanding hydrological dynamics and climate-driven variability in remote and data-scarce regions. Satellite altimetry provides high-precision lake level observations, but its limited spatial and temporal coverage constrain large-scale monitoring. Combining Digital Elevation Model (DEM) and remote sensing imagery offers an alternative, but the accuracy of resultant water level is affected by the uncertainties of inherent elevation and image processing. This study proposes an XGBoost model for correcting systematic errors in inconsistent DEM-derived water levels. Twelve error-influencing parameters were incorporated, spanning lake boundary uncertainty, terrain accuracy, and area-elevation fitting errors. The results achieve a high accuracy (RMSE = 2.99 m, R2 = 0.99, MAE = 0.84 m), and demonstrate robust correction performance across lakes of different sizes. We reconstructed monthly water level time-series (2000-2021) for 965 lakes on the Qiangtang Plateau (QP) using this method. The dataset unravels divergent change trends in lake water levels on QP: a significant rise (0.12 m\/y) in lakes monitored by altimetry and a slight decline (-0.028 m\/y) in lakes without altimetry coverage. This suggests that relying solely on altimetry data may overestimate regional lake expansion. We found both lake types are clustered into three intra-annual variation patterns, reflecting distinct hydrological and climatic influences. Uncertainties in water body extraction during frozen periods significantly reduce the accuracy of lake water level estimations. This study provides the first plateau-scale assessment of monthly lake water level dynamics and may offer a foundation for future research on regional hydrological changes.