An 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 constrains large-scale monitoring. Combining digital elevation model (DEM) and remote sensing imagery offers an alternative, but the accuracy of resultant water levels is affected by the uncertainties of inherent elevation and image processing. This study proposes an extreme gradient boosting tree (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 (root-mean-square error (RMSE) = 2.99 m, $R^{2} =0.99$ , and mean absolute error (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/a) in lakes monitored by altimetry and a slight decline (−0.028 m/a) in lakes without altimetry coverage. This highlights a potential bias when relying solely on altimetry data, as the omission of shrinking, unmonitored lakes could lead to an overestimation of regional lake expansion. We found that the QP lakes 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.