"A XGBOOST ERROR CORRECTION MODEL FOR IMPROVING MONTHLY LAKE WATER LEVEL ESTIMATION ON QIANGTANG PLATEAU"
收藏资源简介：
"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. "
精准且一致的湖泊水位监测，是理解偏远数据匮乏区域水文动态与气候驱动变异性的核心前提。卫星测高技术可获取高精度的湖泊水位观测数据，但其有限的时空覆盖范围，制约了大尺度湖泊监测工作的开展。结合数字高程模型（Digital Elevation Model, DEM）与遥感影像虽可作为替代监测方案，但最终所得水位的精度，会受固有高程不确定性与影像处理误差的影响。本研究提出一种XGBoost模型，用于校正由DEM衍生得到的不一致湖泊水位数据中的系统误差。研究共纳入12项误差影响参数，涵盖湖泊边界不确定性、地形精度以及面积-高程拟合误差三类因素。实验结果展现出优异的精度表现（均方根误差Root Mean Square Error, RMSE=2.99 m，决定系数Coefficient of Determination, R²=0.99，平均绝对误差Mean Absolute Error, MAE=0.84 m），且在不同规模的湖泊上均表现出稳健的校正性能。研究团队借助该方法，重构了羌塘高原（Qiangtang Plateau, QP）965个湖泊2000年至2021年的逐月水位时间序列。该数据集揭示了羌塘高原湖泊水位的差异化变化趋势：经卫星测高数据监测的湖泊水位呈现显著上升趋势（0.12 m/年），而无测高覆盖的湖泊水位则出现小幅下降（-0.028 m/年）。这表明仅依赖卫星测高数据，可能会高估区域湖泊的扩张幅度。研究发现两类湖泊均可被聚类为三种年内变化模式，反映出迥异的水文与气候驱动机制。冰封期水体提取环节的不确定性，会显著降低湖泊水位估算的精度。本研究首次完成了高原尺度下逐月湖泊水位动态的系统性评估，可为未来区域水文变化相关研究提供重要基础支撑。
