ABSTRACT

The present study aims to improve the efficacy of water budget (WB) estimations from various hydrological data products, by (1) evaluating the uncertainties of hydrological data products, (2) merging four precipitation and six evapotranspiration products using their error variances, and (3) employing the constrained Kalman filter (CKF) method to distribute residual errors among water budget components based on their relative uncertainties. The results show that applying bias correction before the merging process improved estimations of precipitation products with decreasing root mean square error (RMSE), except Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Variable Infiltration Capacity (VIC) and bias-corrected Climate Prediction Center Morphing Technique (CMORPH) products outperformed other evapotranspiration and bias-corrected precipitation products, respectively, in terms of mean merging weights. The terrestrial water storage change is the primary reason for non-closure errors, mainly caused by the coarse resolution of Gravity Recovery and Climate Experiment (GRACE). The CKF results were insensitive to variations in uncertainties of runoff. Precipitation derived from the CKF was the best precipitation output, with the highest correlation coefficient (CC) and smallest root mean square deviation (RMSD).

摘要

本研究旨在通过（1）评估水文数据产品的不确定性，（2）利用其误差方差合并四种降水和六种蒸散产品，以及（ 3）采用约束卡尔曼滤波器（CKF）方法根据水收支的相对不确定性在水收支分量之间分配残差。结果表明，在合并过程之前应用偏差校正改进了降水产物的估计，并降低了均方根误差 (RMSE)，但使用人工神经网络 (PERSIANN) 从遥感信息进行降水估计除外。就平均合并权重而言，可变入渗能力 (VIC) 和偏差校正气候预测中心变形技术 (CMORPH) 产品分别优于其他蒸发量和偏差校正降水产品。陆地蓄水量变化是非闭合误差的主要原因，主要是由于重力恢复和气候实验（GRACE）的粗分辨率造成的。CKF 结果对径流不确定性的变化不敏感。来自CKF的降水是最好的降水输出，具有最高的相关系数（CC）和最小的均方根偏差（RMSD）。主要是由于重力恢复和气候实验（GRACE）的粗分辨率造成的。CKF 结果对径流不确定性的变化不敏感。来自CKF的降水是最好的降水输出，具有最高的相关系数（CC）和最小的均方根偏差（RMSD）。主要是由于重力恢复和气候实验（GRACE）的粗分辨率造成的。CKF 结果对径流不确定性的变化不敏感。来自CKF的降水是最好的降水输出，具有最高的相关系数（CC）和最小的均方根偏差（RMSD）。