利用GRACE数据联合新型尺度因子校正法提高陆地水储量变化准确性

杨帅, 郑伟, 尹文杰, 刘杰. 2021. 利用GRACE数据联合新型尺度因子校正法提高陆地水储量变化准确性. 地球物理学报, 64(9): 3068-3082, doi: 10.6038/cjg2021O0431
引用本文: 杨帅, 郑伟, 尹文杰, 刘杰. 2021. 利用GRACE数据联合新型尺度因子校正法提高陆地水储量变化准确性. 地球物理学报, 64(9): 3068-3082, doi: 10.6038/cjg2021O0431
YANG Shuai, ZHENG Wei, YIN WenJie, LIU Jie. 2021. Improve the accuracy of GRACE terrestrial water storage changes using GRACE data combined with a new scale factor correction method. Chinese Journal of Geophysics (in Chinese), 64(9): 3068-3082, doi: 10.6038/cjg2021O0431
Citation: YANG Shuai, ZHENG Wei, YIN WenJie, LIU Jie. 2021. Improve the accuracy of GRACE terrestrial water storage changes using GRACE data combined with a new scale factor correction method. Chinese Journal of Geophysics (in Chinese), 64(9): 3068-3082, doi: 10.6038/cjg2021O0431

利用GRACE数据联合新型尺度因子校正法提高陆地水储量变化准确性

  • 基金项目:

    国家自然科学基金面上项目(41774014,41574014),"兴辽英才计划"攀登学者项目资助(XLYC2002082),中央军委科技委前沿科技创新项目(085015),国防科技创新特区创新工作站项目,中国空间技术研究院杰出青年人才基金联合资助

详细信息
    作者简介:

    杨帅, 男, 1996年生, 硕士研究生, 主要从事卫星重力反演及应用等方面研究.E-mail: yangshuai@home.hpu.edu.cn

    通讯作者: 郑伟, 男, 1977年生, 首席研究员, 博士生导师, 主要从事卫星重力反演和天空海一体化导航与探测等方面研究.E-mail: zhengwei1@qxslab.cn
  • 中图分类号: P223

Improve the accuracy of GRACE terrestrial water storage changes using GRACE data combined with a new scale factor correction method

More Information
  • 本文围绕GRACE数据在信号处理过程中存在泄露误差开展了探索性研究.第一,在传统尺度因子法的基础上,根据模型与CSR-SHc数据的均方根误差和相关性赋予权重,构建了新型尺度因子校正法.第二,以长江流域为例,评估该方法的校正效果,研究结果表明:新型尺度因子校正法校正结果综合GLDAS(Global Land Data Assimilation System)水文模型计算的尺度因子校正结果空间分布趋势的优点,避免了CSR(Center for Space Research)官方提供的尺度因子、WGHM(Water GAP Global Hydrology Model)尺度因子和迭代恢复法校正结果空间分布趋势不均匀的现象.在长期趋势上,该方法校正结果优于GLDAS水文模型计算的尺度因子校正结果;在周年振幅上,新型尺度因子校正法校正结果明显优于迭代恢复法和CSR Mascon数据的结果.第三,基于该方法校正结果显示,长江流域、上游和中下游在2002年4月—2017年1月水储量呈现上升趋势,分别为0.29 cm·a-1、0.14 cm·a-1和0.49 cm·a-1,相比于校正前CSR-SHc数据在长江流域、上游和中下游上升趋势0.21 cm·a-1、0.07 cm·a-1和0.40 cm·a-1,分别提高了38%、100%和23%.长江流域水储量上升趋势主要集中在中下游.

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  • 图 1 

    长江流域位置及其海拔高度

    Figure 1. 

    Location and elevation of the Yangtze River basin

    图 2 

    GLDAS-NOAH模型计算的长江流域TWS空间分布趋势变化

    Figure 2. 

    The spatial distribution trends of TWS in the Yangtze River basin calculated by the GLDAS-NOAH model

    图 3 

    不同模型计算的长江流域尺度因子

    Figure 3. 

    Scale factors of the Yangtze River Basin calculated by different models

    图 4 

    迭代恢复法恢复GRACE质量变化信号

    Figure 4. 

    Forward modeling method to restore GRACE quality change signal

    图 5 

    长江流域不同恢复方法的空间分布趋势

    Figure 5. 

    Spatial distribution trends of different restoration methods in the Yangtze River basin

    图 6 

    长江流域不同泄露误差校正方法恢复的TWS时间序列

    Figure 6. 

    TWS time series recovered by different leakage error correction methods in the Yangtze River Basin

    图 7 

    长江流域不同泄露误差恢复方法恢复的TWS周年振幅和长期趋势

    Figure 7. 

    Annual amplitude and long-term trend of TWS recovered by different leakage error recovery methods in the Yangtze River Basin

    图 8 

    长江子流域的TWS时间序列

    Figure 8. 

    TWS time series of the Yangtze River sub-basin

    图 9 

    长江流域降水与TWS的时间序列

    Figure 9. 

    Time series of precipitation and TWS in the Yangtze River Basin

    图 10 

    长江流域年降水异常

    Figure 10. 

    Annual precipitation anomalies in the Yangtze River Basin

    表 1 

    不同实验模型计算的区域平均趋势变化

    Table 1. 

    Regional average trend changes calculated by different experimental models

    实验模型 区域平均趋势(cm·a-1) 损失率
    NOAH数据 0.08 -
    截断至60阶, 不平滑 0.07 12%
    截断至60阶,300 km平滑 0.06 25%
    截断至60阶,500 km平滑 0.05 37%
    下载: 导出CSV

    表 2 

    不同模型计算的长江流域空间平均尺度因子

    Table 2. 

    Spatial mean scale factor of The Yangtze River Basin calculated by different models

    实验模型 空间平均尺度因子
    WGHM-SF 0.88
    MOS-SF 1.25
    NOAH-SF 1.31
    VIC-SF 1.34
    CLM-SF 1.36
    NSFCM-SF 1.36
    CLM4.0-SF 1.42
    下载: 导出CSV

    表 3 

    长江流域不同恢复方法的区域平均趋势

    Table 3. 

    Regional average trend of different restoration methods in the Yangtze River basin

    实验模型 区域平均趋势
    (cm·a-1)
    实验模型 区域平均趋势
    (cm·a-1)
    WGHM-Trend 0.13 MOS-Trend 0.27
    CSR-SHc-Trend 0.20 NSFCM-Trend 0.28
    NOAH-Trend 0.21 CLM4.0-Trend 0.32
    VIC-Trend 0.25 FM-Trend 0.33
    CLM-Trend 0.27 CSR-M-Trend 0.39
    下载: 导出CSV

    表 4 

    长江子流域的TWS长期趋势和周年振幅

    Table 4. 

    Long-term trends and annual amplitudes of TWS in the Yangtze River sub-basin

    实验模型 长江上游 长江中下游
    长期趋势
    (cm·a-1)
    周年振幅
    (cm)
    长期趋势
    (cm·a-1)
    周年振幅
    (cm)
    CSR-M 0.30 4.28 0.51 6.32
    CSR-SHc 0.07 5.05 0.40 3.71
    TWS-CLM4.0 0.09 5.99 0.66 5.98
    TWS-NSFCM 0.14 6.49 0.49 4.64
    下载: 导出CSV
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出版历程
收稿日期:  2020-01-06
修回日期:  2021-06-25
上线日期:  2021-09-10

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