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摘要: 利用重力恢复与气候实验(gravity recovery and climate experiment, GRACE)时变地球重力场模型计算得到非洲奥卡万戈三角洲地区2003-01—2014-12的陆地水储量变化信息，分别采用主成分分析(principal component analysis, PCA)和独立成分分析(independent component analysis, ICA)提取质量变化信号，并与全球陆地数据同化系统(global land data assimilation system, GLDAS)的水文模型进行对比。结果显示，在奥卡万戈河流域东北部，水储量表现出很强的周期性变化，两种数据空间特征分布的信号出现在相同位置的成分GRACE-IC1和GLDAS-IC1对应的时间序列的相关系数达到0.85。奥卡万戈三角洲地区水储量从2003-01—2011-10呈现上升趋势，两种数据空间特征分布的信号出现在相同位置的成分GRACE-IC2和GLDAS-IC3对应的时间序列的相关系数达到0.81，说明GRACE反演结果与GLDAS水文模型反演结果在研究区域内具有很强的一致性。引入全球降水气候中心降水数据和Water GAP全球水文模型数据对研究区域陆地水储量变化的原因进行分析。实验结果表明，相对于传统的多项式拟合方法，ICA可以在较大区域内直接对特定位置质量变化信号的时空特征进行提取；对比GRACE数据两种方法分解结果的第3成分可以看出，在空间尺度和时间尺度上，ICA方法对信号的分解能力要优于主成分分析方法。Abstract:Objectives Studying regional land water storage changes can better understand the characteristics of water storage changes in an area, and provide better help for the study of extreme natural disasters such as drought and flood.Methods To verify the signal decomposition ability of independent component analysis(ICA), the water storage variations in Africa's Okavango delta region from January 2003 to December 2014 was calculated using gravity recovery and climate experiment (GRACE) time-varying earth gravity field model, and the mass change was extracted by principal component analysis and ICA respectively, which was compared with the Global Land Data Assimilation System (GLDAS) hydrological model.Results The results show periodic changes of the water reserves in the northeast of Okavango river, and the correlation coefficient of the time series corresponding to GRACE-IC1 and GLDAS-IC1 between the two datasets of spatial feature distribution in the same position reaches 0.85. The water variations in the Okavango delta area increase from January 2003 to October 2011, the correlation coefficient of the time series corresponding to GRACE-IC2 and GLDAS-IC3 between the two datasets of spatial feature distribution in the same position reaches 0.81. It indicates that GRACE agrees with the GLDAS hydrological model very well in the research area. In addition, Global Precipitation Climatology Center precipitation data and WaterGAP Global Hydrology Model data were introduced to analyze the variation of terrestrial water reserves in the study area.Conclusions Compared to the traditional polynomial fitting method, the ICA can directly extract the spatial-temporal characteristics of the quality change in a specific location in a large area. By comparing the third component of the analysis results of the two GRACE methods, it can be seen that the ICA has stronger decomposition ability than the principal component analysis.
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图 3 由GRACE数据计算研究区域特征性水储量变化信息
Figure 3. Characteristic Change Information of Water Variations in the Study Area Calculated by GRACE Data
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图 4 2004年前10年和后8年月平均降雨量
Figure 4. Monthly Average Rainfall in the First 10 Years and Last 8 Years of 2004
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图 6 PCA分解前3个成分的空间特征分布
Figure 6. Spatial Feature Distribution of the First Three Components in PCA Decomposition
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图 7 PCA分解前3个成分的时间序列
Figure 7. Time Series of the First Three Components in PCA Decomposition
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图 8 ICA分解前3个成分的空间特征分布
Figure 8. Spatial Feature Distribution of the First Three Components in ICA Decomposition
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图 9 ICA分解前3个成分的时间序列
Figure 9. Time Series of the First Three Components in ICA Decomposition
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图 10 ICA分解前4个成分的空间特征分布
Figure 10. Spatial Feature Distribution of the First Four Components in ICA Decomposition
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图 11 ICA分解前4个成分的时间序列
Figure 11. Time Series of the First Four Components in ICA Decomposition
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图 12 GRACE和GLDAS相似成分结果对比
Figure 12. Comparison of Similar Compositions Between GRACE and GLDAS
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图 13 GRACE-IC2和GLDAS-IC3时间序列13点滑动平均结果对比
Figure 13. Comparison of 13-Point Moving Average Results of GRACE-IC2 and GLDAS-IC3 Time Series
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图 14 WGHM水文模型计算地下水变化结果
Figure 14. WGHM Hydrological Model Calculates Groundwater Variation Results
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图 15 2011-10前后1年平均降雨量
Figure 15. Monthly Average Annual Rainfall of One Year Before and After October 2011
表 1 两区域不同时间段水储量变化趋势
Table 1 Trend of Water Variation in Two Regions in Different Periods
区域 时间/年 趋势/(cm·a-1) 奥卡万戈河地区 2003―2011 1.7 2012―2014 -0.4 维多利亚湖地区 2003―2006 -5.1 2007―2011 0.3 2012―2014 3.1 
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