| Soil moisture is an important and key variable of the land component of climate system. Several studies have been conducted by related soil parameters to atmospheric circulations and weather phenomenon. The sensitivity of soil moisture simulated by Community Land Model (CLM) was analyzed by modifying atmospheric forcing datasetand leaf area index (LAI) as a case study of the north of China. Then different versions of Global Land Data Assimilation System (GLDAS) and reanalysis datasets were compared with observed data. Finally, the spatial and temporal variations of soil moisture were analyzed by the better dataset and observed data from 1993 to 2013.Main conclusions are as follows:(1) Based on the 4 completely combination tests, soil moisture simulation effects by the atmospheric forcing datasetand and LAI were studied to improve soil moisture simulation by CLM. Results showed that the simulation improved by atmospheric forcing dataset or LAI modification in different extent, while the simulation improvement effects by atmospheric forcing dataset modification was significant. And the both could improve the simulation accuracy more significantly. Compared to the model parameters, the bias was reduced by 0.016 m3/m3、0.021 m3/m3、0.018 m3/m3, and root mean square deviation (RMSE) was reduced by 0.021 m3/m3、0.032 m3/m3、0.027 m3/m3 in the area of Northeast, North andNorthwest Eastern China, repectively. In terms of different seasons, all the four simulation tests performed well in the summer and autumn, but relative poorer in the winter and spring, and the simulation improved by modifying both atmospheric forcing dataset and LAI in different seasons. In terms of different years, all of the simulation could reveal the soil moisture characteristics and the simulation by both atmospheric forcing dataset and LAI modification was more closed to observed data.(2) To evaluate the consistency between different products, the 7 different datasetsof soil moisture products from different versions of Global Land Data Assimilation System (GLDAS/CLM, NOAH2.7, NOAH3.3, VIC, MOSAIC) and reanalysis datasets (ERA-Interim, NCEP/DOE) were compared with observed data in temporal andspatial distribution during the period 1995 to 2010. Results showed that all datasets could simulate the spatial distribution of soil moisture in different regions in the north of China. In Northeast China, all of the datasets performed well and have correlation coefficient more than 0.4, and the maximum correlation coefficient was 0.72. Except for MOSAIC, the absolute value of bias and RMSE of all datasets less than 0.04m3/m3 and 0.07m3/m3, repectively. In North China, CLM and ERA-Interim performed better so that the absolute value of bias was less than 0.01m3/m3 and the RMSE was less than 0.05m3/m3, and the correlation coefficient was higher than 0.35 at the same time. In the Eastern of Northwest China, ERA-Interim had better simulation, the correlation coefficient was 0.4. In terms of different seasons, all datasets performed well in the summer, but relative poorer in the winter. In terms ofthe annual cycle of variability, all of the datasets performed well in Northeast China. CLM, VIC, ERA-Interim and NCEP/DOE performed well in North China, and ERA-Interim successfully captured the annual cycle of variability in Northwest Eastern China.(3) Based on observed data and two better datasets of soil moisture products from GLDAS/CLM and ERA-Interim, the spatial and temporal variations of soil moisture were analyzed in north of China from 1993 to 2013 in summer. Observed data showed thatthe soil moisture had the decline trend in Northeast and North China. On the contray, in Northwest Eastern China, the soil moisture showed increasing trend. ERA-Interim could reflect the trend but CLM was opposite to observed data in North China. In addition, CLM performed poorer in 1996 in north of China. The M-K trend analysis showed that in Northeast China, soil moisturehad decling trend from 1995 to 2013 and increasing trend from 1993 to 1995. In North China, soil moisture showed decling trend from 1999 to 2013. In the Eastern of Northwest China, soil moisture showed decling trend from 1993 to 1998 and 2002 to 2010. ERA-Interim and CLM could reflect the trend in Northeast China but relative poorer in North China and the Eastern of Northwest China. In addition, based on observed data and ERA-Interim, the EOF analysis results showed that soil moisture had positive anomal in the first mode. And the EOF of CLM is different to observed data so that it could’t reveal the spatial and temporal variability. |
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