The Simulation and Evaluation of Soil Moisture Based on CLDAS

Han Shuai; Shi Chunxiang; Jiang Lipeng; Zhang Tao; Liang Xiao; Jiang Zhiwei; Xu Bin; Li Xianfeng; Zhu Zhi; Lin Hongjin

doi:10.11898/1001-7313.20170310

Vol.28, NO.3, 2017

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Article Navigation > Journal of Applied Meteorological Science > 2017 > 28(3): 369-378

Han Shuai, Shi Chunxiang, Jiang Lipeng, et al. The simulation and evaluation of soil moisture based on CLDAS. J Appl Meteor Sci, 2017, 28(3): 369-378. DOI: 10.11898/1001-7313.20170310.

Citation:

Han Shuai, Shi Chunxiang, Jiang Lipeng, et al. The simulation and evaluation of soil moisture based on CLDAS. J Appl Meteor Sci, 2017, 28(3): 369-378. DOI: 10.11898/1001-7313.20170310.

Han Shuai, Shi Chunxiang, Jiang Lipeng, et al. The simulation and evaluation of soil moisture based on CLDAS. J Appl Meteor Sci, 2017, 28(3): 369-378. DOI: 10.11898/1001-7313.20170310.

Citation:

Han Shuai, Shi Chunxiang, Jiang Lipeng, et al. The simulation and evaluation of soil moisture based on CLDAS. J Appl Meteor Sci, 2017, 28(3): 369-378. DOI: 10.11898/1001-7313.20170310.

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The Simulation and Evaluation of Soil Moisture Based on CLDAS

DOI: 10.11898/1001-7313.20170310

1.
National Meteorological Information Center, Beijing 100081
2.
Jiangxi Meteorological Information Center, Nanchang 330046
3.
Inner Mongolia Ecology and Agrometeorology Centre, Hohhot 010051

Received Date: 2016-10-23
Rev Recd Date: 2017-02-22
Publish Date: 2017-05-31

Abstract

Abstract

The national weather service modernization is the core and key to the modernization of the national weather, which is an important symbol to enhance China meteorological technology level and professional ability. China Meteorological Administration publishes the national meteorological modernization objectives and evaluation plan (2014-2020), which clearly proposes the development of multi-source data fusion data set, and the land surface data fusion is one of the most important parts. Using the technique of multi-source data fusion, China Meteorological Administration Land Data Assimilation System (CLDAS) integrates observation of ground, satellite and numerical model to obtain the high-quality temperature, pressure, humidity, wind speed, the grid point data of precipitation and radiation and other factors, and then to drive land surface model to simulate different depths of soil temperature and moisture.CLM3.5 land surface model is used to simulate land surface soil moisture of different depths, and then results are assessed using 3 ground datasets. The first is the automatic soil moisture observation of CMA in 2013, which is checked strictly by quality control process, the second is CTP-SMTMN data, and the last is GLDAS soil moisture and ERA-Interim Reanalysis. A comprehensive assessment for soil moisture is conducted and it shows that the correlation coefficient reaches a high level in most provinces, which can better reflect the objective change of soil moisture and has a strong guiding role. In statistical analysis of time series by selecting the representative station, surface soil moisture changing rates are higher than deeper layers, because the interaction between surface soil and the atmospheric boundary layer feedback is more sensitive, and the water heat exchange are more frequent. On the Tibetan Plateau, using Taylor diagrams comparison, it's found simulation results in the appraisal process of different indices are better than the other two kinds of foreign soil moisture data. In summary, the correlation coefficient is up to 0.8, and deviation is about-0.04 mm³·mm^-3 to 0.04 mm³·mm^-3 and root mean square error is lower than 0.04-0.05 mm³·mm^-3 from stations of provinces average and all over the country.
- CLDAS;
- land surface model;
- soil moisture;
- drought monitoring

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References(39)

Figures and Tables

Fig. 1 Time serise of simulated and observated soil moisture in 2013

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Fig. 2 Hourly volume of soil water content of 0-10 cm from 15 Apr 2013 to 31 Oct 2013

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Fig. 3 Hourly volume of soil water content of 10-40 cm from 15 Apr 2013 to 31 Oct 2013

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Fig. 4 Displaying pattern statistics of Taylor based on 3 kinds of soil moisture

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Table 1 Assesment of simulated soil moisture in different provinces

地名	相关系数	偏差/(mm³·mm^-3)	均方根误差/(mm³·mm^-3)
安徽	0.892	0.006	0.032
北京	0.926	0.031	0.038
重庆	0.807	0.036	0.043
福建	0.907	0.028	0.033
甘肃	0.912	0.020	0.024
广东	0.872	0.005	0.018
广西	0.884	0.013	0.017
贵州	0.867	-0.038	0.052
海南	0.944	0.062	0.063
河北	0.965	0.063	0.065
黑龙江	0.737	-0.042	0.049
河南	0.876	0.004	0.018
湖北	0.890	-0.021	0.025
湖南	0.944	0.002	0.012
江苏	0.852	0.033	0.037
江西	0.779	0.051	0.059
吉林	0.685	0.033	0.041
内蒙古	0.802	0.050	0.052
青海	0.890	0.001	0.023
陕西	0.942	0.049	0.051
上海	0.842	0.003	0.021
山西	0.938	0.045	0.048
四川	0.870	0.074	0.076
天津	0.948	0.017	0.024
新疆	0.819	-0.041	0.043
西藏	0.779	0.081	0.085
云南	0.971	-0.056	0.057
浙江	0.850	0.061	0.071

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Table 2 Information of typical stations

站名	区站号	省份	位置
敖汉	54225	内蒙古	42.28°N, 119.92°E
龙游南	58547	浙江	29.03°N, 119.18°E
江油	56195	四川	31.80°N, 104.73°E
琼山	59757	海南	20.00°N, 110.37°E

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