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The Simulation and Evaluation of Soil Moisture Based on CLDAS
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摘要: 中国气象局陆面数据同化系统（CLDAS V1.0）由陆面驱动数据融合和陆面模式模拟两部分组成。基于驱动数据，选取Canmunity Land Model 3.5（CLM3.5）作为CLDAS V1.0系统的陆面模式进行模拟试验，并对土壤模拟结果进行评估。利用2013年经过质量控制的中国气象局业务化自动土壤水分观测站实况数据、青藏高原试验观测数据及国际同类产品对模拟结果进行评估，结果表明：从各省以及全国平均结果看，相关系数普遍在0.8以上，偏差基本为-0.04~0.04 mm3·mm-3，平均均方根误差为0.04~0.05 mm3·mm-3，在青藏高原地区与国际同类产品相比，精度也有一定提高。总体而言，模拟结果已达到较高精度，数据集产品对中国区域干旱监测等具有重要意义。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.
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Key words:
- CLDAS;
- land surface model;
- soil moisture;
- drought monitoring
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图 1 2013年部分省份土壤湿度模拟结果与观测时间序列
Fig. 1 Time serise of simulated and observated soil moisture in 2013
图 2 2013年4月15日—10月31日逐小时0~10 cm土壤体积含水量
Fig. 2 Hourly volume of soil water content of 0-10 cm from 15 Apr 2013 to 31 Oct 2013
图 3 2013年4月15—10月31日逐小时10~40 cm土壤体积含水量
Fig. 3 Hourly volume of soil water content of 10-40 cm from 15 Apr 2013 to 31 Oct 2013
图 4 3种土壤湿度产品泰勒统计图
Fig. 4 Displaying pattern statistics of Taylor based on 3 kinds of soil moisture
表 1 2013年土壤湿度模拟结果分省评估
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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表 2 代表站基本信息
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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