# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 2 Materials and Methods  
The study area is located in eastern China (4\({}^{\circ}\)\(\sim\)53\({}^{\circ}\) N and 100\({}^{\circ}\)\(\sim\)135\({}^{\circ}\) E). It is close to the Pacific Ocean, while the mainland area covers northeast China, east China, and southeast China. Most of these areas are in the eastern monsoon region of China. The average annual rainfall is from 20 mm to 140 m in the east China, while precipitation gradually reduces from southeast to northwest, as shown in Figure 1. The average temperature was between 5 and 20 \({}^{\circ}\)C in east China, decreasing from the south to the north along with the increase of latitude, as indicated in Figure 2. The monsoon climate in east China is significant; it is characterized by hot rain, year-round high-temperatures, and obvious precipitation changes in the rainy and dry seasons, which are advantageous to the tropical economic crops. The region to the south of the Huaihe River and Qinling has a subtropical monsoon climate, with the average temperature over 0 \({}^{\circ}\)C in January. The ample annual precipitation means that crops can be harvested two or three times per year--the paddy plays an essential role in agriculture. To the north of the Huaihe River and Qinling there is a temperate monsoon climate, and the average temperature falls below 0 \({}^{\circ}\)C in January. As annual precipitation is lower than the south of Huaihe River and Qinling, crops can be harvested only once a year, or three times every two years. With the development of national advanced satellites, satellite data provide more valuable information to constrain the land surface state initialization of climate forecasting. At present, there are two ways to constrain the land surface state initialization: Firstly, the observed data (such as climate forecasting) are used to constrain the land surface state initialization. Secondly, the observed data (such as climate forecasting) are used to constrain the land surface state initialization. Figure 1: The spatial distribution of annual average precipitation from 1961 to 2011. Figure 2: The national distribution of annual average temperatures from 1961 to 2011.  
precipitation and radiation data) are used to prevent a land surface model from making serious errors related to the land surface driving data of atmospheric numerical prediction. Secondly, the land surface state observation variables, supported by land surface data assimilation techniques, are used to constrain inaccurate simulated state variables [21]. The Global Land Data Assimilation System was invented by the National Oceanic and Atmospheric Administration (NOAA), the National Center for Environmental Prediction (NCEP), the Goddard Space Flight Center (GSFC) and the National Aeronautics and Space Administration (NASA). It was built based on the aforementioned two criteria. It is an offline global high-resolution land surface simulation system, which combines the ground observation data and satellite observation data to produce better surface state variables [22]. The mode of the version 2.0 GLDAS used in this study is NOAH, the resolution of data is 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), and the time series of monthly data from 1961 to 2011 is longer than for other modes. The data set includes 28 variables such as temperature, precipitation, and soil moisture. Among them, precipitation is calculated by the rate of snow and rainfall. Soil moisture is divided into four levels--0-10 cm, 10-40 cm, 40-100 cm, 100-200 cm--in a unit area of soil water content (kg/m\({}^{2}\)). In the study, the main method includes anomaly analysis and correlation calculation.