Agriculture, Ecosystems and Environment 135 (2010) 111–118

Parameterizing soil and weather inputs for crop simulation models usingthe VEMAP database

Wei Wu a,b,c,*, Ji-long Chen c, Hong-bin Liu c, Axel Garcia y Garcia b, Gerrit Hoogenboom b

a College of Computer and Information Science, Southwest University, Chongqing 400716, Chinab Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223, USAc Chongqing Key Laboratory of Digital Agriculture, Southwest University, Chongqing 400716, China

A R T I C L E I N F O

Article history:

Received 23 January 2009

Received in revised form 23 August 2009

Accepted 25 August 2009

Available online 16 September 2009

Keywords:

Spatial database

Crop simulation model

Scale

Resource management

A B S T R A C T

Soil and weather data are critical for the operation of crop simulation models. However, in many cases

they are not readily available, especially applications at a regional or larger spatial scale. The Vegetation/

Ecosystem Modeling and Analysis Project (VEMAP) provides massive quantities of geo-referenced soil

and weather data variables on a half-degree latitude–longitude grid covering the conterminous USA. The

VEMAP data were derived from a range of products and analyses, including ground observations, cluster

analyses, kriging interpolation, and data assimilation. The objective of this study was to convert the soil

and daily weather data of VEMAP into a format that can be used in the popular modeling software

Decision Support System for Agrotechnology Transfer (DSSAT). Users can select appropriate soil or daily

weather information for the area of interest. The conversion of the VEMAP data resulted in 5927 unique

soil profiles and 3261 unique weather station files that encompass daily data from 1895 to 1993. To

demonstrate the utility of this database in DSSAT, one representative county of the state of Georgia, USA

was selected and a soybean simulation model was employed to simulate final yield using the extracted

soil and daily weather data for the normal year (1961–1990). Meanwhile, the extracted daily weather

data were compared with ground station observations from the National Weather Service Cooperative

Observer Program (COOP). The paired t-test showed that no significant differences were found between

the daily weather data and between simulated yields based on VEMAP and COOP weather data for the

selected location. The outcome of this research showed that the VEMAP data can be used for crop model

applications. However, further research is needed to assess the accuracy of the datasets for a variety of

crops at a regional or national scale.

� 2009 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

journal homepage: www.e lsev ier .com/ locate /agee

1. Introduction

For the last two decades, agriculture has benefited signifi-cantly from the rapid development of computers and associatedinformation technology. Crop growth simulation models anddecision support systems (DSS) have become key instruments inagricultural information technology. Since the development ofthe first crop simulation models over 40 years ago, crop modelinghas gone through a number of developmental stages (Hoogen-boom, 2000). However, they can now be used as an analysis toolto help understand the response of cropping systems for a widerange of management practices and environmental conditions

* Corresponding author. Present address: College of Computer and Information

Science, Southwest University, Chongqing 400716, China. Tel.: +86 23 6825 1069;

fax: +86 23 6825 0444.

E-mail address: wuwei_star@163.com (W. Wu).

0167-8809/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2009.08.016

(Plant et al., 1998; Calvino and Sadras, 1999; Matthews et al.,2002; Park et al., 2005; Pfister et al., 2006; Menendez and Satorre,2007).

Compared to experimental studies which usually are time-consuming and labor intensive, crop simulation models can bereadily obtained and implemented. However, they should beused as a complement to experimental research. They can beused for the interpretation of results and analysis of theresponse of agronomic systems under diverse environmentalconditions and management scenarios (Tsuji et al., 1998). Afterevaluation with local experimental data, the models can be runfor an unlimited number of different scenarios if the appropriateinput data are available (Basso et al., 2001; Walker and Shulze,2006).

The Decision Support System for Agrotechnology Transfer(DSSAT) (Hoogenboom et al., 1999, 2004) was originally developedby the International Benchmark System Network for Agrotechnol-ogy Transfer (IBSNAT) (Tsuji et al., 1994) and is well-known and

Fig. 1. Distribution of mean annual rainfall from 1961 to 1990 of the conterminous USA, and the location of the study site.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118112

widely used software package for agricultural and environmentalapplications. It contains a collection of crop simulation models formore than 28 different crops (Jones et al., 2003) and continues to beimproved and enhanced due to interest by the user community aswell as the expanding range of potential applications of thesemodels and associated decision support tools.

The Vegetation/Ecosystem Modeling and Analysis Project(VEMAP) provides a public database that includes high resolutionclimate and soils data of the conterminous USA on a 0.58 latitude/longitude grid (Fig. 1) (VEMAP, 1995). The VEMAP files areaccessible from the Internet (http://www.cgd.ucar.edu/vemap/).Using the VEMAP database, many researchers have evaluated theresponse of regional or national ecosystems to a changing climate(VEMAP, 1995; Pan et al., 1998; Loaiciga et al., 2000; Rosenberget al., 2003; Coops et al., 2005; Coulson and Joyce, 2006). Inaddition, several studies have evaluated soil erosion, cropmanagement and yield at a regional scale using the VEMAPdatabase with the DSSAT crop simulation models (Southworthet al., 2000; O’Neal et al., 2005; Dhungana et al., 2006).Furthermore, studies such as Jagtap and Jones (2002) and Irmaket al. (2005) used the VEMAP databases to simulate soybean yieldat a regional scale. However, they suggested that some adjustedfactors should be used to correct the simulated yields whencompared to county level reported yields. All these studies confirmthat the public VEMAP database could serve as an important datasource for crop simulation modeling and associated applications ata regional scale.

In general, crop simulation models, such as DSSAT, require alarge amount of input data when implemented at a regional scale.However, model users cannot access soil or daily weather data formany conditions or situations. In these cases, they have to usegeneric data or information from the nearest location, which couldbe at a significant distance from the actual location where themodel is being applied. Gijsman et al. (2007) converted the WorldInventory of Soil Emission Potentials (WISE) database into a formatsuitable for crop model applications. However, this databaseconsisted of 1125 soil profiles that were unevenly distributedacross the world. White et al. (2008) used a spatial weatherdatabase developed by NASA for crop model applications acrossthe USA. However, this database did not include any soilinformation, requiring the model to be applied in a potentialproduction mode. With the help of the VEMAP database it ispossible to identify both soil and daily weather information for aparticular region across the conterminous US. The overall objectiveof this study was to convert the VEMAP database into a cropmodeling format for easy access and applications. The secondobjective was to illustrate the usefulness of the converted data forcrop yield simulation.

2. Materials and methods

2.1. DSSAT soil and weather datasets

The input data required to run the DSSAT crop simulationmodels include daily weather data, i.e., maximum and minimumtemperature, rainfall, and solar radiation and soil characterizationdata, i.e., soil physical, chemical, and morphological properties foreach soil horizon as well as some general characteristics of the soilprofile (Jones et al., 2003; Hunt et al., 2001; Hoogenboom et al.,2004).

2.2. VEMAP soil and weather datasets

The VEMAP database, including both soil and daily weatherdatasets, are on 0.58 latitude/longitude grid across the contermi-nous United States (Fig. 1). The VEMAP soil properties were basedon a 10-km gridded Environmental Protection Agency (EPA) soildatabase developed by Kern (1994, 1995). In order to reduceaggregation bias, the soil information for each grid cell isrepresented by a set of dominant soil profiles, rather than by asimple average of soil properties (VEMAP, 1995). A cluster analysiswas used to group the subgrid 10-km elements into up to fourdominant soil categories for each grid cell. Soil data are organizedinto two layers, one is from 0 to 50 cm and the other from 50 to150 cm.

The weather dataset that is included in the VEMAP wasoriginally developed at the National Center for AtmosphericResearch (NCAR), with collaboration from Oregon State Universityand National Oceanic and Atmospheric Administration (NOAA)’sNational Climate Data Center (NCDC) (VEMAP, 1995). It includesdaily minimum and maximum temperature, precipitation, andsolar radiation from 1895 to 1993 for each grid cell. Precipitation,minimum and maximum temperature were spatially interpolatedto the 0.58 latitude and longitude VEMAP grid for each month ofthe 99-year record using PRISM (Daly et al., 1997). Daily minimumand maximum temperature and precipitation were generatedusing the stochastic weather generator WGEN (Richardson andWright, 1984). Daily solar radiation was generated by MTCLIMfrom daily temperature and precipitation (Thornton and Running,1999).

2.3. Mapping the relationship between VEMAP and DSSAT

The VEMAP database includes elevation, latitude, and longitudevariables for each grid cell which can be used in DSSAT without anymodification. The data were set to ‘�99’ in DSSAT soil and weatherfiles when there is no corresponding data in the VEMAP database.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118 113

2.3.1. Naming conventions

The VEMAP grid contains 5520 cells, with 48 rows and 115columns, 3261 of which are within the boundaries of theconterminous US and predominantly covered by land (Fig. 1).Background cells (ocean and inland water cells) are assigned thevalue of �9999. In order to transfer the VEMAP data into DSSAT,the most important task was to determine how to manage theinformation in DSSAT. In addition, there were some limitationswith respect to naming conventions in DSSAT. To accomplish this,the VEMAP data were organized by grid cell, and each cell wasuniquely identified. For each row, two single digits were used,creating a row number range from 00 to 48. For each column, twosingle characters were used, creating a column identificationranging from 00 to 99, followed by 0A to 0P.

In DSSAT, the overall soil file was named VP.SOL to indicate thatthe soil profiles were originated from the VEMAP database. Eachsoil profile in DSSAT has a unique name that consists of 10characters. As stated earlier, each grid cell has up to four dominantsoil categories. We used one character, ranging from A to D, torepresent these categories. Therefore, in the converted soilprofiles, the unique name was assigned by taking first theabbreviation ‘VP_’, followed by two characters designating thestate, four characters for the grid cell code as explained earlier forthe respect row and column number, and one letter for the soilcategory information. For example, profile name VP_GA3486Arepresents row 34, column 86, category A soil profile located inGeorgia.

The naming convention of weather file in DSSAT consists ofeight characters: the first four reflect the institute and site code,followed by two digits for the starting year and number of years ofdata stored in the file. In our case we replaced the institute and sitecode by row and column, followed by starting and ending years ofthe weather data stored in the file. For example, 34866071.WTHrepresents the row 34, column 86 grid cell, with daily weather datastarting in 1960 and ending in 1971.

2.3.2. Soil parameters

The VEMAP soil datasets are organized into gridded format filescontaining bulk density, sand, silt, clay, organic content, and rockfragment for up to four dominant categories of each grid cell. The

Table 1Soil parameters mapped from VEMAP to DSSAT.

DSSAT header Definition

SITE Site name

COUNTRY Country

SLSOURCE Data source

SLTX Soil texture

SLDESCRIP Soil description

LAT Latitude (degrees and decimal degrees)

LONG Longitude (degrees and decimal degrees)

SCSFAM Soil class

SALB Albedo

SLU1 Evaporation limit (mm)

SLDP Soil depth (cm)

SLDR Drainage rate (fraction day�1)

SLRO Runoff curve no.

SLNF Mineralization factor (0–1)

SLPF Soil fertility factor (0–1)

SLLL Lower limit of plant-extractable soil water

SDUL Drained upper limit (cm3 cm�3)

SSAT Saturated upper limit (cm3 cm�3)

SRGF Root growth factor (0–1)

SSKS Saturated hydraulic conductivity (cm h�1)

SBDM Bulk density (moist) (g cm�3)

SLOC Soil organic carbon content (%)

SLCL Clay (<0.002 mm) (%)

SLSI Silt (0.05–0.002 mm) (%)

SLCF Coarse fraction (>2 mm) (%)

gridded VEMAP soil dataset is stored in text file format. Each soilfiles contains 48 rows � 115 columns with space delimited soilarray. The array starts in the northwest corner of the grid, with thecolumn index running west to east and row index running north tosouth (VEMAP, 1995). In our study, we used Visual Basic 6.0 toextract soil parameters into the DSSAT format for up to fourdominant soil categories of each grid cell.

According to the minimum dataset requirement for DSSAT, thesoil water holding characteristics that define the lower limit ofplant-extractable water (LL), drained upper limit (DUL), andsaturated soil water content (SAT) are the most important soilinputs. However, none of these inputs are available in the VEMAPdatabase. Fortunately, there are many pedotransfer functions thatcan be used to estimate soil water retention values based on soiltexture (Saxton et al., 1986; Rawls et al., 1991; Timlin et al., 1996;Wosten et al., 2001; Gijsman et al., 2002, 2007). In our study, weused the Saxton et al. (1986) method to estimate the soil hydraulicproperties (LL and DUL) on the basis of the comparison of differentmethods reported by Gijsman et al. (2002). For SAT, it wasassumed to be a certain percentage of the porosity (POR) andassociated with the USDA soil texture classes (Dalgliesh and Foale,1998; Gijsman et al., 2007). For sand, loamy sand and sandy loam,the value was 93%; for loam, silt loam, silt, sandy clay loam, andsandy clay, the value was 95%; and for clay, clay loam, silty clayand silty clay loam, the value was 97% (Gijsman et al., 2007). Inaddition, the root distributions and upper limit of stage-1evaporation were calculated using the Ritchie and Crum (1989)methods.

Furthermore, several soil parameters which are not availablein the VEMAP database were set to the default values inDSSAT. For example, soil albedo (SALB), which is the reflectanceof solar radiation by the soil surface, was set to 0.13 (Brown);drainage rate (SLDR), was classified as moderately well drained;runoff curve number (SLRO), the default value is 83; miner-alization factor (SLNF) and soil fertility factor (SLPF), were set to1.00 and 0.92, respectively. The soil parameters mapped fromthe VEMAP to the DSSAT were listed in Table 1. An example of asoil profile for the state of Georgia that was extracted fromVEMAP for the grid cell located in row 34 and column 86 isshown in Fig. 2.

VEMAP source

State+Row+Column

USA

VEMAP

NRCS Soil Conservation Service NATSGO

Lat

Lon

Generic

0.13

0–50 cm, 50–150 cm

0.40

81.0

1.00

0.92

(cm3 cm�3)

Mdb

Moc

Mcl

Msi

Mrf

Fig. 2. An example of two soil profiles for modeling applications in Georgia that were extracted from the VEMAP soil database.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118114

2.3.3. Weather parameters

The VEMAP weather data are stored in Network Common DataForm (NetCDF) binary files. NetCDF is a set of software libraries andself-describing, machine-independent data formats that supportthe creation, access, and sharing of array-oriented scientific data(UNIDATA, 2006). It is freely available and has interfaces for manylanguages, including Python, FORTRAN, C, C++, Java, Perl, Matlab,and IDL. In VEMAP, daily weather data are stored in four NetCDFfiles, i.e., sradTCLMxxD3i.nc contains daily solar radiation,pptxTCLMxxD3i.nc contains daily precipitation, tmaxTCLMxx-D3i.nc contains daily maximum temperature, and tminTCLMxx-D3i.nc contains daily minimum temperature.

In DSSAT, the weather data are stored in ASCII format. Therefore,the daily weather data should be carefully transferred from theNetCFD into the DSSAT format. In our study, we first used Python 2.4to extract daily weather information into temporary text files for theconterminous US. Secondly, we used Visual Basic 6.0 to convert thetemporary files into daily weather files (from 1985 to 1993) for eachgrid cell. The flowchart is illustrated in Fig. 3. The weatherparameters that are used in DSSAT are listed in Table 2. An exampleof a weather data file for the state of Georgia for the grid cell thatcorrespond to row 34 and column 86 in VEMAP is presented in Fig. 4.

2.4. Crop simulation experiment

In the simulation experiments, the aim was to use the extractedsoil and daily weather data from VEMAP as input for the crop

Fig. 3. Flowchart of daily weather data conver

simulation models. Bulloch county, which represents part ofGeorgia’s major row crop region, was selected for running thesoybean yield simulation experiments (Fig. 1). The VEMAP grid cellthat covers Bullock county is located in row 34 and column 86; itscentroid is at latitude 328450N and longitude 828150W with altitude64 m.

The simulated period was from 1961 to 1990. The CroppingSystem Model (CSM)-CROPGRO-soybean of DSSAT Version 4.0.2.0(Jones et al., 2003; Hoogenboom et al., 2004) was run for rainfedconditions. The cultivars included representative varieties of thematurity groups V, VI, and VII, which are common in thesoutheastern USA. The planting dates were May 15, June 10, andJune 25. Row spacing and population were set to 91 cm and27 plants m�2, respectively (Raymer et al., 1990, 1993). The dailysoil water and nitrogen balance were initialized on January 1 ofeach year; initial soil water content was set to the lower limit ofplant-extractable soil water. The previous crop was assumed to bemaize with a root weight of 100 kg ha�1 and crop residue5000 kg ha�1. Simulated yield (Yg) for each year and grid cellwas calculated by averaging yield with nine simulations for eachsoil category (Eq. (1)).

Yg ¼Ps

k¼1

Pmj¼1 yk j

s �m (1)

where m is the number of the combination, s is the number of thesoil category, ykj is the simulated yield of one soil category, and Yg isthe simulated yield of a grid cell.

sion and transfer from VEMAP to DSSAT.

Table 2Weather parameters mapped from VEMAP to DSSAT.

DSSAT header Definition VEMAP source

INSI Institute and site code Row+Column

Lat Latitude (8) Lat

Long Longitude (8) Lon

Elev Elevation (m) Elev

TAV Mean annual temperature (8C)

AMP Half of the mean temperature

difference between the warmest

and coolest month

DATE Date, year + days from January 1

SRAD Daily solar radiation (MJ m�2 day�1) Srad

TMAX Daily temperature maximum (8C) Tmax

TMIN Daily temperature minimum (8C) Tmin

RAIN Daily rainfall (mm day�1) Pptx

Fig. 4. An example of a daily weather data for Georgia, extracted from VEMAP.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118 115

Two experiments were carried out. The first included theextracted soil and weather data from VEMAP for the four grid cellsthat span Bulloch county. A unique yield (Yc, kg ha�1) for the selectedcounty was obtained by weighting the average simulated yield of thefour cells (Eq. (2)). For the second experiment, the same extracted insitu soil profiles as the first experiment were used for each grid cell.However, for daily weather data, we assumed that weather

Fig. 5. Interface of the VEMAP conversion program

information were spatially homogeneous. The daily precipitation,temperature, and generated solar radiation data from a local COOPweather station were applied to simulation model (Hodges et al.,1985; Garcia y Garcia and Hoogenboom, 2005).

Yc ¼Pn

i¼1 YgwiPni¼1 wi

(2)

where n is the number of cells occupied by the county, w is the areaof a grid cell across the county, and Yc is the simulated yield of thecounty.

The paired t-test was applied to evaluate the statisticaldifference between the daily weather and between the simulatedyield based on VEMAP and COOP weather station for the selectedsite. The statistical analyses were performed using the SAS (SASInstitute, 1999).

3. Results

3.1. Implementation of the system

The conversion of the VEMAP database into DSSAT crop modelfiles resulted in numerous soil and weather data files. The full 3261grid cells, which are covered by land, were converted into theDSSAT format. The soil file VP.SOL includes 5927 unique soilprofiles, with some grid cells having up to four different soilcategories. There were 3261 weather files, one for each grid cell,containing 99-year of daily weather data from 1895 to 1993.

A prototype system with a point-and-click user interface wasdeveloped based on Visual Basic 6.0. An example of the interface isshown in Fig. 5. This system can help a user select the soil orweather data for the area of interest by setting appropriatecoordinates. For the weather data, a user can set the starting andending year. There are two options related to the type of theweather files. The ‘One file’ option means the extracted weatherdata will be stored in one weather file, the ‘Multiple files’ optionindicates that multiple weather files will be created, one file for 1year of daily weather data.

3.2. Local weather conditions

Air temperature and precipitation showed a similar tendencyfor both VEMAP and COOP for the selected location (Fig. 6). The

to extract data for crop model applications.

Fig. 6. Monthly average for daily maximum and minimum temperature, solar radiation, and total rainfall from 1960 to 1990 for Bulloch county, Georgia.

Fig. 7. Simulated soybean yield from 1961 to 1990 for Bulloch county, Georgia.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118116

monthly average temperature ranged from 8.6 to 27.3 8C for theVEMAP data, and from 9.0 to 26.9 8C for the COOP data. Themaximum air temperature was recorded from June to August, withJuly as the warmest month, and the minimum air temperature wasfound in January in both VEMAP and COOP. Monthly precipitationvaried from 63.9 to 148.8 mm in VEMAP, and from 58.4 to149.8 mm in COOP, with the lowest rainfall in October and thehighest in August. However, for solar radiation, there were slightdifferences between the VEMAP data and the solar radiation datagenerated based on the COOP data. The maximum solar radiationoccurred in May (24.2 MJ m�2 day�1) in VEMAP, but in June(23.3 MJ m�2 day�1) in the COOP derived data. The monthlyaverage solar radiation ranged from 9.4 MJ m�2 day�1 in Decemberto 24.2 MJ m�2 day�1 in May for VEMAP, while it ranged from9.5 MJ m�2 day�1 in December to 23.2 MJ m�2 day�1 in June forthe COOP derived data.

3.3. Experiment simulations

Two simulated yields from 1961 to 1990 for the selectedlocation are illustrated in Fig. 7. A similar tendency existed for yieldsimulated based on the two different data sources. For the firstexperiment, the maximum, minimum, average and standarddeviation of 30-year simulated yield were 3708, 898, 2575, and865 kg ha�1, respectively. The maximum and minimum simulatedyields were found in 1964 and 1980, respectively. For the secondexperiment, the maximum, minimum, average and standarddeviation of 30-year simulated yield were 3774, 640, 2496, and911 kg ha�1, respectively. The maximum and minimum simulatedyields were found in 1989 and 1968, respectively. No significant

difference was found between the two simulated yields for theselected location.

4. Discussion

The numerous extracted soil and daily weather data fromVEMAP with geo-reference are valuable sources for biophysicalmodels, especially for crop simulation models. However, takinginto account the spatial resolution, each half-degree grid cellcovers approximately 250,000 ha in VEMAP, a potential problem isthe accuracy of the gridded data at a regional scale.

The spatial interpolation accuracy is influenced by manyfactors, such as interpolation method, sample density, terrain,and scale. For example, Goovaerts (2000) documented thatadequate samples and appropriate methods are required inorder to be able to conduct a representative spatial interpola-tion. However, establishing the error associated with developinga spatial database is somewhat difficult. For instance, Daly(2006) found that there is no one satisfactory method forquantitatively estimating the error based on comparing mostcommonly used spatial interpolation methods for climatedatasets.

The comparison of the weather data between VEMAP and COOPfor the selected location showed that there was a good agreementfor air temperatures and precipitation for both data sets (Fig. 6).The difference in solar radiation between the data sources is likelydue in part to the different generated methods. For two differentapproaches, MTCLIM Version 4 (Thornton and Running, 1999) andWGENR (Garcia y Garcia and Hoogenboom, 2005), were used inVEMAP and COOP, respectively.

W. Wu et al. / Agriculture, Ecosystems and Environment 135 (2010) 111–118 117

Although no significant difference existed between the twosimulated yields for the selected location for the normal year(1961–1990), it is also worth noting the difference in inter-annualsimulated yields based on the two source daily weather data. Forexample, the minimum simulated yields were observed in 1980and 1968, for VEMAP and COOP, respectively. The maximumsimulated yields were found in 1964 and 1989, for VEMAP andCOOP, respectively.

Since all weather data were input into the crop simulationmodel, the same soil and management information were used for agiven year, the temporal variability of simulated yield was causednot only by solar radiation variability, but also precipitation and airtemperature. Hubbard (1994) found that air temperature wasspatially similar (r2 > 0.90) up to a scale of 30–60 km, suggestingthat the air temperature might not vary significantly at about25 km scale of the data sources represented here. O’Neal et al.(2002) reported that the difference in simulated yield was of thesame order of spatial precipitation variability. Therefore, decidingwhether or not this variability was significant depended on thescale of interest.

The converted database indisputably represents a potentiallykey source of soil and weather data for research and manage-ment applications concerned with a regional and national scalein the US. Further work is needed to complement data fromother data sources and to account for the temporal and spatialvariability of different crop yields based on the valuable spatialdatabase.

Acknowledgements

The authors gratefully acknowledge the financial support fromChina Scholarship Council. The authors would like to thank theVEMAP data group within the Ecosystem Dynamics and theAtmosphere Section, Climate and Global Dynamics Division,National Center for Atmospheric Research.

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