User's Guide for CTP-SMTMN Data

Version 1.0, October 2013

Kun Yang, Prof., Dr. (yangk@itpcas.ac.cn)

Data Assimilation and Modeling Center for Tibetan Multi-spheres (DAM)

Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (CAS)

1. Introduction

A dense monitoring network that consists of 56 stations is established on the central Tibetan Plateau to measure two state variables (soil moisture and temperature) at three spatial scales (1.0, 0.3, 0.1 degree) and four soil depths (0~5, 10, 20, and 40 cm). Note that some of the stations are shared by both scales (indicated by the station names) and thus the number of station names is up to 69 in total. Elevations of these stations vary over 4470~4950 m. The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle.

As auxiliary parameters of this network, soil texture and soil organic carbon content are measured at each station to support further studies.

As the highest soil moisture network above sea level in the world, this network meets the requirement for evaluating a variety of soil moisture products and for soil moisture scaling analyses.

2. Network configuration

Figure 1 The configuration of the CTP-SMTMN: (a) network position (denoted by the small rectangle) on the central Plateau, (b) the experimental area and station locations (the grey curves are the national/provincial roads), (c)-(e) the large, medium, and small networks. DEM is shown in (a)-(b), land use is shown in (c)-(e).

The CTP-SMTMN is accomplished through several field campaigns, as follows.

2010.08 - set up the large network;

2011.09 - add 14 new stations to accomplish the medium network;

2012.06 - add 5 new stations to accomplish the small network;

Some stations are shared among three scale networks.

Station information (including pictures that indicate land cover and soil profiles), soil texture, soil organic carbon content, and sensor specification are provided in the supplements.

3. Data archive and access

(1) File type

ASCII-text format

(2) File name

'NetworkAbbr_StationID_startDateTimeSer_endDateTimeSer.txt'

with:

Network_Abbr: Abbreviation of the network (CTP-SMTMN)

StationID: Station Identifier (ID)

startDateTimeSer: Begin of the time series, format: YYYYMMDDhhmmss (UTC)

startDateTimeSer: End of the time series, format: YYYYMMDDhhmmss (UTC)

(3) File content

Variables measured at a station. Each file is split up into a header part and a data part - the header part ends at character '$' and marks the beginning of the data part. Each header line refers to a data column (numbered by #1, #2 and so on). Columns #1 and #2 contain timestamp information and columns >= #3 contain the appropriate measurements - with the following metadata for a data column in the header:

#[column]; Sensor name; Quantity; Unit; Sensor depth 'from' [m]; Sensor depth 'to' [m]; Sensor position

These variables are defined according the following notation in our database:

Quantity: Abbreviation used in database;

Unit

- soil moisture: 'sm' ; '% vol'

- soil temperature (measured at same depth as soil moisture measurements): 'ts' ; 'degree C'

- sensor depth: 'm'

Sensor position: oblique (for the first layer) or horizontal (for other three levels).

(4) The sample interval for the time series is 30 minutes.

(5) Missing data are filled with dummy value (-99.00).

(6) File size

The size of each data file varies from 500 KB to 1.7 MB.

The total data set volume (including station pictures) is 31 MB.

(7) Temporal coverage

Data were collected between 1 August 2010 and 31 December 2012.

The latest data can be obtained through collaborative research.

Note that:

a)    For some stations, there might be more than one sensor at a certain soil layer upon deployment in the field. The measured values with different sensors at the same depth may be different, which is rather normal due to the high spatial heterogeneity of soil moisture.

b)   The start dates for the individual time series depend on the installation time.

c)    The sensors have been calibrated by taking account of the impact of soil texture and soil organic carbon content on the measurements.

4. Data Acknowledgement

(1) Users are suggested to refer to the following publications

[1]     Yang., K., J. Qin, L. Zhao, Y. Y. Chen, W. J. Tang, M. L. Han, Lazhu., Z. Q. Chen, N. Lv, B. H. Ding, H. Wu, C. G. Lin,. 2013. A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bull. Am. Meteorol. Soc., DOI: 10.1175/BAMS-D-12-00203.1.

[2]     Qin, J., K. Yang, N. Lu, Y. Y. Chen, L. Zhao, M. L. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sensing of Environment, 138: 1-9.

[3]     Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, M. Han. Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan plateau, Journal of Geophysical Research - Atmospheres, 118, 4466–4475, DOI: 10.1002/jgrd.50301.

[4]     Zhao, L., K. Yang, J. Qin, Y. Y. Chen, W. J. Tang, C. Montzka, H. Wu, C. G. Lin, M. L. Han, H. Vereecken., 2013. Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements, J. Hydrol., 482: 92-104.

(2) The data provider must be acknowledged in any publication, by quoting

"The soil moisture and soil temperature dataset used in this study was provided by Data Assimilation and Modeling Center for Tibetan Multi-spheres, Institute of Tibetan Plateau Research, Chinese Academy of Sciences."

To facilitate the maintenance and improvements of the dataset, please send the data provider a digital copy of scientific publications in which the data is used.

5. Disclaimer

Although we have made tremendous efforts to maintain the network, to calibrate soil moisture sensors, and to carefully process the data, unexpected instrument behaves may occur. The data provider disclaims any kind of liability for quality, performance, and fitness for a particular purpose arising out of the use.

The data can only be utilized for academic research. Use of the data for other purposes (e.g. commercial use) is prohibited. No user is allowed to transfer the data to any third party.