Chinese Ecosystem Research Network Zone II Versions EN1 Vol 4 (4) 2019
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A dataset of soil moisture content in the typical forest ecosystems of Dinghushan (2002 – 2016)
: 2018 - 10 - 09
: 2018 - 12 - 14
: 2018 - 10 - 26
: 2019 - 12 - 25
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Abstract & Keywords
Abstract: As a critical carrier of material cycle and energy exchange, soil moisture plays an important role in the hydrological processes, water balancing, nutrient circulation, forest productivity, and ecological function maintenance of forest ecosystems. It was listed by the Chinese Ecosystem Research Network (CERN) as a fundamental indicator of the water environment of a terrestrial ecosystem. Supported by the Dinghushan Forest Ecosystem Research Station (DHS), this study takes as its object Dinghushan National Nature Reserve, a site under state protection where DHS is located, to build a dataset covering three local typical forest types, namely, Pinus massoniana coniferous forest (PF), mixed Pinus massoniana/broad-leaved forest(MF) and monsoon evergreen broad-leaved forest(MEBF). As part of the preservation endeavor, the station set up standard observation plots for monitoring soil moisture in different forest types of this region as a way to support research, education, and outreach, as per the CERN Observation and Quality Control Protocols. Hereby we present a dataset of soil moisture from 2002 to 2016 in three typical forest types of Dinghushan. This dataset can be used to support studies on the structure and function of forest ecosystems under global warming and regional vegetation change, contributing to regional forest management and ecosystem service evaluations.
Keywords: Dinghushan; soil moisture; neutron probe; long-term observation
Dataset Profile
TitleA dataset of soil moisture content in the typical forest ecosystems of Dinghushan (2002 – 2016)
Data authorsLiu Peiling, Zhang Qianmei, Liu Xiaodong, Liu Shizhong, Chu Guowei, Zhang Deqiang, Meng Ze
Data corresponding authorsZhang Qianmei (zqm@scib.ac.cn);
Liu Xiaodong (liuxd@scib.ac.cn)
Time rangeFebruary 2002 to June 2016
Geographical scopeDinghushan National Nature Reserve, China (23°09′21"N – 23°11′30"N, 112°30′39"E – 112°33′41"E)
Data format*.xlsx
Data volume1.07 MB (8005 entries)
Data service system<http://dhf.cern.ac.cn/meta/detail/FC012002>;
<http://www.sciencedb.cn/dataSet/handle/667>
Sources of fundingDinghushan Forest Ecosystem Positioning Research Station of the Chinese Ecosystem Research Network (CERN) of the National Science and Technology Infrastructure Platform; Operation Service Project of National Scientific Observation and Research Field Station for Dinghushan Forest Ecosystem in Guangdong of the Ministry of Science and Technology of the People’s Republic of China
Dataset compositionThe dataset consists of one data file comprised of 8005 entries. It contains data of the volumetric soil moisture content in Pinus massoniana coniferous forests, mixed Pinus massoniana/broad-leaved forests and monsoon evergreen broad-leaved forests measured by the neutron probe method.
1.   Introduction
Soil moisture plays an important role in matter cycling in forest ecosystems, especially at the land-atmosphere and soil-root interfaces, among others. It hence regulates the spatial distribution of nutrients and energy, and has a profound effect on ecosystem structure and functional evolution.1,2 As soil layer is the main aquifer of forest ecosystems, water content monitoring can help us further understand the functional mechanisms involved in the ecological processes of forest ecosystems,3 which is of great significance to water cycle, soil and water conservation, and watershed ecosystem management.
The Chinese Ecosystem Research Network (CERN) undertakes projects related to national ecosystem monitoring, research and demonstration. Over a long period, CERN has formed a standardized system for systematic observations and has generated large amounts of ecological observation data through its long-term in-situ monitoring of key elements, such as water, soil, atmosphere and biology of typical ecosystems nationwide. Meanwhile, the sharing of integral, consistent and reliable long-term observation data has ushered scientific research into a new big data era, which transformed the mode of scientific research and expanded the capability of scientific discovery. As soil moisture is an important element of CERN’s long-term in-situ water environment observations, analysis of the data would be conducive to revealing the dynamics of soil moisture and tackling relevant theoretical and practical predicaments.4
Typical forest vegetation types distributed in the Dinghushan National Nature Reserve include Pinus massoniana coniferous forest, mixed Pinus massoniana/broad-leaved forest, monsoon evergreen broadleaved forest, among others. Supported by the Dinghushan Forest Ecosystem Research Station (Dinghushan station), observation sites were set up in respective areas of the above-mentioned three forest types, where a complete set of work was conducted including long-term in-situ observations, data exchange, quality control, scientific research and demonstration. This dataset is an assemblage of the long-term soil moisture data observed in a real-time fashion from 2002 to 2016. It aims to provide background data for in-depth research on the structure and function of forest ecosystems under the influence of climate change and vegetation cover change. It can also be used to support forest management and the evaluation of ecosystem service functions in this area.
2.   Data collection and processing
2.1   Description of the sampling plots
The observation sites were established through identification of typical forest area, observation field, and lastly, sampling plot. They were, respectively, Tangeling auxiliary observation site as the Pinus massoniana forest sampling plot, Hankeng survey site as the mixed forest I sampling plot, Feitianyan auxiliary observation site as the mixed forest II sampling plot, and Sanbaofeng comprehensive observation field as the monsoon forest sampling plot. Among them, the mixed forest I sampling plot, established in Hankeng in 1978, was too faraway to be used for observation, so the mixed forest II sampling plot was set up in 1999, which represented the middle stage of Dinghushan forest succession. Tables 1 – 3 outline some basic information of the sampling plots.
Table 1   Forest parameters of the sampling plots
No.ParameterPFMFIMFIIMEBF
1Elevation (m)50–150200–300100–200230–350
2AspectSESESENE
3Gradient (°)15–2530–4530–4525–35
4Stand age (a)50–6070–8070–80>400
5Canopy density (%)70>90>90>95
6Soil typelateritic red soillateritic red soillateritic red soillateritic red soil
Notes: PF – Pinus massoniana coniferous forest; MF – mixed Pinus massoniana/broad-leaved forest; MEBF – monsoon evergreen broad-leaved forest.
Table 2   Soil physical and chemical properties in the sampling plots5
Forest typeSoil depth (cm)TextureTP (%)FC
(%)
MWC
(%)
SOM
(g/kg)
BD
(g·cm-3)
Sand
(%)
Silt
(%)
Clay
(%)
PF0–10
loam
39.626.145.226.31.543.042.512.5
10–2038.525.838.511.81.735.348.314.3
20–4039.38.91.631.650.316.0
40–60Silty (sandy) loam39.77.71.628.352.317.3
60–8039.86.51.327.553.317.2
MF
II
0–10Silty (sandy) loam42.325.353.745.21.212.565.819.7
10–2039.226.549.621.11.312.067.718.3
20–4047.713.21.210.068.719.3
40–6044.510.31.410.567.220.3
MEBF0–10Silty (sandy) loam56.334.659.549.30.918.861.317.8
10–2053.832.850.221.41.315.663.019.3
20–4049.614.51.321.558.318.2
40–6044.79.91.622.557.518.0
60–8040.28.31.329.153.315.5
Notes: TP – Total porosity; FC – Field capacity; MWC – Maximal water holding capacity; SOM – Soil organic matter; BD – Bulk density.
Table 3   Overview of the sampling plots
No.Forest typeDeployment of moisture observation facility and coding description
1MFThe sampling plot has an area of approximately 8000 m2, with each unit (quadrat) being 10*10 m with a 5 m buffer zone in between. Neutron tubes were deployed in the pine forest sampling plot, numbered 1, 2 and 3 from bottom to top which were coded as: DHFFZ01CTS_01_01, 02, and 03, respectively.
2MF IThe sampling plot has an area of 1200 m2, with each unit (quadrat) being 10*10 m. Neutron tubes were deployed on the lowerleft side below the plot, coded from downhill to uphill as: DHFZQ01CTS_01_01, 02, and 03, respectively.
3MF IIThe sampling plot has an area of 10000 m2, with each unit (quadrat) being 20*20 m. Due to the terrain, the class II quadrat in the northwestern corner of the sampling plot was moved to the northeastern corner. Neutron tubes were deployed inside the sampling plot, numbered 1, 2, and 3 from bottom to top which were coded as DHFFZ02CTS_01_01, 02, and 03, respectively.
4MEBFThe sampling plot has an area of 2500 m2, with each unit (quadrat) being 10*10 m. Seven neutron tubes were deployed in the upper, middle and lower parts of the hillside, within or at the edge of the plot. The tubes were coded as: DHFZH01CTS_01_01, 02, 03, 04, 05, 06, and 07, respectively.
2.2   Observation facilities
The volumetric water content (%) of soil was manually measured in each forest type and soil layer on a regular basis. Facilities used for the measurement included neutron meter and neutron tube instrument (CNC503B, Beijing Super Energy Technology Co., LTD.) purchased and equipped by the CERN.
2.3   Data sources
Dinghushan station has been striving to be an intelligent station for many years. Devoted to actively promoting the automatic monitoring, collection and transmission of field monitoring data, the station has greatly improved the coverage of digital monitoring and the timeliness of data collection within its observation scope.6
This dataset is derived from field observations of the soil moisture content of the three forest types as mentioned above started in 1999. But the dataset only covers the period from February 2002 to June 2016 due to data ambiguity (e.g., sampling layer ambiguity or time record ambiguity) and data missing during 1999 – 2001. The content of soil moisture was measured manually on a regular basis using a neutron meter, and the measured results were recorded and converted into soil volumetric moisture content (%). The soil moisture of PF and MF was measured once each month, while that of MEBF was measured every 5 days, from 0 to 90 cm at intervals of 15 cm. Restricted by certain natural conditions in deep soil (e.g., stone blockage), data measurement was hindered at certain points.
2.4   Data processing methods and procedures
The use of neutron meters to measure soil moisture content operates as follows: when a neutron source is placed in soil, the number of slow neutrons formed around the source can be used to calculate soil water content.2 This method involves placing a fast neutron source and a slow neutron detector in a casing and burying them underground. When the fast neutrons collide with hydrogen atoms in water, they change direction and lose some of their energy, becoming slow neutrons. The larger amount of water in soil, the larger number of hydrogen atoms it has, and the larger quantity of slow neutrons it would produce. When the number of slow neutrons is detected, soil moisture can be calculated based on the rectified data.7 Compared with the drying method, the neutron probe method acts as an indirect way of soil moisture observation. A neutron meter measures the volumetric moisture content of soil, which can be converted directly into millimeter units.
Neutron meter observation mainly involves three steps, namely, standard record reading, neutron meter reading and data output, which can be expressed by the following formula:
VWC = m (R/Rw ) + c (1)
where VWC is the soil volumetric water content (%); R is the neutron count rate in soil; Rw is the neutron count rate in water; and m and c are constants, at values of 12.272 and -1.2683, respectively.
3.   Data sample description
Table 4 shows the main indicators of soil moisture content in the typical forest ecosystems of Dinghushan observed by our study.
Table 4   Indicators of the soil volumetric moisture content
Data codeYearMonthDateSampling plot codeSampling plotTube code15 cm
SWC (%)
30 cm
SWC (%)
45 cm
SWC (%)
60 cm
SWC (%)
75 cm
SWC (%)
90 cm
SWC (%)
Note
Data typeNumberNumberNumberCharacterCharacterCharacterNumberNumberNumberNumberNumberNumberCharacter
Notes: SWC – Soil water content.
4.   Quality control and evaluation
4.1   Quality management system
CERN’s long-term ecological monitoring project consists of a network of joint monitoring plans among field stations,4 which conduct long-term observations of various ecological indicators. Management and quality control of the resulting data are carried out by specialized sub-centers and integrated centers. To ensure data quality and promote effective sharing, CERN has formulated a rigorous quality management system, which adopts a front- and back-end management mode to audit, inspect and evaluate data through planning, implementation and appraisal. The specific quality management process and organizational responsibilities are shown in Figure 1.


Figure 1   CERN’s quality management procedures and organizational responsibilities – the three-level quality management system4
4.2   Quality control for data generation
The data generation process involves site management, site maintenance, sampling, field observation, indoor analysis, automatic detection and data entry. For efficient data quality management, CERN lays down rigorous control measures to tackle possible problems arisen throughout the process, including to ensure the typicality of long-term observation sites in terms of both ecological types and observation purposes, to develop a special site management system and maintenance procedures, and in the case of sampling, to formulate a thorough plan, clear sampling procedures and sample monitoring specifications.
The person in charge of quality management is critical because they control every part of the data generation process. In view of this, Dinghushan station has always attached great importance to researcher training and professionalism improvement. Since it became a national pilot station in 1999, Dinghushan station has sent young researchers overseas for further study and has encouraged them to actively participate in major academic activities at home and abroad. The station has also sent technicians for the operational training of CERN and CNERN (National Ecosystem Observation and Research Network) sub-centers, focused on facility operation and maintenance related to conventional monitoring, field monitoring technology and methods, etc., so as to ensure a smooth progress of field monitoring.
4.3   Data verification and evaluation
CERN mainly checks data quality in terms of data integrity, accuracy and consistency, through which to determine the rationality of the structure, measures and other aspects of the quality control process. To detect data errors, observation results of multiple consecutive years were validated against each other and the outliers were removed. Missing data, which could be caused by technical failures of the device or thin soil layers that prevented the neutron tube from going deep enough into soil, are denoted by blank spaces. The content of soil moisture measured by the neutron meter was compared with, and validated against, results by the drying method, so outliers could be detected, attributed and corrected through measures such as a re-calibration of the neutron meter curve.
Soil moisture is a systematic reflection of both local water status and other ecological factors, such as soil physical and chemical properties, vegetation and the microclimate of forest ecosystems. Soil moisture provides an important reference for forest ecological hydrologists to understand the hydrological characteristics of forest ecosystems. As part of the larger scholarly endeavor of data sharing and scientific progress, this study presents a dataset of soil moisture in the Dinghushan forest ecosystem from 2002 to 2016, which enables convenient data queries. The dataset can be used to support research on the characteristics of, and changes in, local soil water content. It also provides bases for a systematic analysis on the soil moisture of forest ecosystems at large.8
This dataset can be applied to multiple fields related to climate, ecology, agriculture, and water resource management. It can be used for comparative analysis between different typical regions and terrestrial ecosystems. When used in combination with other biological and soil data, the dataset supports comprehensive analyses of the long-term changes and coupling mechanisms of different ecological factors, providing important data for studying the evolution of forest ecosystem structures and functions in different typical areas.9 Users should pay attention to the missing data during data application.
5.   Usage notes
This dataset can be viewed at the Dinghushan Station Data Resource Service Platform (http://dhf.cern.ac.cn/meta/detail/FC012002), where users can download the data upon login. Alternatively, users can access the dataset at Science Data Bank (http://www.sciencedb.cn/dataSet/handle/667).
1.
Liu XD, Qiao YN & Zhou GY. Controlling action of soil organic matter on soil moisture retention and its availability. Chinese Journal of Plant Ecology 35(2011): 1209-1218.
2.
Yuan GF, Tang DY, Sun XM et al. Protocols for Standard Water Environmental Observation and Measurement in Terrestrial Ecosystems. Beijing: China Environmental Science Press, 2007.
3.
Zhou G, Wei X, Wu Y et al. Quantifying the hydrological responses to climate change in an intact forested small watershed in Southern China. Global Change Biology 17(2011): 3736-3746.
4.
Yuan GF, Zhang XY, Tang XZ et al. Quality Assurance and Quality Control for Long-term Water Environmental Observation in Terrestrial Ecosystems. Beijing: China Environmental Science Press, 2012.
5.
Zhang QM. Datasets from Chinese Ecosystem Positioning Observation and Research (Volume on Forest Ecosystems: Dinghushan Station of Guangdong (1998 - 2008)). Beijing: China Agricultural Press, 2011.
6.
Zhang QM, Zhang DQ, Li YL et al. Construction of an intelligent field observation station: a case study from Dinghushan Forest Ecosystem Research Station. Ecological Science 34(2015): 139-145.
7.
Lu YZ & Li BG. Soil Science. Beijing: China Agricultural Press, 2016.
8.
Xu WT, Ge JL, Xiong GM et al. A dataset of species composition in a typical subtropical mixed evergreen and deciduous broad-leaved forest (2001). China Scientific Data 2(2017). DOI: 10.11922/csdata.180.2016.0108.
9.
Tang XZ, Yuan GF, Zhu YL et al. A soil moisture dataset observed in weather sites of CERN (2005 – 2014). China Scientific Data 2(2017). DOI: 10.11922/csdata.170.2016.0101.
Data citation
Liu P, Zhang Q, Liu X et al. A dataset of soil moisture content in the typical forest ecosystems of Dinghushan (2002 – 2016). Science Data Bank, 2018. (2018-10-26). DOI: 10.11922/sciencedb.667.
Article and author information
How to cite this article
Liu P, Zhang Q, Liu X et al. A dataset of soil moisture content in the typical forest ecosystems of Dinghushan (2002 – 2016). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0063.zh.
Liu Peiling
data analysis and manuscript writing.
MSc candidate; research area: forest ecohydrology.
Zhang Qianmei
data pre-processing and quality control.
zqm@scib.ac.cn
Senior Engineer; research area: forest ecology.
Liu Xiaodong
data quality control.
liuxd@scib.ac.cn
Lecturer; research area: forest cultivation and forest ecology.
Liu Shizhong
data acquisition and quality control.
Senior Engineer; research area: forest ecology.
Chu Guowei
data acquisition and quality control.
Senior Engineer; research area: environmental ecology.
Zhang Deqiang
project organization and management.
Professor; research area: soil ecology.
Meng Ze
data acquisition and quality control.
technician.
Dinghushan Forest Ecosystem Positioning Research Station of the Chinese Ecosystem Research Network (CERN) of the National Science and Technology Infrastructure Platform; Operation Service Project of National Scientific Observation and Research Field Station for Dinghushan Forest Ecosystem in Guangdong of the Ministry of Science and Technology of the People’s Republic of China
Publication records
Published: Dec. 25, 2019 ( VersionsEN1
Released: Oct. 26, 2018 ( VersionsZH2
Published: Dec. 25, 2019 ( VersionsZH3
References
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