Chinese Ecosystem Research Network Zone II Versions EN1 Vol 6 (1) 2021
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A dataset of water content of litter in typical forest ecosystems of Dinghushan (2012–2018)
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Abstract & Keywords
Abstract: Litter water content is an important component of the hydrological cycle of forest ecosystems and plays a key role in various hydrological processes, such as soil evaporation, water infiltration, and runoff generation at the surface soil interface. The litter type, composition, and decomposition degree, among others, are important factors affecting the water content of litter. The water content of litter is considered a hydrological factor in the long-term positional observation of forest ecosystem experimental stations by the Chinese Ecosystem Research Network (CERN); this factor can provide a solid foundation for studies on the temporal dynamics of hydrological processes in the major types of ecosystems in China. Dinghushan Forest Ecosystem Research Station strictly follows the observational requirements of the CERN to standardize the monitoring of the water content of litter and accumulate a large amount of data. Pinus massoniana coniferous forest, mixed P. massoniana/broad-leaved forest, and monsoon evergreen broad-leaved forest are typical forest types in the southern subtropical regions of China. The dataset presented in this work publicly reports the long-term observational data of the litter water content of these three typical forest types in Dinghushan from 2012 to 2018. This study aims to reveal the ecological and hydrological effects of typical forest vegetation in this region and provide data support for the quantitative research and service evaluation of the hydrological regulation of different forest ecosystems.
Keywords: water content of litter; ecohydrology; representative vegetation; Dinghushan
Dataset Profile
TitleA dataset of water content of litter in typical forest ecosystems of Dinghushan (2012–2018)
Data authorsLIU Peiling, ZHANG Qianmei, LIU Xiaodong, LIU Shizhong, CHU Guowei, ZHANG Deqiang, LIU Juxiu, MENG Ze
Data corresponding authorZHANG Qianmei (zqm@scbg.ac.cn);
LIU Xiaodong (liuxd@scau.edu.cn)
Time rangeJanuary 2012–December 2018
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 volume116 KB (756 entries)
Data service system< http://www.dx.doi.org/10.11922/sciencedb.j00001.00175>
Sources of fundingDinghushan Forest Ecosystem Research Station of the Chinese Ecosystem Research Network (CERN), National Scientific Observation and Research Field Station of Dinghushan Forest Ecosystem in Guangdong of National Ecosystem Research Network, Ministry of Science and Technology of the People’s Republic of China (CRERN), the Archives of Chinese Academy of Sciences (Y821341001), and the Forestry Science and Technology Innovation Platform in Guangdong Province (2020-KYXM-09).
Dataset compositionThe dataset consists of one data file with 756 entries. The file contains raw data on the fresh weight, dry weight, and litter water content of a Pinus massoniana coniferous forest, mixed P. massoniana/broad-leaved forest, and monsoon evergreen broad-leaved forest.
1.   Introduction
Litter water content is an important component of the hydrological cycle of forest ecosystems, including soil evaporation, water infiltration, and runoff generation at the surface interface of forest ecosystems, and mainly exists in the form of tissue capillary water, tissue-surface attached water, and void gaseous water [1-3]. Efforts to monitor and study litter water content can help clarify the interactions of water elements in forest ecosystems and improve the understanding of the processes of water, heat, and nutrients in the surface soil, as well as the formation and regulation mechanisms of forest ecosystem services[1,4].
The litter layer of a forest ecosystem is composed of plant organs or residues, including leaves, branches, flowers, and fruits[5]. The litter layer can be divided according to its decomposition degree into the undecomposed layer, the semi-decomposed layer, and the decomposed layer[6]. Litter water content is controlled by the type, composition, and decomposition degree of litter, and its variation is closely related to the dynamic process of water exchange between the atmosphere and soil in forest ecosystems[4,7]. Driven by the evaporative force of the environment, part of the moisture retained by the litter layer is transferred to the atmosphere, where it exerts an important effect on the humidity of the forest. In addition, under the action of gravity, untrapped water in the litter layer is replenished to the soil through infiltration. Given the pivotal role of litter water content in the hydrological cycle of forest ecosystems, the Chinese Ecosystem Research Network (CERN) listed this parameter as a hydrological factor in the long-term positional observation of forest ecosystem experimental stations with the aim of providing a solid foundation for extensive dynamic studies on the hydrological processes of major ecosystem types in China. Dinghushan Forest Ecosystem Research Station, hereafter referred to as Dinghushan Station, is a member of the CERN. This station strictly abides by the observational requirements of the water environment of terrestrial ecosystems to standardize the monitoring of litter water content and provide a high-quality platform for the comprehensive research of the hydrological cycle elements of forest ecosystems; thus, Dinghushan Station plays an important role in research on forest hydrology and ecological hydrology[7-8].
Pinus massoniana coniferous forest (PF), mixed P. massoniana/broad-leaved forest (MF), and monsoon evergreen broad-leaved forest (MEBF) are typical forest vegetation types in Dinghushan. The dataset constructed in this work publicly reports the long-term observational data of the litter water content of the above forests in 2012–2018. The aim of this study is to strengthen quantitative research efforts on the eco-hydrological effect of typical forest vegetation and provide important data support for understanding and evaluating the hydrological service functions of ecosystems under specific regional climate conditions and vegetation cover.
2.   Data collection and processing
2.1   Description of the sampling plots
The sampling plots of litter water content were established near long-term positional observation fields of the above stands according to the requirements of the CERN monitoring manual. The long-term positional observation fields of each stand are introduced in Table 1. PF was planted in 1954 and represents the early stages of forest succession. The sampling plots were located in Tangeling, Dinghushan Nature Reserve. P. massoniana was the dominant species in the tree layer, Evodia lepta and Rhodomyrtus tomentosa were the dominant species in the shrub layer, and Dicranopteris pedata was the dominant species in the herb layer. MF was formed by the succession of the P. massoniana plantation after the invasion of broad-leaved species and, thus, represents the middle stage of forest succession. The sampling plots were located in the buffer zone of the reserve (Feitianyan). In MF, Castanopsis chinensis, Schima superba,and P. massoniana were the dominant species in the tree layer, Psychotria asiatica and Ardisia quinquegona were the dominant species in the shrub layer, and D. pedata and Lophatherum gracile were the dominant species in the herb layer. MEBF represents the climax community, and the corresponding sampling plots were established at the core area of the reserve (Sanbaofeng). The community remained evergreen throughout the year and included three tree layers, one shrub layer, and one herb layer. The canopy density of the MEBF tree layer was approximately 80%, and the dominant species were C. chinensis, S. superba, and Aporosa yunnanensis. The coverage of the shrub layer was approximately 50%, and the dominant species were Aidia canthioides, Blastus cochinchinensis, P. asiatica,and Cryptocarya concinna. The coverage of the herb layer was approximately 40%, and the dominant species included Alpinia oblongifolia and Tectaria harlandii. While the proportions of litter components varied in different forests, leaves were the main litter type, and the litter amount peaked during the rainy season[6,9].
Table 1   Forest parameters of the sample plots[6]
NumberSample namePlot area /m2Altitude /mSlope /(°)Forest age /aCanopy density /%Leaf area index
1Sampling plot of Pinus massoniana forest in an auxiliary observation field8000200–30025–3060–70703.6
2Sampling plot of mixed P. massoniana/broad-leaved forest II in an auxiliary observation field10 000220–30028–3580–90>904.8
3Sampling plot of monsoon evergreen broad-leaved forest in a comprehensive observation field10 000220–30025–33>400>956.2
2.2   Data processing
Monitors collected samples on a day without rainfall in the middle of each month. Three 1 m × 1 m quadrats were randomly established under a relatively uniform canopy structure near the sampling plot of each stand. All of the dead branches and leaves in the small plot were collected during sampling, the mixed soil was removed, and all of the litter was wrapped in a transparent and airtight sealed plastic bag. Each sample was numbered, and the sampling time, sampler, sampling area, and location information were recorded[9]. The samples were transported to the laboratory, and their fresh weight ma (g) was recorded using an electronic balance (CP2102, Aohaosi Instrument Co., Ltd.) within 1 h. The samples were then dried in an oven at 105 °C for approximately 6–8 h to a constant weight, cooled, and weighed once more to obtain their dry weight m (g). The raw data of fresh and dry weights were recorded by the surveyors on special recording paper, inputted into an Excel worksheet, and then submitted to the station administrator to complete the calculation of litter water content WL (%). The specific calculation formula is as follows:
WL (%) = (mam)×100/m (1)
3.   Description of the data samples
3.1   Structure of the data table
The main indicators of the litter water content dataset of typical forest ecosystems in Dinghushan are shown in Table 2.
Table 2   Indicators of the litter water content dataset
Ecological station codeYearMonthDayName of sampling plotVegetation typesFresh weight /gDry weight /gLitter water content /%
CharacterNumberNumberNumberCharacterCharacterNumberNumberNumber
3.2   Data time dynamic
The temporal dynamic changes in litter water content in different stands are shown in Figure 1.


Figure 1   Temporal dynamics of litter water content in different stands (error lines represent the standard error)
(A) Annual dynamic, (B) Monthly dynamicPF: Pinus massoniana coniferous forest, MF: mixed P. massoniana/broad-leaved forest, MEBF: monsoon evergreen broad-leaved forest
4.   Data quality assurance and quality control
As a base for ecosystem monitoring and ecological environment research in China, the CERN is an important member of the knowledge innovation project of the Chinese Academy of Sciences and the global monitoring network for ecological environment change[10]. The CERN has established a standardized and institutionalized management system to improve the comparability of monitoring data of typical ecosystems in different climatic regions in China. The managers and monitors of each station jointly formulate targeted manuals of quality management for long-term observations according to CERN monitoring standards and the actual situation of the relevant stations. Strict verification of the operating specifications and implementation rules for plot setting, field observation and sampling, and observational data recording and arrangement, are also conducted to ensure the total quality of the observational data.
The error caused by the improper setting of medium- and long-term observation sites in ecosystem research is far greater than the error of the experimental analysis. Therefore, the premise of data quality is the reasonable selection of typical and representative long-term observation sites[7]. The specific locations of long-term observation plots were determined on the basis of background investigations, the community structure, and dynamic analyses in Dinghushan Station. The managers of the station ensured the long-term stability of each site by developing a unique site management and maintenance system that could enable continuous data observation[8].
The litter water content is among the factors considered in the long-term monitoring of water environments in terrestrial ecosystems. Dinghushan Station is operated by designated monitoring personnel with clearly defined responsibilities. Quality management requirements are strictly followed in each step of sample collection, sample analysis, and data processing. For example, during sample collection, personnel are required to use airtight collection bags and control the transportation time to reduce the measurement error caused by water loss. When recording the original data, the surveyor and time of measurement should be noted to aid subsequent inspections. The monitoring data are strictly checked thrice. First, the surveyor conducts a preliminary check of the original data. Following confirmation, the data are then submitted to the station administrator for further processing and review. Finally, the data are reported to the water sub-center for final verification and storage[7]. The specific processes adopted for quality management are shown in Figure 2. The seasonal distribution of rainfall in Dinghushan shows strong fluctuations, and the characteristics of rainfall throughout the year are fairly complex. The measurement results obtained from sampling at fixed periods of each month may be affected by weather changes. Therefore, the dataset could be used in combination with local meteorological data to achieve comprehensive analyses.


Figure 2   Flow chart of data quality management
5.   Data value
The litter water content in different stands reflects the precipitation interception capacity of the litter layer, as well as the comprehensive environmental characteristics of the woodland[11-12]. Many scholars have achieved notable success in the study of the hydrological effects of litter. However, these studies feature some prominent problems, such as the lack of measurement data and the insufficient representativeness of the data obtained on account of the inherent limitations of the equipment and methods used[4]. The public reporting of monitoring data over an extensive time series could provide an accurate basis for the dynamic analysis of hydrological elements in the same region or even across several regions.
Given the persistence of environmental problems related to water resources, water conservation and the hydrological regulation ability of forest ecosystems have become a key focus of forestry workers. Thus, conducting in-depth and quantitative research on the formation mechanisms and processes of water stock and flow at different spatial levels is imperative to evaluate the hydrological service benefits of forest ecosystems in a scientific manner[13]. The hydrological cycle on the vertical interface of forest ecosystems mainly occurs in the canopy, litter layer, and soil layer; the litter layer is the transfer station of water and heat exchange and the main functional layer of soil and water conservation[1,14]. The dataset presented in this work can be used to analyze the water storage potential, intercepted rainfall intensity, and runoff-generation mechanism of different stands, all of which contributes to the dynamic processes and scientific mechanisms of water conservation in forest ecosystems.
Global warming continues to this day, the variability of precipitation is still increasing, and the regional climate consistently exhibits extreme trends. Changes in the distribution patterns of water and heat resources will inevitably affect regional forests in terms of population diversity, canopy structure, and branch and leaf biological characteristics. Changes in these factors, in turn, may be expected to modify the litter rhythm, litter amount, and litter decomposition speed in different stands, thus affecting the water content of the litter layer and the forest hydrological cycle[4,15]. The dataset can be used to study the sensitivity and response mechanism of forest hydrology and nutrient cycling processes to global climate change. Ultimately, the dataset provides important basic information for the evaluation of forest hydrological ecological benefits, decision-making of forest management, ecological environment maintenance, and sustainable utilization of water resources.
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Data citation
LIU PL, ZHANG QM, LIU XD, et al. A dataset of water content of litter in typical forest ecosystems of Dinghushan (2012–2018). Science Data Bank, 2020. (November 20, 2020). DOI: 10.11922/sciencedb.j00001.00175.
Article and author information
How to cite this article
LIU PL, ZHANG QM, LIU XD, et al. A dataset of water content of litter in typical forest ecosystems of Dinghushan (2012–2018). China Scientific Data 6 (2021). (December 15, 2020). DOI: 10.11922/csdata.2020.0084.zh.
LIU Peiling
Contribution: data analysis and manuscript writing.
MSc candidate, research area: forest ecohydrology.
ZHANG Qianmei
Contribution: data pre-processing and quality control.
zqm@scbg.ac.cn
Professor, research area: forest ecology.
LIU Xiaodong
Contribution: data quality control.
liuxd@scau.edu.cn
Lecturer, research area: forest cultivation and forest ecology.
LIU Shizhong
Contribution: data acquisition and quality control.
Senior Engineer, research area: forest ecology.
CHU Guowei
Contribution: data acquisition and quality control.
Senior Engineer, research area: environmental ecology.
ZHANG Deqiang
Contribution: project organization and management.
Professor, research area: soil ecology.
LIU Juxiu
Contribution: project organization and management.
Researcher, research area: forest ecology.
MENG Ze
Contribution: data acquisition and quality control.
Technician.
Dinghushan Forest Ecosystem Research Station of the Chinese Ecosystem Research Network (CERN), National Scientific Observation and Research Field Station of Dinghushan Forest Ecosystem in Guangdong of National Ecosystem Research Network, Ministry of Science and Technology of the People’s Republic of China (CRERN), the Archives of Chinese Academy of Sciences (Y821341001), and the Forestry Science and Technology Innovation Platform in Guangdong Province (2020-KYXM-09).
Publication records
Published: March 29, 2021 ( VersionsEN1
Released: Nov. 20, 2020 ( VersionsZH2
Published: March 29, 2021 ( VersionsZH3
References
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