Chinese Ecosystem Research Network Research Articles

2011–2015年东洞庭湖洲滩典型植物群落样方调查数据集

The fixed sample plot investigation obtains the dynamic characteristics of ecosystem through long-term data accumulation and accurate space-time comparison, which provides a solid basis for long-term ecosystem research. In accordance with the unified monitoring specifications set forth by the Chinese Ecosystem Research Network (CERN), Dongting Lake Wetland Ecosystem Research Station conducts long-term positioning monitoring on the species composition and community characteristics of typical beach vegetation in the wetland ecosystem in response to the changing hydrological conditions of Dongting Lake. We processed the data of long-term monitoring sample plots of three typical wetland plant communities (Carex sp, Miscanthus lutarioriparius and Polygonum hydropiper) in Dongting Lake to obtain the dataset of sample survey of typical plant communities on East Dongting Lake beach from 2011 to 2015. This dataset covers a total of 14 indicators, including species name, Latin name, number of plants (clusters) (plants or clusters/quadrats), average height of leaf layer (cm), average height of reproductive branches (cm), coverage (%), phenological period, dominant species, number of plant species, density (plants or clusters/m2), leaf layer height (cm) of dominant species, reproductive branch height (cm) of dominant species, total coverage (%), and total dry weight of aboveground green parts (g/m2). This dataset comes with detailed background information to ensure its consistency across both spatial and temporal dimensions. Moreover, to ensure data accuracy and reliability, the dataset underwent comprehensive quality control throughout the entire process and is subject to expert review and verification. It can serve as an important source of background data for studying the process and succession trends of the beach wetland ecosystem in response to the hydrological conditions of Dongting Lake, and provide data support for vegetation remote sensing monitoring, biodiversity protection, wetland ecological restoration and adaptive management of Dongting Lake.

2011–2015年洞庭湖洲滩群落植物生态型、生活型和生长型组成数据集

Analyzing plant life-forms and ecotypes can provide deep insights into the relationship between plant community structure and environmental conditions, and plant life-forms play an important role in understanding the emergence, development, and succession laws of plant community. An investigation was made on the species life-forms and ecotypes of wetland plants in Dongting lake wetland. In accordance with the unified monitoring specifications of the Chinese Ecosystem Research Network (CERN), the Dongting Lake Wetland Ecosystem Observation and Research Station conducts long-term positioning monitoring on the species composition, species life-forms, growth forms and ecotypes of typical beach vegetation in the wetland ecosystem in response to the changing hydrological conditions of Dongting Lake. The dataset of long-term monitoring conducted on three typical plant communities (Carex sp., Miscanthus lutarioriparius and Polygonum hydropiper) in Dongting Lake wetland has been verified by experts to ensure the accuracy of the data. We then classified and processed the data to obtain the dataset of community composition of plant life-forms, growth forms and ecotypes on Dongting Lake beach (2011–2015). The dataset covers a total of 8 indicators, namely species name, Latin name, life-form, growth form, ecotype according to light intensity, ecotype according to water requirement, ecotype based on soil pH and ecotype according to responses sediment burial. This dataset comes with detailed background information to ensure its consistency across both spatial and temporal dimensions. This dataset comes with detailed background information about site information and site management records to ensure its consistency across both spatial and temporal dimensions. This dataset can provide support for the analysis of the classification of plant life forms and the composition and structure of ecotypes, and serve as valuable reference for further study of vegetation succession in Dongting Lake wetland.

2010–2014年哀牢山亚热带常绿阔叶林土壤呼吸数据集

Subtropical forests, as the largest forest type in China, play an important role in regulating global climate change and maintaining atmospheric carbon balance. The carbon emitted by soil through respiration is the main contributor of atmospheric CO2 levels, so even slight changes in soil carbon pool can significantly affect the concentration of atmospheric CO2. Therefore, at present, the magnitude and dynamic change characteristics of soil carbon storage in subtropical forests are attached great importance to. Ailao Mountain in Yunan Province stands as a crucial region for the distribution of subtropical forests in China. This study used a multichannel automated chamber system to estimate soil respiration in a subtropical evergreen broad-leaved forest ecosystem of Ailao Mountain. Ailao Mountain Station for Subtropical Forest Ecosystem Studies is a national field station and a basic observation station of the Chinese Ecosystem Research Network. We gathered soil respiration data from the subtropical evergreen broad-leaved forest in Ailao Mountain from 2011 to 2014, including soil temperature 5cm (TS), soil moisture content 10cm (SWC) and soil respiration data (RS), organized at daily, monthly and annual scales. This dataset is of great significance in revealing the effects of climate change on soil ecological processes in subtropical evergreen broad-leaved forests. It can facilitate precise evaluation of soil organic carbon emissions and forest ecosystem management, and provide both empirical and theoretical basis for further research on the effects of global changes (e.g. climate warming) on soil respiration components, especially with a focus on soil organic carbon emissions.

2005–2017年中国干旱半干旱区典型生态系统土壤储水量数据集

Soil water is one of the important components of the earth's water resources, and also the main source of water directly consumed by vegetation and crops. Therefore, the storage, recharge, consumption, renewal and balance of soil water are of great significance to agriculture, animal husbandry, forestry, ecological environment and water resources balance, especially for the arid and semi-arid region of China. We collect and sort out the long-term observation data of soil moisture from eight ecosystem observation and research stations of the Chinese Ecosystem Research Network (CERN) located in the arid and semi-arid region of China during 2005 - 2017, and generates the corresponding soil water storage dataset through quality control, spatial and temporal normalization, statistical calculation, in order to provide long-term data support for understanding distribution and change of soil water storage in typical ecosystems in arid and semi-arid areas of China.

2005–2020年中国干旱半干旱区典型生态系统植被地上部生物量数据集

Plant biomass are important components of global carbon cycle, important part of carbon exchange between terrestrial ecosystems and atmosphere, as well as is a basis of quantitative analysis about feedback and regulation effects between grassland and desert ecosystems and global climate change. Grassland and desert ecosystems in arid and mid-arid area are important carbon stock in China. This paper chooses 2 grassland field sites (Haibei station and Inner Mongolia station) and 5 desert field stations (Erdos station, Fukang station,Linze station,Naiman station ,Shapotou station) in China 's arid and mid-arid area which belongs to Chinese Ecosystem Research Network (CERN)， We collect these 7 field station's originally observed data about plant aboveground biomass in monthly frequency according to CERN's observational protocol from the year of 2005–2020, and make quality control ，then calculate originally observed data in sub-plot into statistical data in the scale of plot, build dataset of plant aboveground biomass, which can be used as ground measured data to support the research of grassland and desert ecosystems' response to the global climate change, vegetation conservation and sustainable development in the arid and mid-arid area.

氮磷添加下典型温带、亚热带森林土壤氮循环功能基因丰度和微生物群落属性数据集

Nitrogen (N) and phosphorus (P) are essential nutrients for soil microbial growth and activities. There are notable differences in the concentrations of N and P and the characteristics of microbial communities observed between temperate and subtropical forests soils, and this is also true for soil vertical profiles. As a consequence of the rise in global atmospheric N deposition, soil nutrient imbalances and P limitation are increasingly aggravated in eastern China. However, there is a lack of systematic dataset showing how N deposition and P addition affect soil N cycling microbes and soil physicochemical properties in eastern China; hence a comprehensive dataset is needed to a better understanding of the impact of N deposition and P addition on soil nitrogen cycle. This dataset is constructed based on the field experimental setup in the forests of Chinese Ecosystem Research Network (CERN). To obtain a systematic dataset on N-cycling microbial communities, we conducted measurements and collected data of soil N-cycling functional genes abundance, N-cycling microbial community diversities and compositions and enzyme activities in three typical forests with N and/or P additions. We collected 0-10 cm top soils from a temperate forest (Changbai Mountain) and two subtropical forests (Qianyanzhou and Dinghu Mountain) with field N and/or P additions, and the soils along 0-80 cm vertical profiles without nutrient additions. The N-cycling functional genes involved in the datasets cover ammonia oxidizing archaea amoA, ammonia oxidizing bacteria amoA, nirK, nirS, nosZ, qnorB, narG, nir, nifH and chiA. The potential activities included in the datasets cover nitrification activity, denitrification activity, nitrogen fixation activity and organic nitrogen decomposition enzyme activity. The datasets also include soil physiochemical properties, including soil pH, moisture content, N2O emission, net nitrification rate and the concentrations of organic carbon, NH4+ , NO3− , available phosphorus, total nitrogen, total carbon, total phosphorus and dissolved organic carbon. The creation and sharing of the datasets can provide data and support for understanding the microbial mechanisms of soil N-cycling and the modelling processes under the scenario of aggravated N deposition and P addition. The datasets will also provide support for forest management efforts with regard to greenhouse gas N2O emission, N and nutrient loss in forest ecosystems.

Improving Phenology Representation of Deciduous Forests in the Community Land Model: Evaluation and Modification Using Long‐Term Observations in China

AbstractPhenology is an important factor indicating environmental changes and regulates the variations of carbon, water, and energy exchange. However, phenology models exhibit large uncertainties due to limited understanding of its mechanisms. In this study, we modified deciduous phenology scheme based on the evaluation of different phenological models using long‐term observations at Chinese Ecosystem Research Network with CLM4.5. The alternating leaf unfolding model and summer‐influenced autumn leaf falling model that we proposed, performed best in simulating leaf‐unfolding and leaf‐falling. Compared with the observed and remote‐sensed phenology, the modified model could better simulate the phenological dates at the site and regional scale. Moreover, the modified model improved the simulation of gross primary productivity (GPP) by decreasing the errors of modeled carbon uptake duration and amplitude. Furthermore, the advance in leaf‐unfolding slowed down from 0.20 days/year during 1981–2015 to 0.11 days/year during 2016–2100 under RCP4.5 because of the slowdown of climate warming, but the delay in leaf‐falling changed little. By the last decade of the twenty‐first century, the leaf‐unfolding would advance (8 days) and leaf‐falling would delay (16 days). The subtropical region had large interannual variation (IAV) in leaf‐unfolding because of the high sensitivity to temperature. The phenological dates IAV in the cold temperate region increased due to enhanced temperature IAV. We suggest that the deciduous phenology models, especially the leaf‐falling process, used in Community Land Model need to be improved to reduce the errors in predicting phenology and carbon flux in the future.

A dataset of atmospheric silicon wet deposition in China's terrestrial ecosystems (2013–2020)

As a "quasi essential" nutrient element for plants, Silicon (Si) serves as an essential nutrient for some phytoplankton. Atmospheric deposition has gradually become an important supplement way for silicon nutrients to enter the ecosystem. At present, there is no national observation data report on atmospheric silicon deposition. In 2013, China Wet Deposition Observation Network (ChinaWD) was established based on the Chinese Ecosystem Research Network (CERN) and stations across the country. Since 2013, the observation network has covered 43 stations nationwide. These stations are evenly distributed in various ecological regions, and almost cover the main types of ecosystems in China. This paper focuses on the compilation and collation of atmospheric silicon wet deposition flux data of 43 stations in ChinaWD network from 2013 to 2020. ChinaWD network boasts rigorous quality assurance and high quality control standards. We adopted standard solutions and blank samples for testing and calibration, and carried out repeated processing to ensure the accuracy of data. All the parameters indicate the high accuracy and reliability of test results. The dataset is expected to make up for the deficiency of regional silicon deposition data, provide data support for the follow-up regional, national and global silicon biogeochemical cycle research. Moreover, it can be used for reference to comprehensively evaluate the proportions and ecological effect of nutrient elements (nitrogen, phosphorus, silicon).

A dataset of dryland ground temperature in typical small watersheds in the hilly areas of Central Sichuan Basin (1997-2021)

The underlying surface temperature (i.e. land surface temperature) and soil temperature at different depths are collectively referred to as ground temperature. The variations and variation rules of ground temperature comprehensively depict the exchange of energy and materials between the atmosphere and the land surface. Hence ground temperature is an important parameter for assessing soil thermal conditions. Located in the agro-ecological zone of subtropical Sichuan Basin, Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network (also referred to as Yanting Station, code YGA) serves as an experimental observation and research platform of Agro-Ecosystem set by the Chinese Ecosystem Research Network (CERN) and the National Science and Technology Infrastructure Platform in this ecological region. This dataset is based on the long-term automatic station observation data and manual observation data of the comprehensive meteorological observation field of Yanting Station. It consists of four data files, namely, hourly ground temperature data from the automatic observation stations on a daily basis (1997-2021), daily ground temperature statistical data from the automatic observation stations (2004-2021), monthly statistical data of ground temperature from the automatic observation stations (2004-2021) and daily and monthly surface temperature data form the manual observation stations (2004-2021). The data volume is about 8.85 MB. Ground temperature is the networked observation index of field stations. Observation instruments, data acquisition procedures, and data quality control adhere to standardized norms and protocols, ensuring the utmost reliability and comparability of ground temperature data. The accumulation of long time series and multi-level geothermal data can provide scientific data support for the energy and material exchange of ecosystem, the response to environmental change, and the application research of cultivated land quality assessment and food security in the main agricultural areas of the basin.

A dataset of litter decomposition residual mass and nutrient concentrations under nitrogen and phosphorus addition in subtropical plantations (2016–2019)

The increase in atmospheric nutrient deposition resulted from human activities and changes of natural factors have a dramatically influence on the ecosystem nutrient cycling. The subtropical plantations in the South of China account for more than 40% of the national plantation area, which have become the central zone of nutrient deposition. Therefore, the scientific understanding of nutrient cycling process in plantations is the basis of coping with global climate changes and ensuring the construction of national ecological civilization. According to the unified specification of Chinese Ecosystem Research Network, and on the basis of the long-term observation of the field plots of Qianyanzhou Ecological Research Station, Jiangxi (referred to as Qianyanzhou Station), we integrated residual mass and nutrient concentrations of leaf and absorptive roots of Pinus massoniana and Schima superba plantations under different treatments (control, lower nitrogen addition, higher nitrogen addition, phosphorus addition, adding both higher nitrogen and phosphorus) from 2016 to 2019. The production and sharing of this dataset can help to provide basic data support for the dynamic study of litter decomposition as well as for the stock of soil carbon pool under the background of global changes. Additionally, it can promote the sustainable development of subtropical plantations.

Evaluation and intercomparison of multiple satellite-derived and reanalysis downward shortwave radiation products in China

ABSTRACT Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth’s surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, their performances in different time scales have not been well-documented, particularly in China. This study intended to evaluate several DSR products across multiple time scales (i.e. instantaneous, 1-hourly, daily, and monthly average) and ecosystems in China. Six DSR products, including GLASS, BESS, CLARA-A2, MCD18A1, ERA5 and MERRA-2, were evaluated against ground measurements at Chinese Ecosystem Research Network (CERN) and integrated land-atmosphere interaction observation (TPDC) sites from 2009 to 2012. The instantaneous DSR of MCD18 showed a root mean square error (RMSE) of 146.02 W/m2. The hourly RMSE of ERA5 (155.52 W/m2) was largely smaller than MERRA-2 (188.53 W/m2). On the daily and monthly scale, BESS had the most optimized accuracy among the six products (RMSE of 36.82 W/m2). For the satellite-derived DSR products, the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization (WMO) for Global Numerical Weather Prediction (20 W/m2).

土壤碳氮特征沿干旱梯度时空格局及对气候变化的响应——基于中国生态系统研究网络(CERN)长期定位监测数据

PDF HTML阅读 XML下载 导出引用 引用提醒 土壤碳氮特征沿干旱梯度时空格局及对气候变化的响应——基于中国生态系统研究网络(CERN)长期定位监测数据 DOI: 10.5846/stxb202205311527 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院国际合作局对外合作重点项目(121311KYSB20170004);中国科学院战略性先导科技专项(XDA19020305);中国博士后科学基金面上资助项目(2020M670439) Spatio-temporal patterns of soil carbon and nitrogen characteristics along aridity gradients and their responses to climate change: Based on long-term field observation data of Chinese Ecosystem Research Network Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土壤碳氮含量及其化学计量特征是表征生态系统碳汇能力和土壤质量的重要指标,在支撑生态系统结构功能以及缓解气候变化中起着关键作用。利用中国生态系统研究网络(CERN)长期定位监测数据,分析了土壤碳氮特征沿干旱梯度的时空规律及其对气候变化的响应。结果表明:空间上,典型荒漠草原生态系统随着干旱加剧,土壤有机碳和全氮含量减少,土壤有机碳对干旱响应的敏感性降低,而土壤全氮对干旱响应的敏感性增加,土壤有机碳随土壤全氮含量的增加而增加。时间上,2005-2018年,荒漠草原生态系统土壤有机碳和全氮含量变化速率沿干旱梯度表现出由负转正的增加趋势,其中,干旱区呈减少趋势,半干旱和半湿润地区呈增加趋势,鄂尔多斯站和沙坡头站呈显著增加趋势。从影响因素来看,土壤碳氮特征对降水量增加的敏感性沿干旱梯度呈现出先增强后减弱的"上凸"抛物线趋势,温度变化对土壤碳氮特征的调控没有表现出明显的干旱梯度效应。土壤碳氮比、土壤有机碳含量、土壤全氮含量对降水量和平均温度变化响应的敏感性均依次降低。不同干旱梯度土壤碳氮特征的变化规律为未来气候变化下生态系统结构与功能预测提供科学依据。 Abstract:Soil organic carbon content (SOC), soil total nitrogen content (TN) and its ecological stoichiometric characteristics indicate the carbon sink efficiency and soil quality of ecosystem, and play the critical role in supporting ecosystem structure and function and mitigating climate change. Using long-term field observation data of ecological stations of the Chinese Ecosystem Research Network (CERN), this study analyzed the spatio-temporal patterns of the SOC, TN and its stoichiometric characteristics of soil, and their responses to climate change along aridity gradient. Results showed that with the aridity intensifying spatially, the SOC and TN decreased, the response sensitivity of SOC to aridity decreased and that of the TN increased. The SOC was increased along the TN increasing. Temporally, the rate of SOC and TN changes with aridity gradient showed increased with the aridity mitigating, which were negative in the aridity region and positive in semi-arid region from 2005 to 2018. The SOC and TN of Shapotou station and Erdos station were significant increased with aridity change in past more than one decade. Additionally, the response sensitivity of the SOC, TN and stoichiometric characteristics to precipitation increasing showed an "upwardly convex" pattern, which firstly increased and then decreased with the aridity gradient, but none for temperature. The response sensitivity of the SOC, TN and carbon-nitrogen ratio to changes in precipitation and temperature were as follows: the sensitivity of soil carbon-nitrogen ratio > the sensitivity of SOC > the sensitivity of TN. It is implied that the rule of the SOC, TN and its ecological stoichiometric characteristics changes in different aridity gradient region provide the scientific basis for the of ecosystem structure and function prediction under future climate change. 参考文献 相似文献 引证文献

Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques

Diffuse radiation is a major component of solar radiation that is important in carbon exchanges and material, energy, and information flows in agricultural ecosystems; however, measuring diffuse radiation is difficult and expensive, leaving only few stations in China that can record diffuse radiation. Therefore, five high-speed and highly accurate hybrid models were developed and compared to simulate diffuse radiation based on the aerosol optical properties and radiation parameters provided by the Aerosol Robotic Network (AERONET), Baseline Surface Radiation Network (BSRN), Wuhan University, Chinese Ecosystem Research Network (CERN), GLASS surface albedo data, and combined radiative transfer model (RTM) with machine learning (ML) models that include random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), deep neural networks (DNN), and convolutional neural network (CNN). Furthermore, the uncertainty in the simulated diffuse radiation due to the measurement uncertainties of aerosol optical properties and land surface albedo was quantified, and the relative contributions of multiple variables to diffuse radiation were analyzed. The results showed that RTM-RF was the most successful, with determination coefficients (R2) of 0.95, 0.94, and 0.98, and minimum root mean square errors (RMSE) of 9.56, 10.05, and 13.27 W m−2 at the Lulin, Wuhan, and Xianghe sites, respectively. The largest measurement uncertainty in the aerosol optical depth (AOD) was found at the Lulin site, while that of the single-scattering albedo led to the largest errors in Wuhan and Xianghe. AOD, solar zenith angle (SZA), and single-scattering albedo contributed significantly more than the asymmetry factor, land surface albedo, precipitable water vapor, and ozone. This was especially true for AOD, which was higher than 28 % at all sites. Overall, the proposed RTM-RF method exhibited superior performance, therefore we recommend it for estimating diffuse radiation in China.

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

The start of the growing season (SOS) is a vital ecological indicator for climate change and the terrestrial ecosystem. Previous studies have reported that the SOS over the Northern Hemisphere (NH) has experienced remarkable changes in the past few decades. However, because of the different spatial and temporal coverages of existing SOS studies, a coherent and robust account for SOS changes in the NH has been lacking. Using satellite-retrieved vegetation-phenology datasets, ground observations, and several auxiliary datasets, this study evaluated the performance of the latest MODIS vegetation-dynamics dataset (MCD12Q2-C6) and explored the distribution and attribution of the SOS to climate change over the NH for the period 2001–2018. The validation results using the Chinese Ecosystem Research Network (CERN) and Lilac-leafing observations (Lilac) displayed that the MCD12Q2-C6 has a good performance in SOS monitoring over the NH mid-latitudes. Meanwhile, evidence from MCD12Q2-C6 pointed out that the SOS was advanced by 2.08 days on average over the NH during 2001–2018, especially for Europe, China, and Alaska, United States. In addition, detailed-sensitivity analysis showed that the increased surface air temperature (Ts) (−1.21 ± 0.34 days °C−1) and reduced snow-cover fraction (Sc) (0.62 ± 0.29 days%−1) were the key driving factors of the observed SOS changes over the NH during 2001–2018. Compared with Ts and Sc, the role of total precipitation (Pt) was minor in dominating the spring vegetation-phenology changes at the same period. The findings of this study contribute to our understanding of the responses of SOS to the competing changes of Ts, Pt, and Sc over the NH.

Mapping clear-sky surface solar ultraviolet radiation in China at 1 km spatial resolution using Machine Learning technique and Google Earth Engine

Ultraviolet (UV) radiation is an important fundamental data for solar energy utilization, climate change, human health, photochemical reaction studies, etc. However, it is still a problem to get UV radiation estimations with high spatial resolution rapidly. This study attempted to develop a Machine Learning (ML) model to estimate clear-sky UV radiation with high accuracy and high spatial resolution (1 km) in China. Based on Moderate-resolution Imaging Spectro-radiometer (MODIS) data and ERA5 reanalysis data obtained from Google Earth Engine (GEE), we established input dataset composed of different variables and developed 29 ML models to estimate clear-sky UV radiation using 37 Chinese Ecosystem Research Network (CERN) stations measurements for model training and validation. The results showed that compared with other ML models the Deep Neural Networks (DNN) model had a high and stable performance with a determination coefficient (R2) of 0.904, a Root Mean Square Error (RMSE) of 3.100 Wm-2, a Mean Absolute Error (MAE) of 2.274 Wm-2 for 10-fold cross-validation. To realize fast estimation of online clear-sky UV radiation, the DNN model was deployed to Google Cloud Platform. The online estimation results showed that northern China had more UV radiation than southern China, and eastern China had less radiation than western China. This study would provide a useful reference for the study of solar energy resources, human health, and ecological system studies.

The effects of climate on soil microbial diversity shift after intensive agriculture in arid and semiarid regions

In arid and semiarid desert areas, climate factors distinctly impact soil microbial community, which can also be greatly altered after agricultural practices at multiple spatial scales. However, it is still poorly unknown whether the effects of climate on soil microbial diversity change after intensive agriculture at a large spatial scale. To uncover this concern, we used time-interval archived soils, taken from paired desert and agricultural experiments at five field stations of the Chinese Ecosystem Research Network across northern China, and performed high-throughput sequencing. Herein, we discovered that the clustering pattern of soil microbial communities was influenced by precipitation at some extent in desert ecosystem, while not impacted by climate factors in agricultural ecosystem. In addition, the analyses on microbial communities presented that the effects of climate factors on the communities decreased after agricultural practices. Soil microbial richness was significantly correlated with environmental temperature in deserts (R = −0.39, P < 0.001) and croplands (R = 0.34, P = 0.004), while the coefficients were opposite; the richness-precipitation relationship was significant in deserts (R = 0.63, P < 0.001) while nonsignificant in croplands (R = −0.03, P = 0.815). Moreover, for the dominant microbial groups (the top 10 phyla), the relationships between their richness and climate factors differed in two land use types, and fewer significant correlations were observed in croplands. In summary, it can be indicated that the influences of climate on soil microbial communities are shifted after intensive agriculture, and the relations of the richness with climate factors are also weakened for both the total and dominant microbial groups. These results improve our comprehension about the effects of climate on soil microbial diversity after intensive agriculture in desert areas, which can help to project microbial diversity in varied land uses under the context of global climate changes.

Evaluation of satellite land surface albedo products over China using ground-measurements

ABSTRACT Land surface albedo (LSA) is an important parameter in surface energy balance and global climate change. It has been used in the fields of energy budgets, climate dynamics, and land surface processes. To apply satellite LSA products more widely, the product accuracy needs to be evaluated at different scales and under atmospheric and surface conditions. This study validates and analyzes the errors of the LSA datasets from the Global LAnd Surface Satellites (GLASS) product, the European Space Agency’s Earth Observation Envelope Programme (GlobAlbedo), the Quality Assurance for Essential Climate Variables (QA4ECV) project, the Gap-filled Snow-free Bidirectional Reflectance Distribution Function (BRDF) parameters product (MCD43GF), and the Satellite Application Facility on Climate Monitoring (CM SAF) Albedo dataset from the AVHRR data (CLARA-SAL) against the Chinese Ecosystem Research Network (CERN) measurements at different spatiotemporal scales over China from 2005 to 2015. The results show that LSA estimated by GLASS agrees well with the CERN measurements on a continental scale. The GLASS product is characterized by a correlation coefficient of 0.80, a root-mean-square error of 0.09, and a mean absolute error of 0.06. The consistency between GLASS, GlobAlbedo, and CLARA-SAL is slightly lower over the regions with high aerosol optical depth (AOD) (e.g. Sichuan Basin, northern China) and high cloud cover compared with that in regions with lower AOD and low cloud cover. The estimation errors are related to varying atmospheric and surface conditions and increase with increasing AOD and cloud cover and decreasing enhanced vegetation index. Therefore, algorithms under complex atmospheric and surface conditions (e.g. high AOD, sparse vegetation) should be optimized to improve the accuracy of LSA products.

Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation

Chinese forests cover most of the representative forest types in the Northern Hemisphere and function as a large carbon (C) sink in the global C cycle. The availability of long-term C dynamics observations is key to evaluating and understanding C sequestration of these forests. The Chinese Ecosystem Research Network has conducted normalized and systematic monitoring of the soil-biology-atmosphere-water cycle in Chinese forests since 2000. For the first time, a reference dataset of the decadal C cycle dynamics was produced for 10 typical Chinese forests after strict quality control, including biomass, leaf area index, litterfall, soil organic C, and the corresponding meteorological data. Based on these basic but time-discrete C-cycle elements, an assimilated dataset of key C cycle parameters and time-continuous C sequestration functions was generated via model-data fusion, including C allocation, turnover, and soil, vegetation, and ecosystem C storage. These reference data could be used as a benchmark for model development, evaluation and C cycle research under global climate change for typical forests in the Northern Hemisphere.

Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model.

Accuracy analysis of a process-based model is important for evaluating the reliability of model estimates of crop growth. Uncertainties in projections of crop growth may derive from different sources in modelling. The parameter-induced uncertainty is one of the important aspects. Here we calibrated the parameters for rice, wheat and maize combined with observed data of aboveground biomass (AGB) and leaf area index (LAI) at 16 Chinese Ecosystem Research Network (CERN) sites under different rotation systems and subsequently validated the model at these sites using the data independent of calibration. The results showed that the simulated AGB and LAI exhibited good agreement with the observations. The model performance for rice and maize was better than that for wheat. The statistical analysis of model performance showed that the RMSE (root mean square error), RMD (relative mean deviation) and EF (model efficiency) were 32.52%, - 0.95% and 0.87 of the means, respectively. The three components of the modelling uncertainty, bias of mean (UM), bias of slope (UR) and random residue (UE) accounted 0.1%, 0.9% and 99% of the total errors, respectively. The main contributor to the error was the random disturbances, indicating that the parameters calibration in this study had reached relatively reasonable conditions on the whole. Although the model displayed an overall good prediction in crops AGBs and LAI, there were still notable bias at some sites due to non-random errors (UM and UR). This indicated that there were still uncertainties in the modelling procedure, e.g. the model mechanism or parameterization. The uncertainty of the simulated results may greatly restrict the application of a model. To effectively and reasonably apply a model, it is necessary to evaluate and analyse the main sources of uncertainty in the simulated results. The parameter-induced uncertainty analysis in this study showed that, at the site scale, the range of uncertainty brought by the changes in three parameters (SLA, PL and α) to the modelling results (95% CI) of Agro-C covered more than 90% of the observations and brought approximately 21% uncertainty to the simulated AGBs of the three major crops.

鼎湖山不同演替阶段森林土壤水分时空变异研究

PDF HTML阅读 XML下载 导出引用 引用提醒 鼎湖山不同演替阶段森林土壤水分时空变异研究 DOI: 10.5846/stxb201912022603 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31600353，41430529）；广东省自然科学基金项目（2016A030310450）；广东省林业科技创新平台项目（2020-KYXM-09） Temporal and spatial variability of soil moisture in a forest succession series in Dinghushan Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土壤水分作为森林生态系统水分蓄库的主体，森林土壤水分储量及其时空动态与变异对揭示区域植被恢复与气候变化背景下的森林生态系统水文过程响应与服务功能变化机制具有重要意义。本研究以南亚热带地区典型森林植被演替序列马尾松人工林（Pinus massoniana coniferous forest，PF）-马尾松针阔叶混交林（mixed Pinus massoniana/broad-leaved forest，MF）-季风常绿阔叶林（monsoon evergreen broad-leaved forest，MEBF）为研究对象，依托中国生态系统研究网络森林样地建设与监测统一规范对鼎湖山森林生态系统定位站站区内分布的上述森林类型土壤水分的长期定位观测（2005-2015年），通过分析各演替阶段森林土壤不同土层（0-15、15-30、30-45、45-60、60-75和75-90 cm）土壤体积含水量观测数据，探究该区域森林植被恢复过程中的土壤水分变化及其时空变异。结果表明：在雨热同期且干湿季明显的南亚热带地区，鼎湖山森林土壤储水量及其时间动态受降雨量的影响显著，森林土壤层对降雨具有强烈的调蓄和稳定作用，伴随PF→MF→MEBF自然演替进程，调蓄水分能力逐步增强。林型间，由初期阶段PF到顶级群落MEBF，森林土壤水分储量逐渐提高，且演替后期林型相对于早期林型，土壤储水量均呈现为较小的年际与年内变幅。干、湿季而言，干季时林型间的土壤储水量差异大于湿季，干季时MEBF和MF土壤含水量分别是PF的1.33倍和1.11倍。从土壤含水量的干、湿季期间变异来看，不同林型各土层土壤含水量的变异系数大小均表现为干季大于湿季；垂直剖面方向上，突出表现为无论干湿季MEBF各层土壤含水量变异均比其他两种林型较为缓和，充分体现了MEBF优越的土壤水分时空调配能力。整体上，伴随PF→MF→MEBF自然演替进程，土壤水分储量及其稳定性逐步提升。 Abstract:Soil moisture is the main component of water storage in forest ecosystems. The soil water reserve of forests and its spatiotemporal dynamics and variations are of great significance for revealing the response of hydrological processes and the change mechanism of service functions in forest ecosystems under the background of regional vegetation restoration and climate change. In this study, we focused on the typical forests in lower subtropical China: a Pinus massoniana forest (PF), mixed Pinus massoniana/broad-leaved forest (MF), and monsoon evergreen broad-leaved forest (MEBF), which formed a natural successional sequence in this region. Based on the unified standards for the construction and monitoring of forest plots by the Chinese Ecosystem Research Network, long-term positioning observation of soil moisture in the above forest types of the Dinghushan Forest Ecosystem Research Station was conducted during the 2005-2015 period. Through analysis of the measured soil volumetric water content in different soil layers (0-15, 15-30, 30-45, 45-60, 60-75 and 75-90 cm) of the above mentioned forests at each stage of succession, the change in soil water storage and its spatiotemporal variation in the process of forest vegetation restoration in this region were explored. The data showed that in the southern subtropical regions with simultaneous rain and heat and distinct dry and wet seasons, soil water storage and its seasonal dynamics in Dinghushan were significantly affected by rainfall, and the soil layer had a strong regulatory and stabilizing effect on rainfall. With natural succession from the planted (PF) to the climax (MEBF) stage, the ability to regulate and store water gradually increased. Among forest types, from the initial stage (PF) to the top community (MEBF), the soil water storage of the forest ecosystem gradually increased, and compared with the early forest types, the soil water storage of late succession forest types showed small interannual and intra-annual variation. At a seasonal scale, the difference in soil water storage between forest types in the dry season was greater than that in the wet season. The soil water content of MEBF and MF in the dry season was 1.33 times and 1.11 times that of PF, respectively. According to the variation in the soil water content in dry and wet seasons, the coefficient of variation of the soil water content in each soil layer of the different forest types was greater in the dry season than in the wet season. In the vertical direction, this coefficient of variation highlighted that the variation in the soil water content in each layer of MEBF was more moderate than that in the other two forest types in both the dry and wet seasons, which reflected the superior water regulation ability of MEBF. On the whole, with natural succession from the planted (PF) to the climax (MEBF) forest, the soil water storage and its stability improved. 参考文献 相似文献 引证文献