7 research outputs found
Effects of Mowing on Methane Uptake in a Semiarid Grassland in Northern China
Background: Mowing is a widely adopted management practice for the semiarid steppe in China and affects CH4 exchange. However, the magnitude and the underlying mechanisms for CH 4 uptake in response to mowing remain uncertain. Methodology/Principal Findings: In two consecutive growing seasons, we measured the effect of mowing on CH 4 uptake in a steppe community. Vegetation was mowed to 2 cm (M2), 5 cm (M5), 10 cm (M10), 15 cm (M15) above soil surface, respectively, and control was set as non-mowing (NM). Compared with control, CH4 uptake was substantially enhanced at almost all the mowing treatments except for M15 plots of 2009. CH4 uptake was significantly correlated with soil microbial biomass carbon, microbial biomass nitrogen, and soil moisture. Mowing affects CH 4 uptake primarily through its effect on some biotic factors, such as net primary productivity, soil microbial C\N supply and soil microbial activities, while soil temperature and moisture were less important. Conclusions/Significance: This study found that mowing affects the fluxes of CH4 in the semiarid temperate steppe of north China
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Differentiating anthropogenic modification and precipitation-driven change on vegetation productivity on the Mongolian Plateau
Satellite-Based Analysis of Evapotranspiration and Water Balance in the Grassland Ecosystems of Dryland East Asia
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Abstract
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible