Javascript must be enabled to continue!
Carbon Dioxide Emissions from the Littoral Zone of a Chinese Reservoir
View through CrossRef
The continuous increase in the number of reservoirs globally has raised important questions about the environmental impact of their greenhouse gases emissions. In particular, the littoral zone may be a hotspot for production of greenhouse gases. We investigated the spatiotemporal variation of CO2 flux at the littoral zone of a Chinese reservoir along a wet-to-dry transect from permanently flooded land, seasonally flooded land to non-flooded dry land, using the static dark chamber technique. The mean total CO2 emission was 346 mg m−2 h−1 and the rate varied significantly by water levels, months and time of day. The spatiotemporal variation of flux was highly correlated with biomass, temperature and water level. Flooding could play a positive role in carbon balance if water recession occurs at the time when carbon gains associated with plant growth overcomes the carbon loss of ecosystem. The overall carbon balance was analysed using cumulative greenhouse gases fluxes and biomass, bringing the data of the present study alongside previously published, simultaneously measured CH4 and N2O fluxes. For the growing season, 12.8 g C m−2 was absorbed by the littoral zone. Taking CH4 and N2O into the calculation showed that permanently flooded sites were a source of greenhouse gases, rather than a sink. Our study emphasises how water level fluctuation influenced CO2, CH4 and N2O in different ways, which greatly affected the spatiotemporal variation and emission rate of greenhouse gases from the littoral zone.
Title: Carbon Dioxide Emissions from the Littoral Zone of a Chinese Reservoir
Description:
The continuous increase in the number of reservoirs globally has raised important questions about the environmental impact of their greenhouse gases emissions.
In particular, the littoral zone may be a hotspot for production of greenhouse gases.
We investigated the spatiotemporal variation of CO2 flux at the littoral zone of a Chinese reservoir along a wet-to-dry transect from permanently flooded land, seasonally flooded land to non-flooded dry land, using the static dark chamber technique.
The mean total CO2 emission was 346 mg m−2 h−1 and the rate varied significantly by water levels, months and time of day.
The spatiotemporal variation of flux was highly correlated with biomass, temperature and water level.
Flooding could play a positive role in carbon balance if water recession occurs at the time when carbon gains associated with plant growth overcomes the carbon loss of ecosystem.
The overall carbon balance was analysed using cumulative greenhouse gases fluxes and biomass, bringing the data of the present study alongside previously published, simultaneously measured CH4 and N2O fluxes.
For the growing season, 12.
8 g C m−2 was absorbed by the littoral zone.
Taking CH4 and N2O into the calculation showed that permanently flooded sites were a source of greenhouse gases, rather than a sink.
Our study emphasises how water level fluctuation influenced CO2, CH4 and N2O in different ways, which greatly affected the spatiotemporal variation and emission rate of greenhouse gases from the littoral zone.
Related Results
Research on the Win-win Performance of Carbon Dioxide Emissions Reduction and High-quality Development under the “Dual-carbon” Goal in China
Research on the Win-win Performance of Carbon Dioxide Emissions Reduction and High-quality Development under the “Dual-carbon” Goal in China
Abstract
As the country with the largest total and incremental carbon dioxide emissions in the world, China is under increasing international pressure to reduce carbon diox...
Modeling Climate Impacts of Hydrogen Transition Pathways
Modeling Climate Impacts of Hydrogen Transition Pathways
Hydrogen has emerged as a key contender for decarbonizing hard-to-abate sectors, as it has the advantage of emitting no direct carbon dioxide emissions during combustion. However, ...
Prediction of Carbon Emissions in Guizhou Province-Based on Different Neural Network Models
Prediction of Carbon Emissions in Guizhou Province-Based on Different Neural Network Models
Abstract
Global warming caused by greenhouse gas emissions has become a major challenge facing people all over the world. The study of regional human activities and...
Optimization and Design of Carbon Dioxide Flooding
Optimization and Design of Carbon Dioxide Flooding
Abstract
Increasing energy demand coupled with public concern for the environment has placed the oil industry in an awkward position as profit-making energy provider...
Genetic-Like Modelling of Hydrothermal Dolomite Reservoir Constrained by Dynamic Data
Genetic-Like Modelling of Hydrothermal Dolomite Reservoir Constrained by Dynamic Data
This reference is for an abstract only. A full paper was not submitted for this conference.
Abstract
Descr...
Effects of the rate of carbon dioxide injection at the initial gas-water contact on the recovery factor
Effects of the rate of carbon dioxide injection at the initial gas-water contact on the recovery factor
The process of carbon dioxide injection into the initial gas-water contact with different rates of its injection, using a 3D model of a gas condensate reservoir, has been investiga...
Peat forest disturbances in tropical regions: direct drivers and GHG emissions
Peat forest disturbances in tropical regions: direct drivers and GHG emissions
We estimated and compared driver-specific GHG (CO₂, CH₄, and N₂O) emissions from biomass and peat soil carbon loss caused by peat forest disturbances ...
Granite Reservoir Prediction Based on Amplitude Spectrum Gradient Attribute Post-Stack Cube Attribute and Pre-Stack Fracture Prediction with Wide Azimuth Seismic Data
Granite Reservoir Prediction Based on Amplitude Spectrum Gradient Attribute Post-Stack Cube Attribute and Pre-Stack Fracture Prediction with Wide Azimuth Seismic Data
Abstract
Granite "buried hill" oil pool is an unconventional oil pool which can be formed a large and highly effective oilfield in some basins such as Bach Ho oilfie...

