Robust modeling and predictions of greenhouse gas fluxes from forest and wetland ecosystems
Page: 1-324
2015
- 12Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage12
- Abstract Views12
Thesis / Dissertation Description
The land-atmospheric exchanges of carbon dioxide (CO2) and methane (CH4) are major drivers of global warming and climatic changes. The greenhouse gas (GHG) fluxes indicate the dynamics and potential storage of carbon in terrestrial and wetland ecosystems. Appropriate modeling and prediction tools can provide a quantitative understanding and valuable insights into the ecosystem carbon dynamics, while aiding the development of engineering and management strategies to limit emissions of GHGs and enhance carbon sequestration. This dissertation focuses on the development of data-analytics tools and engineering models by employing a range of empirical and semi-mechanistic approaches to robustly predict ecosystem GHG fluxes at variable scales. (Abstract shortened by ProQuest.)
Bibliographic Details
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