NDLI: Relative Linkages of Canopy-Level CO2 Fluxes with the Climatic and Environmental Variables for US Deciduous Forests

Content Provider	Springer Nature Link
Author	Ishtiaq, Khandker S. Abdul Aziz, Omar I.
Copyright Year	2015
Abstract	We used a simple, systematic data-analytics approach to determine the relative linkages of different climate and environmental variables with the canopy-level, half-hourly CO2 fluxes of US deciduous forests. Multivariate pattern recognition techniques of principal component and factor analyses were utilized to classify and group climatic, environmental, and ecological variables based on their similarity as drivers, examining their interrelation patterns at different sites. Explanatory partial least squares regression models were developed to estimate the relative linkages of CO2 fluxes with the climatic and environmental variables. Three biophysical process components adequately described the system-data variances. The ‘radiation-energy’ component had the strongest linkage with CO2 fluxes, whereas the ‘aerodynamic’ and ‘temperature-hydrology’ components were low to moderately linked with the carbon fluxes. On average, the ‘radiation-energy’ component showed 5 and 8 times stronger carbon flux linkages than that of the ‘temperature-hydrology’ and ‘aerodynamic’ components, respectively. The similarity of observed patterns among different study sites (representing gradients in climate, canopy heights and soil-formations) indicates that the findings are potentially transferable to other deciduous forests. The similarities also highlight the scope of developing parsimonious data-driven models to predict the potential sequestration of ecosystem carbon under a changing climate and environment. The presented data-analytics provides an objective, empirical foundation to obtain crucial mechanistic insights; complementing process-based model building with a warranted complexity. Model efficiency and accuracy (R 2 = 0.55–0.81; ratio of root-mean-square error to the observed standard deviations, RSR = 0.44–0.67) reiterate the usefulness of multivariate analytics models for gap-filling of instantaneous flux data.
Starting Page	943
Ending Page	960
Page Count	18
File Format	PDF
ISSN	0364152X
Journal	Environmental Management
Volume Number	55
Issue Number	4
e-ISSN	14321009
Language	English
Publisher	Springer US
Publisher Date	2015-01-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Relative linkages CO2 fluxes Data-analytics Pattern recognition Empirical modeling Deciduous forests Environmental Management Ecology Nature Conservation Atmospheric Protection/Air Quality Control/Air Pollution Forestry Management Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Ecology Pollution Global and Planetary Change

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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