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Grant: Alliance Grants

McMaster University

$496,830.00

Assessing the potential of enhanced forest management practices for offsetting greenhouse gas emissions using integrated multi-platform observations and carbon cycle models

FY 2024-25

Summary
Canada's forests cover ~40% or 397 Mha of the country's surface area. A large portion of these forests (~230 Mha) are managed or plantation/afforested forests, which has enormous potential to sequester and store atmospheric carbon to offset anthropogenic greenhouse gas (GHG) emissions. Developing a clear understanding of how these managed forest ecosystems function and respond to climatic change and extreme events is urgently needed to maintain or further enhance their capacity to sequester atmospheric CO2 to achieve net-zero GHG emission goals. This proposal will develop new innovative approaches by integrating next generation of sensors, data monitoring and assimilation techniques and ecosystem models to determine the carbon uptake capabilities of Canadian managed forest ecosystems of different ages, species and management regimes (harvesting intensities and spatial patterns) in Eastern Canada. For this purpose, ground based (e.g. eddy covariance, soil CO2 efflux, biometric) and airborne (e.g. active and passive drone and satellite remote sensing) observations and datasets at eight flux tower sites in Ontario and Quebec will be utilized. The proposal will further develop and apply the next generation of Canadian Land Surface Scheme (CLASSIC) and coupled hydrologic-biogeochemical model, Modélisation Environmentale communautaire Surface and Hydrology system (MESH- CLASSIC) to simulate energy, water and carbon cycles at stand, ecosystem and regional levels. Proposal will provide regional scale estimates of carbon uptake of eastern Canadian forests and determine how it may be impacted by different scenarios of management, disturbances, and climate change and associated extreme events. Quantification of the carbon uptake capabilities of Canadian managed forests and their response to climate change will help planners to develop climate adopted forest management practices to achieve net-zero GHG emission goals. Further development of the CLASSIC model will help to advance predictive capabilities of Canadian regional and global climate models and provide an assessment tool for climate change policy development.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
577190-2022
Name nom
Arain, Muhammad Altaf MAA
Department département
Earth, Environment & Society, School of
Organization id
27
Institution établissement
McMaster University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
496830
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
201
Area of application group en
Natural resources (economic aspects)
Area of application group fr
Ressources naturelles (aspects économiques)
Area of application en
Forestry (silviculture, forest management)
Area of application fr
Foresterie (sylviculture, aménagement forestier)
Research subject code
4400
Research subject group en
Atmospheric science
Research subject group fr
Étude de l'atmosphère
Research subject en
Atmospheric science
Research subject fr
Étude de l'atmosphère
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Assessing the potential of enhanced forest management practices for offsetting greenhouse gas emissions using integrated multi-platform observations and carbon cycle models
Keywords
active and passive optical sensors; canadian land surface scheme; carbon sequestration; climate change impacts; eddy covariance technique; forest management; forest water use efficiency; net ecosystem productivity; remote sensing; solar-induced fluorescence (sif)
Application summary
Canada's forests cover ~40% or 397 Mha of the country's surface area. A large portion of these forests (~230 Mha) are managed or plantation/afforested forests, which has enormous potential to sequester and store atmospheric carbon to offset anthropogenic greenhouse gas (GHG) emissions. Developing a clear understanding of how these managed forest ecosystems function and respond to climatic change and extreme events is urgently needed to maintain or further enhance their capacity to sequester atmospheric CO2 to achieve net-zero GHG emission goals. This proposal will develop new innovative approaches by integrating next generation of sensors, data monitoring and assimilation techniques and ecosystem models to determine the carbon uptake capabilities of Canadian managed forest ecosystems of different ages, species and management regimes (harvesting intensities and spatial patterns) in Eastern Canada. For this purpose, ground based (e.g. eddy covariance, soil CO2 efflux, biometric) and airborne (e.g. active and passive drone and satellite remote sensing) observations and datasets at eight flux tower sites in Ontario and Quebec will be utilized. The proposal will further develop and apply the next generation of Canadian Land Surface Scheme (CLASSIC) and coupled hydrologic-biogeochemical model, Modélisation Environmentale communautaire Surface and Hydrology system (MESH- CLASSIC) to simulate energy, water and carbon cycles at stand, ecosystem and regional levels. Proposal will provide regional scale estimates of carbon uptake of eastern Canadian forests and determine how it may be impacted by different scenarios of management, disturbances, and climate change and associated extreme events. Quantification of the carbon uptake capabilities of Canadian managed forests and their response to climate change will help planners to develop climate adopted forest management practices to achieve net-zero GHG emission goals. Further development of the CLASSIC model will help to advance predictive capabilities of Canadian regional and global climate models and provide an assessment tool for climate change policy development.