Quantifying and forecasting tree mortality risks and uncertainties of Canadian boreal forests under a changing climate

量化和预测气候变化下加拿大北方森林的树木死亡风险和不确定性

• 批准号: RGPIN-2019-06547
• 负责人: Peng, Changhui
• 金额: $ 4.33万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765316
• 关键词: Quantifying; forecasting; tree; mortality; risks

Climate-induced tree mortality has emerged as a global concern that is expected to increase under the projections of future climate change. Canadian boreal forests, which occupy 77% of Canada's total forest land, play a critical role in the Earth's surface albedo and the global carbon budget. Recent studies suggested that Canadian boreal forests may be highly vulnerable to rapid increases in tree mortality due to warmer temperatures and more severe droughts and defoliation by spruce budworm. Forecasting the potential impacts of increasing tree mortality on carbon budgets requires both local and regional scale. These impacts, however, have generally been ignored in large-scale carbon cycle modeling studies because data on tree mortality over large areas are expensive to collect, and therefore not widely available. There is an urgent need to develop a better scientific understanding and to quantify and forecasting the impacts of future climate change and associated insect disturbances on boreal forest ecosystems in Canada, including ecosystem productivity, tree mortality and changes in the timing and magnitude of fluxes between the land and the atmosphere. To my knowledge, no comparable studies on changes in tree mortality rates have been conducted in boreal forests using a model-data fusion (MDF) approach which integrates observed forest plots and remote sensing data with climate data and a process-based forest model in Canada. The central objective of this 5 year research program is to develop and use state-of-the-art of TRIPLEX-mortality to 1) better understand and predict forest growth, mortality and biomass carbon; and 2) quantify and reduce risks and uncertainties by using new model-data fusion approach on the loss of forest growing stock and biomass carbon due to tree mortality in the face of climate change. Our approach will provide a powerful and tractable way of incorporating tree mortality into forest models to reduce uncertainty over the fate of Canadian boreal forest ecosystems in a changing climate. The present research program will focus on the boreal forests of eastern Canada. This work will fill in our knowledge gaps in the area of the direct effects of climate change on tree mortality, biomass, and carbon balance of Canada's boreal forests. This study will pave the way for better quantifying and forecasting drought and insect impacts and better understanding of how climate change will alter Earth's forests in the 21st century. The knowledge gained from this research will be transferred to forest managers, industry partners and policy makers. This Discovery program will contribute to the training of 15 highly qualified personnel (10 undergraduates, 2 MSc, 2 PhDs and 1 post-doctorate) in the areas of forest growth, mortality, remote sensing, climate change and biomass/carbon relationships.

气候引起的树木死亡已成为全球关注的问题，根据对未来气候变化的预测，预计这一问题还会增加。加拿大北方森林占加拿大森林总面积的77%，在地球表面碳平衡和全球碳预算中发挥着关键作用。最近的研究表明，由于气温升高、更严重的干旱和云杉芽虫落叶，加拿大北方森林可能非常容易受到树木死亡率迅速增加的影响。预测树木死亡率增加对碳收支的潜在影响需要地方和区域尺度。然而，这些影响在大规模碳循环建模研究中通常被忽略，因为收集大面积树木死亡率的数据成本很高，因此无法广泛获得。现在迫切需要更好地从科学上了解未来气候变化和相关的昆虫干扰对加拿大北方森林生态系统的影响，包括生态系统生产力、树木死亡率以及土地和大气之间通量的时间和规模的变化，并对这些影响进行量化和预测。据我所知，没有可比的研究，树木死亡率的变化进行了在北方森林使用模型数据融合的方法，将观测到的森林地块和遥感数据与气候数据和过程为基础的森林模型在加拿大。这个为期5年的研究计划的中心目标是开发和使用最先进的TRIPLEX死亡率，以1）更好地了解和预测森林生长，死亡率和生物量碳;和2）通过使用新的模型数据融合方法来量化和减少风险和不确定性森林生长量和生物量碳的损失，由于面对气候变化的树木死亡率。我们的方法将提供一个强大的和易于处理的方式，将树木死亡率纳入森林模型，以减少加拿大北方森林生态系统在气候变化中的命运的不确定性。目前的研究计划将集中在加拿大东部的北方森林。这项工作将填补我们在气候变化对加拿大北方森林的树木死亡率，生物量和碳平衡的直接影响方面的知识空白。这项研究将为更好地量化和预测干旱和昆虫的影响铺平道路，并更好地了解气候变化将如何改变地球的森林在世纪。从这项研究中获得的知识将转让给森林管理人员、行业伙伴和决策者。该发现计划将有助于在森林生长，死亡率，遥感，气候变化和生物量/碳关系等领域培训15名高素质人员（10名本科生，2名硕士，2名博士和1名博士后）。