Southern Forests Water Futures
PI: Altaf Arain, McMaster University
Co-I's: Joe Boyce; San-Tae Kim; Jing Chen; Michael Pisaric, McMaster University
Forest ecosystems cover about 40% (397 Mha) of Canada's surface area and play a major role in providing sustainable water resources and healthy environments for communities in cold regions in Canada and across the world. A large portion of these forests (230 Mha) has traditionally been managed or harvested for wood production and resource extraction – practices which can impact regional water resources. Forest management has evolved from stand replacement practices to reduced disturbances such as thinning or regeneration enhancement methods. The impacts of these partial canopy disturbances on processes affecting energy, carbon and water balances are challenging to observe and quantify. In addition, climate change and extreme weather events introduce additional stresses that can impact forest growth, composition, water budgets, resilience and overall survival. In southeastern Canada, large population centres and industrial, municipal and agricultural land use activities put a strain on forest ecosystems and local water resources, which will be further complicated by future climatic changes.
The proposed work will develop and improve process-based land surface and ecosystem models (e.g. Canadian Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS-CTEM) and Boreal Ecosystems Productivity Simulator (BEPS) to more accurately account for cold region processes, through development and testing of new novel canopy conductance, evapotranspiration and ground water interflow algorithms. This modelling work will help to improve the predictive capabilities of the Canadian regional and global climate models. BEPS is incorporated in the Canadian regional (Global Environmental Multiscale Model, (GEM)) model and CLASS-CTEM is used in the Canadian Earth System Model (Can-ESM). Project data will be available to Canadian and international user communities and to the public through GWF Big-Data archives.