Climate Smart Forecasting and Robust Optimization of Forest Management System under Uncertainty

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The increasing demand for more goods and services interacts with global change and natural disturbances and, consequently, increases the pressure on forest resources. On the other hand, there is inherent uncertainty about the degree of changes in climate and the outcomes of forest system processes. Therefore, the integrity of ecosystems must be safeguarded in response to the impacts of global change and the associated risks and the uncertainties. This is crucial to provide a solid base for continued improvement in forest system management. This research proposal will establish a unique Canadian research team to pursue the following project objectives: 1)Analyze Canadian forest systems by developing state-of-the-art Climate Smart Forecasting system of forest processes equipped with the indicators of ecosystem services and simulate the expected effects of anthropogenic climate change and management interventions on forest resilience and evaluate forests contribution to the society (forest system analysis); 2)Evaluate resilience and resilience drivers of Canadian forests and their services by quantifying the uncertainty of forecasts over time regarding forest disturbances and the impacts of climate change; and 3)Develop decision-analysis approaches to find optimal and robust solutions for the adaptation of Canadian forests to uncertain future conditions and scenarios. The project applies a widely used process-based forest system model 3PG (Physiological Processes Predicting) a version developed by the applicant in Europe to predict the dynamics of ecosystem goods (e.g. timber) and services (e.g. Carbon, Water, Biodiversity) in response to detailed management interventions (thinning, fertilization, harvesting, habitat tree retention, regeneration) and global change (IPCC (Intergovernmental Panel on Climate Change) scenarios) and natural disturbances (e.g. wind, fire, drought, insects). A Bayesian inference system is specifically adopted by the applicant to simultaneously calibrate 3PG to the specific site conditions (e.g. species, management, soil, productivity) and analyze forecasting uncertainty (i.e. deep uncertainty of multiple scenarios and model-parameters' stochasticity). These uncertainties will be propagated, solely and in combination, to the final performance of forest decisions. Finally, advanced optimization techniques (genetic algorithms) will be applied to find the optimal multi-objective robust solutions. Compromise programming approaches (e.g. a regret minimization and a maximum weighted Value-at-Risk) will be applied to deal with the expectations' uncertainty of multiple objectives and risks. This research will train 3PhD, 6 MSc, and 9 BSc students to support process-based forest system decisions of Canadian forest industry to the expected and uncertain changes in Canadian forest futures e.g. in forest growth, large scale disturbances, and adopting many-objective and robust forest management strategies.