scholarly journals Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

2020 ◽  
Author(s):  
Martin Bagaram ◽  
Sándor Tóth
Keyword(s):  
Climate Change ◽  
Net Present Value ◽  
Timber Harvest ◽  
Sample Average Approximation ◽  
Expected Value ◽  
Forest Growth ◽  
Growth And Yield ◽  
Present Value ◽  
Sample Average ◽  
Average Approximation

Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. We proposed in this paper to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different climate scenarios compared to the approach using the expected growth and yield. The SAA methods requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

scholarly journals Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1230
Author(s):  
Martin B. Bagaram ◽  
Sándor F. Tóth
Keyword(s):  
Climate Change ◽  
Net Present Value ◽  
Timber Harvest ◽  
Sample Average Approximation ◽  
Expected Value ◽  
Forest Growth ◽  
Growth And Yield ◽  
Present Value ◽  
Sample Average ◽  
Average Approximation

Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different growth scenarios compared to the approach using the expected growth and yield. The SAA method requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

Accounting for climate change in a forest planning stochastic optimization model

2016 ◽  
Vol 46 (9) ◽  
pp. 1111-1121 ◽  
Author(s):  
Jordi Garcia-Gonzalo ◽  
Cristóbal Pais ◽  
Joanna Bachmatiuk ◽  
Andrés Weintraub
Keyword(s):  
Climate Change ◽  
Forest Harvesting ◽  
Forest Growth ◽  
Growth And Yield ◽  
Forest Planning ◽  
Planning Problem ◽  
Climate Scenarios ◽  
Present Value ◽  
Eucalyptus Forest ◽  
Climate Change Uncertainty

An approach is proposed for incorporating the variations in timber growth and yield due to climate change uncertainty into the forest harvesting decision process. A range of possible climate scenarios are transformed by a forest growth and yield model into tree growth scenarios, which in turn are integrated into a multistage stochastic model that determines the timber cut in each future period so as to maximize net present value over the planning horizon. For comparison purposes, a deterministic model using a single average climate scenario is also developed. The performance of the deterministic and stochastic formulations are tested in a case study of a medium-term forest planning problem for a Eucalyptus forest in Portugal where climate change is expected to severely impact production in the coming years. Experiments conducted using 32 climate scenarios demonstrate the stochastic model’s superior results in terms of present value, particularly in cases of relatively high minimum timber demand. The model should therefore be useful in supporting forest planners’ decisions under climate uncertainty.

scholarly journals Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 248
Author(s):  
Tyler Searls ◽  
James Steenberg ◽  
Xinbiao Zhu ◽  
Charles P.-A. Bourque ◽  
Fan-Rui Meng
Keyword(s):  
Climate Change ◽  
Model Validation ◽  
21St Century ◽  
Black Spruce ◽  
Abies Balsamea ◽  
Acer Rubrum ◽  
Forest Growth ◽  
Balsam Fir ◽  
Growth And Yield ◽  
Climate Projections

Models of forest growth and yield (G&Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&Y projections more robust for use under periods of climate change.

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