Estimating components of propagated variance in growth simulation model projections

1991 ◽  
Vol 21 (3) ◽  
pp. 379-386 ◽  
Author(s):  
H. Todd Mowrer
Keyword(s):  
Simulation Model ◽  
Taylor Series ◽  
Variance Estimation ◽  
Forest Growth ◽  
Growth And Yield ◽  
Model Estimation ◽  
Growth Simulation ◽  
First Order ◽  
Estimation Equations ◽  
Estimation Sequence

First-order Taylor series variance estimation equations were embedded in a growth simulation model to estimate propagated variances during growth and yield projections. Variance equations estimated three error components: covariances propagated through predictor variables, covariances from estimated regressor coefficients, and covariances between regressor coefficients and variables. A separate Monte Carlo process was used to estimate the total variance in projected variables caused by simultaneous perturbations in values of initialization variables and in regressor coefficients. Variances estimated by these two procedures were compared over five consecutive projection periods for six variables in a forest growth simulation model. While results agreed closely for the variance in mean stand diameter, disparities increased for other variables later in the model estimation sequence. Disparities were attributed to differences between the populations used in both variance estimation procedures and to possible violations of Taylor series assumptions in the variance estimation equations.

A Blind Spot in the Use of the Weibull Function for Modeling Diameter Distributions

2020 ◽  
Author(s):  
Adrian Norman Goodwin
Keyword(s):  
Lower Bound ◽  
Blind Spot ◽  
Forest Growth ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Absolute Difference ◽  
Weibull Function ◽  
Distribution Models ◽  
Difference Statistic ◽  
Diameter Distributions

Abstract Diameter distribution models based on probability density functions are integral to many forest growth and yield systems, where they are used to estimate product volumes within diameter classes. The three-parameter Weibull function with a constrained nonnegative lower bound is commonly used because of its flexibility and ease of fitting. This study compared Weibull and reverse Weibull functions with and without a lower bound constraint and left-hand truncation, across three large unthinned plantation cohorts in which 81% of plots had negatively skewed diameter distributions. Near-optimal lower bounds for the unconstrained Weibull function were negative for negatively skewed data, and the left-truncated Weibull using these bounds was 14.2% more accurate than the constrained Weibull, based on the Kolmogorov-Smirnov statistic. The truncated reverse Weibull fit dominant tree distributions 23.7% more accurately than the constrained Weibull, based on a mean absolute difference statistic. This work indicates that a blind spot may have developed in plantation growth modeling systems deploying constrained Weibull functions, and that left-truncation of unconstrained functions could substantially improve model accuracy for negatively skewed distributions.

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.

Ontario's forest growth and yield modelling program: Advances resulting from the Forestry Research Partnership

2008 ◽  
Vol 84 (5) ◽  
pp. 694-703 ◽  
Author(s):  
Mahadev Sharma ◽  
John Parton ◽  
Murray Woods ◽  
Peter Newton ◽  
Margaret Penner ◽  
...  
Keyword(s):  
Tree Species ◽  
Stand Density ◽  
Forest Growth ◽  
Growth And Yield ◽  
Research Partnership ◽  
Wood Supply ◽  
Permanent Sample Plots ◽  
Forestry Research ◽  
Research Activities ◽  
Forest Growth And Yield

The province of Ontario holds approximately 70.2 million hectares of forests: about 17% of Canada’s and 2% of the world’s forests. Approximately 21 million hectares are managed as commercial forests, with an annual harvest in the early part of the decade approaching 200 000 ha. Yield tables developed by Walter Plonski in the 1950s provide the basis for most wood supply calculations and growth projections in Ontario. However, due to changes in legislation, policy, and the planning process, they no longer fully meet the needs of resource managers. Furthermore, Plonski`s tables are not appropriate for the range of silvicultural options now practised in Ontario. In October 1999, the Canadian Ecology Centre- Forestry Research Partnership (CEC-FRP) was formed and initiated a series of projects that collectively aimed at characterizing, quantifying and ultimately increasing the economically available wood supply. Comprehensive, defensible, and reliable forecasts of forest growth and yield were identified as key knowledge gaps. The CEC-FRP, with support from the broader science community and forest industry, initiated several new research activities to address these needs, the results of which are outlined briefly in this paper. We describe new stand level models (e.g., benchmark yield curves, FVS Ontario, stand density management diagrams) that were developed using data collected from permanent sample plots and permanent growth plots established and remeasured during the past 5 decades. Similarly, we discuss new height–diameter equations developed for 8 major commercial tree species that specifically account for stand density. As well, we introduce a CEC-FRP-supported project aimed at developing new taper equations for plantation grown jack pine and black spruce trees established at varying densities. Furthermore, we provide an overview of various projects undertaken to explore measures of site productivity. Available growth intercept and site index equations are being evaluated and new equations are being developed for major commercial tree species as needed. We illustrate how these efforts are advancing Ontario’s growth and yield program and supporting the CEC-FRP in achieving its objective of increasing the supply of fibre by 10% in 10 years while maintaining forest sustainability. Key words: permanent sample plots (PSPs), permanent growth plots (PGPs), normal yield tables, sustainable forest management, NEBIE plot network, forest inventory, Forest Vegetation Simulator

scholarly journals How random are predictions of forest growth? The importance of weather variability

2020 ◽  
Author(s):  
Joanna Horemans ◽  
Olga Vindušková ◽  
Gaby Deckmyn
Keyword(s):  
Climate Change ◽  
Uncertainty Analysis ◽  
Climate Models ◽  
Global Climate ◽  
Basal Area ◽  
Random Order ◽  
Forest Growth ◽  
Global Climate Models ◽  
Growth And Yield ◽  
Weather Variability

Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of model studies. We perform such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focus on the effect of the inter-annual climate variability. For that, the climate years in the model input (daily resolution) were randomly shuffled within each 5-year period. In total, 540 simulations (10 parameter sets, 9 climate shuffles, 3 global climate models and 2 mitigation scenarios), were made for one growing cycle (80 years) of a Scots pine forest growing in Peitz (Germany). Our results show that, besides the important effect of the parameter set, the random order of climate years can significantly change results such as basal area and produced volume, and the response of these to climate change. We stress that the effect of weather variability should be included in the design of impact model ensembles, and the accompanying uncertainty analysis. We further suggest presenting model results as likelihoods to allow risk assessment. For example, in our study the likelihood of a decrease in basal area of >10% with no mitigation was 20.4%, while the likelihood of an increase >10% was 34.4%.

scholarly journals Generalized nonlinear height–diameter models for a <i>Cryptomeria fortunei</i> plantation in the Pingba region of Guizhou Province, China

Web Ecology ◽  
2018 ◽  
Vol 18 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Zongzheng Chai ◽  
Wei Tan ◽  
Yuanyuan Li ◽  
Lan Yan ◽  
Hongbo Yuan ◽  
...  
Keyword(s):  
Model Performance ◽  
Forest Growth ◽  
Performance Criteria ◽  
Forest Monitoring ◽  
Growth And Yield ◽  
Guizhou Province ◽  
Biomass Estimation ◽  
Multiple Model ◽  
Nonlinear Growth ◽  
The Relationship

Abstract. The relationship between height and diameter (H-D) is an important component in forest growth and yield models, and a better understanding of the relationship will improve forest monitoring, management, and biomass estimation. Sixteen nonlinear growth functions were fitted to H-D data for 1261 trees from a Cryptomeria fortunei plantation in the Pingba region of Guizhou Province, China. Of the 1261 trees, 80 % were randomly selected for model calibration, while the remaining trees were reserved for model validation. All models were evaluated and compared by means of multiple-model performance criteria. Although all 16 models showed a good fit to the dataset and each of them accounted for more than 75 % of the total variation in height, a large difference in asymptotic estimates was observed. The Chapman–Richards, Weibull, and Näslund models were recommended for C. fortunei plantations, due to their satisfactory height prediction and biological interpretability.

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