The process-based forest growth model 3-PG for use in forest management: A review

Gupta, Rajit; Sharma, Laxmi Kant

doi:10.1016/j.ecolmodel.2019.01.007

Cited by 64 publications

(46 citation statements)

References 132 publications

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“…3‐PG is a process‐based forest ecosystem model that consists of five submodels in a causal chain, starting with light absorption and assimilation, and ending with the conversion of biomass into output variables (Landsberg & Waring, ; Sands & Landsberg, ). A simple structure, readily obtainable input data, and a low number of parameters have facilitated the widespread use of 3‐PG in various forest types around the world (Gupta & Sharma, ). Initially developed for simulating evergreen, even‐aged, monospecific forests, the model has recently been further developed for deciduous, uneven‐aged, and mixed‐species forests (Forrester & Tang, ).…”

Section: Methodsmentioning

confidence: 99%

“…A simple structure, readily obtainable input data, and a low number of parameters have facilitated the widespread use of 3-PG in various forest types around the world (Gupta & Sharma, 2019). Initially developed for simulating evergreen, even-aged, monospecific forests, the model has recently been further developed for deciduous, uneven-aged, and mixed-species forests (Forrester & Tang, 2016).…”

Section: Dynamic Vegetation Modelmentioning

confidence: 99%

“…Thinning from below or above is achieved by specifying the fraction of the foliage, root, and SB of an average tree that was thinned (Landsberg, Mäkelä, Sievänen, & Kukkola, 2005). All submodels were evaluated by comparing predictions of the given process against empirical data of that process for many different forest types (Gupta & Sharma, 2019;Landsberg & Sands, 2011), including central European forests (Forrester, Ammer, et al, 2017;Nolè et al, 2009).…”

Section: Dynamic Vegetation Modelmentioning

confidence: 99%

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Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

Trotsiuk

Härtig

Cailleret

et al. 2020

Global Change Biology

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The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long‐term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3‐PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960–2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha−1 year−1 km−1 for P. abies and 0.93 ± 0.010 Mg C ha−1 year−1 km−1 for F. sylvatica). During warm–dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm‐dry extremes. Importantly, cold–dry extremes had negative impacts on regional forest NPP comparable to warm–dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

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Section: Methodsmentioning

confidence: 99%

Section: Dynamic Vegetation Modelmentioning

confidence: 99%

Section: Dynamic Vegetation Modelmentioning

confidence: 99%

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Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

Trotsiuk

Härtig

Cailleret

et al. 2020

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…Similarly, a current and potential forest biomass scenario was established for Tasmania [74]. The studies by Farine et al (2012) and Crawford et al (2016) also rely on the use of the 3-PG model [75], which is a process-based forest growth model, to estimate the theoretical biomass potential of forest biomass in the future. A similar method was applied in the Green Triangle in South Australia to assess the year-by-year biomass availability [76].…”

Section: Theoretical Biomass Potentialmentioning

confidence: 99%

A Review on the Potential of Forest Biomass for Bioenergy in Australia

et al. 2020

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The use of forest biomass for bioenergy in Australia represents only 1% of total energy production but is being recognized for having the potential to deliver low-cost and low-emission, renewable energy solutions. This review addresses the potential of forest biomass for bioenergy production in Australia relative to the amount of biomass energy measures available for production, harvest and transport, conversion, distribution and emission. Thirty-Five Australian studies on forest biomass for bioenergy are reviewed and categorized under five hierarchical terms delimiting the level of assessment on the biomass potential. Most of these studies assess the amount of biomass at a production level using measures such as the allometric volume equation and form factor assumptions linked to forest inventory data or applied in-field weighing of samples to predict the theoretical potential of forest biomass across an area or region. However, when estimating the potential of forest biomass for bioenergy production, it is essential to consider the entire supply chain that includes many limitations and reductions on the recovery of the forest biomass from production in the field to distribution to the network. This review reiterated definitions for theoretical, available, technological, economic and environmental biomass potential and identified missing links between them in the Australian literature. There is a need for further research on the forest biomass potential to explore lower cost and lowest net emission solutions as a replacement to fossil resources for energy production in Australia but methods the could provide promising solutions are available and can be applied to address this gap.

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“…Today, there are numerous spatial and non-spatial ecological models that can be used to explore effects of management actions (Keane et al, 2004;He, 2008). Moreover, many of today's modelers incorporate climate into models' design, enabling them to be used for future climate change forecasts (Canelles et al, 2019;Gupta and Sharma, 2019). Box (1979) observed that no real-world system can be exactly represented by a model, suggesting that we ask not whether any model is "true, " but rather whether it is illuminating and useful.…”

Section: Introductionmentioning

confidence: 99%

Simulation Modeling of Complex Climate, Wildfire, and Vegetation Dynamics to Address Wicked Problems in Land Management

Loehman

Keane

Holsinger

2020

Front. For. Glob. Change

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Complex, reciprocal interactions among climate, disturbance, and vegetation dramatically alter spatial landscape patterns and influence ecosystem dynamics. As climate and disturbance regimes shift, historical analogs and past empirical studies may not be entirely appropriate as templates for future management. The need for a better understanding of the potential impacts of climate changes on ecosystems is reaching a new level of urgency, especially in highly perturbed or vulnerable ecological systems. Simulation models are extremely useful tools for guiding management decisions in an era of rapid change, thus providing potential solutions for wicked problems in land management-those that are difficult to solve and inherently resistant to easily definable solutions. We identify three experimental approaches for landscape modeling that address management challenges in the context of uncertain climate futures and complex ecological interactions: (1) an historical comparative approach, (2) a future comparative approach, and (3) threshold detection. We provide examples of each approach from previously published studies of simulated climate, disturbance, and landscape dynamics in forested landscapes of the western United States, modeled with the FireBGCv2 ecosystem process model. Cumulatively, model outcomes indicate that typical land management strategies will likely not be sufficient to counteract the impacts of rapid climate change and altered disturbance regimes that threaten the stability of ecosystems. Without implementation of new, adaptive management strategies, future landscapes are very likely to be different than historical or contemporary ones, with significant and sometimes persistent changes triggered by interactions of climate and wildfire.

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The process-based forest growth model 3-PG for use in forest management: A review

Cited by 64 publications

References 132 publications

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

A Review on the Potential of Forest Biomass for Bioenergy in Australia

Simulation Modeling of Complex Climate, Wildfire, and Vegetation Dynamics to Address Wicked Problems in Land Management

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