Productivity of Fagus sylvatica under climate change – A Bayesian analysis of risk and uncertainty using the model 3-PG

Augustynczik, Andrey Lessa Derci; Härtig, Florian; Minunno, Francesco; Kahle, Hans‐Peter; Diaconu, Daniela; Hanewinkel, Marc; Yousefpour, Rasoul

doi:10.1016/j.foreco.2017.06.061

Cited by 41 publications

(38 citation statements)

References 94 publications

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“…Our study demonstrates that it is possible to integrate monitoring data from multiple networks across a wide bioclimatic gradient into a process‐based forest ecosystem model 3‐PG. The resulting uncertainty in the parameter estimates was relatively low (Tables S3 and S4) and comparable to other studies that calibrated the 3‐PG model (Augustynczik et al, ; Thomas et al, ). Not surprisingly, the monitoring data were most informative for constraining parameters that are directly related to stand structure.…”

Section: Discussionsupporting

confidence: 82%

“…We assumed uniform (i.e., non-informative) prior distributions for each of the 54 model parameters. The ranges of the priors (Tables S3 and S4) were set to the minimum (maximum) value found in the literature minus (or plus) half of the range for this parameter (following Augustynczik et al, 2017). The likelihood function was constructed to be robust against outliers by modeling the residual error as a Student's t distribution with sampled degrees of freedom (see Code S1; Lange, Little, & Taylor, 1989).…”

Section: Parameter Estimationmentioning

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: Discussionsupporting

confidence: 82%

Section: Parameter Estimationmentioning

confidence: 99%

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

Trotsiuk

Härtig

Cailleret

et al. 2020

Global Change Biology

Self Cite

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“…() approach, which is similar to others (Augustynczik et al. ), is not linked to the model time step and does not increase through a simulation (Fig. b).…”

Section: Methodsmentioning

confidence: 55%

“…3a ;Clark 2007), thus explicitly linking the process uncertainty term with the time step of the model (i.e., the standard deviation, r add , represents the process error that is added each month of a monthly time step model). In contrast, the Thomas et al (2017) approach, which is similar to others (Augustynczik et al 2017), is not linked to the model time step and does not increase through a simulation (Fig. 3b).…”

Section: Overview Of Data Assimilation Approachmentioning

confidence: 77%

A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity

Thomas

Jersild

Brooks

et al. 2018

Ecological Applications

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Ecological forecasting of forest productivity involves integrating observations into a process‐based model and propagating the dominant components of uncertainty to generate probability distributions for future states and fluxes. Here, we develop a forecast for the biomass change in loblolly pine (Pinus taeda) forests of the southeastern United States and evaluate the relative contribution of different forms of uncertainty to the total forecast uncertainty. Specifically, we assimilated observations of carbon and flux stocks and fluxes from sites across the region, including global change experiments, into a forest ecosystem model to calibrate the parameter distributions and estimate the process uncertainty (i.e., model structure uncertainty revealed in the residuals of the calibration). Using this calibration, we forecasted the change in biomass within each 12‐digit Hydrologic (H12) unit across the native range of loblolly pine between 2010 and 2055 under the Representative Concentration Pathway 8.5 scenario. Averaged across the region, productivity is predicted to increase by a mean of 31% between 2010 and 2055 with an average forecast 95% quantile interval of ±15 percentage units. The largest increases were predicted in cooler locations, corresponding to the largest projected changes in temperature. The forecasted mean change varied considerably among the H12 units (3–80% productivity increase), but only units in the warmest and driest extents of the loblolly pine range had forecast distributions with probabilities of a decline in productivity that exceeded 5%. By isolating the individual components of the forecast uncertainty, we found that ecosystem model process uncertainty made the largest individual contribution. Ecosystem model parameter and climate model uncertainty had similar contributions to the overall forecast uncertainty, but with differing spatial patterns across the study region. The probabilistic framework developed here could be modified to include additional sources of uncertainty, including changes due to fire, insects, and pests: processes that would result in lower productivity changes than forecasted here. Overall, this study presents an ecological forecast at the ecosystem management scale so that land managers can explicitly account for uncertainty in decision analysis. Furthermore, it highlights that future work should focus on quantifying, propagating, and reducing ecosystem model process uncertainty.

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“…The topic of uncertainty and its quantification have attracted increasing attention in recent years (Cramer et al 2001), both due to the realization that quantitative uncertainty estimates are key for sound management recommendations, and due to the increasing computing power, which makes the systematic exploration of uncertainty possible for larger models. However, while many studies have recently started to quantify parametric uncertainty (Hartig et al 2012), and some results exist on the contributions of different model sectors to overall parametric uncertainty (Augustynczik et al 2017), the quantification of structural uncertainty is less advanced. Most studies on this topic are model comparisons and multi-model projections (Warszawski et al 2014), which are helpful to explore differences between models regarding the effective variability in outputs due to structural differences.…”

Section: Introductionmentioning

confidence: 99%

Tree mortality submodels drive simulated long‐term forest dynamics: assessing 15 models from the stand to global scale

et al. 2019

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Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.

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Productivity of Fagus sylvatica under climate change – A Bayesian analysis of risk and uncertainty using the model 3-PG

Cited by 41 publications

References 94 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 mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity

Tree mortality submodels drive simulated long‐term forest dynamics: assessing 15 models from the stand to global scale

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