Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory

Minunno, Francesco; Peltoniemi, Mikko; Härkönen, Sauli; Kalliokoski, Tuomo; Mäkinen, Harri; Mäkelä, Annikki

doi:10.1016/j.foreco.2019.02.041

Cited by 49 publications

(69 citation statements)

References 69 publications

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“…Linked with downscaled global circulation model projections, PRELES has been used to predict boreal forest productivity under climate change scenarios, and its parametric uncertainty is marginal when compared with other sources of uncertainty (Kalliokoski, Mäkelä, Fronzek, Minunno, & Peltoniemi, ). Furthermore, PRELES has been linked with a process‐based carbon allocation model CROBAS (Mäkelä, ; Valentine & Mäkelä, ) in simulating forest variables with a country‐generic calibration in Finland (Minunno et al, ). Mäkelä et al () showed that daily temperature, vapour‐pressure deficit (VPD) and absorbed photosynthetic photon flux density (PPFD) accounted for most of the daily variation in GPP in the model, but unexplained variation remained in the site‐specific maximum LUE, which correlated linearly with canopy nitrogen (Peltoniemi et al, ).…”

Section: Introductionmentioning

confidence: 99%

Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

Tian

Minunno

Cao

et al. 2020

Global Change Biology

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Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semiempirical ecosystem flux model, PREdict Light-use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site-specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the sitespecific and PFT-specific parameter vectors for PRELES. A systematically designed cross-validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT-specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the sitespecific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light-use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite-driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT-specific parameterizations were applied. K E Y W O R D S evapotranspiration, geographical variations, gross primary production, inverse modelling, light-use efficiency, multisite calibration, plant functional type S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Tian X, Minunno F, Cao T, Peltoniemi M, Kalliokoski T, Mäkelä A. Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate. Glob Change

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Section: Introductionmentioning

confidence: 99%

Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

Tian

Minunno

Cao

et al. 2020

Global Change Biology

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“…Despite the fact that this method allows us to combine multiple data sources and types, most studies have focused on the local scale. Hence, an important step forward is now to use large and diverse datasets in combination with DVMs at the regional scale (Cailleret, Bircher, Hartig, Hülsmann, & Bugmann, 2019;Fer et al, 2018;Minunno, Peltoniemi, et al, 2019;Thomas et al, 2017;Van Oijen et al, 2013).…”

Section: Introductionmentioning

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

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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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“…So far, only few studies have assimilated extensive forest monitoring datasets into a DVM through techniques of parameter estimation (but see Cailleret et al, 2019;Fer et al, 2018;Minunno, Peltoniemi, et al, 2019;Thomas et al, 2017), even though recommended by several authors to improve large-scale model projections (Dietze et al, 2014;Hartig et al, 2012). 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.…”

Section: Parameter Estimationmentioning

confidence: 86%

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

Trotsiuk¹

2020

Preprint

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Under unprecedent climate change and increased frequency of extreme events, e.g. drought, it is important to assess and forecast forest ecosystem vulnerability and stability. Large volumes of data from observational and experimental networks, increases in computational power, advances in ecological models, and optimization methodologies are the main measures to improve quantitative forecasting in ecology. Data assimilation is a key tool to improve ecosystem state prediction and forecasting by combining model simulations and observations. We assimilated observations of carbon stocks and fluxes from 271 permanent long-term forest monitoring plots across Switzerland 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, respectively. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018 and tested for climate-induced shifts in productivity along elevational gradient and in extreme years. Overall, we demonstrated a high potential of using data assimilation to improve predictions of forest ecosystem productivity. Furthermore, our calibrated model simulations suggest that climate extremes affect forest productivity in more complex ways than by simply shifting the response upwards in elevation.

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Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory

Cited by 49 publications

References 69 publications

Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

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

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