Accuracy, realism and general applicability of European forest models

Abstract

Forest models are widely used to assess the impacts of changing environmental conditions such as climate, atmospheric CO 2 concentration and nitrogen deposition on forest functioning, dynamics and structure (e.g., Reyer et al., 2013). Yet, because of our incomplete understanding of forest ecosystems and computational constraints, these models differ in the way specific processes are represented, leading to differences in their predictions (Bugmann et al., 2019;Collalti et al., 2019;Huber et al., 2021). Hence, models need to be comprehensively evaluated using different data types at different spatio-temporal scales before we can judge their structural uncertainties and suitability for answering specific questions (Marechaux et al., 2021;Oberpriller et al., 2021).Model simulations need to be in adequate agreement with independent observations. Moreover, models have to be sensitive to environmental drivers to ensure that system responses are realistically predicted under a wide range of environmental and climatic

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

“…The 3D-CMCC-FEM v.5.5 (Three Dimensional - Coupled Model Carbon Cycle - Forest Ecosystem Model; Collalti et al, 2014, 2016; Marconi et al, 2017; Engel et al, 2021; Mahnken et al, 2022) simulates daily GPP through the Farquhar-von Caemmerer-Berr photosynthesis model (Farquhar et al, 1980), modified for sun and shaded leaves (de Pury and Farquhar, 1997) and acclimation to temperature history (Kattge and Knorr, 2007). Plant respiration (R a ) is simulated explicitly and partitioned into growth (R g ) and maintenance (R m ) fractions as in the growth-and-maintenance-respiration paradigm (Amthor, 2000; McCree, 1970; Thornley, 2000).…”

“…Rotation age is the stand age at which a full harvest occurs, after which the stand is replanted with saplings of the species adopted in the Inter-Sectoral Impact Model Intercomparison Project (ISIMPI, https://www.isimip.org, Warszawski et al, 2014) protocol. The model benchmark was the BAU forest management scheme for the most common European species as described in Reyer et al (2020) and Mahnken et al (2022) and applied in three contrasting forest stands as in Collalti et al (2018).…”

“…The model was parameterized for, and simulated C fluxes and tree growth in three even-aged, long-monitored, managed European forest sites which are part of the Fluxnet network (Pastorello et al, 2020), the ISIMIP initiative and the PROFOUND database (Reyer et al, 2020; Mahnken et al 2022): (1) the temperate European beech ( Fagus sylvatica L.) forest of Sorø, Denmark; (2) the Norway spruce ( Picea abies (L.) H. Karst) stand of Bílý Křìž in Czech Republic, and (3) the boreal Scots pine ( Pinus sylvestris L.) forest of Hyytiälä, Finland (Table 1). These sites were selected because they represent the dominant forest types in Europe covering the temperate and boreal climatic zones (Figure S1),which contain more than 50% of European forest biomass (Avitabile et al, 2020), and their management best corresponds to ‘the intensive even-aged forestry’ as defined by Duncker et al (2012).…”

“…This was used to identify the most influential factor the modelled carbon cycle variables GPP, R a , NPP, carbon-use efficiency (CUE = NPP/GPP), net woody productivity (NPP woody ), and potential Carbon Woody Stocks. The model was evaluated using 1997-2005 annual GPP and NPP woody data for Sorø and Hyytiälä and 2000-2005 for Bílý Křìž by comparing simulated GPP against eddy covariance estimates (Pastorello et al, 2020), and compared modeled wood growth against measured NPP woody (Principal Investigator’s site for Hyytiälä and Bílý Křìž under personal communication , and Wu et al, 2013 for the site of Sorø) and stem diameter at breast height (DBH; Reyer et al, 2020; Mahnken et al, 2022). Subsequent years were excluded from the model evaluation since the scenario period in the ESMs started in 2006, and hence was driven by different atmospheric CO 2 concentration trajectories after 2006.…”

Calibrating a Process-based Model to Enhance Robustness in Carbon Sequestration Simulations: the Sase of Cedrus Atlantica (Endl.) Manetti ex Carrière

“…(PBMs) can compensate for the lacks in the empirical growth models and showed to be more accurate for long-term projections [15][16][17][18]. Among the most widely used PBMs in forestry literature, the Physiological Principles Predicting Growth (3-PG) model [19], uses simple equations to simulate physiological processes controlling carbon balance such as: solar radiation absorption, carbon sequestration and allocation, mortality, as well as water balance such as: canopy interception and evapotranspiration [20].…”