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Large-scale forest scenario models are intensively used to make projections of forest areas of up to hundreds of millions of hectares. Within Europe, such projections have been done for 11 countries at the individual national scale, most often to foresee the long-term implications of the ongoing forest management. However, the validity of the model...
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... other projections have been made for Finnish forests (Table 1). The MELA model (Sii- tonen and Nuutinen 1996) has been used for the projections for the European Timber Trend Studies V (Pajuoja 1995) and for the projections for the Ministry of Agriculture in Finland. ...
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Citations
... Although the above results are based on the application of a model in a projection case, those can be used to discuss reasonable time spans for future forest projections. For instance, Nabuurs et al. (2000) suggested a maximum feasible time span of 50-60 years, based on a qualitative analysis of scenarios to 2050, but parameterized by forest inventory data to the 1990s. Vauhkonen et al. (2021) considered a time span of 30 years, but argued that uncertainties related to markets, climate and management could make a maximum feasible time span for realistic projections much shorter. ...
An important modifier of forests and forestry practices is browsing by cervids. As high populations of moose (Alces alces L.) cause extensive forest damage in the Fennoscandian boreal forests, models should be able to predict the susceptibility of projected forest structures to browse damage. We augmented the European Forestry Dynamics Model (EFDM) for the area of seedling stands damaged by moose. The augmented model was tested in projecting both forest resources and moose damage for 18 million hectares of forest land in Finland, based on input data from the National Forest Inventory (NFI). Modeling the area of seedling stands damaged as a function of moose population density, forest characteristics, and region-specific interactions of these variables was found to work realistically for 30 years, predicting that the area of seedling stands damaged by moose would increase by up to a third from the last NFI observation. Our work lays the groundwork for modeling consequential, large-scale ecological and socio-economic effects of moose browsing.
... Biome-BGC, Running -Gower 1991, CENTURY, Metherall et al. 1993, 3-PG, Landsberg -Waring 1997, TEM, Tian et al. 1999 require input datasets such as leaf-area index (Running -Gower 1991), climate variables, and soil variables (McGuire et al. 2002). Empirical yield data-driven models like EFISCEN (Nabuurs et al. 2000), CO2FIX (Masera et al. 2003), or FORMICA (Böttcher et al. 2008a) require data on merchantable wood volume as a function of stand type and age. These are the same data represented in national forest inventories (NFI) and used by operational foresters in timber supply analysis and forest management planning tools (Kurz et al. 2009, Pilli et al. 2013. ...
This paper presents the DAS forest model (Distributions Applied on Stands model), a forest stand-based model suitable for projecting standing volume, increment, harvest, and carbon sequestration on the stand, regional, or country levels. The forest subcompartment is the modelling unit of the DAS model, which uses National Forestry Database (NFD) data, including geospatial data. The model is suitable for further processing spatially explicit input parameters such as climate change forecasts. The model output is also georeferenced and can be further processed using GIS software. The model handles the data of approximately 600,000 forest subcompartments. Data on tree species, origin, age, growing stock, increment etc. of each subcompartment are stored in “tree-species rows”, which are the sub-units of the model. The DAS model simultaneously processes the data of 1.2 million tree species rows and describes their development in time. It uses parameters based on the actual processes of the reference period. It also uses empiric cutting age distributions and a regeneration matrix derived from historic NFD data. The ForestLab project (TKP2021-NKTA-43) is currently engaged in the re-parametrization of the model based on 2016–2021 data. This study discusses the functions of the harvesting ratio distribution in the modelling process and in determining the subcompartments selected for harvest. The paper presents the latest results regarding the 2016–2021 cutting age distributions and the preparation of the new set of species-specific and yield class-specific average harvesting ratio distributions.
... Therefore, systems such as EFISCEN, EFDM, CBM-CFS3, and GLOBIOM /G4M were developed to inform European and national policy-and decision-making (e.g., Verkerk et al., 2019Verkerk et al., , 2011aVerkerk et al., , 2011b. Still, such models can also provide reliable country-wide estimates; for example, EFISCEN was used to calculate the sustainable harvest levels for Finland, taking into account trends promoting close-to-nature management (Nabuurs et al., 2000). ...
Forest management decisions increasingly rely on modelling tools, which help identify future risks, optimize management decisions, and provide a suite of indicators beyond timber production. Here we developed and tested a novel simulation and upscaling framework (SUF) and used it for prognosing forest resources of the Czech Republic (Central Europe), which is currently one of Europe's hotspots of disturbance intensification.
The SUF is based on an empirical forest model that simulates the development of 8 240 forest stands representing forest conditions in 206 administrative districts of the Czech Republic. The effect of natural disturbances is considered via empirical species- and age-specific mortality probability (MP) functions parameterized based on the national forest damage reports and remote sensing data. An upscaling procedure was developed to obtain district- and country-wide estimates. We tested this framework for its ability to (i) reproduce the initial forest conditions from the year 2003, (ii) reproduce forest dynamics in 2003–2016 (i.e., before the recent disturbance wave), (iii) reproduce the recent mortality pulse in 2017–2020, and (iv) generate plausible and consistent outputs under several disturbance and management settings in 2003–2050.
The SUF reliably reproduced forest dynamics in both testing periods. The country-wide growing stock (GS) simulated for 2004–2050 oscillated around the initial value of 661 mill. m³ if the reference MP and management were considered. Using the elevated MP (corresponding with the recent disturbance period) increased the mean annual mortality rate from 0.78 % to 1.19 % and caused GS to decrease by 21 % in 2050. The wave of elevated mortality lasted 16 years, ceasing in 2033 due to the depletion of vulnerable stands. Reducing the rotation length by 40 % increased the harvests temporarily and caused GS to decrease by 29 and 33 % in 2050 under reference and elevated MP, respectively. At the same time, mortality was reduced by up to 18 % due to the removal of potentially vulnerable stands.
The presented SUF is able to accommodate diverse forestry data, reproduce real forest dynamics, and generate outputs that correspond with the national forestry statistics. Flexible adoption of different mortality and management regimes makes it a versatile tool for supporting management decisions and policies. The presented simulations highlighted the negative prospects of the regional forests and the need for a profound transformation of management practices and the regional forest-based sector.
... The model was run for 100 years from 2016, even though 50 years or less (e.g. [33]) is recommended. The results for total growing stock, age distribution, and harvest income were estimated (Fig 1). ...
... observed past development, models derived from experimental data, or market forecasts. In that sense all simulations are wrong [39], and therefore the results should be interpreted against the model limitations [33]. ...
Scenario tools are widely used to support policymaking and strategic planning. Loss of biodiversity, climate change, and increase in biomass demand ways to project future forest resources considering e.g. various protection schemes, alterations to forest management, and potential threats like pests, wind, and drought. The European Forestry Dynamics Model (EFDM) is an area-based matrix model that can combine all these aspects in a scenario, simulating large-scale impacts. The inputs to the EFDM are the initial forest state and models for management activities such as thinning, felling or other silvicultural treatments. The results can be converted into user-defined outputs like wood volumes, the extent of old forests, dead wood, carbon, or harvest income. We present here a new implementation of the EFDM as an open-source R package. This new implementation enables the development of more complex scenarios than before, including transitions from even-aged forestry to continuous cover forestry, and changes in land use or tree species. Combined with a faster execution speed, the EFDM can now be used as a building block in optimization systems. The new user interface makes the EFDM more approachable and usable, and it can be combined with other models to study the impact of climate change, for example.
... In addition, processbased models are usually complex and require detailed measurements of leaf area index, climate variables, and soil variables [10], which are unavailable for many areas [12,20]. On the other hand, empirical models, such as EFISCEN [21], CO2FIX [22], FORMICA [23], and CBM-CFS3 [23], consider C dynamics in various forest C pools while incorporating the impacts of disturbances and forest management into forest C simulations, thus providing detailed C budget information and decision support for the scientific management of forest ecosystems. Moreover, empirical models are better suited than processbased models for using data collected from small-scale investigations in plots in the field or from large-scale surveys at the regional and national levels [20]. ...
Background:
Countries seeking to mitigate climate change through forests require suitable modelling approaches to predict carbon (C) budget dynamics in forests and their responses to disturbance and management. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a feasible and comprehensive tool for simulating forest C stock dynamics across broad levels, but discrepancies remain to be addressed in China. Taking Guizhou as the case study, we customised the CBM-CFS3 model according to China's context, including the modification of aboveground biomass C stock algorithm, addition of C budget accounting for bamboo forests, economic forests, and shrub forests, improvement of non-forest land belowground slow dead organic matter (DOM) pool initialisation, and other model settings.
Results:
The adequate linear relationship between the estimated and measured C densities (R2 = 0.967, P < 0.0001, slope = 0.904) in the model validation demonstrated the high accuracy and reliability of our customised model. We further simulated the spatiotemporal dynamics of forest C stocks and disturbance impacts in Guizhou for the period 1990-2016 using our customised model. Results shows that the total ecosystem C stock and C density, and C stocks in biomass, litter, dead wood, and soil in Guizhou increased continuously and significantly, while the soil C density decreased over the whole period, which could be attributed to deforestation history and climate change. The total ecosystem C stock increased from 1220 Tg C in 1990 to 1684 Tg C in 2016 at a rate of 18 Tg C yr-1, with significant enhancement in most areas, especially in the south and northwest. The total decrease in ecosystem C stock and C expenditure caused by disturbances reached 97.6 Tg C and 120.9 Tg C, respectively, but both represented significant decreasing trends owing to the decline of disturbed forest area during 1990-2016. Regeneration logging, deforestation for agriculture, and harvest logging caused the largest C stock decrease and C expenditure, while afforestation and natural expansion of forest contributed the largest increases in C stock.
Conclusions:
The forests in Guizhou were a net carbon sink under large-scale afforestation throughout the study period; Our customised CBM-CFS3 model can serve as a more effective and accurate method for estimating forest C stock and disturbance impacts in China and further enlightens model customisation to other areas.
... For the forest sector, the economic development in such future scenarios are converted to demands for forest products with models such as the global forest sector model EFI-GTM (Kallio et al. 2004;Moiseyev et al. 2011). This can be complemented with forecasts on the availability of harvestable forest resources in different areas with models such as European Forest Information Scenario model EFISCEN (Nabuurs et al. 2000;Schelhaas et al. 2007;Verkerk et al. 2016b). Now we have a reference future to work with, so that when we next create the alternative containing our ex-ante question, we can compare the effect of just the question, by eliminating background noise caused by it sitting in the future. ...
... On the national level there are tools such as the forest management planning system MELA (Kilkki et al. 1977;Siitonen 1993), which has been applied for regional case studies with ToSIA (Haatanen et al. 2014;den Herder et al. 2017). The European Forest Information Scenario Model (EFISCEN) produces projections of forest resource development for many European countries in a harmonized manner (Nabuurs et al. 2000;Schelhaas et al. 2007;Verkerk et al. 2016b). This information in spatial availability of forest resources (Verkerk et al. 2011(Verkerk et al. , 2015 can be used to initialize value chains in ToSIA based on actual or projected forest resources (Hengeveld et al. 2016;Verkerk et al. 2016a). ...
Decision making for sustainable development calls for scientific support in anticipating the possible consequences of decision alternatives and identifying the trade-offs between these alternatives. At the EU level, there has been a consistent movement toward the utilization of Sustainability Impact Assessments (SIA). First, the EU Strategy for Sustainable Development voiced the need to look at how EU policies contribute to sustainable development. Next, the European Commission committed to perform impact assessments of all proposed major initiatives. SIA can be used to study how factors such as policy, management, or technology development affect the sustainability of a sector or value chain and helps to inform decision makers about consequences of decision alternatives.
The Tool for Sustainability Impact Assessment (ToSIA) was developed to achieve a holistic assessment method for structuring sustainability questions as value chains of interlinked processes that enable evaluating the impacts of changes in these chains. To evaluate these changes, indicators of ecological, economic and social sustainability are utilised to describe different sustainability dimensions. Selecting the preferred alternative within these calculated differences in sustainability indicators may imply trade-offs and is enabled for example by the multi criteria analysis appended on top of ToSIA. The use of ToSIA is demonstrated through its application in numerous case studies conducted by various organizations and scholars.
This thesis presents the developed ToSIA from a methodological point of view, describing how the method is used. ToSIA is the first software implementation of a method that combines material flow based value chain analysis with indicators of different sustainability dimensions and harmonized system boundaries. ToSIA is a valid tool for evaluating consequences of the difficult decisions ahead that need to be made as we strive to enact a transition both to a 1.5 degree warming future, as well as a more sustainable humankind.
... Cette étude permet de conclure que le modèle paysage hybride PICUS est celui repoduisant le mieux la productivité du domaine d'étude. -Une quatrième approche consiste à tester un modèle à grande échelle sur un autre domaine d'étude que celui qui a permis de le calibrer, pour en tester la généricité (ex : Nabuurs et al., 2000 ;Thürig & Schelhaas, 2006 ;Pilli et al., 2013). L'exemple le plus récent d'extrapolation de modèle est celui du modèle CBM-CFS3 (Kurz et al., 2009), développé au Canada, et testé sur les forêts européennes sous l'impulsion du Joint Research Center (JRC) en 2009 (Barreiro et al., 2017). ...
Contexte. Depuis la révolution industrielle, les forêts européennes connaissent une dynamique d’expansion de leur surface et de leur stock de bois. Cette expansion, conjuguée au changement climatique, entraîne des modifications des processus de dynamique forestière. L’émergence de la bioéconomie européenne augure dans ce contexte d’évolutions des stratégies de gestion forestière à l’échelle européenne et nationale. La simulation des ressources forestières futures et de leur pilotage par des modèles à grande échelle spatiale est donc indispensable pour fournir des outils de planification stratégique. En France, les ressources forestières se caractérisent par une diversité marquée par rapport à d’autres pays européens. Le modèle de dynamique forestière MARGOT (MAtrix model of forest Resource Growth and dynamics On the Territory scale), a été mis en place par l’inventaire forestier national (IFN) en 1993 pour simuler les ressources forestières françaises à partir des données de cet inventaire, mais n’a été l’objet que de travaux de recherche restreints depuis son origine. Ses simulations restent limitées à un horizon temporel de moyen terme (inférieur à 30 ans), sous des scénarios de gestion de type business as usual, et ne tenant pas compte des contextes forestiers et environnementaux non-stationnaires.Objectifs. Cette thèse a pour ambition générale de consacrer un effort de recherche de rupture sur le modèle MARGOT, afin d’aborder les enjeux forestiers actuels. Les objectifs précis sont : i) de déterminer la capacité du modèle MARGOT à restituer l’expansion forestière française sur une période rétrospective longue (1971-2016), ii) de prendre en compte de façon synthétique de l’hétérogénéité des forêts à grande échelle, iii) de prendre en compte le phénomène de densification des forêts dans la dynamique démographique, iv) d’inclure les forçages climatiques externes dans la dynamique de croissance des forêts, v) dans un contexte devenu très incertain, de pouvoir mesurer le niveau d’incertitude des simulations résultant de l’erreur d’échantillonnage de l’inventaire forestier au regard des évolutions tendancielles considérées. Le développement de scénarios de gestion forestière reste hors du champ de ce travail. Principaux résultats. Une méthode générique de partition des forêts selon leur hétérogénéité géographique et compositionnelle a été mise en place, avec une vocation applicative à d’autres contextes forestiers européens. Une méthode de propagation de l’incertitude d’échantillonnage aux paramètres du modèle, puis aux simulations, a été développée à partir d’approches de ré-échantillonnage de données et de modélisation d’erreurs. Une approche originale d’intégration des phénomènes de densité-dépendance démographique, fondée sur une métrique de densité et la réintroduction d’un concept de « peuplement forestier » adapté à ce modèle, a été développée. Une stratégie d’intégration des forçages climatiques des paramètres démographiques du modèle a été développée à partir d’une approche d’hybridation entrées-sorties avec le modèle fonctionnel CASTANEA pour un sous-ensemble de la forêt française incluant les espèces de chênes, de hêtre, d’épicéa commun, et de pin sylvestre. L’ensemble de ces développements a permis de réduire très notablement le biais de prédiction du modèle initial. Conclusions. Les développements consentis font du modèle MARGOT un outil d’exploration et de planification plus fiable des ressources forestières, et reposant sur une approche de modélisation originale et unique en Europe. L’utilisation de statistiques forestières anciennes permettra d’évaluer le modèle et de simuler le stock de carbone de la forêt française sur un horizon temporel plus importante (de plus de 100 ans). Une évaluation approfondie des performances de ce nouveau modèle par des simulations intensives doit être conduite.
... EFISCEN (European Forest Information Scenario Model, version EFISCEN 4.2.0) is a detailed forest resource model (wood stocks, increment, harvests) based on about 5000 forest types in Europe, while allowing new data and parameters to be incorporated. It depicts forest areas at regional scale (e.g., NUTS-2) in terms of age classes, growing stocks and increment, using data obtained from the latest available national forest inventory data [22][23][24][25][26][27]. It has been used to investigate the impacts of forest-management changes, biomass availability and carbon balances [24]. ...
Background
Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices.
Results
The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m ³ ha ⁻¹ , while the carbon stock in tree biomass will reach 76.5 tC ha ⁻¹ . The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060.
Conclusions
This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use.
... For scenario analyses, the multiple unknown factors that affect the future outcomes should be included in the assumptions of the projections [13][14][15]. For instance, Nabuurs et al. [16] suggest 50-60 years as a maximum feasible time span for the projections. However, this conclusion is based on reproducing historical development of 1923-1963 and qualitatively analyzing scenarios run until 2050 but parameterized by forest inventory data until 1990's. ...
Background: The current EU LULUCF regulation calls for member state-specific Forest Reference Levels (FRLs) for benchmark in the accounting of greenhouse gas emissions and removals of managed forest land during the compliance period (2021–2030). According to the technical guidance on developing and reporting the FRLs, it could be actualized by projecting a ratio of harvested to total available biomass. We tested how the initial age distribution may affect the aforementioned ratio by simulating the continuation of forest management based on several descriptive shapes of forest age class distribution.
Results: Our simulations suggest that when the FRLs are prepared by employing the harvest ratio and forest management is assumed strictly age dynamics driven, the shape of the initial forest age class distribution gives rise to computational sinks or sources of carbon in managed forest land. Harvests projected according to the ratio corresponded those resulting from the age dynamics only in the case of uniform age distribution.
Conclusions: The result calls for a better consideration of variation in initial states between countries when determining the future LULUCF regulation. Our exercise demonstrates how generic simulations in a standardized modeling framework could help in ex-ante impact assessment of proposed changes to the LULUCF regulation.
Keywords:
Land use, land use change and forest; Biomass available for wood supply; Harvest Fraction of Management; Harvesting intensity; European Forestry Dynamics Model (EFDM); Chapman-Richards function; Growth and yield model; Simulation; Projection; Modelling
... However, it is a challenging task for the researchers of different works of life to build an appropriate global forest C model because of diverse tree species composition, soil resources, geography, and climatic conditions. Literature revealed that forest carbon dynamics empirical models such as CBM-CFS3 (Kurz et al., 2009), CO2FIX (Masera et al., 2003), CASMOFOR (Kim et al., 2015), and EFISCEN (Nabuurs et al., 2000) are frequently used in forestry and agroforestry systems, out of which CO2FIX model is stated to be good over other models because of giving precised results and easily assessable. Literature reported that there are several studies which reported carbon storage potential of forest and agroforestry system under different land-use systems in India (Lal & Singh, 2000;Haripriya, 2001;Swamy et al., 2003;Pala et al., 2012;Banday et al., 2018;Rana et al., 2020) but either very less or no study has been reported for the agroforestry system of waterlogged areas under per-humid climate. ...
The present study reports the potential of carbon (C) storage in traditional agroforestry
systems (i.e., a set of age-old agroforestry systems) under waterlogged environmental conditions from north-eastern India. An experiment was conducted in a farmer’s field and further used CO2FIX model, allometric equations, and destructive sampling methods to know the potential of C sequestration. In this study area, agroforestry system is dominated by woody perennials like Areca catechu, Cocos nucifera, Mangifera indica, Artocarpus heterophyllus, Melocanna baccifera, and Colocasia esculenta as annual crop component. Need-based management of the drainage system has been built-up by making broad/narrow bunds for maintaining water levels at different stages of plant growth. The total annual carbon storage potential of this traditional agroforestry system was estimated as 103.760±8.630 t ha-1 yr-1. The highest annual carbon storage potential (97.900±8.090 t ha-1 yr-1) was recorded in annual crop components (i.e., Colocasia) followed by trees & its underlaid soil (4.250±0.340 t ha-1 yr-1) and lowest for bamboos (1.610± 0. 200 t ha-1 yr-1). However the estimated carbon stored, annually, was 24.992±1.502 t ha-1 yr-1 in which Colocasia share maximum contribution (19.600±1.080 t ha-1 yr-1) followed by trees + soil (3.798±0.229 t ha-1 yr-1) and the minimum contribution from bamboos (1.594±0.193 t ha-1 yr-1). Moreover, total carbon loss from harvesting of this system was 78.768±7.128 t ha-1 yr-1. The study, therefore, recommends this
agroforestry system for other waterlogged ecosystems at regional and/or global scale under a warm per humid climate for both livelihood opportunities and environmental sustainability.
