Dr. Markus Didion

The objective of Task 1.3 was to analyse the NFI, ICP Forests, and LUCAS data to harmonize and improve the assessment of carbon fluxes among living biomass, deadwood, litter and soil pools. After exploration of the available data sets, the ICP Forests Level II data set was found the most suitable to set the initial modelling conditions as it is the only data set that comprises above- and below-ground compartments and contains repeated assessments on a subset of about 200 plots across Europe. This deliverable describes the pre-processing of the soil data in order to calculate the soil carbon stocks and changes over time. The pre-processing of the growth and litterfall data is included in the Deliverable D.3.3. Based on the improved data sets where the field observations on growth, litterfall and soil were available, the Yasso model will be further adopted, validated, and improved within Task 3.3.

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40'349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27'297 and 18'980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.

Deliverable 3.3 'Framework for estimating soil carbon changes and fluxes' is the foundation towards achieving consistent and comparable estimates of organic carbon (C) fluxes in deadwood, litter, and mineral soil across the diverse country-specific forest dynamics within Europe. The primary objective of Task 3.3 'Modelling carbon flux among pools' was to develop the framework for estimating soil C changes and fluxes between living biomass, dead organic matter (DOM) and soil pools. To this end the work toward Deliverable 3.3 focused on obtaining, cleaning, and analysing data relevant for the further development and accuracy evaluation of C fluxes from forest biomass to the mineral soil and the atmosphere, as well as implementing a link between the European Forest Dynamics Model and the C cycling model Yasso that are the primary modelling tools for the Pathway analysis in PathFinder. The established linked model framework can be applied in the pathway assessment dependent on the completion of Task 3.2 and in the trade-off analysis in Task 3.6.

Gegitterte Klimadaten mit einer regelmässigen räumlichen Auflösung sind eine wichtige Grundlage für Umweltstudien. Sie werden mithilfe statistischer Methoden aus Punktdaten von Messstationen erzeugt und haben insbesondere in topografisch komplexen Gebirgsregionen wie den Schweizer Alpen eine limitierte Genauigkeit. Dieser Artikel vergleicht vier für die Schweiz entwickelte gegitterte Datensätze unterschiedlicher Temperatur- und Niederschlagsvariablen mit unabhängigen Messdaten von 14 Dauerbeobachtungsflächen der Langfristigen Waldökosystemforschung (LWF) über einen Zeitraum von 20 Jahren. Die Resultate dienen als Entscheidungshilfe zur Wahl eines für eine grossflächige Anwendung zuverlässigen und homogenen Datensatzes. Die untersuchten Datensätze mit individuellen Stärken und Schwächen kommen für Anwendungen in der Schweiz infrage.

Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon (SOC) stocks are poorly quantified. Here, we assessed the SOC stocks of windthrown forests at 19 sites across Switzerland spanning an elevation gradient from 420 to 1550 m, encompassing a strong climatic gradient. Results show that the effect size of disturbance on SOC stocks increases with the size of the initial SOC stocks. The largest windthrow-induced SOC losses of up to 29 t C ha^-1 occurred in high-elevation forests with a harsh climate developing thick organic layers. In contrast, SOC stocks of low-elevation forests with thin organic layers were hardly affected. A mineralization study further revealed high elevation forests to store higher amounts of easily mineralizable C in thick organic layers that got lost following windthrow. These findings are supported by a meta-analysis of available windthrow studies, showing an increase of storm-induced SOC losses with the size of the initial SOC stocks. Modelling simulations further indicate longer-lasting SOC losses and a slower recovery of SOC stocks after windthrow at high compared to low elevations, due to a slower regeneration of mountain forests and associated lower C inputs into soils in a harsh climate. Upscaling the experimental findings/observed patterns by linking them to a data base of Swiss forest soils shows a total SOC loss of ∼0.4 Mt. C for the whole forested area of Switzerland after two major storm events, counteracting the forest net carbon sink of decades. Our study provides strong evidence that the vulnerability of SOC stocks to windthrow is particularly high in forests featuring thick and slowly forming organic layers, such as mountain soils. Thus, the risk of losing SOC to more frequent windthrows in mountain forests strongly limits their potential to mitigate climate change.

Key message: Dead wood in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. While standing and downed dead wood are typically monitored in National Forest Inventories (NFI), stumps have not received comparable attention. Based on the detailed stump inventory in the current Swiss NFI, this study demonstrates the important contribution of stumps to the dead wood pool.
Context: Dead wood (DW) in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. NFIs provide representative DW estimates focusing primarily on standing and downed DW. Little is known on stumps as a DW pool.
Aims: The aim of this study is to obtain an accurate assessment of the stump volume and biomass in the Swiss NFI to identify its significance for the DW pool, to evaluate the development over the last 30 years, and to examine the need for additional measurements for improving estimates compared to commonly applied assumptions for stump height such as a constant stump height or a fraction of tree height.
Methods: The current NFI includes a detailed stump inventory to improve accuracy and completeness of the aboveground DW pool estimate. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW pool over time.
Results: Based on the extended stump inventory in the NFI5, the contribution of stumps to the total DW pool is approximately 25%. The effect of simplifying assumptions or limited measurements to estimate stump volume can result in a significant underestimation of up to 2/3
of the more accurate and comprehensive assessment of this pool.
Conclusion: This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.

Wälder sind zunehmend von Sturmschäden betroffen. In einem Projekt der Eidgenössischen Forschungsanstalt WSL wurde untersucht, wie sich Windwürfe auf die Kohlenstoffspeicherung im Waldboden auswirken. Hoch gelegene Nadelwälder sind besonders anfällig für Kohlenstoffverluste aus dem Boden. Im Unterschied zu Wäldern tieferer Lagen werden dort grössere Mengen an Kohlenstoff in rein organischen, mächtigen Humusauflagen gespeichert. Eine Stabilisierung durch mineralische Interaktionen fehlt weitgehend, was die Humusauflagen empfindlich macht für Kohlenstoffverluste durch störungsbedingte Veränderungen des Bestandesklimas. Die langsame Baumverjüngung und die geringen Streueinträge in den Boden bremsen den Humusaufbau nach einer Störung zusätzlich. Eine weitere Zunahme von Windwürfen könnte die Bodenkohlenstoffvorräte in Bergwäldern drastisch reduzieren, was negative Auswirkungen auf das Klima hätte. Darüber hinaus könnten störungsbedingte Humusverluste die Bodenqualität nachhaltig verschlechtern.

Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon stocks are poorly known. The Swiss Federal Institute WSL assessed the soil organic carbon stocks of windthrown forests and neighbouring unaffected stands across Switzerland. The largest soil carbon losses occurred in high-elevation forests with thick organic layers. In contrast, soil organic carbon stocks of low-elevation forests with thin organic layers were hardly affected. The likely reason for this pattern is the high amount of easily mineralizable carbon in thick organic layers which got lost following windthrow. At low elevations, on the other hand, a greater carbon fraction may be stabilized by mineral interactions preventing fast decomposition. Modelling simulations further show longer-lasting soil carbon losses and a slower recovery of carbon stocks after windthrow at high elevations compared to low elevations, due to a slower regeneration of mountain forests and associated lower carbon inputs into soils. The study provides strong empirical evidence that the vulnerability of soil organic carbon stocks to windthrow is particularly high in mountain forests featuring thick and slowly forming organic layers.

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.

Introduction: Among terrestrial ecosystems, forests represent large carbon stocks threatened by changing climatic conditions, deforestation, overexploitation, and forest degradation. Close to nature forestry may help forests to continue to acting as carbon sinks by promoting their resilience against disturbances. The EU decided to carry out carbon accounting of emissions and removals from managed forests under the Paris Agreement (PA) by using a projected Forest Reference Level (FRL) based on the continuation of recent management practices.
Methods: We developed four conceptual scenarios that could build the Swiss Forest Reference Level and performed simulations over 50 years using Swiss National Forest Inventory (NFI) data and the empirical forest model MASSIMO. To improve MASSIMO, we further developed a new tree species-specific model for small scale mortality that accounts for the Swiss NFI design. Then, using projected biomass and mortality from MASSIMO, carbon budgets of mineral soil, litter, and dead wood were estimated using the Yasso07 model.
Results: The U-shaped mortality model performed well (AUC 0.7). Small as well as large trees had the highest mortality probabilities, reflecting both young trees dying due to self-thinning and old trees from age, pests or abiotic influences. All scenarios matched their given harvesting and growing stock targets, whereby the share of broadleaves increased in all regions of Switzerland. This resulted in decreasing biomass growth, possibly due to a species shift from typically fast growing and more shade tolerant conifers to broadleaves. The CO₂-balance of the conceptual scenarios ranged from 1.06 to -3.3 Mt CO₂ a^-1 under Increased Harvesting and Recent Management Practices (RMP), respectively. Rotation periods are shortened under Increased Harvesting, which is an important climate adaptive management strategy, but forests were predicted to become a net carbon source. In contrast, RMP resulted in similar harvesting amounts and forests as carbon sinks, as reported in the past. Further, the RMP scenario does not involve political assumptions and reflects the idea of the CMP approach used by the EU member states, which makes it comparable to other countries. Therefore, we propose the scenario RMP as a suitable and ideal candidate for the Swiss FRL.

La forêt suisse a une superficie de 1,32 million d'hectares et couvre presque un tiers de la surface du pays. Elle remplit de nombreuses fonctions importantes : elle protège contre les glissements de terrain, les laves torrentielles, les chutes de pierres et les avalanches, elle produit du bois, matière première renouvelable, fournit de l'eau potable, constitue l'habitat de nombreuses espèces végétales et animales, et offre aux humains un espace de détente et de récréation. Sur près de la moitié de la surface forestière, la protection contre les dangers naturels est la fonction prioritaire de la forêt, et sur près d'un tiers, c'est la production de bois. L'épicéa est de loin l'espèce la plus fréquente dans la forêt suisse, suivie du hêtre et du sapin. Le volume des arbres vifs est de 421 millions de m³ ou 350 m³/ha. Il a continué à augmenter en moyenne nationale depuis l'IFN3. De nouvelles augmentations du volume dans les Alpes et au Sud des Alpes sont compensées par de nouvelles diminutions sur le Plateau. La surface forestière a elle aussi augmenté. Les nouvelles surfaces couvrent 31 000 ha et se situent principalement dans les zones supérieures des Alpes. En revanche, sur le Plateau, la surface n'a pas changé. L'augmentation du volume et de la surface forestière suisse a aussi contribué à augmenter la prestation de puits de carbone de la forêt suisse. Si l'on tient compte des méthodes utilisables pour la récolte des bois en fonction de la topographie, on peut considérer que la moitié environ de la forêt suisse est bien desservie. Pour l'autre moitié, le degré de desserte est moyen ou faible.

Der Schweizer Wald hat eine Fläche von 1,32 Mio. ha und bedeckt knapp einen Drittel der Landesfläche. Er erfüllt viele wichtige Funktionen: Er schützt vor Rutschungen, Murgängen, Steinschlag und Lawinen, produziert den erneuerbaren Rohstoff Holz, sorgt für sauberes Trinkwasser, ist Lebensraum für viele Tier- und Pflanzenarten und bietet uns Menschen Raum für Freizeit und Erholung. Auf fast der Hälfte der Waldfläche ist der Schutz vor Naturgefahren die vorrangige Waldfunktion, auf knapp einem Drittel ist es die Holzproduktion. Die Fichte ist mit Abstand die häufigste Baumart im Schweizer Wald. Auf sie folgen die Buche und die Tanne. Der Holzvorrat der lebenden Bäume beläuft sich auf 421 Mio. m³ oder 350 m³/ ha. Er hat damit seit dem LFI3 im Landesdurchschnitt weiter zugenommen. Dabei stehen weitere Vorratszunahmen in den Alpen und auf der Alpensüdseite einer weiteren Vorratsabnahme im Mittelland gegenüber. Auch die Waldfläche hat weiter zugenommen. Die neuen Waldflächen im Umfang von 31 000 ha befinden sich dabei weitgehend in den höheren Lagen der Alpen. Im Mittelland hat sich die Waldfläche dagegen nicht verändert. Mit der Zunahme des Vorrats und der Waldfläche in der Schweiz ist auch die CO₂-Senkenleistung des Schweizer Waldes gestiegen. Unter Berücksichtigung der aufgrund der Topografie einsetzbaren Holzernteverfahren darf etwa die Hälfte des Schweizer Waldes als gut erschlossen betrachtet werden. Bei der anderen Hälfte ist der Erschliessungsgrad dagegen mässig oder schlecht.

Background: National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing st'ock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs.
Results: Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R² = 0.65, p = 0; for non-ligneous biomass: R² = 0.76; p = 0) as well as on cover classes (R² = 0.83; p = 0; and R² = 0.79, p = 0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6 ± 7.7, 575.2 ± 7.6, and 586.7 ± 7.9 kg·ha^- 1, respectively.
Conclusions: The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.

Background: Forests are an important component of the global carbon (C) cycle and can be net sources or sinks of CO₂, thus mitigating or exacerbating the effects of anthropogenic greenhouse gas emissions. While forest productivity is often inferred from national-scale yield tables or from satellite products, forest C emissions resulting from dead organic matter decay are usually simulated, therefore it is important to ensure the accuracy and reliability of a model used to simulate organic matter decay at an appropriate scale. National Forest Inventories (NFIs) provide a record of carbon pools in ecosystem components, and these measurements are essential for evaluating rates and controls of C dynamics in forest ecosystems. In this study we combine the observations from the Swiss NFIs and machine learning techniques to quantify the decay rates of the standing snags and downed logs and identify the main controls of dead wood decay.
Results: We found that wood decay rate was affected by tree species, temperature, and precipitation. Dead wood originating from Fagus sylvatica decayed the fastest, with the residence times ranging from 27 to 54 years at the warmest and coldest Swiss sites, respectively. Hardwoods at wetter sites tended to decompose faster compared to hardwoods at drier sites, with residence times 45-92 and 62-95 years for the wetter and drier sites, respectively. Dead wood originating from softwood species had the longest residence times ranging from 58 to 191 years at wetter sites and from 78 to 286 years at drier sites.
Conclusions: This study illustrates how long-term dead wood observations collected and remeasured during several NFI campaigns can be used to estimate dead wood decay parameters, as well as gain understanding about controls of dead wood dynamics. The wood decay parameters quantified in this study can be used in carbon budget models to simulate the decay dynamics of dead wood, however more measurements (e.g. of soil C dynamics at the same plots) are needed to estimate what fraction of dead wood is converted to CO₂, and what fraction is incorporated into soil.

While the Swiss NFI (NFI) delivers detailed information on the state of forest resources at the time of the field visit, data on the annual carbon (C) balance in dead organic matter (DOM) and soil are beyond the scope of the NFI. The annual C balance of DOM and soil on NFI sample plots is thus estimated with the C cycling model YASSO07.
 

Information on tree biomass and carbon (C) stock has become more relevant in the context of renewable energy production and reporting obligations, such as the Global Forest Resources Assessment of the FAO or the Greenhouse Gas Inventories under the UNFCCC and the Kyoto Protocol. Biomass and C stock cannot be measured easily in the field, and they are typically derived from existing estimates of volume or relationships to measured attributes such as tree diameter. In the Swiss NFI (NFI), whole tree biomass and its respective C content are derived separately for each individual tree element, including the aboveground elements stemwood, foliage, and large and small branches, and belowground coarse roots, based on volume estimates of stemwood and branchwood, and on foliage and root biomass correlations with tree diameter. Biomass and C stock are estimated at the individual tree level, thereby ensuring accuracy and consistency of the estimates.
 

In the Swiss National Forest Inventory (NFI), wood volume and changes in wood volume are estimated based on the stem volume of individual trees using various models: stem volume models and tariff models, models estimating volumes of large and small branches and growth models. Many of the models applied in the fourth NFI were described in previous publications, but some of them have subsequently been completed or adjusted based on methodological developments. This chapter mainly updates descriptions published previously.

In a context of global changes, modeling and predicting the dynamics of soil carbon stocks (CSs) in forest ecosystems are vital but challenging. Yasso07 is considered to be one of the most promising models for such a purpose. We examine the accuracy of its prediction of soil carbon dynamics over the whole French metropolitan territory at a decennial timescale.
We used data from 101 sites in the RENECOFOR network, which encompasses most of the French temperate forests. These data include (i) the quantity of above-ground litterfall from 1994 to 2008, measured yearly, and (ii) the soil CSs measured twice at an interval of approximately 15 years (once in the early 1990s and around 2010). We used Yasso07 to simulate the annual changes in carbon stocks (ACCs; in tC ha⁻¹ yr⁻¹) for each site and then compared the estimates with actual recorded data. We carried out meta-analyses to reveal the variability in litter biochemistry in different tree organs for conifers and broadleaves. We also performed sensitivity analyses to explore Yasso07's sensitivity to annual litter inputs and model initialization settings.
At the national level, the simulated ACCs (+0.00±0.07 tC ha⁻¹ yr⁻¹, mean ± SE) were of the same order of magnitude as the observed ones (+0.34±0.06  tC ha⁻¹ yr⁻¹). However, the correlation between predicted and measured ACCs remained weak (R²<0.1). There was significant overestimation for broadleaved stands and underestimation for coniferous sites. Sensitivity analyses showed that the final estimated CS was strongly affected by settings in the model initialization, including litter and soil carbon quantity and quality and also by simulation length. Carbon quality set with the partial steady-state assumption gave a better fit than the model with the complete steady-state assumption.
With Yasso07 as the support model, we showed that there is currently a bottleneck in soil carbon modeling and prediction due to a lack of knowledge or data on soil carbon quality and fine-root quantity in the litter.

Forest development models have been used to predict growth and yield for a long time. Yield tables, for example, have been optimised to predict the yield of pure even-aged stands on the basis of long-term observations from experimental forest plots. However, forests in Switzerland cover a broad ecological gradient and forest structures are diverse, with several even- and uneven-aged stand types that can include various species mixtures. Given such diverse forest ecosystems, we predict harvesting potentials and forest management reference levels using the Swiss National Forest Inventory-based individual-tree growth simulator MASSIMO.
 

Forest development models have been used to predict future harvesting potentials and forest management reference levels under the Kyoto guidelines. This contribution aims at presenting the individual-tree simulator MASSIMO and demonstrating its scope of applications with simulations of two possible forest management reference levels (base or business as usual) in an example application. MASSIMO is a suitable tool to predict timber harvesting potentials and forest management reference levels to assess future carbon budgets of Swiss forests. While the current version of MASSIMO accurately accounts for legacy effects and management scenarios, effects of climate and nitrogen deposition on growth, mortality, and regeneration are not yet included. In addition to including climate sensitivity, the software may be further improved by including effects of species mixture on tree growth and assessing ecosystem service provision based on indicators.

National forest inventory based growth simulators are an important tool to assess long-term consequences of forest management in many European countries. MASSIMO is the empirically-based growth simulator used in Switzerland. This individual-tree model has been developed to simulate the growth of trees using the spatial grid of the Swiss National Forest Inventory (NFI) and its repeated measurements. MASSIMO has been used at the national scale to predict timber harvesting potentials, to assess the CO2 effects of Swiss forests and their potential for carbon sequestration, especially regarding the full timber chain, and for simulating the forest management reference level (FMRL) under the Kyoto protocol. Further, MASSIMO has been used to evaluate different timber mobilisation scenarios in a mountainous landscape and to evaluate timber-mobilisation strategies and habitat-tree retention in low-elevation Swiss forests.