Petra Lasch-Born

Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide

Abstract• ContextProjecting changes in forest productivity in Europe is crucial for adapting forest management to changing environmental conditions.• AimsThe objective of this paper is to project forest productivity changes under different climate change scenarios at a large number of sites in Europe with a stand-scale process-based model.• MethodsWe applied the process-based forest growth model 4C at 132 typical forest sites of important European tree species in ten environmental zones using climate change scenarios from three different climate models and two different assumptions about CO2 effects on productivity.• ResultsThis paper shows that future forest productivity will be affected by climate change and that these effects depend strongly on the climate scenario used and the persistence of CO2 effects. We find that productivity increases in Northern Europe, increases or decreases in Central Europe, and decreases in Southern Europe. This geographical pattern is mirrored by the responses of the individual tree species. The productivity of Scots pine and Norway spruce, mostly located in central and northern Europe, increases while the productivity of Common beech and oak in southern regions decreases. It is important to note that we consider the physiological response to climate change excluding disturbances or management.• ConclusionsDifferent climate change scenarios and assumptions about the persistence of CO2 effects lead to uncertain projections of future forest productivity. These uncertainties need to be integrated into forest management planning and adaptation of forest management to climate change using adaptive management frameworks.

Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity

The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine (Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included.

A framework for modeling adaptive forest management and decision making under climate change

Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the “no-change,” the “reactive,” the “trend-adaptive,” and the “forward-looking adaptive” decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe.

Realizing Mitigation Efficiency of European Commercial Forests by Climate Smart Forestry

European temperate and boreal forests sequester up to 12% of Europe’s annual carbon emissions. Forest carbon density can be manipulated through management to maximize its climate mitigation potential, and fast-growing tree species may contribute the most to Climate Smart Forestry (CSF) compared to slow-growing hardwoods. This type of CSF takes into account not only forest resource potentials in sequestering carbon, but also the economic impact of regional forest products and discounts both variables over time. We used the process-based forest model 4 C to simulate European commercial forests’ growth conditions and coupled it with an optimization algorithm to simulate the implementation of CSF for 18 European countries encompassing 68.3 million ha of forest (42.4% of total EU-28 forest area). We found a European CSF policy that could sequester 7.3–11.1 billion tons of carbon, projected to be worth 103 to 141 billion euros in the 21st century. An efficient CSF policy would allocate carbon sequestration to European countries with a lower wood price, lower labor costs, high harvest costs, or a mixture thereof to increase its economic efficiency. This policy prioritized the allocation of mitigation efforts to northern, eastern and central European countries and favored fast growing conifers Picea abies and Pinus sylvestris to broadleaves Fagus sylvatica and Quercus species.

Mistletoe-induced growth reductions at the forest stand scale

The hemiparasite European mistletoe (Viscum album L.) adversely affects growth and reproduction of the host Scots pine (Pinus sylvestris L.) and in consequence may lead to tree death. Here, we aimed to estimate mistletoe-induced losses in timber yield applying the process-based forest growth model 4C. The parasite was implemented into the eco-physiological forest growth model 4C using (literature-derived) established impacts of the parasite on the trees water and carbon cycle. The amended model was validated simulating a sample forest stand in the Berlin area (Germany) comprising trees with and without mistletoe infection. At the same forest stand, tree core measurements were taken to evaluate simulated and observed growth. A subsample of trees were harvested to quantify biomass compartments of the tree canopy and to derive a growth function of the mistletoe population. The process-based simulations of the forest stand revealed 27% reduction in basal area increment (BAI) during the last 9 years of heavy infection, which was confirmed by the measurements (29% mean growth reduction). The long-term simulations of the forest stand before and during the parasite infection showed that the amended forest growth model 4C depicts well the BAI growth pattern during >100 years and also quantifies well the mistletoe-induced growth reductions in Scots pine stands.

Wald und Forstwirtschaft

In der Vergangenheit haben sich Walder an die geringen Veranderungen des am Wuchsort herrschenden Klimas angepasst. Die gegenwartige Geschwindigkeit des Klimawandels in Verbindung mit der aktuellen Verteilung der Baumarten uberfordert jedoch die naturliche Anpassung. Vegetationszonen, Verbreitungsgebiete der Baumarten und Artzusammensetzung der Walder verschieben sich. Das Kapitel charakterisiert die Folgen, die der Klimawandel fur die Walder mit sich bringt, stellt Schadfaktoren im Einzelnen vor und schildert die Auswirkungen auf die Produktivitat. Daruber hinaus wird detailliert auf die Rolle des Waldes als Kohlenstoffspeicher eingegangen, denn Walder produzieren nicht nur den nachwachsenden Rohstoff Holz, sondern sie leisten auch viel fur die Umwelt und wirken ausgleichend auf das Klima. Auch mogliche Anpassungsoptionen werden dargestellt.

Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

Abstract Process-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBACC, and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.