Michael Flechsig

Regional impacts of climatic change on forests in the state of Brandenburg, Germany

The changes of climate projected for the next century will most likely alter both the environment and the growth of forests. In a regional case study, the two forest gap models FORSKA and FORCLIM were applied to simulate vegetation composition using spatially differentiated site data on a 10 × 10-km grid across the state of Brandenburg, Northeast Germany. Three climate scenarios were used to investigate the possible consequences of a changing climate on the environmental constraints of forest growth in the state. To test the plausibility of the forest composition simulated by the two models, their results were compared with a map of potential natural vegetation as well as with each other. The simulation results show that both models respond realistically to the spatial variability of the environment and thus are suitable for regional applications. However, there are a number of quantitative differences between the simulation results of the models. FORSKAs strength is in simulating the ecological effects of the spatial variability of soil water holding capacity and nitrogen availability, whereas FORCLIM realistically portrays the climate-induced distribution limits of trees, e.g. beech (Fagus sylvatica L.). The study suggests that climatic change could have considerable consequences for future competitive relationships between species. According to the two models, the main driving force of vegetation change would be the increased occurrence of drought, which already today determines some distribution limits of tree species in Brandenburg. Under the strongest change of climate investigated in the present study, none of the species currently present on the landscape could grow any more in certain areas of Brandenburg. Conclusions are drawn concerning the importance of regional model applications for testing model performance under a wide variety of environmental conditions as well as for forest planning. Regional analyses of the impacts of climate change on forests may help to develop forest management strategies to cope with the risk of changing environmental conditions.

Scaling issues in forest succession modelling

This paper reviews scaling issues in forest succession modelling, focusing on forest gap models. Two modes of scaling are distinguished: (1) implicit scaling, i.e. taking scale-dependent features into account while developing model equations, and (2) explicit scaling, i.e. using procedures that typically involve numerical simulation to scale up the response of a local model in space and/or time. Special attention is paid to spatial upscaling methods, and downscaling is covered with respect to deriving scenarios of climatic change to drive gap models in impact assessments. When examining the equations used to represent ecological processes in forest gap models, it becomes evident that implicit scaling is relevant, but has not always been fully taken into consideration. A categorization from the literature is used to distinguish four methods for explicit upscaling of ecological models in space: (1) Lumping, (2) Direct extrapolation, (3) Extrapolation by expected value, and (4) Explicit integration. Examples from gap model studies are used to elaborate the potential and limitations of these methods, showing that upscaling to areas as large as 3000 km2 is possible, given that there are no significant disturbances such as fires or insect outbreaks at the landscape scale. Regarding temporal upscaling, we find that it is important to consider migrational lags, i.e. limited availability of propagules, if one wants to assess the transient behaviour of forests in a changing climate, specifically with respect to carbon storage and the associated feedbacks to the atmospheric CO2 content. Regarding downscaling, the ecological effects of different climate scenarios for the year 2100 were compared at a range of sites in central Europe. The derivation of the scenarios is based on (1) imposing GCM grid-cell average changes of temperature and precipitation on the local weather records; (2) a qualitative downscaling technique applied by the IPCC for central and southern Europe; and (3) statistical downscaling relating large-scale circulation patterns to local weather records. Widely different forest compositions may be obtained depending on the local climate scenario, suggesting that the downscaling issue is quite important for assessments of the ecological impacts of climatic change on forests.

Visual exploration and evaluation of climate-related simulation data

Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.

May land use change reduce the water deficiency problem caused by reduced brown coal mining in the state of Brandenburg

Abstract Surface mining alters the water regime not only locally, but also regionally. The reduced brown coal mining in the south-east of the state Brandenburg (Germany) leads to decreasing river discharge and consequently to a shortage in the water supply. Land use change is one possible option to counteract this development. In this simulation study, we explored the impact of temporary and permanent set-aside of arable land on Brandenburg’s regional water balance. Temporary and permanent set-aside were considered as major measures towards deintensification of agriculture. Simulations were performed using the regional ecohydrological model SWIM, which integrates hydrological processes, vegetation growth, erosion and nutrient dynamics. The model was used to simulate the consequences of different land use change scenarios on main components of the regional water balance. Changes in the use of arable land altered clearly its water balance. The impact of these changes on the regional water balance for Brandenburg did not exceed ±10% for its single components. Opposite tendencies were established in the simulations by contrasting effects of temporary and permanent set-aside of arable cropland. While temporary set-aside increased runoff from the whole area up to 6.7% due to lower evapotranspiration and higher soil moisture in arable land, the conversion of agricultural land within river corridors to meadows had an opposite effect on regional runoff (6.9% decrease) due to higher water retention coefficients and higher evapotranspiration losses. Therefore, only temporary set-aside may compensate to some extent for the anticipated decrease in river discharge.

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.

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.