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To develop measures to reduce the vulnerability of forests to drought, it is necessary to estimate specific water balances in sites and to estimate their development with climate change scenarios. We quantified the water balance of seven forest monitoring sites in northeast Germany for the historical time period 1961–2019, and for climate change projections for the time period 2010–2100. We used the LWF-BROOK90 hydrological model forced with historical data, and bias-adjusted data from two models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) downscaled with regional climate models under the representative concentration pathways (RCPs) 2.6 and 8.5. Site-specific monitoring data were used to give a realistic model input and to calibrate and validate the model. The results revealed significant trends (evapotranspiration, dry days (actual/potential transpiration < 0.7)) toward drier conditions within the historical time period and demonstrate the extreme conditions of 2018 and 2019. Under RCP8.5, both models simulate an increase in evapotranspiration and dry days. The response of precipitation to climate change is ambiguous, with increasing precipitation with one model. Under RCP2.6, both models do not reveal an increase in drought in 2071–2100 compared to 1990–2019. The current temperature increase fits RCP8.5 simulations, suggesting that this scenario is more realistic than RCP2.6
To cope with the challenges in forest management that are contemporarily caused by climate change, data on current chemical and physical soil properties are more and more necessary. For this purpose, we present a further amalgam of depth functions and SCORPAN modelling to provide data at arbitrary depth layers. In this concept, regionalisation is split up into the modelling of plot totals and the estimation of vertical distributions. The intended benefits by splitting up are: consistency between estimates on plot level and depth layer level, avoidance of artificial depth gradients, straightforward interpretation of covariates in the sense of pedogenetic processes, and circumnavigation of the propagation of uncertainties associated with separation between horizons during field sampling. The methodology was tailored to the circumstances within the north-eastern lowlands and the utilisation of current inventory data of the National Forest Soil Inventory (NFSI) in Brandenburg (Germany). Using the regionalisation of soil organic carbon (SOC) as an example, the application is demonstrated and discussed in detail. The depth to groundwater table and terrain parameters related to the catchment area were the main factors in SOC storage. The use of kriging did not improve the model performance. The relative depth gradients of SOC were especially distinguished by tree species composition and stand age. We suppose that interesting fields of application may be found in scenario-based modelling of SOC and when SOC serves as a basis for hydrological modelling.
An important component of the National Forest Soils Inventory (NFSI) is the investigation of inorganic and organic pollutants. Forests are able to filter out large quantities of these substances from the atmosphere and incorporate them into the soil for a long time. The aim of this study was the integrative evaluation of organic and inorganic pollutant concentrations in forest soils in the state of Brandenburg, Germany. With the help of principle component analysis, the pollutant concentrations can essentially be explained by three significant environmental components, which explain 76% of the total variance of all pollutants examined within the scope of the NFSI. The first component characterizes the extent of the atmospheric pollution caused by flue gases and fly ash from lignite combustion in the 1970s and 1980s and is mainly charged by the organic pollutants HCB and PAH, and the elements arsenic and chromium. This component shows positive relation to both spatially interpolated calcium-deposition data from the 1980s (as an indicator for the dust emission from coal combustion) and crown defoliation data of pine stands from the forest condition survey in the early 1990s. The depositions of zinc and cadmium from industrial sources, vehicle traffic and the use of fertilizers in agriculture mainly characterize the second principle component. The use of the pesticides DDT and lindane in the early 1980s and the associated pollutants input into the forest soils are expressed by the third component. In expanding the term archiving function of soils, the results illustrate their particular importance for the long-term archiving of anthropogenic inputs and the associated potential stress factors for forests.
Nowadays regional data on forest soil properties are increasingly demanded for various questions concerning forest management practices like tree species selection, liming, harvest intensity or the detection of risk areas. Thus data related to climate change and its site specific drought effects on one hand and current soil nutrient status on the other is especially required to support decision making. Besides soil texture, soil organic carbon is one of the most important key factors controlling soil nutrient status (eg. cation-exchange capacity) and soil water storage (e.g. field capacity) in the glacial deposits of north eastern lowlands. In contrast to soil texture, which can be considered as stable over periods relevant to practical forest management, for mapping soil organic carbon and its distribution throughout the soil profile a more dynamic regionalization approach considering contemporary measurements is needed. For this purpose, data on contemporary soil organic carbon stocks is taken from the second National Forest Soil Inventory and additional regional sampling points in Brandenburg (Germany). Potential covariates representing the soil forming factors: parent material, organisms (vegetation), age, relief and climate are obtained with high spatial resolution from forest site mapping, forest inventory, digital terrain analysis and climate models. The proposed regionalization approach captures the concepts of SCORPAN modelling and depth functions. While for estimation of carbon stocks in the entire soil solum stepwise regression analysis and geostatistical techniques are involved, the methodology to derive and map relative depth functions is based on cluster analysis and classification tree approach. The intended benefits by splitting regionalisation into plot level and relative vertical depth gradients are to ensure consistency of solum stocks and single soil depths and to avoid the construction of artificial depth gradients. Furthermore, the procedure allows a straightforward interpretation of covariates in the sense of pedogenetic processes, which: (a) supports variable selection and exclusion of spurious covariates during model development and (b) may provide direct decision support regarding environmental and management effects on soil organic carbon. The obtained statistical models contain covariates related to all five soil forming factors. But, the conducted analyses especially point towards high influences of depth to groundwater table and mean slope of catchment area on soil organic carbon storage. Invoking geostatistical techniques shows no remaining variation, to be explained by spatial position and thus don’t improve overall model performance. Cluster analysis of relative depth gradients results in five Clusters of acceptable heterogeneity. The highest differences between the clusters are observed across the portions of carbon stored in the forest floor. Relative depth gradients are especially distinguished by tree species composition and stand age.