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- Boden; Wald; Inventur; Probenahme; Karten; Konzentrationen; Vorräte; Kohlenstoff; Stickstoff; pH-Wert; Trockenrohdichte; Unsicherheiten; Harmonisierung; Repräsentativität (1)
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Recent studies have identified strong relationships between delayed recovery of tree growth after drought and tree mortality caused by subsequent droughts. These observations raise concerns about forest ecosystem services and post-drought growth recovery given the projected increase in drought frequency and extremes. For quantifying the impact of extreme droughts on tree radial growth, we used a network of tree-ring width data of 1689 trees from 100 sites representing most of the distribution of two drought tolerant, deciduous oak species (Quercus petraea and Quercus robur). We first examined which climatic factors and seasons control growth of the two species and if there is any latitudinal, longitudinal or elevational trend. We then quantified the relative departure from pre-drought growth during droughts, and how fast trees were able to recover the pre-drought growth level. Our results showed that growth was more related to precipitation and climatic water balance (precipitation minus potential evapotranspiration) than to temperature. However, we did not detect any clear latitudinal, longitudinal or elevational trends except a decreasing influence of summer water balance on growth of Q. petraea with latitude. Neither species was able to maintain the pre-drought growth level during droughts. However, both species showed rapid recovery or even growth compensation after summer droughts but displayed slow recovery in response to spring droughts where none of the two species was able to fully recover the pre-drought growth-level over the three post-drought years. Collectively, our results indicate that oaks which are considered resilient to extreme droughts have also shown vulnerability when droughts occurred in spring especially at sites where long-term growth is not significantly correlated with climatic factors. This improved understanding of the role of drought seasonality and climate sensitivity of sites is key to better predict trajectories of post-drought growth recovery in response to the drier climate projected for Europe.
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
Global climate change is expected to further raise the frequency and severity of extreme events, such as droughts. The effects of extreme droughts on trees are difficult to disentangle given the inherent complexity of drought events (frequency, severity, duration, and timing during the growing season). Besides, drought effects might be modulated by trees’ phenotypic variability, which is, in turn, affected by long-term local selective pressures and management legacies. Here we investigated the magnitude and the temporal changes of tree-level resilience (i.e., resistance, recovery, and resilience) to extreme droughts. Moreover, we assessed the tree-, site-, and drought-related factors and their interactions driving the tree-level resilience to extreme droughts. We used a tree-ring network of the widely distributed Scots pine (Pinus sylvestris) along a 2,800 km latitudinal gradient from southern Spain to northern Germany. We found that the resilience to extreme drought decreased in mid-elevation and low productivity sites from 1980–1999 to 2000–2011 likely due to more frequent and severe droughts in the later period. Our study showed that the impact of drought on tree-level resilience was not dependent on its latitudinal location, but rather on the type of sites trees were growing at and on their growth performances (i.e., magnitude and variability of growth) during the predrought period. We found significant interactive effects between drought duration and tree growth prior to drought, suggesting that Scots pine trees with higher magnitude and variability of growth in the long term are more vulnerable to long and severe droughts. Moreover, our results indicate that Scots pine trees that experienced more frequent droughts over the long-term were less resistant to extreme droughts. We, therefore, conclude that the physiological resilience to extreme droughts might be constrained by their growth prior to drought, and that more frequent and longer drought periods may overstrain their potential for acclimation.
The productivity of forest ecosystems depends on the supply of both nutrients and water by soils. Therefore, the knowledge of the chemical and physical soil properties is crucial for assessing the soil
condition as well as the quality of forest sites. For this purpose, data are available from two nationwide Forest Soil Inventories (NFSI) on more than 1,800 sample plots of an 8 x 8 km grid. The inventories were conducted between 1986 and 1994 (NFSI I) and 2006 and 2009 (NFSI II) in accordance with harmonized surveying techniques. The LUCAS Project aims at the development of a harmonized dataset on land cover
and land use within the European Union (EU). Approximately 265.000 georeferenced plots on a 2 x 2 km grid were available from the European Soil Data Centre (ESDAC). Soil samples were taken in 2009 and in 2015 at approximately 10 % of these plots at the same locations. Considering two almost parallel running soil inventories, questions have arisen to their potential links to one another. In this study, the LUCAS soil data from the 2015 inventory were compared with the corresponding NFSI II data and reviewed under the aspect of representativeness. In order to enable an area weighted estimation for Germany the plots of the LUCAS program were attributed to the same 16 soil parent material groups used in the German NFSI data
using geographical coordinates. All classes were tested for significant and systematic differences between the inventories. To quantify C stocks, it is important to compare the depth level 0-20 cm of the LUCAS program to Germanies NFSI sampling scheme, where data available for the organic layer and the mineral soil down to 90 cm. The comparison of various chemical soil parameters showed clear differences between the inventories. The pH(H2O) and the C/N ratio derived from LUCAS 2015 was lower while carbon (C) and nitrogen (N) concentrations were higher compared to the NFSI II. The C and N stocks deviate even more from the NFSI II due to uncertainties in the estimation of bulk densities derived from maps. By contrast
Germany’s NFSI, were volume-based sampling was obligatory for almost all plots. As a result, fine earth stock estimates from map derived bulk densities could result in highly uncertain and overestimated stocks change rates. Higher C and N concentrations of the LUCAS inventory could be attributed to an insufficient separation of the organic layer from the mineral soil because if the separation is not practiced carefully, residues of the organic layer can contaminate the mineral soil sample and cause significant higher concentrations. The soil sampling of the NFSI focused explicitly of the systematic separation of both compartments. The number of the sampled forest plots of LUCAS 2015 showed a lack of representativeness because in the northern and southern parts of Germany an insufficient number of plots were sampled. Nevertheless, the proportion of the forest area of the total LUCAS inventory is comparable with Germany’s Third National Forest Inventory. The selected LUCAS 2015 samples comprise 25 % of the NFSI II plots which results in an insufficient coverage of various soil groups. This means that soil properties remain unconsidered and that the area-weighted extrapolation could become more difficult. Chemical soil parameter within in individual substrate groups differed between the sampling grids due to the smaller sample of LUCAS 2015 compared to the NFSI II. Moreover, the reduction of sampling plots results in an increase of uncertainties. Thus, the detection of changes in soil conditions could be more difficult in respect to repeated sampling. Soil inventory data are of importance in greenhouse gas reporting because C stocks the organic layer and the mineral soil down to 90 cm must be reported. The analysis of the NFSI II revealed
that the organic layer comprises 16 % of the C stored in the entire soil profile. The organic layer was not sampled in the LUCAS inventory. Nevertheless, changes in C stored in the organic layer is vulnerable to impacts of climate and other environmental and anthropogenic variables but the effects cannot be considered with LUCAS 2015. Due to the LUCAS 2015 mineral soil sampling depth down to 20 cm only 42 % of the stored C was detected in the entire soil profile. Therefore, data based on LUCAS 2015 are insufficiently qualified for the greenhouse gas reporting due to the lack of a sampled organic layer and the limitation to the sampling depth of 20 cm. Compared to Germany’s NFSI, the results based on the LUCAS
inventory revealed a smaller representativity and were associated with larger uncertainties as well as discrepancies. Consequently, the union of both data sets will not result in additional synergies.