Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
Understanding tree-response to extreme drought events is imperative for maintaining forest ecosystem services under climate change. While tree-ring derived secondary growth measurements are often used to estimate direct and lagging drought impacts, so-called drought legacies, underlying physiological responses remain difficult to constrain across species and site conditions. As extreme droughts may alter the functioning of plants in terms of resource allocation being shifted towards repair and physiological adjustments, climate control on growth may consequently be altered until physiological recovery is completed. In this context, we here advance the concept of drought legacy effects by quantifying 'functional legacies' as climate sensitivity deviations (CSD) of secondary growth after droughts, i.e. temporary alterations of climate-growth relations. We quantified climate sensitivity deviations after extreme drought events by applying linear mixed-effects models to a global-scale, multi-species tree-ring dataset and differentiated responses by clades, site aridity and hydraulic safety margins (HSMs). We found that while direct secondary growth legacies were common across these groups, responses in post-drought climate sensitivity deviations were nuanced. Gymnosperms showed weaker coupling between secondary growth and the dominant climatic driver after droughts, a response that was narrowed down to gymnosperms with a small HSM, i.e. risky hydraulic strategy. In comparison, angiosperms instead showed stronger coupling between secondary growth and the dominant climatic driver following droughts, which was narrowed down to the angiosperms growing in arid sites. These results are consistent with current understanding of physiological impairment and carbon reallocation mechanisms, and the distinct functional responses suggest functional legacies quantified by climate sensitivity deviations is a promising avenue for detecting and thus studying physiological mechanisms underlying drought-responses in tree growth on large scales.
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Andreas Krause,
Phillip Papastefanou,
Konstantin Gregor,
Lucia Layritz,
Prof. Dr. Christian Zang,
Allan Buras,
Xing Li,
Jingfeng Xiao,
Prof. Dr. Anja Rammig
Historically, humans have cleared many forests for agriculture. While this substantially reduced ecosystem carbon storage, the impacts of these land cover changes on terrestrial gross primary productivity (GPP) have not been adequately resolved yet. Here, we combine high-resolution datasets of satellite-derived GPP and environmental predictor variables to estimate the potential GPP of forests, grasslands, and croplands around the globe. With a mean GPP of 2.0 kg C m−2 yr−1 forests represent the most productive land cover on two thirds of the total area suitable for any of these land cover types, while grasslands and croplands on average reach 1.5 and 1.8 kg C m−2 yr−1, respectively. Combining our potential GPP maps with a historical land-use reconstruction indicates a 4.4% reduction in global GPP from agricultural expansion. This land-use-induced GPP reduction is amplified in some future scenarios as a result of ongoing deforestation (e.g., the large-scale bioenergy scenario SSP4-3.4) but partly reversed in other scenarios (e.g., the sustainability scenario SSP1-1.9) due to agricultural abandonment. Comparing our results to simulations from state-of-the-art Earth System Models, we find that all investigated models deviate substantially from our estimates and from each other. Our maps could be used as a benchmark to reduce this inconsistency, thereby improving projections of land-based climate mitigation potentials.
Prof. Dr. Jörg Ewald,
Prof. Dr. Christian Ammer,
Markus Blaschke,
Dr. Jonas Hagge,
Andreas Henkel,
Prof. Dr. Sören Hese,
Alisa Klamm,
Dr. Birgit Reger,
Prof. Dr. Andreas Rothe,
Dr. Sebastian Seibold,
Prof. Dr. Rupert Seidl,
Prof. Dr. Christian Zang,
Dr. Michelangelo Olleck
Prof. Dr. Jörg Ewald,
Prof. Dr. Christian Ammer,
Markus Blaschke,
Dr. Jonas Hagge,
Andreas Henkel,
Prof. Dr. Sören Hese,
Alisa Klamm,
Dr. Birgit Reger,
Prof. Dr. Andreas Rothe,
Dr. Sebastian Seibold,
Prof. Dr. Rupert Seidl,
Prof. Dr. Christian Zang,
Dr. Michelangelo Olleck
Dynamik und Anpassung der Naturwälder an den Klimawandel - Verbundprojekt vergleicht Reaktion von unbewirtschafteten und bewirtschafteten Wäldern auf Dürreperioden (2022) Waldklimafonds-Kongress 2022, 11.-12. Oktober 2022, Göttingen .
Phillip Papastefanou,
Prof. Dr. Christian Zang,
Zlatan Angelov,
Aline Anderson de Castro,
Juan Carlos Jimenez,
Luiz Felipe Campos De Rezende,
Romina Ruscica,
Boris Sakschewski,
Anna A. Sörensson,
Kirsten Thonicke,
Carolina Vera,
Nicolas Viovy,
Celso von Randow,
Prof. Dr. Anja Rammig
Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (mean =2.7) ×106 km2 (37 %–51 % of the Amazon basin, mean =45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly ). In 2010, the affected area was about 16 % larger, ranging from 3.0 up to 4.4 (mean =3.6) ×106 km2 (51 %–74 %, mean =61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean km2; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106 km2 (40 %–69 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60 %), followed by the choice of the precipitation dataset (20 %) and the evapotranspiration dataset (20 %). Using a fixed, constant evapotranspiration rate instead of variable evapotranspiration can lead to an overestimation of drought stress in the parts of Amazon basin that have a more pronounced dry season (for example in 2010). We highlight that even for well-known drought events the spatial extent and intensity can strongly depend upon the drought indicator and the data sources it is calculated with. Using only one data source and drought indicator has the potential danger of under- or overestimating drought stress in regions with high measurement uncertainty, such as the Amazon basin.
Mehr
Paul Bodesheim,
F. Babst,
David C. Frank,
Claudia Hartl,
Prof. Dr. Christian Zang,
Martin Jung,
Markus Reichstein,
M. D. Mahecha
Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.
Mehr
Anja Zmegac,
Julia Rieder,
Bernhard Schuldt,
Prof. Dr. Christian Zang
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