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Quantifying, Understanding and Predicting Forest growth In Switzerland

 

Background: Forests world-wide are known as an important net carbon sink and are thus a key component of the terrestrial carbon cycle. However, carbon fluxes and storage vary regionally and with inter-annual to long-term environmental change (Luyssaert et al. 2010; Körner 2017) . A higher frequency of drought events and other negative impacts on growth (increased autotrophic respiration and disturbances) are predicted to outweigh enhanced productivity as a result of increasing temperatures (Hoch and Körner 2012) or CO2, Ozone (Cailleret et al. 2018) and nitrogen fertilization (Sitch et al. 2008). Existing models of forest growth dynamics include large uncertainties, which ramify and lead to divergence in forecasts how climate change will impact the future terrestrial carbon cycle. To reduce these uncertainties, it is necessary to extend and combine assessments of current observation networks using novel analytical approaches and data sources. Swiss forests are of particular interest: the climatic, pedologic and biogeographical conditions of Switzerland correspond closely with the European gradient, on a relatively small geographical scale. WSL and its partners own a wealth of diverse data on forest growth and health in Switzerland, originating from various sources and of diverse types that cover a wide range of spatial and temporal (seasons to decades) scales. We plan to exploit this real data treasure within the proposed activity.

Aims and Scope/Hypothesis: Our aim is to estimate Swiss forest net ecosystem productivity (NEP) at monthly or seasonal resolution for each individual year in order to link biomass changes over time with global drivers (climate, soils, landscapes, N deposition). We hypothesize that a combination of available high-quality long-term data sets from WSL and its Swiss partners provides an excellent data basis for a data-model-fusion approach within SwissForestLab. This activity is expected (a) to merge data with dif- ferent temporal and spatial resolution so that they can be used more easily by SwissForestLab members, and (b) to provide a first visible result within two years that will foster follow-up research projects focusing on different aspects of forest growth and development.

Data Networks: QUPFIS aim to make use of different data-networks awailable at SFL. Main focus is on repeated ground observations, but remote sensing (or its parts) will be also incorporated.

 

Please refer to the documentation file for more information on data submission and structure.