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Combining observational data with modelling approaches for ecosystems
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Edited by Alexandre Belleflamme, Christian Poppe Terán, Harrie-Jan Hendricks-Franssen
Climate and global change increase the pressure on ecosystems, their functions, and services. Accurate data products of critical environmental variables at high spatial and temporal resolution offer a basis for decision making. This supports management practices that increase the resilience of ecosystems against droughts and heatwaves, wildfires, biodiversity loss, soil degradation, and others. Such data products can for example be produced by combining observational data (both in-situ and remote sensing) with simulation models. In this context, we welcome methodological studies and applications on producing these types of data products. A wide range of methods is of interest, like AI-based model-data fusion, data assimilation, inverse modelling, purely statistical methods or purely simulation based approaches. We are also interested in studies using site specific data for model parameterization, calibration, and validation, to address model uncertainties. Studies at all spatial scales (sites, catchments, continental, global scale) are welcome. Case studies may focus on ecosystem biodiversity, carbon and water cycles, and their interfaces with society, their response to (extreme) events, or long-term changes. This session will showcase the advantages and challenges of combining observational data with modelling approaches, highlighting their potential to inform policy decisions and support ecological resilience to adapt to environmental changes.
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