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| Home > Publications database > Parameter sensitivity analysis of vegetation and carbon dynamics using land surface model (CLM5-FATES) at European forest sites. |
| Home > Publications database > Parameter sensitivity analysis of vegetation and carbon dynamics using land surface model (CLM5-FATES) at European forest sites. |
| Conference Presentation (Plenary/Keynote) | FZJ-2024-01260 |
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2023
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Please use a persistent id in citations: doi:10.5194/egusphere-egu23-11629
Abstract: Changing environmental conditions impact ecosystem dynamics which have important implications for the land–atmosphere carbon and water exchanges. Land surface models coupled with dynamic vegetation models can be used to understand the impact of changes in terrestrial ecosystems on carbon and water cycles and their interactions with climate. However, process-based vegetation models are highly parameterized and have a large number of uncertain parameters, which lead to uncertainties in the model outputs. Here, we use a dynamic vegetation model, the Functionally Assembled Terrestrial Simulator (FATES) coupled to the Community Land Model (CLM v5) to analyze parameter sensitivities and its effects on forest growth, carbon storage and fluxes. We first calibrate allometry parameters to accurately describe plant functional types, representative of most abundant tree species across Europe (such as Norway spruce and European Beach), at three different European sites. Further, an ensemble of model simulations with perturbed parameters were performed and compared against observations to explore uncertainties in simulated vegetation structure and distributions (forest density, tree basal areas and above ground biomass) and their effects on ecosystem functioning (carbon, water and energy fluxes). Comparison with observation shows that the CLM5-FATES model is able to capture the interannual variability well for water and carbon fluxes (such as ET and GPP), but shows larger uncertainties for simulated forest structure (growth, establishment, and mortality). Future work will focus on parameter optimization to further improve model performance in simulating vegetation growth and composition for different vegetation distributions and climate conditions.
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