Search Results
A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: An example from the Amazon region
2018, Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, Rödig, Edna
Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25% and up to 60%, respectively. Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products.
Amazon forest ecosystem responses to elevated atmospheric Co2 and alterations in nutrient availability: Filling the gaps with model-experiment integration
2016, Hofhansl, Florian, Andersen, Kelly M., Fleischer, Katrin, Fuchslueger, Lucia, Rammig, Anja, Schaap, Karst J., Valverde-Barrantes, Oscar J., Lapola, David M.
The impacts of elevated atmospheric CO2 (eCO2) and alterations in nutrient availability on the carbon (C) storage capacity and resilience of the Amazon forest remain highly uncertain. Carbon dynamics are controlled by multiple eco-physiological processes responding to environmental change, but we lack solid experimental evidence, hampering theory development and thus representation in ecosystem models. Here, we present two ecosystem-scale manipulation experiments, to be carried out in the Amazon, that examine tropical ecosystem responses to eCO2 and alterations in nutrient availability and thus will elucidate the representation of crucial ecological processes by ecosystem models. We highlight current gaps in our understanding of tropical ecosystem responses to projected global changes in light of the eco-physiological assumptions considered by current ecosystem models. We conclude that a more detailed process-based representation of the spatial (e.g., soil type; plant functional type) and temporal (seasonal and inter-annual) variability of tropical forests is needed to enhance model predictions of ecosystem responses to projected global environmental change.