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@Article{RammigHHVBCCDFGGHJKLMPOTVZR:2018:ExAmRe,
               author = "Rammig, Anja and Heinke, Jens and Hofhansl, Florian and Verbeeck, 
                         Hans and Baker, Timothy R. and Christoffersen, Bradley and Ciais, 
                         Philippe and De Deurwaerder, Hannes and Fleischer, Katrin and 
                         Galbraith, David and Guimberteau, Matthieu and Huth, Andreas and 
                         Johnson, Michelle and Krujit, Bart and Langerwisch, Fanny and 
                         Meir, Patrick Meir and Papastefanou, Phillip and Oliveira, Gilvan 
                         Sampaio de and Thonicke, Kirsten and Von Randow, Celso and Zang, 
                         Christian and R{\"o}dig, Edna",
          affiliation = "{Technical University of Munich} and {Potsdam Institute for 
                         Climate Impact Research} and {IIASA International Institute for 
                         Applied Systems Analysis} and {CAVElab Computational \& Applied 
                         Vegetation Ecology} and School of Geography, University of Leeds 
                         and {The University of Texas Rio Grande Valley} and 
                         {Universit{\'e} Paris-Saclay} and 4CAVElab Computational \& 
                         Applied Vegetation Ecology, Department of Applied Ecology and 
                         Environmental Biology, Faculty of Bioscience Engineering and 
                         {Technical University of Munich} and {University of Leeds} and 
                         {Universit{\'e} Paris-Saclay} and {Helmholtz Centre for 
                         Environmental Research (UFZ)} and {University of Leeds} and 
                         ALTERRA and {Potsdam Institute for Climate Impact Research} and 
                         {University of Edinburgh} and {Technical University of Munich} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Potsdam 
                         Institute for Climate Impact Research} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Technical University of Munich} 
                         and {Helmholtz Centre for Environmental Research (UFZ)}",
                title = "A generic pixel-to-point comparison for simulated large-scale 
                         ecosystem properties and ground-based observations: an example 
                         from the Amazon region",
              journal = "Geoscientific Model Development",
                 year = "2018",
               volume = "11",
                pages = "5203--5215",
             abstract = "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 modeldata 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.",
                  doi = "10.5194/gmd-11-5203-2018",
                  url = "http://dx.doi.org/10.5194/gmd-11-5203-2018",
                 issn = "1991-959X",
             language = "en",
           targetfile = "rammig_generic.pdf",
        urlaccessdate = "28 nov. 2020"
}


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