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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-182
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-182
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 27 Jul 2018

Methods for assessment of models | 27 Jul 2018

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

Anja Rammig1,*, Jens Heinke2,*, Florian Hofhansl3, Hans Verbeeck4, Timothy R. Baker5, Bradley Christoffersen6, Phillipe Ciais7, Hannes De Deurwaerder4, Katrin Fleischer1, David Galbraith5, Matthieu Guimberteau7, Andreas Huth8, Michelle Johnson5, Bart Krujit9, Fanny Langerwisch2, Patrick Meir10,11, Phillip Papastefanou1, Gilvan Sampaio12, Kirsten Thonicke2, Celso von Randow12, Christian Zang1, and Edna Rödig8 Anja Rammig et al.
  • 1Technical University of Munich, TUM School of Life Sciences Weihenstephan, Hans-Carl-von-Carlowitz-Platz 2, 85356 Freising, Germany
  • 2Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 3Department of Botany & Biodiversity Research, Division of Conservation Biology, Vegetation- and Landscape Ecology, University of Vienna, Austria
  • 4CAVElab Computational & Applied Vegetation Ecology, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience Engineering, Gent, Belgium
  • 5School of Geography, University of Leeds, Leeds, UK
  • 6Department of Biology, The University of Texas Rio Grande Valley, Edinburg, USA
  • 7Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
  • 8Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
  • 9ALTERRA, Wageningen-UR, Wageningen, The Netherlands
  • 10School of Geosciences, University of Edinburgh, Edinburgh, UK
  • 11Research School of Biology, Australian National University, Canberra, Australia
  • 12INPE, Sao Jose dos Campos, SP, Brazil
  • *These authors contributed equally to this work.

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 that accounts for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables, and provide statistical measures. 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, a scale mismatch and 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.

Anja Rammig et al.
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Anja Rammig et al.
Anja Rammig et al.
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