In:
Geoscientific Model Development, Copernicus GmbH, Vol. 11, No. 12 ( 2018-12-21), p. 5203-5215
Abstract:
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 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.
Type of Medium:
Online Resource
ISSN:
1991-9603
DOI:
10.5194/gmd-11-5203-2018
DOI:
10.5194/gmd-11-5203-2018-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2456725-5
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