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

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 Published by Copernicus Publications on behalf of the European Geosciences Union. 5204A. Rammig et al.: Pixel-to-point comparison for simulated large-scale ecosystem properties 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.

From small-scale forest structure to Amazon-wide carbon estimates

“…Uncertainty in tree allometries is a common source of model structural and parameter uncertainties in dynamic vegetation models 44 . New approaches based on terrestrial laser scanning 45 will help to reduce allometric uncertainties in the future.…”

Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model

“…Thus, even a simulated AGB pattern that perfectly matches the real large-scale pattern would not yield a correlation coefficient of one when compared to small-scale plot observations. To address this problem, we apply the method from Rammig et al (2018), which was specifically developed to compare spatial patterns of simulated large-scale ecosystem properties ( ) to ground-based observations ( ). The method assumes that a small scale "point" measurement consists of two components: the large-scale average and a normally-distributed random component originating from small-scale variability and measurement error.…”

“…The uncertainty ranges for these two properties as well as for the pattern average (which does not require a correction and therefore no differentiation of 'largescale') are estimated by bootstrapping. For further details on the underlying methodology see (Rammig et al, 2018).…”

Assessing regional drought impacts on vegetation and evapotranspiration: a case study in Guanacaste, Costa Rica

“…A novel approach for pixel‐to‐point comparisons proposed by Rammig et al. () determines the statistical properties of “within‐pixel” variability and observational errors, and uses this information to correct for their effect when large‐scale area averages (pixels) are compared to small‐scale point estimates. First, this approach characterizes the global variability of the point data set with the global variance and mean.…”

“…Conducting a validation with only 3 towers would suffer from scale mismatch issues, which could not be addressed by the Rammig et al. () approach due to small sample size. We decided to adopt an error estimate from other validation studies of the TMPA‐3B43V7 product that had access to a denser network of weather stations.…”