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

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-topoint comparison that accounts for the uncertainty resulting from landscape variability and measurement errors in ecosystem 30 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 35 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.Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-182 Manuscript under review for journal Geosci. Model Dev. Discussion started: 27 July 2018 c Author(s) 2018. CC BY 4.0 License. 2Our 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 5 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.

An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation

“…Parameters for DBH growth and shade tolerance should be calibrated for each PFT. The model has been applied to tropical rain forests in Africa and South America [39,71,74]. However, there are few applications recorded in temperate coniferous forests and the growth parameters for cedar and cypress have not been reported [75,76].…”

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

“…Many studies address this scale mismatch by conducting a validation of remote sensing pixels that contain multiple measurement towers and use the mean value as the ground truth for each pixel (Nicholson et al 2003, Dinku et al 2007, Chokngamwong and Chiu 2008, Yong et al 2010. A novel approach for pixel-topoint comparisons proposed by Rammig et al (2018) 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.…”

“…The network of weather stations from the Costa Rica Instituto Meteorologico Nacional comprises about 13 towers, only 3 of which cover the entirety of the time period of this study. Conducting a validation with only 3 towers would suffer from scale mismatch issues, which could not be addressed by the Rammig et al (2018) 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.…”