Reducing the impact of model scale on simulated, gridded switchgrass yields

Abstract

a b s t r a c tResults of gridded ecosystem simulations of bioenergy crops are used for estimating economic viability, environmental impacts, and potential land use change. Gridded model uncertainty propagates through these uses, thus we propose a simple method for estimating regional, spatial model error from sparse field data. We apply this method to the Agricultural-BioGeochemical Cycles (Agro-BGC) model to examine and reduce the model uncertainty associated with grid scale for simulated switchgrass yields in a 6 latitude  5 longitude (w300,000 km 2 ) region covering Illinois, United States of America. Based on three evaluation sites, changes in yield with scale result from complex intra-model interactions driven by a combination of meteorological rather than soil or terrain variables. Spatial bias of the regional mean significantly increases with increasing cell size for 11 of 15 measurement dates. This bias is primarily due to grid scale, thus bias correction of output yield reduces the model uncertainty associated with grid scale. The corresponding Root Mean Squared Error and Bias-Corrected RMSE (RMSE BC ) have effectively negligible trends with inconsistent signs. The range of RMSE BC for 2-year Average Mature August Yield (AMAY) is 267e285 g C m À2 across 3-to 3600-arcsec resolution (w90 mew100 km) with biases from 9 to 61 g C m À2 . AMAY bias significantly increases with increasing cell size. Spatial bias of the regional mean is relatively consistent for resolutions 1200 arcsec (w33 km) (AMAY bias <3%), and larger AMAY biases (4e13%) at coarse resolutions indicate poorly characterized spatial heterogeneity. Including the 68% confidence interval around bias-corrected values, AMAY ranges from 0 to 1116 g C m À2 across a 150-arsec grid, which is similar to the range reported for 24 eastern United States field sites. Spatial bias of the regional mean yield can vary across grid resolution by as much as 31% of the observed regional mean and can dramatically affect calculations dependent on the resolution of the estimate. We conclude that grid scale profoundly affects model accuracy such that regional studies must match evaluation and simulation scales and should utilize multi-scale analyses to determine robustness of results.

Representing agriculture in Earth System Models: Approaches and priorities for development

“…Recent studies stress that the representativeness of site‐based parameters commonly used in process‐based crop models deteriorates at larger scales [ Iizumi et al ., ]. When larger‐scale parameters are utilized in ESM‐crop model formulations, the resulting yields and outputs must be compared against values of a similar scale and bias corrected before interpretation [ Di Vittorio and Miller , ; Iizumi et al ., ]. Iizumi et al .…”

“…One challenge lies specifically in the scale at which crop parameters are obtained, as a mismatch between these and ESM resolutions may compromise simulated crop growth and enhance prediction errors [Baron Journal of Advances in Modeling Earth Systems 10. 1002/2016MS000749 et al, 2005Di Vittorio and Miller, 2014;Iizumi et al, 2014]. Recent studies stress that the representativeness of site-based parameters commonly used in process-based crop models deteriorates at larger scales [Iizumi et al, 2014].…”

“…Recent studies stress that the representativeness of site-based parameters commonly used in process-based crop models deteriorates at larger scales [Iizumi et al, 2014]. When larger-scale parameters are utilized in ESM-crop model formulations, the resulting yields and outputs must be compared against values of a similar scale and bias corrected before interpretation [Di Vittorio and Miller, 2014;Iizumi et al, 2014]. Iizumi et al [2014] suggest that parameters for major crop growth relationships should be obtained at scales appropriate for simulation, and warn that these could differ substantially from the corresponding site-based parameters due to aggregation.…”

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

“…Olesen et al (2000) found that a 10 km or finer spatial resolution of input data can improve the simulation accuracy in modeling the winter wheat yields in Denmark. Mearns et al (2004) and Di Vittorio & Miller (2014) found that aggregation of weather data resulted in high uncertainty for simulation of crop yields at regional scales. Mummery & Battaglia (2002) found that by using low-resolution input data, the mean productivity for large geographic extents can be biased, especially for regions with heterogeneous soil conditions.…”

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

“…For example, recent studies have highlighted large spreads in regional multi-model climate ensemble projections, at both coarse and fine resolutions (e.g., Mearns et al, 2013;Qiao et al, 2014, van der Linden andMitchell, 2009). While these differences are often attributed to model differences in general, inappropriate resolution or spatial aggregation in models can cause significant biases in outputs (Di Vittorio and Miller, 2014;Ogle et al, 2006;Riley et al, 2009). …”