Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models. (August 2017)

• Record Type: Journal Article
• Authors: Van Oijen, Marcel; Cameron, David; Levy, Peter E.; Preston, Rory
• Is Part Of: Environmental modelling & software. Volume 94(2017)
• Page Start: 157
• Page End: 165
• Publication Date: 2017-08
• Subjects: Block support -- Ecosystem modelling -- Greenhouse gas emission -- Point support -- Spatial upscaling -- Taylor expansion; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.03.023
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Ecological models are used to quantify processes over large regions. When the model is nonlinear and input variables are spatially averaged, the regional mean will be in error. A formula for estimating the upscaling error can be derived from Taylor expansion of the model (Bresler and Dagan 1988). We test this for simple models under three different input distributions (Gaussian, exponential, lognormal). In several cases the formula is exact, in others it provides a reasonable approximation. We then study models for emissions of methane, ammonia, and nitrous oxide across the UK. We scale from 1 × 1 km to 32 × 32 km. The UK-average upscaling errors are −12%, −48% and −3%, well estimated using the formula. The formula is a useful tool for modellers desiring to correct upscaling error for their application. Calculation of second-order partial derivatives of model output is required, for which we provide R-code. Highlights: We study a formula, based on Taylor expansion, which estimates errors induced by aggregating the inputs of nonlinear models. The formula provides exact error estimates for some analytical models and input distributions, good approximations for others. Spatial upscaling error for greenhouse gas models in the United Kingdom is strongly reduced for CH4 and NH3 but less for N2 O.