Model evaluation in relation to soil N2O emissions: An algorithmic method which accounts for variability in measurements and possible time lags. (October 2016)

• Record Type: Journal Article
• Authors: Myrgiotis, Vasileios; Williams, Mathew; Rees, Robert M.; Smith, Kate E.; Thorman, Rachel E.; Topp, Cairistiona F.E.
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 251
• Page End: 262
• Publication Date: 2016-10
• Subjects: Agro-ecosystems -- Soil modelling -- Model evaluation -- Nitrous oxide -- Time lag; 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.2016.07.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The loss of nitrogen from fertilised soils in the form of nitrous oxide (N2 O) is a side effect of modern agriculture and the focus of many model-based studies. Due to the spatial and temporal heterogeneity of soil N2 O emissions, the measured data can introduce limitations to the use of those statistical methods that are most commonly employed in the evaluation of model performance. In this paper, we describe these limitations and present an algorithm developed to address them. We implement the algorithm using simulated and measured N2 O data from two UK arable sites. We show that possible time lags between the measured and simulated data can affect model evaluation and that their consideration in the evaluation process can reduce measures such as the Mean Squared Error (MSE) by 30%. We also analyse the algorithm's results to identify patterns in the estimated lags and to narrow down their possible causes. Highlights: The statistics used in soil N2 O model evaluation have limitations. We use new measures to assess the prediction of measured magnitudes and patterns. We present a method to identify time lags between measured and simulated data. We use an algorithm to show how time lags affect common evaluation statistics. We show how the algorithm can be used to examine possible causes of time lags.