Addressing the ability of a land biosphere model to predict key biophysical vegetation characterisation parameters with Global Sensitivity Analysis. (March 2015)

• Record Type: Journal Article
• Authors: Ireland, Gareth; Petropoulos, George P.; Carlson, Toby N.; Purdy, Sarah
• Is Part Of: Environmental modelling & software. Volume 65(2015:Mar.)
• Page Start: 94
• Page End: 107
• Publication Date: 2015-03
• Subjects: Sensitivity analysis -- CO2 flux -- Ambient CO2 -- O3 flux -- SimSphere -- Gaussian process emulators -- BACCO GEM-SA; 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.2014.11.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Sensitivity Analysis (SA) of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model has been performed in this study using a cutting edge and robust Global Sensitivity Analysis (GSA) approach, based on the use of the Gaussian Emulation Machine for Sensitivity Analysis (GEM-SA) tool. The sensitivity of the following model outputs was evaluated: the ambient CO2 concentration, the rate of CO2 uptake by the plant, the ambient O3 concentration, the flux of O3 from the air to the plant/soil boundary and the flux of O3 taken up by the plant alone. The most sensitive model inputs for the majority of outputs were: The Leaf Area Index (LAI), Fractional Vegetation Cover (Fr), Cuticle Resistance (CR) and Vegetation Height (VH). The influence of the external CO2 on the leaf and O3 concentration in the air as input parameters was also significant. Our study provides an important step forward in the global efforts towards SimSphere verification given the increasing interest in its use as an independent modelling or educational tool. Results of this study are also timely given the ongoing global efforts focused on deriving, at an operational level, spatio-temporal estimates of energy fluxes and soil moisture content using SimSphere synergistically with Earth Observation (EO) data. Highlights: First study to examine the sensitivity of specific key outputs simulated from SimSphere. First study to examine the effect of simulation time on model sensitivity. Results extend our understanding of the SimSphere model's structure and coherence. Results are potentially important for the development of operational EO products.