Abstract
In today’s changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earth’s terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalisation of any physically based model. A Global Sensitivity Analysis (GSA) was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs) for the model inputs, when examining sensitivity of key quantities simulated by SimSphere at different output times, were examined. The development of topographic model input parameters (e.g., slope, aspect, and elevation) were derived within a Geographic Information System (GIS) before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, fractional vegetation cover, vegetation height and surface moisture availability in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values of the output variance decomposition, but it did not seem to change the relative importance of each input parameter. Sensitivity Analysis (SA) results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling and educational tool. Furthermore, this study is very timely given on-going efforts towards the development of operational products based on the synergy of SimSphere with Earth Observation (EO) data. In this context, results also provide additional support for the potential applicability of the assimilation of spatial analysis data derived from GIS and EO data into an accurate modelling framework.
Highlights
Advances in computer science over recent decades have led to the increased use of sophisticated deterministic models in the simulation and prediction of processes, feedbacks and mechanisms related to a number of science and engineering fields [1,2]
“roughness value” parameters, ..., which represents emulator performance, unrelated to the surface roughness of physical model input parameter. They include the “cross-validation root mean square error”, “cross-validation root mean squared relative error”, “cross-validation root mean squared standardised error” and the “sigma-squared” value. These cross-validation measures work by estimating a series of “left-out” points for which we know the true values generated as output from the code
This study provides a further demonstration of the capability of the Global Sensitivity Analysis (GSA) meta-modelling method adopting Bayesian theory to decompose the total uncertainty of the SimSphere model and confirm results from previous sensitivity analyses on the model showing that the model’s yield output sensitivity to important parameters depend strongly on topographic conditions
Summary
Advances in computer science over recent decades have led to the increased use of sophisticated deterministic models in the simulation and prediction of processes, feedbacks and mechanisms related to a number of science and engineering fields [1,2]. Soil-Vegetation-Atmosphere Transfer (SVAT) models are one such type of deterministic models designed to simulate the continuously evolving interactions and feedback processes within the soil-plantatmosphere continuum [5] These models are essentially mathematical representations of 1-dimensional “views” of the increasingly complex physical mechanisms governing radiative, turbulent and water transfers [6]. Some SVAT models provide a framework for assessing the spatial variability of mass and energy exchanges by combining the remotely sensed surface conditions within a Surface Temperature (Ts) and Vegetation Index (VI) feature space with a land surface process model to derive regional maps of energy fluxes [8] One such approach is the so-called “triangle”
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