A sensitivity analysis of the SimSphere SVAT model in the context of EO-based operational products development

Use of simulation process models, often combined with Earth Observation (EO) data, has played a key role in extending our abilities to study land surface interaction processes and enhancing our understanding of how different components of the Earth system interplay. These techniques aim to improve the estimates of key parameters characterising land surface interactions by combining the horizontal coverage and spectral resolution of remote sensing data with the vertical coverage and fine temporal continuity of simulation process models. This study performs a Global Sensitivity Analysis (GSA) on the SimSphere land surface model aiming to further extend our understanding of the model structure and establish its coherence. It builds on previous works conducted to the model using a sophisticated and cutting edge method adopting Bayesian theory to develop an emulator based on which the GSA is conducted. Our first objective is to examine the effect of assuming uniform probability distribution function (PDFs) assumptions for the model inputs to the sensitivity of key quantities simulated by SimSphere. A further objective is to explore the sensitivity of new, previously unexplored outputs simulated by the model, namely of the Daily Evaporative and Non-Evaporative Fractions and Radiometric Temperature. The GSA conducted assuming uniform PDFs showed comparable results to previous studies in terms of identifying the most sensitive model inputs to each of the outputs considered. Yet, in absolute terms the statistical parameters measuring the sensitivity of the model inputs were notably different. SA results of the newly model outputs showed largely explainable results and allowed identification of the most responsive model inputs and interactions. In general, our results provided further evidence supporting the model coherence and correspondence to the behaviour of a natural system. The overall implications of our findings are discussed in the framework of the general model use either as a stand-alone tool or synergistically with EO data, particularly so the operational development of regional parameters derived from its synergy with EO data.JRC.G.3-Econometrics and applied statistic