A comparative analysis of emulators for the sensitivity analysis of a land surface process model

Abstract Sophisticated mathematical models simulating land surface interaction processes are increasingly employed today in performing various types of scientific investigations and analysis, providing important assistance to scientists in better understanding the complicated physical phenomena that occur in nature. Sensitivity analysis (SA) in particular, is generally regarded nowadays as a key step in verifying the concept and relevance to real world of any mathematical model, before that is used for carrying out any kind of operation or analysis for which it has been developed. The present work provides a comparative analysis of both Gaussian and Non-Gaussian process emulators for performing a global sensitivity analysis (GSA) to a land surface process model named SimSphere. Until today, only few SA studies have been conducted to this specific model, even more, GSA ones, and thus results from the work conducted here are expected to contribute decisively towards an all-inclusive assessment of its overall verification.

[1]  George P. Petropoulos,et al.  An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions , 2009, Sensors.

[2]  Jon C. Helton,et al.  Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models , 2009, Reliab. Eng. Syst. Saf..

[3]  Frederick E. Boland,et al.  Analysis of Urban-Rural Canopy Using a Surface Heat Flux/Temperature Model , 1978 .

[4]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[5]  D. Vidal-Madjar,et al.  Evapotranspiration over an agricultural region using a surface flux/temperature model based on NOAA-AVHRR data , 1986 .

[6]  R. Gillies A verification of the 'triangle' method for obtaining surface water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature , 1997 .

[7]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[8]  George P. Petropoulos,et al.  A global Bayesian sensitivity analysis of the 1d SimSphere soil-vegetation-atmospheric transfer (SVAT) model using Gaussian model emulation. , 2009 .

[9]  Peter C. Young,et al.  State Dependent Parameter metamodelling and sensitivity analysis , 2007, Comput. Phys. Commun..

[10]  S. Miller,et al.  Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach , 2003 .

[11]  Brian J Reich,et al.  Surface Estimation, Variable Selection, and the Nonparametric Oracle Property. , 2011, Statistica Sinica.

[12]  George P. Petropoulos,et al.  A global sensitivity analysis study of the 1d SimSphere SVAT model using the GEM SA software , 2009 .

[13]  Jeremy E. Oakley,et al.  Bayesian Analysis of Computer Model Outputs , 2002 .

[14]  Albert Olioso,et al.  Simulation of diurnal transpiration and photosynthesis of a water stressed soybean crop , 1996 .

[15]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .