Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

Abstract. This paper describes the validation of the SimSphere SVAT (Soil–Vegetation–Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (Rg), net radiation (Rnet), latent heat (LE), sensible heat (H), air temperature at 1.3 m (Tair 1.3 m) and air temperature at 50 m (Tair 50 m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation. Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the Rg, Rnet, Tair 1.3 m and Tair 50 m parameters. The simulated Rg parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ~ 24 % of the observed fluxes was reported for the Rnet parameter for all days of study (RMSD = 58.69 W m−2). A systematic underestimation of Rg and Rnet (mean bias error (MBE) = −19.48 and −16.46 W m−2) was also found. Simulations for the Tair 1.3 m and Tair 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 °C (within ~ 15 and ~ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m−2, ~ 34 and ~ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash–Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations. To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products.

[1]  Volker Liebig,et al.  The changing earth : New scientific challenges for esa's living planet programme , 2006 .

[2]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[3]  Timothy R. Oke,et al.  Tests of three urban energy balance models , 1988 .

[4]  M. Williams,et al.  Multi-site evaluation of the JULES land surface model using global and local data , 2014 .

[5]  Toby N. Carlson,et al.  A stomatal resistance model illustrating plant vs. external control of transpiration , 1990 .

[6]  Prasad S. Thenkabail,et al.  Global Croplands and Their Water Use from Remote Sensing and Nonremote Sensing Perspectives , 2011 .

[7]  Mike Rivington,et al.  Validation of biophysical models: issues and methodologies. A review , 2011, Agronomy for Sustainable Development.

[8]  A. Pitman,et al.  Evaluating the Performance of Land Surface Models , 2008 .

[9]  Henrik Madsen,et al.  Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment , 2012 .

[10]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[11]  Ü. Rannik,et al.  Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology , 2000 .

[12]  Ann Henderson-Sellers,et al.  Modelling tropical deforestation: A study of GCM land-surface parametrizations , 1988 .

[13]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[14]  A. Monin,et al.  Basic laws of turbulent mixing in the surface layer of the atmosphere , 2009 .

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

[16]  Toby N. Carlson,et al.  The effects of plant water storage on transpiration and radiometric surface temperature , 1991 .

[17]  Yasushi Yamaguchi,et al.  Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area , 2006 .

[18]  I. Prentice,et al.  Reliable, robust and realistic: the three R's of next-generation land-surface modelling , 2014 .

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

[20]  George P. Petropoulos,et al.  Sensitivity Exploration of SimSphere Land Surface Model Towards Its Use for Operational Products Development from Earth Observation Data , 2014 .

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

[22]  P. Mascart,et al.  Canopy resistance formulation and its effect in mesoscale models: A HAPEX perspective , 1991 .

[23]  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 .

[24]  T. Vesala,et al.  Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation , 2006 .

[25]  Werner H. Terjung,et al.  Intercomparison of three urban climate models , 1988 .

[26]  Alan G. Barr,et al.  Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux , 2006 .

[27]  T. Foken,et al.  The energy balance closure problem , 2004 .

[28]  Omar T. Farouki,et al.  The thermal properties of soils in cold regions , 1981 .

[29]  Pedro Viterbo,et al.  An Improved Land Surface Parameterization Scheme in the ECMWF Model and Its Validation. , 1995 .

[30]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[31]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[32]  Syukuro Manabe,et al.  THE ATMOSPHERIC CIRCULATION AND THE HYDROLOGY OF THE EARTH ’ S SURFACE , 1969 .

[33]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[34]  Toby N. Carlson,et al.  Observations and model simulations link stomatal inhibition to impaired hydraulic conductance following ozone exposure in cotton , 1999 .

[35]  Dennis D. Baldocchi,et al.  Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America , 2000 .

[36]  A. Ibrom,et al.  A modelling approach for simulation of water and carbon dioxide exchange between multi-species tropical rain forest and the atmosphere , 2008 .

[37]  Xavier Briottet,et al.  Monitoring land surface processes with thermal infrared data : Calibration of SVAT parameters based on the optimisation of diurnal surface temperature cycling features , 2008 .

[38]  Jonas Ardö,et al.  Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach , 2013 .

[39]  Yann Kerr,et al.  Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[40]  P. Kerkides,et al.  New empirical formula for hourly estimations of reference evapotranspiration , 2003 .

[41]  Ann Henderson-Sellers,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1995 .

[42]  Toby N. Carlson,et al.  Feedback significantly influences the simulated effect of CO2 on seasonal evapotranspiration from two agricultural species , 1999 .

[43]  George P. Petropoulos,et al.  SimSphere model sensitivity analysis towards establishing its use for deriving key parameters characterising land surface interactions , 2014 .

[44]  Jan-Tai Kuo,et al.  Procedure to Calibrate and Verify Numerical Models of Estuarine Hydrodynamics , 1999 .

[45]  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.

[46]  J. Deardorff Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[47]  George P. Petropoulos,et al.  A sensitivity analysis of the SimSphere SVAT model in the context of EO-based operational products development , 2013, Environ. Model. Softw..

[48]  A. Martínez-cob,et al.  Estimating sensible and latent heat fluxes over rice using surface renewal , 2006 .

[49]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[50]  S. Manabe CLIMATE AND THE OCEAN CIRCULATION1 , 1969 .

[51]  T. A. Black,et al.  Validation of the Integrated Biosphere Simulator over Canadian deciduous and coniferous boreal forest stands , 2001 .

[52]  D. Holzworth,et al.  Common Sense In Model Testing , 2005 .

[53]  Ari Nissinen,et al.  Evaluation of six process‐based forest growth models using eddy‐covariance measurements of CO2 and H2O fluxes at six forest sites in Europe , 2002 .

[54]  T. Carlson,et al.  Satellite Estimation of the Surface Energy Balance, Moisture Availability and Thermal Inertia. , 1981 .

[55]  L. Wilson,et al.  Handbook of Vadose Zone Characterization & Monitoring , 1994 .

[56]  George P. Petropoulos,et al.  Retrievals of turbulent heat fluxes and soil moisture content by Remote Sensing , 2011 .

[57]  Zong-Liang Yang,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[58]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

[59]  W. James Shuttleworth,et al.  Towards a comprehensive approach to parameter estimation in land surface parameterization schemes , 2013 .

[60]  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 .

[61]  D. Vidal-Madjar,et al.  Evaluation of a Surface/Vegetation Parameterization Using Satellite Measurements of Surface Temperature , 1986 .

[62]  Dennis D. Baldocchi,et al.  Estimating parameters in a land‐surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites , 2007 .

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