An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions (original) (raw)
Related papers
2009
Soil Vegetation Atmosphere Transfer (SVAT) models consist of deterministic mathematical representations of the physical processes involved between the land surface and the atmosphere and of their interactions, at time-steps acceptable for the study of land surface processes. The present article provides a comprehensive and systematic review of one such SVAT model suitable for use in mesoscale or boundary layer studies, originally developed by [1]. This model, which has evolved significantly both architecturally and functionally since its foundation, has been widely applied in over thirty interdisciplinary science investigations, and it is currently used as a learning resource for students in a number of educational institutes globally. The present review is also regarded as very timely, since a variation of a method using this specific SVAT model along with satellite observations is currently being considered in a scheme being developed for the operational retrieval of soil surface moisture by the US National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellites that are due to be launched from 2016 onwards.
SimSphere is a land biosphere model that provides a mathematical representation of vertical 'views' of the physical mechanisms controlling Earth's energy and mass transfers in the soil/vegetation/atmo-sphere continuum. Herein, we present recent advancements introduced to SimSphere code, aiming at making its use more integrated to the automation of processes within High Performance Computing (HPC) that allows using the model at large scale. In particular, a new interface to the model is presented, so-called " SimSphere-SOA " which forms a command line land biosphere tool, a Web Service interface and a parameters verification facade that offers a standardised environment for specification execution and result retrieval of a typical model simulation based on Service Oriented Architecture (SOA). SimSphere-SOA library can now execute various simulations in parallel. This allows exploitation of the tool in a simple and efficient way in comparison to the currently distributed approach. In SimSphere-SOA, an Application Programming Interface (API) is also provided to execute simulations that can be publicly consumed. Finally this API is exported as a Web Service for remotely executing simulations through web based tools. This way a simulation by the model can be executed efficiently and subsequently the model simulation outputs may be used in any kind of relevant analysis required. The use of these new functionalities offered by SimSphere-SOA is also demonstrated using a " real world " simulation configuration file. The inclusion of those new functions in SimSphere are of considerable importance in the light of the model's expanding use worldwide as an educational and research tool.
A Downloadable Soil-Vegetation-Atmosphere-Transfer (SVAT) Model for Teaching and Research
can be downloaded from the web for use by students and researchers. In existence for several decades, Simsphere has figured in both the classroom and in research at several universities. As such, Simsphere has been supported by a knowledgeable group of academic users and has been applied in a variety of applications, such as in remote sensing of surface soil water content and in the assessment of water and ozone stresses on plants. This paper describes the model and how it can be downloaded and run.
Water Resources Research, 2008
1] A Markov chain Monte Carlo (MCMC) based algorithm was developed to derive upscaled land surface parameters for a soil-vegetation-atmosphere-transfer (SVAT) model using time series data of satellite-measured atmospheric forcings (e.g., precipitation), and land surface states (e.g., soil moisture and vegetation). This study focuses especially on the evaluation of soil moisture measurements of the Aqua satellite based Advanced Microwave Scanning Radiometer (AMSR-E) instrument using the new MCMC-based scaling algorithm. Soil moisture evolution was modeled at a spatial scale comparable to the AMSR-E soil moisture product, with the hypothesis that the characterization of soil microwave emissions and their variations with space and time on soil surface within the AMSR-E footprint can be represented by an ensemble of upscaled soil hydraulic parameters. We demonstrated the features of the MCMC-based parameter upscaling algorithm (from field to satellite footprint scale) within a SVAT model framework to evaluate the satellite-based brightness temperature/soil moisture measurements for different hydroclimatic regions, and identified the temporal effects of vegetation (leaf area index) and other environmental factors on AMSR-E based remotely sensed soil moisture data. The SVAT modeling applied for different hydroclimatic regions revealed the limitation of AMSR-E measurements in high-vegetation regions. The study also suggests that inclusion of soil moisture evolution from the proposed upscaled SVAT model with AMSR-E measurements in data assimilation routine will improve the quality of soil moisture assessment in a footprint scale. The technique also has the potential to derive upscaled parameters of other geophysical properties used in remote sensing of land surface states. The developed MCMC algorithm with SVAT model can be very useful for land-atmosphere interaction studies and further understanding of the physical controls responsible for soil moisture dynamics at different scales. Citation: Das, N. N., B. P. Mohanty, and E. G. Njoku (2008), A Markov chain Monte Carlo algorithm for upscaled soil-vegetationatmosphere-transfer modeling to evaluate satellite-based soil moisture measurements, Water Resour. Res., 44, W05416,
Sim2DSphere: A novel modelling tool for the study of land surface interactions
Herein we present Sim2DSphere, an open-source software tool that offers a complete environment for mapping key parameters characterizing land surface interactions (LSIs) at varying spatiotemporal resolution using standard and freely available geospatial data. The tool has been developed in Java programming language as an addon module to the SimSphere Soil Vegetation Atmosphere Transfer model. Its use is demonstrated herein using Earth Observation (EO) data products from ASTER satellite, acquired over a European site belonging to FLUXNET global in-situ monitoring network. Results obtained in overall confirmed Sim2DSphere's ability to replicate the diurnal spatiotemporal variability of key LSIs parameters at the ecosystem and environmental conditions in which it was tested. The presented herein tool can be applied by the scientific community, managers and policymakers as a form of acquiring technical and scientifically-based information for improving our understanding of LSIs and to achieve a more sustainable environment. In addition, this new add-on modelling tool consists an important step towards advancing the deployment of geo-processing tools utilising the state-of-the-art EO data available today. The software tool systematic validation at variant testing conditions as well as further software developments to improve its use are between the key priorities of future work with it.
A one-dimensional simulation of the interaction between land surface processes and the atmosphere
Boundary-Layer Meteorology, 1992
A one-dimensional soil-vegetation model is developed for future incorporation into a mesoscale model. The interaction of land surface processes with the overlying atmosphere is treated in terms of three coupled balance equations describing the energy and moisture transfer at the ground and the energy state of the vegetation layer. For a complete description of the interaction, the coupled processes of heat and moisture transport within the soil are included as a multilayer soil model. As model verification, successful reproductions of the observed energy fluxes over vegetated surfaces from the HAEEX-MOBILHY experiment in southwestern France and from the LOTREX-lOE/HIBE88 field experiment in Germany are presented. Finally, some sensitivity studies are performed and discussed in order to investigate the influence of different soil and vegetation types on the energy state of the atmosphere.
Frontiers in Environmental Science, 2014
This paper presents a method to produce long term climatic forcing fields to force Soil-Vegetation-Atmosphere transfer (SVAT) models in off-line mode. The objective is to increase the temporal frequency of existent climate projections databases from daily frequency to hourly time step to be used in impact climate studies. A statistical clustering k-means method is used. A tens of clusters seems to be enough to describe the climate variability in term of wind regimes, precipitation and thermal and humidity amplitude. These clusters are identified in the future projections of climate and reconstructed sequences at hourly frequency are obtained for all the forcing variables needed by a SVAT model, typically: air temperature, specific humidity, wind speed and direction, precipitation, direct short-wave radiation, downward long-wave radiation, and scattered short-wave radiation. Eleven years of observations from two sites in France are used to illustrate the method: the Chartres station (Paris) and Blagnac station (Toulouse). The reconstruction algorithm is able to produce diurnal cycles that fits well with hourly series from observations (1998-2008; 1961-1990) and from climatic scenarios . The diurnal amplitude and mean value is well represented for variables with marked daily cycle as temperature or humidity. Changes in the mean wind direction are represented and, to a certain extent, changes in wind intensity are also retained. The mean precipitation is conserved during the day even if the method is not able to reproduce the short rain picks variability. Precipitation is used as input in the clusterization process so in clusters representative of rainy days some diurnal variability is nevertheless retained. Citation: Hidalgo J, Masson V and Baehr C (2014) From daily climatic scenarios to hourly atmospheric forcing fields to force Soil-Vegetation-Atmosphere transfer models. Front. Environ. Sci. 2:40.
2019
The present study investigates the ability of SimSphere, a Soil Vegetation Atmosphere Transfer (SVAT) model, to predict key parameters in characterising land Surface interactions. In particular, the model’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), andSensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation. In overall, SimSphere was largely able to accurately predict the variables against which it was evaluated for most of the experimental sites. Statistical analysis showed highest agreement of H fluxes to the measured in-situ values for all ecosystems, with an average RMSD of 55.36 Wm-2. Predicted LE fluxes and Rnet also agreed well with the corresponding in-situ data with RSMDs of 62.75 Wm-2 and 64.65 Wm-2 respectively....
A global analysis of soil moisture derived from satellite observations and a land surface model
Hydrology and Earth System Sciences, 2012
Soil moisture availability is important in regulating photosynthesis and controlling land surface-climate feedbacks at both the local and global scale. Recently, global remote-sensing datasets for soil moisture have become available. In this paper we assess the possibility of using remotely sensed soil moisture-AMSR-E (LPRM)-to similate soil moisture dynamics of the process-based vegetation model ORCHIDEE by evaluating the correspondence between these two products using both correlation and autocorrelation analyses. We find that the soil moisture product of AMSR-E (LPRM) and the simulated soil moisture in ORCHIDEE correlate well in space and time, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE has on average a higher temporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements. This may be due to the different vertical depth of the two products-AMSR-E (LPRM) at the 2-5 cm surface depth and ORCHIDEE at the root zone (max. 2 m) depth-to uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consists of a single-layer deep soil, which does not allow simulation of the proper cascade of time scales that characterize soil drying after each rain event. We conclude that assimilating soil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE with an improved hydrological model of more than one soil layer, may significantly improve the soil moisture dynamics, which could lead to improved CO 2 and energy flux predictions.