A simple water and energy balance model designed for regionalization and remote sensing data utilization

A simple soil–vegetation–atmosphere transfer (SVAT) model designed for scaling applications and remote sensing utilization will be presented. The study is part of the Semi-Arid Land Surface Atmosphere (SALSA) program. The model is built with a single-bucket and single-source representation with a bu...

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Bibliographic Details
Published in:Agricultural and forest meteorology 2000-11, Vol.105 (1), p.117-132
Main Authors: Boulet, G, Chehbouni, A, Braud, I, Vauclin, M, Haverkamp, R, Zammit, C
Format: Article
Language:eng
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Summary:A simple soil–vegetation–atmosphere transfer (SVAT) model designed for scaling applications and remote sensing utilization will be presented. The study is part of the Semi-Arid Land Surface Atmosphere (SALSA) program. The model is built with a single-bucket and single-source representation with a bulk surface of mixed vegetation and soil cover and a single soil reservoir. Classical atmospheric forcing is imposed at a reference level. It uses the concept of infiltration and evaporation capacities to describe water infiltration or exfiltration from a bucket of depth d r corresponding to the average infiltration and evaporation depth. The atmospheric forcing is divided into storm and interstorm periods, and both evaporation and infiltration phenomena are described with the well-known three stages representation: one at potential (energy- or rainfall-limited) rate, one at a rate set by the soil water content and one at a zero rate if the water content reaches one of its range limits, namely saturation or residual values. The analytical simplicity of the model is suitable for the investigation of the spatial variability of the mass and energy water balance, and its one-layer representation allows for the direct use of remote sensing data. The model is satisfactorily evaluated using data acquired in the framework of SALSA and a mechanistic complex SVAT model, Simple Soil-Plant-Atmosphere Transfer (SiSPAT) model.
ISSN:0168-1923
1873-2240