Theories and models used in soil hydrological sciences rely on the knowledge of the spatially and temporally variable soil hydraulic properties, i.e. the water retention and hydraulic conductivity curves. Obtaining reliable experimental information on these curves for an area of interest is not easy and it still remains a challenge for soil scientists. In 2006, a simple and physically based methodology was proposed to completely characterize the soil using a single ring infiltration experiment in the field and determination of soil particle size distribution, initial and final soil water content and dry soil bulk density. This methodology, named BEST – Beerkan Estimation of Soil Transfer parameters, has received great attention and interest from the worldwide scientific community and it shows promise for further developments. This review paper aims to take the reader into the BEST world. Initially, the BEST experiment is described and the different algorithms that can be applied to analyze the infiltration data are illustrated. Then, an overview of the use that has been made of BEST up to now in the world is provided. Subsequently, infiltration prediction by BEST equations is discussed from both a theoretical and a practical point of view, i.e. with reference to the shape and geometric parameters of the assumed infiltration model and to the effects of the available information for the transient and steady-state phases of the process. Then, the application of BEST is presented with reference to different types of heterogeneous soils. Finally, practical recommendations and theoretical and experimental perspectives are provided.

Angulo-Jaramillo R., Bagarello V., Di Prima S., Gosset A., Iovino M., Lassabatere L. (2019). Beerkan Estimation of Soil Transfer parameters (BEST) across soils and scales. JOURNAL OF HYDROLOGY, 576, 239-261 [10.1016/j.jhydrol.2019.06.007].

Beerkan Estimation of Soil Transfer parameters (BEST) across soils and scales

Bagarello V.;Iovino M.;
2019-01-01

Abstract

Theories and models used in soil hydrological sciences rely on the knowledge of the spatially and temporally variable soil hydraulic properties, i.e. the water retention and hydraulic conductivity curves. Obtaining reliable experimental information on these curves for an area of interest is not easy and it still remains a challenge for soil scientists. In 2006, a simple and physically based methodology was proposed to completely characterize the soil using a single ring infiltration experiment in the field and determination of soil particle size distribution, initial and final soil water content and dry soil bulk density. This methodology, named BEST – Beerkan Estimation of Soil Transfer parameters, has received great attention and interest from the worldwide scientific community and it shows promise for further developments. This review paper aims to take the reader into the BEST world. Initially, the BEST experiment is described and the different algorithms that can be applied to analyze the infiltration data are illustrated. Then, an overview of the use that has been made of BEST up to now in the world is provided. Subsequently, infiltration prediction by BEST equations is discussed from both a theoretical and a practical point of view, i.e. with reference to the shape and geometric parameters of the assumed infiltration model and to the effects of the available information for the transient and steady-state phases of the process. Then, the application of BEST is presented with reference to different types of heterogeneous soils. Finally, practical recommendations and theoretical and experimental perspectives are provided.
2019
Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-Forestali
Angulo-Jaramillo R., Bagarello V., Di Prima S., Gosset A., Iovino M., Lassabatere L. (2019). Beerkan Estimation of Soil Transfer parameters (BEST) across soils and scales. JOURNAL OF HYDROLOGY, 576, 239-261 [10.1016/j.jhydrol.2019.06.007].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/363252
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