The Beerkan method along with BEST algorithms is an alternative technique to conventional laboratory or field measurements for rapid and low-cost estimation of soil hydraulic properties. The Beerkan method is simple to conduct but requires an operator to repeatedly pour known volumes of water through a ring positioned at the soil surface. A cheap infiltrometer equipped with a data acquisition system was recently designed to automate Beerkan infiltration experiments. In this paper, the current prototype of the automated infiltrometer was tested to validate its applicability to the Beerkan infiltration experiment under several experimental circumstances. In addition, the accuracy of the estimated saturated soil hydraulic conductivity, Ks, and sorptivity, S, was assessed by applying different BEST algorithms to the data obtained with the infiltrometer. At this purpose, both analytically generated and real experimental data were used. The analytical assessment showed that the use of the infiltrometer along with BEST methods could lead to accurate estimates of the considered soil properties in most cases, which validated the design of the infiltrometer and its combination with BEST algorithms. Loamy soils and high initial water contents led to misestimating Ks and S or to failure of BEST algorithms, but advices about the infiltrometer design were developed to alleviate such problems. A comparison between the automated procedure and the original BEST procedure was made at three field sites in Sicily (Italy). Other experiments were carried out in an infiltration basin located in the pumping well field of Crépieux-Charmy (Lyon, France), in order to assess the ability of the automated infiltrometer to check clogging effects on Ks. The experiments showed that the automatic data collection increased measurement speed, allowed a more efficient data handling and analysis, and reduced sensitivity of the calculated hydraulic parameters on the applied BEST algorithm.
|Number of pages||15|
|Publication status||Published - 2016|
All Science Journal Classification (ASJC) codes
- Soil Science