Comparison of devices for measuring soil matric potential and effects on soil hydraulic functions and related parameters

Alcione Guimarães Freire, Thiago Leite de Alencar, Arilene Franklin Chaves, Ícaro Vasconcelos do Nascimento, Raimundo Nonato de Assis, Quirijn de Jong van Lier and Jaedson Cláudio Anunciato Mota

Agricultural Water Management, 2018, vol. 209, issue C, 134-141

Abstract: The tensiometer represents an excellent instrument for measuring in situ soil water status. However, measuring soil matric potential requires a tensiometer reading system sensitive enough to accurately record the matric potential. Hence, an instantaneous profile-type experiment was conducted in the field, to measure the soil matric potentials within a moisture range from saturation to field capacity. After that, matric potential, soil moisture, total potential gradient, flow density and hydraulic conductivity were calculated and van Genuchten equation parameters were estimated through inverse modeling. This study aimed to test the Bourdon pressure gauge and digital tensiometer compared with the mercury manometer to measure the soil matric potential and to examine the differences related to the estimations of soil water content and to the associated variables. In addition, the study also aimed to evaluate soil hydraulic parameters by inverse modeling, based on the matric potentials from each reading system. Bourdon pressure gauge replaces the Hg manometer in the measurement of soil water matric potential within the moisture range from saturation to field capacity; The use of digital tensiometer and Bourdon pressure gauge reduced hydraulic conductivity by four and three times and flow density by approximately three and two times, respectively, at 6 kPa tension and, therefore, are not recommended for the estimation of these hydraulic parameters; Regardless of reading system used in the tensiometer, inverse modeling estimates well van Genuchten equation parameters and, consequently, soil water matric potential.

Keywords: Tensiometer; Inverse modeling; Propagation of errors (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:209:y:2018:i:c:p:134-141

DOI: 10.1016/j.agwat.2018.07.008

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