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Estimation of saturated hydraulic conductivity from double‐ring infiltrometer measurements
Author(s) -
Fatehnia M.,
Tawfiq K.,
Ye M.
Publication year - 2016
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12322
Subject(s) - infiltrometer , ponding , hydraulic conductivity , infiltration (hvac) , soil science , saturation (graph theory) , mean squared error , soil water , geotechnical engineering , mathematics , finite element method , correlation coefficient , hydrology (agriculture) , materials science , environmental science , geology , statistics , physics , thermodynamics , composite material , combinatorics , drainage , ecology , biology
Summary This research aims to determine soil vertical saturated hydraulic conductivity ( K s ) in situ from the measured steady infiltration rate ( I ), initial soil properties and double‐ring infiltrometer ( DRI ) test data. Characterizing the effects of these variables on the measured steady infiltration rate will enable more accurate prediction of K s . We measured the effects of the ring diameter, head of ponding, ring depth, initial effective saturation and soil macroscopic capillary length on measured steady infiltration rates. We did this by simulating 864 DRI tests with the finite element program HYDRUS‐2D and by conducting 39 full‐scale in situ DRI tests, 30 M ini‐ D isk infiltrometer experiments and four G uelph P ermeameter tests. The M5 ′ model trees and genetic programming ( GP ) methods were applied to the data to establish formulae to predict the K sof sandy to sandy‐clay soils. The nine field DRI tests were used to verify the computer models. We determined the accuracy of the methods with 30% of the simulated DRI data to compare I/K Svalues of the finite element models with estimates from the suggested formulae. We also used the suggested formulae to predict the K s values of 30 field DRI experiments and compared them with values measured by G uelph P ermeameter tests. Compared with the GP method, the M5 ′ model was better at predicting K S , with a correlation coefficient of 0.862 and root mean square error ( RMSE ) of 0.282 cm s −1 . In addition, the latter method estimated K s values of the field experiments more accurately, with an RMSE of 0.00346 cm s −1 .