Comparison of Saturated Hydraulic Conductivity Methods for Sandy Loam Soil with Different Land Uses

2018 ◽  
pp. 99-117 ◽  
Author(s):  
Aminul Islam ◽  
D. R. Mailapalli ◽  
Anuradha Behera
Keyword(s):  
Hydraulic Conductivity ◽  
Sandy Loam ◽  
Saturated Hydraulic Conductivity ◽  
Sandy Loam Soil ◽  
Land Uses ◽  
Loam Soil

scholarly journals Source shape and data analysis procedure effects on hydraulic conductivity of a sandy-loam soil determined by ponding infiltration runs

2017 ◽  
Vol 48 (2) ◽  
pp. 71 ◽  
Author(s):  
Vincenzo Bagarello ◽  
Andrea De Santis ◽  
Giuseppe Giordano ◽  
Massimo Iovino
Keyword(s):  
Steady State ◽  
Hydraulic Conductivity ◽  
Sandy Loam ◽  
Analysis Procedure ◽  
Sandy Loam Soil ◽  
Second Phase ◽  
General Validity ◽  
Equivalent Radius ◽  
Loam Soil ◽  
Ks Values

Performing ponding infiltration runs with non-circular sources could represent a good means to sample completely an area of interest. Regardless of the shape of the source, predicting the expected reliability of the collected data by infiltrometers should facilitate soil hydraulic characterisation and also allow a more conscious use of the field data. The influence of the shape of the infiltration source (i.e., circular or square) and the analysis procedure of the steady-state infiltration data on the saturated hydraulic conductivity, Ks, of a sandy-loam soil was tested in this investigation. Circular and square surfaces sampled with the pressure infiltrometer (PI) yielded similar estimates of Ks (i.e., differing by a factor of 1.05-1.16, depending on the calculation method) when an equivalent radius was considered to geometrically describe the square source. With the simplified falling head (SFH) technique, the shape of the source was irrelevant (i.e., circular and square sources yielding Ks values that differed by a factor of 1.19), as theoretically expected. For the steady-state PI experiment, the twoponding depth approach yielded two times smaller Ks values than the one-ponding depth (OPD) approach, probably due to lower steady-state flow rates than those expected for the second phase of the two-level run. The conclusions were that: i) simple infiltrometer experiments (PI, SFH) can be carried out with square sources; and ii) the simplest PI run (OPD approach) is expected to yield the most reliable predictions of Ks. Sampling other soils is advisable in an attempt to make these conclusions of general validity.

scholarly journals Comparative Study of Soil Tillage Practices Effects on Hydraulic Conductivity and Bulk Density of a Sandy Loam Soil in Tunisia

2019 ◽  
Vol 3 (1) ◽  
pp. ep19013
Author(s):  
Roua Amami ◽  
Khaled Ibrahimi ◽  
Khaoula Abrougui ◽  
Amira Hmila ◽  
Sayed Chehaibi
Keyword(s):  
Hydraulic Conductivity ◽  
Comparative Study ◽  
Bulk Density ◽  
Sandy Loam ◽  
Sandy Loam Soil ◽  
Soil Tillage ◽  
Tillage Practices ◽  
Loam Soil

scholarly journals Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

Water SA ◽  
2019 ◽  
Vol 45 (1 January) ◽  
Author(s):  
Naji Mordi N Al-Dosary ◽  
Mohammed A Al-Sulaiman ◽  
Abdulwahed M Aboukarima
Keyword(s):  
Hydraulic Conductivity ◽  
Gaussian Process ◽  
Unsaturated Soil ◽  
Sandy Loam ◽  
Gaussian Process Regression ◽  
Sandy Loam Soil ◽  
Soil Hydraulic Conductivity ◽  
Unsaturated Hydraulic Conductivity ◽  
Loam Soil ◽  
Water And Solute Transport

Unsaturated soil hydraulic conductivity is a main parameter in agricultural and environmental studies, necessary for predicting and managing water and solute transport in soils. This parameter is difficult to measure in agricultural fields; thus, a simple and practical estimation method would be preferable, and quantitative methods (analytical and numerical) to predict the field parameters should be developed. Field experiments were conducted to collect water quality data to model the unsaturated hydraulic conductivity of a sandy loam soil. A mini disk infiltrometer (MDI) was used to measure soil infiltration rate. Input variables included electrical conductivity and the sodium adsorption ratio of irrigation water. Suction rate (pressure head), soil bulk density, and soil moisture content acted as inputs, with unsaturated soil hydraulic conductivity as output. The performance of Gaussian process regression (GPR) was analysed, with multiple linear regression (LR) and multi-layer perceptron (MLP) models used for comparison. Three performance criteria were compared: correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE). The simulations employed the Waikato environment for knowledge analysis (WEKA) open source tool. The results indicate that the GPR with Pearson VII function-based universal kernel (PUK kernel), cache size 250007, Omega 1.0 and Sigma 1.0 performs better than other kernels when evaluating test split data, with a correlation coefficient of 0.9646. The RMSEs for GPR (PUK kernel), MLP, and LR were 1.16 × 10−04, 1.87 × 10−04, and 2.22 × 10−04 cm·s−1, respectively. Predictive data mining algorithms (DMA) enable an estimate of unknown values based on patterns in a database. Therefore, the present methodology can be put to use in predictive tools to manage water and solute transport in soils, as the GPR model provides much greater accuracy than the LR and MLP models in predicting the unsaturated hydraulic conductivity of a sandy loam soil.

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