scholarly journals An improved drip infiltrometer measuring the near-saturated hydraulic conductivity: Pedotransfer development and macropore transport

2020 ◽  
Author(s):  
Bo Vangsø Iversen ◽  
Michael Koppelgaard ◽  
Ali M. Kotlar
Keyword(s):  
Hydraulic Conductivity ◽  
Root Zone ◽  
Soil Column ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Ceramic Plate ◽  
Undisturbed Soil ◽  
Flow Rates ◽  
Spatially Distributed ◽  
High Level

<p>The near-saturated hydraulic conductivity is an important parameter in relation to the analysis of heterogeneous transport in the soil macropore system. To a high degree, leaching of phosphorus out of the root zone takes place in the macropores either in a dissolved form or as phosphorus bound to colloids. In this work, a newly constructed and improved drip infiltrometer (DIM) is presented being able to measure the unsaturated hydraulic conductivity in the near-saturated range (i.e. in the range of matric potentials between -0.1 and 3 -kPa) on undisturbed soil columns (20 cm by 20 cm). The DIM is a modified version of the classical multistep system establishing gravity flow at decreasing flow rates. The procedure is that the soil column is placed on top of a ceramic plate. Five tensiometers measure the change in the matric potential a different flow rates applied by a drip-irrigation device mounted on the top of the column. By applying a certain inflow at the top and suction at the bottom of the sample, a steady state flow is established based on tensiometer readings showing a constant gradient along the soil sample. This allows the determination of the near-saturated hydraulic conductivity by applying Darcy’s equation. Compared to an earlier version of the infiltrometer, the instrument has been improved in several ways. This involves a high level of automation of the computer program controlling the analysis making it possible to setup a number of settings and constrains in order to optimize the analysis. Examples are given for newly developed pedotransfer functions predicting the saturated and near-saturated hydraulic conductivity. Results were used to model water transport in the vadose zone spatially distributed over Denmark using variation in the hydraulic properties as well as spatially distributed metrological data. Models results ended up with a map pointing out risk areas of macropore transport in relation to the leaching of phosphorus.</p>

Global Mapping of Soil Saturated Hydraulic Conductivity Combining Legacy Data, Spatial Covariates and Machine Learning

2021 ◽  
Author(s):  
Surya Gupta ◽  
Peter Lehmann ◽  
Andreas Papritz ◽  
Tomislav Hengl ◽  
Sara Bonetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydraulic Conductivity ◽  
Cross Validation ◽  
Arable Land ◽  
Remotely Sensed ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Data Set ◽  
Global Database ◽  
Spatially Distributed

<p>Saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climatic modeling applications, as it controls the partitioning between precipitation, infiltration and runoff. Values of Ksat are often deduced from Pedotransfer Functions (PTFs) using maps of soil attributes. To circumvent inherent limitations of present PTFs (heavy reliance of arable land measurements, ignoring soil structure, and geographic bias to temperate regions), we propose a new global Ksat map at 1–km resolution by harnessing technological advances in machine learning and availability of remotely sensed surrogate information (terrain, climate and vegetation). We compiled a comprehensive Ksat data set with 13,258 data geo-referenced points from literature and other sources. The data were standardized and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB was then applied to develop a Covariate-based GeoTransfer Function (CoGTF) model for predicting spatially distributed Ksat values using remotely sensed information on various environmental covariates. The model accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root meansquare error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC=0.79 and RMSE=0.72 for non spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on soil texture information and bulk density. The validation indicates that Ksat could be modeled without bias using CoGTFs that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.</p>

scholarly journals Pedotransfer Functions for Estimating Saturated Hydraulic Conductivity of Selected Benchmark Soils in Ghana

2019 ◽  
pp. 1-11
Author(s):  
Henry Oppong Tuffour ◽  
Awudu Abubakari ◽  
Alex Amerh Agbeshie ◽  
Abdul Aziz Khalid ◽  
Erasmus Narteh Tetteh ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Organic Matter Content ◽  
Direct Methods ◽  
Matter Content ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Local Data ◽  
Undisturbed Soil ◽  
Exchangeable Calcium ◽  
Relative Improvement

Aims: Direct methods of measuring saturated hydraulic conductivity (Ks), either in situ or in the laboratory, are time consuming and very expensive. Several Pedotransfer functions (PTFs) are available for estimating Ks, with each having its own limitations. In this study, the performances of four popular PTFs were evaluated on different soil classes in the semi deciduous zone of Ghana. The PTFs considered herein were Puckett et al. (1985), Campbell and Shiozawa (1994), Dane and Puckett (1994), and Ferrer-Julià et al. (2004). In addition, five local data derived PTFs were used to study the possibility of using local datasets to validate PTF accuracy. Materials and Methods: A total of 450 undisturbed soil cores were collected from the 0 – 15 cm depth from three benchmark soils, namely, Stagni-Dystric Gleysol (SDG), Plinthi Ferric Acrisol (PFA) and Plinthic Acrisol (PA). The Ks of samples were measured by the falling-head permeameter method in the laboratory. Sand, silt and clay fractions, bulk density, organic matter content, and exchangeable calcium and sodium were measured and used as input parameters for the newly derived PTFs. Accuracy and reliability of the predictions were evaluated by the root mean square error (RMSE), coefficient of correlation (r), index of agreement (d), and the Nash-Sutcliffe efficiency (NSE) between the measured and predicted values from both tested and newly derived PTFs. The relative improvement (RI) of the newly derived PTFs from this study over the existing ones were also evaluated. Results: The newly derived PTFs in this study had higher prediction accuracy with r, d, RMSE and NSE ranging from 0.80 – 0.99, 0.79 – 0.94, 0.14 – 1.74 and 0.84 – 0.98, respectively, compared with 0.32 – 0.45, 0.27 – 0.50, 4.00 – 4.90 and 0.41 – 0.47 for the tested PTFs. The relative improvement of the newly derived over the tested PTFs ranged from 56.50 – 95.71% in the SDG, 70.73 – 96.89% in the PFA, and 65.37 – 95.81% in the PA. Generally, RI was observed to be highest for Model 1 in the SDG, and Model 4 in both PFA and PA, and lowest for Model 5 in all three soils. It was observed that the inclusion of exchangeable calcium and sodium as predictors increased the predictability of the newly derived PTFs.

scholarly journals Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 705
Author(s):  
Josué Trejo-Alonso ◽  
Carlos Fuentes ◽  
Carlos Chávez ◽  
Antonio Quevedo ◽  
Alfonso Gutierrez-Lopez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hydraulic Conductivity ◽  
Input Data ◽  
Logistic Function ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Back Propagation Algorithm ◽  
Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

Evaluation of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for U.S. Soils

2009 ◽  
Author(s):  
Ahmed M Abdelbaki ◽  
Mohamed A Youssef ◽  
Esmail M. F Naguib ◽  
Mohamed E Kiwan ◽  
Emad I El-giddawy
Keyword(s):  
Hydraulic Conductivity ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions

scholarly journals Effects of Temperature Change on the Soil Water Characteristic Curve and a Prediction Model for the Mu Us Bottomland, Northern China

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1235
Author(s):  
Xiaoying Qiao ◽  
Shaoyang Ma ◽  
Guixing Pan ◽  
Guanglu Liu
Keyword(s):  
Hydraulic Conductivity ◽  
Prediction Model ◽  
Soil Water ◽  
Direct Measurement ◽  
Empirical Formula ◽  
Characteristic Curve ◽  
Soil Column ◽  
Distribution Model ◽  
Undisturbed Soil ◽  
Soil Water Characteristic Curve

The soil-water characteristic curve (SWCC) is the basis for obtaining the hydraulic conductivity parameters of a soil as well as for using soil water and heat transport models. At present, the curve can be obtained by two methods: by direct measurement and by empirical formula. Direct measurement is both difficult and time-consuming. By contrast, fitting the SWCC with a suitable empirical formula is stable and convenient. The van Genuchten (VG) model has the advantage of universal applicability due to its use of a statistical aperture distribution model for estimating hydraulic conductivity. This study selected the Mu Us Bottomland as a study area. Data on the water content and water potential of undisturbed soil from this site were obtained with a Ku-pF instrument and a self-designed soil column experiment with temperature settings of 13 °C, 18 °C, 23 °C, 27 °C, and 30 °C. The variation of four main parameters in the VG model with temperature was analyzed based on thermodynamic theory and considering the effect of temperature on soil capillary pressure via its effects on surface tension and contact angle. A prediction model for the soil-water characteristic curve of the Mu Us Bottomland was then constructed, and its applicability was further analyzed. The temperature dependence of the SWCC demonstrated here provides an important scientific basis for agricultural production, farmland water conservancy, and the design of soil and water conservation engineering projects.

Surface-positioned double-ring to improve traditional infiltrometer for measuring soil infiltration

Soil Research ◽  
2020 ◽  
Vol 58 (3) ◽  
pp. 314
Author(s):  
Jing Zhang ◽  
Shaopeng Li
Keyword(s):  
Hydraulic Conductivity ◽  
Measurement Accuracy ◽  
Soil Column ◽  
Saturated Hydraulic Conductivity ◽  
Experimental Results ◽  
Water Infiltration ◽  
Maximum Relative Error ◽  
Wetting Front ◽  
Double Ring ◽  
Improve Measurement

The installation of a traditional double-ring infiltrometer (DRI) into soil is difficult and time consuming. It results in reduced accuracy because of soil disturbance and water leakage along the gaps between the ring wall and the soil. In this study, a surface-positioned DRI (SPDRI) was suggested to improve measurement accuracy and convenience of the DRI. Laboratory experiments were conducted to evaluate performance of the method in terms of the influence of the lateral flow of water on the accuracy of infiltration rate, average vertical wetting front depth and saturated hydraulic conductivity. A cylindrical soil column was used to simulate the ideal ring infiltrometer (IRI) of the one-dimensional vertical infiltration process for comparison purposes. Experimental results indicated that the infiltration rates measured by the SPDRI and IRI were nearly identical, with maximum relative error (RE) of 18.75%. The vertical wetting front depth of the SPDRI was nearly identical to that of the IRI, with proportional coefficients of 0.97 and R2 > 0.95. Comparison of the soil saturated hydraulic conductivity with those from IRI indicated that the REs were 7.05–10.63% for the SPDRI. Experimental results demonstrated that the SPDRI could improve the measurement accuracy and facilitate the soil water infiltration measurement process.

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