scholarly journals Evaluation and Development of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for Mexican Soils

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1516
Author(s):  
Josué Trejo-Alonso ◽  
Antonio Quevedo ◽  
Carlos Fuentes ◽  
Carlos Chávez
Keyword(s):  
Hydraulic Conductivity ◽  
Bulk Density ◽  
Saturated Hydraulic Conductivity ◽  
The Other ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Del Rio ◽  
Clay Percentage ◽  
Prediction Capability ◽  
Better Than

In the present work, we evaluate the prediction capability of six pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (KS). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were constructed for KS from clay percentage, bulk density, and saturation water content data. The results show, for the evaluated models, that one model presents an overestimation for KS > 0.5 cm h−1 values, three models have an underestimation for KS > 1.0 cm h−1, and two models have a good correlation (R2 > 0.98) but more than three parameters are necessary. Nevertheless, the last two models require 3–4 parameters in order to obtain optimization. On the other hand, the models proposed in this work have a similar correlation with fewer parameters. The fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and R2 = 0.9901 when we use two.

scholarly journals Evaluation and Development of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for Mexican Soils

2020 ◽  
Author(s):  
Josué Trejo-Alonso ◽  
Antonio Quevedo ◽  
Carlos Fuentes ◽  
Carlos Chávez
Keyword(s):  
Hydraulic Conductivity ◽  
Water Content ◽  
Bulk Density ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Del Rio ◽  
Clay Percentage ◽  
Prediction Capability ◽  
Better Than

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

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.

Prediction of three key hydraulic properties in a soil survey of a small forested catchment

Soil Research ◽  
2002 ◽  
Vol 40 (2) ◽  
pp. 191 ◽  
Author(s):  
D. A. O'Connell ◽  
P. J. Ryan
Keyword(s):  
Hydraulic Conductivity ◽  
Water Content ◽  
Bulk Density ◽  
Hydraulic Properties ◽  
Saturated Hydraulic Conductivity ◽  
Soil Survey ◽  
Coarse Grained ◽  
Pedotransfer Functions ◽  
Explanatory Variables ◽  
Tree Models

Direct measurement of ψ(θ) and K(θ) relationships at all observation sites in soil survey is not feasible. Three key hydraulic properties — water content at field capacity (θ–5 kPa), water content at wilting point (θ–1.5 MPa), and saturated hydraulic conductivity (Ks) — can be used to derive K(θ) and ψ(θ) when combined with bulk density. These properties were measured in 'calibration' horizons in a soil survey in Yambulla State Forest in south-east New South Wales. Pedotransfer functions (PTFs) for predicting θ-5 kPa, θ–1.5 MPa, and Ks from the physical and morphologic soil attributes are presented and evaluated here. Models for predicting θ–5 kPa and θ–1.5 MPa relied on per cent clay. An R2 of 0.64 (for θ–5 kPa) to 0.67 (for θ–1.5 MPa) was obtained for linear regressions using only morphologic explanatory variables. An R2 of 0.73 (for θ–5 kPa) to 0.90 (for θ–1.5 MPa) was obtained if laboratory-measured clay content was included as an explanatory variable. Ks was measured in situ using well permeameters, and used for developing PTFs. Large cores were taken from a small subsample of horizons and measurements of Ks, K–0.1 kPa, K–0.2 kPa, and K–0.5 kPa were made in the laboratory. Ks measurements from well permeameters were similar to K-0.5 kPa from laboratory measurements. Regression and tree models were used to predict Ks. The linear regression had an R2 of 0.55, while the tree models accounted for approximately 40% reduction in deviance. Bulk density was the most useful predictor in all Ks models. The inclusion of per cent rock fragments, bulk density, and estimated percentage clay as useful explanatory variables demonstrated the utility of functional descriptors not routinely measured in soil survey. The models are empirical and were locally calibrated for use in a soil survey. They may be applicable in target domains similar to the source domain (i.e. coarse-grained adamellite soils in similar climatic regimes). surrogates, saturated hydraulic conductivity, K(θ), ψ(θ), Ks, pedotransfer functions, soil survey, soil morphology, PTF.

scholarly journals SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications

2021 ◽  
Vol 13 (4) ◽  
pp. 1593-1612
Author(s):  
Surya Gupta ◽  
Tomislav Hengl ◽  
Peter Lehmann ◽  
Sara Bonetti ◽  
Dani Or
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hydraulic Conductivity ◽  
Soil Properties ◽  
Bulk Density ◽  
Learning Algorithm ◽  
Field Measurements ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Global Database

Abstract. The saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climate models. Ksat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of Ksat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 Ksat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of Ksat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived Ksat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these Ksat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between Ksat data collected in temperate and tropical regions and Ksat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.

scholarly journals SoilKsatDB: global soil saturated hydraulic conductivity measurements for geoscience applications

2020 ◽  
Author(s):  
Surya Gupta ◽  
Tomislav Hengl ◽  
Peter Lehmann ◽  
Sara Bonetti ◽  
Dani Or
Keyword(s):  
Hydraulic Conductivity ◽  
Bulk Density ◽  
Regional Scale ◽  
Field Measurements ◽  
Soil Samples ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Tropical Regions ◽  
Global Database ◽  
Field Samples

Abstract. 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. Ksat values are primarily determined from soil textural properties and soil forming processes, and may vary over several orders of magnitude. Despite availability of Ksat datasets at catchment or regional scale, significant efforts are required to import and bind the data before it could be used for modeling. In this work, a total of 1,910 sites with 13,267 Ksat measurements were assembled from published literature and other sources, standardized, and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most global regions, with the highest data density from the USA, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11,667 measurements), bulk density (11,151 measurements), soil organic carbon (9,787 measurements), field capacity (7,389) and wilting point (7,418) are also included in the dataset. The results of using the SoilKsatDB to fit Ksat pedotransfer functions (PTFs) for temperate climatic regions and laboratory based soil samples based on soil properties (sand and clay content, bulk density) show that reasonably accurate models can be fitted using Random Forest (best CCC = 0.70 and CCC = 0.73 for temperate and lab based measurements, respectively). However when temperate and laboratory based Ksat PTFs are applied to soil samples from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.51 for tropical and CCC = 0.13 for field samples). PTFs derived for temperate soils and laboratory measurements might not be suitable for estimating Ksat for tropical regions or field measurements, respectively. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 and the code used to produce the compilation is publicly available under an open data license.

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

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 Effect of Biochar Application and Re-Application on Soil Bulk Density, Porosity, Saturated Hydraulic Conductivity, Water Content and Soil Water Availability in a Silty Loam Haplic Luvisol

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1005 ◽  
Author(s):  
Lucia Toková ◽  
Dušan Igaz ◽  
Ján Horák ◽  
Elena Aydin
Keyword(s):  
Hydraulic Conductivity ◽  
Water Content ◽  
Bulk Density ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Bulk Density ◽  
Relative Increase ◽  
Saturated Hydraulic Conductivity ◽  
Soil Drought ◽  
Silty Loam

Due to climate change the productive agricultural sectors have started to face various challenges, such as soil drought. Biochar is studied as a promising soil amendment. We studied the effect of a former biochar application (in 2014) and re-application (in 2018) on bulk density, porosity, saturated hydraulic conductivity, soil water content and selected soil water constants at the experimental site in Dolná Malanta (Slovakia) in 2019. Biochar was applied and re-applied at the rates of 0, 10 and 20 t ha−1. Nitrogen fertilizer was applied annually at application levels N0, N1 and N2. In 2019, these levels were represented by the doses of 0, 108 and 162 kg N ha−1, respectively. We found that biochar applied at 20 t ha−1 without fertilizer significantly reduced bulk density by 12% and increased porosity by 12%. During the dry period, a relative increase in soil water content was observed at all biochar treatments—the largest after re-application of biochar at a dose of 20 t ha−1 at all fertilization levels. The biochar application also significantly increased plant available water. We suppose that change in the soil structure following a biochar amendment was one of the main reasons of our observations.

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