Saturated hydraulic conductivity from field measurements compared to pedotransfer functions in a heterogeneous arable landscape

2010 ◽  
Vol 21 (6) ◽  
pp. 923-930 ◽  
Author(s):  
Florian Winter ◽  
Markus Disse
Keyword(s):  
Hydraulic Conductivity ◽  
Field Measurements ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions

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 Determining a composite value for the saturated hydraulic conductivity in a recharge area of the Guarani Aquifer System, using pedotransfer functions

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Marcelo Eduardo Dias de Oliveira ◽  
Didier Gastmans ◽  
Marcelo Donadelli Sacchi ◽  
Rodrigo Esteves Rocha ◽  
Camila de Lima ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Field Measurements ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Essential Property ◽  
Sampling Point ◽  
Catchment Scale ◽  
Recharge Area ◽  
Aquifer System ◽  
Guarani Aquifer

ABSTRACT The saturated hydraulic conductivity (Ks) is an essential property for modeling water and contaminants movement into aquifers. However, Ks is extremely variable, even when considering nearby locations, which poses a challenge for modeling at catchment scales. Field measurements of Ks are most of the time expensive, time-consuming and labor-intensive. This study aimed to obtain, for modeling purposes, and using pedotransfer functions (PTFs), a composite value of Ks at a catchment scale, in a recharge area of the Guarani Aquifer System. Soil samples were taken across the study area, and the Ks for each sampling point were determined by several PTF methods. At the same locations, Ks field measurements were taken using a Guelph permeameter. Average values of Ks for all the sampling points calculated by PTFs were similar to the average value obtained by field measurements. The use of PTFs proved to be a faster and simpler method to efficiently determine the Ks value for the watershed and to capture the stochastic variation in terms of soil pore combination at the watershed scale.

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.

Saturated Hydraulic Conductivity of Clayey Tills and the Role of Fractures

1990 ◽  
Vol 21 (2) ◽  
pp. 119-132 ◽  
Author(s):  
Johnny Fredericia
Keyword(s):  
Hydraulic Conductivity ◽  
American Studies ◽  
Field Measurements ◽  
Present Knowledge ◽  
Saturated Hydraulic Conductivity ◽  
Field Observations ◽  
Laboratory Measurements ◽  
Vertical Hydraulic Conductivity ◽  
Data Field

The background for the present knowledge about hydraulic conductivity of clayey till in Denmark is summarized. The data show a difference of 1-2 orders of magnitude in the vertical hydraulic conductivity between values from laboratory measurements and field measurements. This difference is discussed and based on new data, field observations and comparison with North American studies, it is concluded to be primarily due to fractures in the till.

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

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>

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