scholarly journals Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework

2020 ◽  
Author(s):  
Surya Gupta ◽  
Tomislav Hengl ◽  
Peter Lehmann ◽  
Sara Bonetti ◽  
Andreas Papritz ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydraulic Conductivity ◽  
Transfer Functions ◽  
Saturated Hydraulic Conductivity

Data forcing errors resulting from lack of harmonization and standardization in measurement methodologies: A comparison of soil hydrophysical data from a large EU database.

2020 ◽  
Author(s):  
Martine van der Ploeg ◽  
Attila Nemes
Keyword(s):  
Organic Matter ◽  
Hydraulic Conductivity ◽  
Bulk Density ◽  
Large Scale ◽  
Water Retention ◽  
Transfer Functions ◽  
Landscape Heterogeneity ◽  
Organic Matter Content ◽  
Saturated Hydraulic Conductivity ◽  
Soil Water Retention

<p>Soil hydro-physical properties —such as soil water retention, (un)saturated hydraulic conductivity, shrinkage and swelling, organic matter content, texture (particle distribution), structure (soil aggregation/pore structure)and bulk density— are used in many sub(surface) modeling applications. Reliable soil-hydrophysical properties are key to proper predictions with such models, yet the harmonization and standardization of these properties has not received much attention. Lack of harmonization and standardization may lead to heterogeneity in data as a result of differences in methodologies, rather than real landscape heterogeneity. A need and scope has been identified to better harmonize, innovate, and standardize methodologies regarding measuring soil hydraulic properties that form the information base of many derived products in support of EU policy. With this identified need in mind the Soil Program on Hydro-Physics via International Engagement (SOPHIE) was initiated in 2017. Besides developing new activities that may advise future measurements, we also explore historic data and metadata and mine its relevant contents. The European Hydro-pedological Data Inventory (EU-HYDI), the largest European database on measured soil hydrophysical properties, is – to date – rather under-explored in this sense, which served as motivation for this work.</p><p>From EU-HYDI we selected those records that were complete for soil texture, bulk density and organic matter, and fitted pedo-transfer functions separately for particular water retention points (at heads of 0, 2.5, 10, 100, 300, 1000, 3000, 15000 cm) and saturated hydraulic conductivity by multi-linear regression. We then subtracted the observed retention and hydraulic conductivity values from their estimated counterparts, and grouped the residuals by measurement methodologies. The results show that there can be significant differences between different methodologies and sample sizes used to obtain the water retention and hydraulic conductivity in the laboratory. The results thus show that the EU-data that may underlie large scale modelling may introduce errors in the forcing data that are attributed to a lack of harmonization and standardization in currently used measurement protocols.</p>

scholarly journals Improved calibration of the Green–Ampt infiltration module in the EROSION-2D/3D model using a rainfall-runoff experiment database

SOIL ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 241-253
Author(s):  
Hana Beitlerová ◽  
Jonas Lenz ◽  
Jan Devátý ◽  
Martin Mistr ◽  
Jiří Kapička ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydraulic Conductivity ◽  
Soil Erosion ◽  
3D Model ◽  
Transfer Functions ◽  
State Of The Art ◽  
The State ◽  
Saturated Hydraulic Conductivity ◽  
Infiltration Process ◽  
Calibration Parameter

Abstract. Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process, and additional calibration parameters are required. The Green–Ampt-based infiltration module in the EROSION-2D/3D model introduces a calibration parameter “skinfactor” to adjust saturated hydraulic conductivity. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable, and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study brings together an extensive database of rainfall simulation experiments, the state-of-the-art model parametrisation method and linear mixed-effect models to statistically analyse relationships between soil and vegetation conditions and the model calibration parameter skinfactor. New empirically based transfer functions for skinfactor estimation significantly improving the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance are provided in this study. Soil moisture and bulk density were identified as the most significant predictors explaining 82 % of the skinfactor variability, followed by the soil texture, vegetation cover and impact of previous rainfall events. The median absolute percentage error of the skinfactor prediction was improved from 71 % using the currently available method to 30 %–34 % using the presented transfer functions, which led to significant decrease in error propagation into the model results compared to the present method. The strong logarithmic relationship observed between the calibration parameter and soil moisture however indicates high overestimation of infiltration for dry soils by the algorithms implemented in EROSION-2D/3D and puts the state-of-the-art parametrisation method in question. An alternative parameter optimisation method including calibration of two Green–Ampt parameters' saturated hydraulic conductivity and water potential at the wetting front was tested and compared with the state-of-the-art method, which paves a new direction for future EROSION-2D/3D model parametrisation.

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 Evaluation and Development of Pedo-Transfer Functions for Predicting Soil Saturated Hydraulic Conductivity in the Alpine Frigid Hilly Region of Qinghai Province

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1581
Author(s):  
Yafan Zuo ◽  
Kangning He
Keyword(s):  
Hydraulic Conductivity ◽  
Water Conservation ◽  
Transfer Functions ◽  
Regional Scale ◽  
Predictive Performance ◽  
Saturated Hydraulic Conductivity ◽  
Soil Conditions ◽  
Qinghai Province ◽  
Hilly Region ◽  
Input Variables

In recent years, Pedo-Transfer Functions (PTFs) have become a commonly used tool to predict the hydraulic properties of soil. As an important index to evaluate the function of forest water conservation, the prediction of saturated hydraulic conductivity (KS) on the regional scale is of great significance to guide the vegetation construction of returning farmland to forest area. However, if the published PTFs are directly applied to areas where the soil conditions are different from those where the PTFs are established, their predictive performance will be greatly reduced. In this study, 10 basic soil properties were measured as input variables for PTFs to predict KS in the three watersheds of Taergou, Anmentan, and Yangjiazhai in the alpine frigid hilly region of Qinghai Province, China. The parameters of the eight published PTFs were modified by the least-squares method and new PTFs were also constructed, and their prediction performance was evaluated. The results showed that the KS of coniferous and broad-leaved mixed forests and broad-leaved pure forests in the study area were significantly higher than those of pure coniferous forests, and grassland and farmland were the lowest (p > 0.05). Soil Organic Matter plays an important role in predicting KS and should be used as an input variable when establishing PTFs. The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln2Cl, SOM2, and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.

scholarly journals Development of Pedo-Transfer Functions for the Saturated Hydraulic Conductivity of Forest Soil in South Korea Considering Forest Stand and Site Characteristics

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2217
Author(s):  
Honggeun Lim ◽  
Hyunje Yang ◽  
Kun Woo Chun ◽  
Hyung Tae Choi
Keyword(s):  
Hydraulic Conductivity ◽  
South Korea ◽  
Soil Properties ◽  
Forest Soil ◽  
Transfer Functions ◽  
Forest Stand ◽  
Saturated Hydraulic Conductivity ◽  
Temperate Climate ◽  
Topographic Features ◽  
Site Characteristics

The saturated hydraulic conductivity (Ks) is one of the most important soil properties for many hydrological simulation models. Especially in South Korea, analyzing the Ks of the forest soil is essential for understanding the water cycle throughout the country, because forests cover almost two-thirds of the whole country. However, few studies have focused on the forest soil in the temperate climate zone on a nationwide scale. In this study, 1456 forest soil samples were collected throughout South Korea and pedo-transfer functions employed to predict the Ks were developed. The non-linearities of the soil and topographic features were considered with the pretreatment of variables, and the variance inflation factor was used for treating the multicollinearity problem. The forest stand and site characteristics were also categorized by an ANOVA and post hoc test due to their diversity. As a result, the Ks values were different for various forest stands and site characteristics, which was statistically significant. Additionally, the model performance was higher when both soil properties and topographic features were considered. The sensitivity analysis showed that the Ks was highly affected by the bulk density, sand fraction, slope, and upper catchment area. Therefore, the topographic features were as important in predicting the Ks as the soil properties of the forest soil.

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.

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