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

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.

Author(s):  
Josué Trejo-Alonso ◽  
Antonio Quevedo ◽  
Carlos Fuentes ◽  
Carlos Chávez

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.


Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1005 ◽  
Author(s):  
Lucia Toková ◽  
Dušan Igaz ◽  
Ján Horák ◽  
Elena Aydin

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.


2020 ◽  
Vol 34 (3) ◽  
pp. 310-324
Author(s):  
Leonardo Ezequiel Scherger ◽  
Victoria Zanello ◽  
Claudio Lexow

The aim of this work is to compare the use of the inverse solution approach in the estimation of soil hydraulic properties with traditional tension disk infiltrometer (TDI) data analysis, field retention data and commonly used pedotransfer functions (PTFs). Field data were collected in an experimental plot located at Bahía Blanca, Argentina. Field infiltration under saturated conditions was measured by the inverse auger hole method and infiltration under unsaturated conditions were carried out with TDI. Field retention data (θ(h)) were also collected periodically. The HYDRUS 2D/3D software was used to optimize soil hydraulic parameters by inverse solution according to TDI data. The saturated hydraulic conductivity measured by inverse auger hole method (5.53 cm.h-1) and calculated by Wooding analytical approach (5.35 cm.h-1) and inverse numerical simulations (5.36 cm.h-1) showed very close values. According to macroporosity estimates infiltrated water is mainly conducted through soils micro and mesopores.  Macropores only channeled 15.9% of total infiltrated flow.  Soil water retention curves (SWRC) predicted by PTFs did not represented correctly field retention data. The best adjustment between water content at specific pressure heads predicted by SWRCs and field measured water content was reached by the TDI inverse solution approach (RMSE: 0.050 cm3.cm-3). The inverse solution approach probed to be a simple and practical method to obtain an accurate estimate of both, SWRC and hydraulic conductivity curve.


Soil Research ◽  
2013 ◽  
Vol 51 (2) ◽  
pp. 94 ◽  
Author(s):  
Rogerio Cichota ◽  
Iris Vogeler ◽  
Val O. Snow ◽  
Trevor H. Webb

Modelling water and solute transport through soil requires the characterisation of the soil hydraulic functions; however, determining these functions based on measurements is time-consuming and costly. Pedotransfer functions (PTFs), which make use of easily measurable soil properties to predict the hydraulic functions, have been proposed as an alternative to measurements. The better known and more widely used PTFs were developed in the USA or Europe, where large datasets exist. No specific PTFs have been published for New Zealand soils. To address this gap, we evaluated a range of published PTFs against an available dataset comprising a range of different soils from New Zealand and selected the best PTFs to construct an ensemble PTF (ePTF). Assessment (and adjustment when required) of published PTFs was done by comparing measurements and estimates of soil water content and the hydraulic conductivity at selected matric suction values. For each point, the best two or three PTFs were chosen to compose the ePTF, with correcting constants if needed. The outputs of the ePTF are the hydraulic properties at selected matric suctions, akin to obtaining measurements, thus allowing the fit of different equations as well as combining any available measurements. Testing of the ePTF showed promising performance, with reasonably accurate estimates of the water retention of an independent dataset. Root mean square error values averaged 0.06 m3 m–3 for various New Zealand soils, which is within the accuracy level of published PTF studies. The largest errors were found for soils with high clay content, for which the ePTF should be used with care. The performance of the ePTF for estimating soil hydraulic conductivity was not as reliable as for water content, exhibiting large scatter. Predictions of saturated hydraulic conductivity were of the same magnitude as the measurements, whereas the unsaturated values were generally under-predicted. The conductivity data available for this study were limited and highly variable. The estimates for hydraulic conductivity should therefore be used with much care, and future research should address measurements and analysis to improve the predictions. The ePTF was also used to parameterise the SWIM soil module for use in Agricultural Production Systems Simulator (APSIM) simulations. Comparisons of drainage predicted by APSIM against results from lysimeter experiments suggest that the use of the derived ePTF is suited for the estimation of soil parameters for use in modelling. The ePTF is not envisaged as a substitute for measurements but is a useful tool to complement datasets with limited amounts of measured data.


2021 ◽  
Vol 13 (4) ◽  
pp. 1593-1612
Author(s):  
Surya Gupta ◽  
Tomislav Hengl ◽  
Peter Lehmann ◽  
Sara Bonetti ◽  
Dani Or

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.


2020 ◽  
Author(s):  
Surya Gupta ◽  
Tomislav Hengl ◽  
Peter Lehmann ◽  
Sara Bonetti ◽  
Dani Or

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.


2021 ◽  
Author(s):  
Brigitta Szabó ◽  
Melanie Weynants ◽  
Tobias Weber

<p>We present improved European hydraulic pedotransfer functions (PTFs) which now use the machine learning algorithm random forest and include prediction uncertainties. The new PTFs (euptfv2) are an update of the previously published euptfv1 (Tóth et al., 2015). With the derived hydraulic PTFs soil hydraulic properties and van Genuchten-Mualem model parameters can be predicted from easily available soil properties. The updated PTFs perform significantly better than euptfv1 and are applicable for 32 predictor variables combinations. The uncertainties reflect uncertainties from the considered input data, predictors and the applied algorithm. The euptfv2 includes transfer functions to compute soil water content at saturation (0 cm matric potential head), field capacity (both -100 and -330 cm matric potential head) and wilting point (-15,000 cm matric potential head), plant available water content computed with field capacity at -100 and -330 cm matric potential head, saturated hydraulic conductivity, and Mualem-van Genuchten parameters of the moisture retention and hydraulic conductivity curves. The influence of predictor variables on predicted soil hydraulic properties is explored and suggestions to best predictor variables given.</p><p>The algorithms have been implemented in a web interface (https://ptfinterface.rissac.hu) and an R package (https://doi.org/10.5281/ZENODO.3759442) to facilitate the use of the PTFs, where the PTFs’ selection is automated based on soil properties available for the predictions and required soil hydraulic property.</p><p>The new PTFs will be applied to derive soil hydraulic properties for field- and catchment- scale hydrological modelling in European case studies of the OPTAIN project (https://www.optain.eu/). Functional evaluation of the PTFs is performed under the iAqueduct research project.</p><p> </p><p>This research has been supported by the Hungarian National Research, Development and Innovation Office (grant no. KH124765), the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4), and the German Research Foundation (grant no. SFB 1253/12017). OPTAIN is funded by the European Union’s Horizon 2020 Program for research and innovation under Grant Agreement No. 862756.</p>


1999 ◽  
Vol 50 (7) ◽  
pp. 1259 ◽  
Author(s):  
K. R. J. Smettem ◽  
K. L. Bristow

Regional scale application of water and solute transport models is often limited by the lack of available data describing soil hydraulic properties and their variability. Direct measurement over large areas is expensive and time consuming. Physico-empirical models derived from soil survey data are therefore an attractive alternative. If the Marshall method of estimating the saturated hydraulic conductivity is simplified to depend primarily on the maximum pore radius, given by the bubbling pressure, then it is equivalent to the Campbell model of saturated hydraulic conductivity which relies entirely on an estimate of the bubbling pressure obtained from particle size data. We apply this simplified physico-empirical model to estimate the ‘matrix’, or textural saturated hydraulic conductivity, K m, using estimates of the bubbling pressure derived entirely from clay content data that are readily available in soil surveys. Model estimates are compared with in situ measurements on surface soils obtained using a disc permeameter with a negative pressure head at the supply surface of 40 mm. Results appear to be satisfactory for broad-scale water balance and leaching risk models that require specification of a matching point for the unsaturated hydraulic conductivity function and for modelling applications requiring generalised application of results from experimental sites.


Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1516
Author(s):  
Josué Trejo-Alonso ◽  
Antonio Quevedo ◽  
Carlos Fuentes ◽  
Carlos Chávez

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.


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