SPLINTEX: A physically-based pedotransfer function for modeling soil hydraulic functions

Silva, Alessandra Calegari da; Armindo, Robson André; Brito, Alexsandro dos Santos; Schaap, M. G.

doi:10.1016/j.still.2017.07.011

Cited by 26 publications

(15 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RMSE values of 0.046 and 0.050 m 3 m −3 for the prediction of this water content were also obtained by Barros et al (2013) using soil samples from the northeast of Brazil. For sandy and clayey soils from different parts of Brazil, da Silva et al (2017) reported RMSE values of about 0.030 and 0.050 m 3 m −3 obtained by predictions from a semi‐deterministic PTF.…”

Section: Resultsmentioning

confidence: 99%

Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads

Kotlar

Lier

Barros

et al. 2019

Vadose Zone Journal

View full text Add to dashboard Cite

PTFs for water contents at specific pressure heads were developed. Covariate shift increased uncertainty in PTF predictions. Relative importance of predictors in machine learning PTFs was determined. There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov–Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF‐predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.

show abstract

Section: Resultsmentioning

confidence: 99%

Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads

Kotlar

Lier

Barros

et al. 2019

Vadose Zone Journal

View full text Add to dashboard Cite

show abstract

“…The second PTF in Splintex (PTF2) uses the same input information as PTF1, but does not include the correction based on the Arruda et al () model. Instead, PTF2 uses an additional experimentally measured θ ( h ) value in addition to the value of θ (0 m) (more detail is given in Silva et al , ). The performance of PTF2 based on a measured database and the PTF from the Rosetta H4‐model (Schaap et al , ) was evaluated by Silva et al ().…”

Section: Methodsmentioning

confidence: 99%

“…The Splintex 1.0 model is public freeware and comprises two PTFs to estimate the soil hydraulic properties included in its algorithm. It is a physical–empirical model that uses a simplification of the Arya & Paris () formulation (AP) with the cubic spline function and assumes a similarity between the mirrored shapes of particle and pore distribution functions (Silva et al ., ,b). Both PTFs available in Splintex can be applied to any porous medium without the need for calibration (Silva et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…However, its performance for a wider soil science community was only systematically evaluated when Silva et al . () reported on the quality of PTF2 and when Splintex‐PTF2 was used to simulate the spatial variation of soil hydraulic properties (Silva et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…In this study we aimed to examine the functionality of this PTF1 against measured data and also against PTF2, whose performance had already been evaluated by Silva et al . (,b). In addition, we tested the capability of Splintex to predict VGM parameters and also the sensitivity to both PTFs of some soil physical–structural variables derived from these parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating pedotransfer functions of the Splintex model

Reis

Armindo

Durães

et al. 2018

European J Soil Science

View full text Add to dashboard Cite

The soil water retention curve (SWRC) is an essential hydraulic function for the understanding and modelling of soil hydraulic processes. Its direct determination is time consuming and sometimes expensive because it requires extensive sampling, especially when spatial and temporal variation of soil hydraulic properties are an issue. The use of pedotransfer functions (PTFs) is a viable alternative to predict the parameters of SWRC from more easily determined soil attributes. Splintex is a physically based engineering model containing two PTFs to estimate SWRC parameters and saturated hydraulic conductivity, but its performance and functionality have not yet been fully evaluated and disseminated for soil science. We examined the functionality of the PTFs available in Splintex to estimate parameters of the Van Genuchten–Mualem (VGM) SWRC equation with a laboratory‐measured dataset containing information on particle size, bulk and particle density, saturated water content and parameters of the SWRCs. In addition, we tested the performance of both PTFs for deriving some soil physical–structural variables calculated from estimated SWRC parameters. Compared with VGM parameters fitted to the laboratory dataset, the model performed well. Its predictive performance for soil air capacity, relative field capacity and soil physical–structural quality indices was also evaluated. The performance of Splintex for estimating the VGM parameters depended on the combined effect of all input variables rather than on isolated correlations between an input variable and a model parameter. Highlights Splintex provides two PTFs to estimate the Van Genuchten–Mualem parameters of the SWRC. In addition to PTF1 input, PTF2 needs an additional soil water content–retention observation. Performance of Splintex‐PTF1 against measured data and ‐PTF2 prediction is presented. PTF2 provided better results than PTF1 for estimating SPQ indices.

show abstract

Estimating the optimal vegetation coverage for the dominant tree and shrub species over China’s northwest drylands

Zhang,

Jia,

Zhu

et al. 2024

Sci. China Earth Sci.

View full text Add to dashboard Cite

SPLINTEX: A physically-based pedotransfer function for modeling soil hydraulic functions

Cited by 26 publications

References 38 publications

Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads

Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads

Evaluating pedotransfer functions of the Splintex model

Estimating the optimal vegetation coverage for the dominant tree and shrub species over China’s northwest drylands

Contact Info

Product

Resources

About