Pedotransfer functions for the soil water characteristics of New Zealand soils using S-map information

McNeill, Stephen; Lilburne, Linda; Carrick, Sam; Webb, T. H.; Cuthill, T.

doi:10.1016/j.geoderma.2018.04.011

Cited by 56 publications

(55 citation statements)

References 38 publications

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“…Analyses on the prediction of soil hydraulic properties has been started extensively in the 1980s (Ahuja et al, 1985;Pachepsky et al, 1982;Rawls and Brakensiek, 1982;Saxton et al, 1986;Vereecken et al, 1989) and are continuously updated to increase performance of predictions (pedotransfer functions -PTFs) when newer statistical methods and/or new data become available. Latest works on it among others are McNeill et al (2018); Román Dobarco et al (2019); Zhang and Schaap (2017).…”

Section: Introductionmentioning

confidence: 99%

“…They calculated sub-grid variability of the coupled Mualem-van Genuchten model parameters for a coarse 0.25° grid based on fitting water retention and hydraulic conductivity model for each grid cell of the 1 km resolution SoilGrids. Román Dobarco et al (2019) and McNeill et al (2018) also provided information on uncertainty of the prediction of soil hydraulic properties.…”

Section: Introductionmentioning

confidence: 99%

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Mapping soil hydraulic properties using random forest based pedotransfer functions and geostatistics

Szabó¹,

Szatmári²,

Takács³

et al. 2018

Preprint

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Abstract. Spatial 3D information on soil hydraulic properties for areas larger than plot scale are usually derived with indirect methods due to lacking measured information on those. Soil hydraulic properties are calculated with applying pedotransfer functions (PTFs) – which describe the relationship between the desired soil hydraulic parameter and easily available soil properties determined on a soil hydraulic point dataset – on available soil maps. Our aim was to analyse difference in performance and spatial patterns between soil hydraulic maps derived with indirect (using PTFs) and direct (geostatistical) mapping methods. We performed the study on Balaton catchment in Hungary, where density of measured soil hydraulic data fulfils the requirements of geostatistical methods. Maps of saturated water content (THS), field capacity (FC) and wilting point (WP) for 0–30, 30–60 and 60–90 cm soil depth were prepared. PTFs were derived with random forest method on the whole Hungarian soil hydraulic dataset (MARTHA: soil chemical, physical, taxonomical and hydraulic information of some 12000 samples) complemented with information on topography, climate, parent material, vegetation and land use. As a direct method random forest combined with kriging (RFK) was applied on 359 MARTHA soil profiles located in the Balaton catchment. There was no significant difference between the direct and indirect methods in case of six out of nine maps having root mean squared error values between 0.052 and 0.074 cm3 cm−3, which is in accordance with the internationally accepted performance of hydraulic PTFs. The PTFs based mapping method performed significantly better than the RFK for the THS at 30–60 and 60–90 cm soil depth, in case of WP the RFK outperformed the PTFs at 60–90 cm depth. Difference between the PTF based and RFK mapped values are less than 0.025 cm3 cm−3 for 65–86 % of the catchment. In RFK uncertainty of input environmental covariate layers is less influential on the mapped values which is preferable. In the PTFs based method the uncertainty mapping of the soil hydraulic properties is less computational intensive. Detailed comparison of the maps derived by the PTF based and the RFK is presented in the paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping soil hydraulic properties using random forest based pedotransfer functions and geostatistics

Szabó¹,

Szatmári²,

Takács³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In most cases there are no available point data for applying geostatistical methods; therefore in several studies soil hydraulic maps were generated with a PTF applied on easily available spatial soil data (Chaney et al, 2016;Dai et al, 2013;Marthews et al, 2014;Montzka et al, 2017;Wu et al, 2018).…”

mentioning

confidence: 99%

“…In this way extreme conditions might be better described. A possible calculation of this kind of uncertainty was provided by Montzka et al (2017). They calculated sub-grid variability of the coupled Mualem-van Genuchten model parameters for a coarse 0.25 • grid based on fitting water retention and hydraulic conductivity model for each grid cell of the 1 km resolution SoilGrids.…”

mentioning

confidence: 99%

“…They calculated sub-grid variability of the coupled Mualem-van Genuchten model parameters for a coarse 0.25 • grid based on fitting water retention and hydraulic conductivity model for each grid cell of the 1 km resolution SoilGrids. Román Dobarco et al (2019) and McNeill et al (2018) also provided information on the uncertainty of the prediction of soil hydraulic properties. The root mean square error (RMSE) of published PTFs predicting soil water retention is usually between 0.02 and 0.07 cm 3 cm −3 depending on the predicted soil hydraulic property and available input informa- tion (e.g.…”

mentioning

confidence: 99%

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Szatmári¹

2019

Preprint

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Spatial 3-D information on soil hydraulic properties for areas larger than plot scale is usually derived using indirect methods such as pedotransfer functions (PTFs) due to the lack of measured information on them. PTFs describe the relationship between the desired soil hydraulic parameter and easily available soil properties based on a soil hydraulic reference dataset. Soil hydraulic properties of a catchment or region can be calculated by applying PTFs on available soil maps. Our aim was to analyse the performance of (i) indirect (using PTFs) and (ii) direct (geostatistical) mapping methods to derive 3-D soil hydraulic properties. The study was performed on the Balaton catchment area in Hungary, where density of measured soil hydraulic data fulfils the requirements of geostatistical methods. Maps of saturated water content (0 cm matric potential), field capacity ( − 330 cm matric potential) and wilting point (−15 000 cm matric potential) for 0-30, 30-60 and 60-90 cm soil depth were prepared. PTFs were derived using the random forest method on the whole Hungarian soil hydraulic dataset, which includes soil chemical, physical, taxonomical and hydraulic properties of some 12 000 samples complemented with information on topography, climate, parent material, vegetation and land use. As a direct and thus geostatistical method, random forest combined with kriging (RFK) was applied to 359 soil profiles located in the Balaton catchment area. There were no significant differences between the direct and indirect methods in six out of nine maps having root-mean-square-error values between 0.052 and 0.074 cm 3 cm −3 , which is in accordance with the internationally accepted performance of hydraulic PTFs. The PTF-based mapping method performed significantly better than the RFK for the saturated water content at 30-60 and 60-90 cm soil depth; in the case of wilting point the RFK outperformed the PTFs at 60-90 cm depth. Differences between the PTF-based and RFK mapped values are less than 0.025 cm 3 cm −3 for 65 %-86 % of the catchment. In RFK, the uncertainty of input environmental covariate layers is less influential on the mapped values, which is preferable. In the PTF-based method the uncertainty of mapping soil hydraulic properties is less computationally intensive. Detailed comparisons of maps derived from the PTF-based method and the RFK are presented in this paper.

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An improved method for measuring wetting front advancing velocity

Liu

et al. 2021

Soil Science Soc of Amer J

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The unsaturated soil hydraulic conductivity function is one of the keys to solving unsaturated seepage issues. The wetting front advancing method (WFAM) is a proposed method for quickly measuring the coefficient of permeability of the unsaturated soils, but it has some demerits and limitations. In this study, a series of capillary rise tests were conducted and the coefficient of permeability of six kinds of unsaturated soils were obtained by using the WFAM. Based on the analyzing of characteristic changes in volumetric water content via soil moisture sensors in the capillary rise test, improvements were made to the method of determining the wetting front advancing velocity. The RMSE evaluation index was used to analyze the deviation of the data obtained by the improved method from the original visual method. The RMSE of the wetting front advancing velocity ranged from 0.0007 to 0.0074 cm s -1 , and the RMSE of the coefficient of permeability ranged from 0 to 5.2 × 10 -6 m s -1 . The results indicated the applicability of the improved method.

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Pedotransfer functions for the soil water characteristics of New Zealand soils using S-map information

Cited by 56 publications

References 38 publications

Mapping soil hydraulic properties using random forest based pedotransfer functions and geostatistics

Mapping soil hydraulic properties using random forest based pedotransfer functions and geostatistics

author comments

An improved method for measuring wetting front advancing velocity

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