Measurement and estimation of the soil water retention curve using the evaporation method and the pseudo continuous pedotransfer function

Haghverdi, Amir; Öztürk, Hasan Sabri; Durner, Wolfgang

doi:10.1016/j.jhydrol.2018.06.007

Cited by 33 publications

(30 citation statements)

References 30 publications

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“…The RMSE values reported for parametric PTFs in the literature typically range from 0.034 cm 3 cm −3 to 0.085 cm 3 cm −3 [11]. Therefore, PTFs developed using the HYPROP data, both in this study and by Haghverdi et al [18], rank high among previously published parametric PTFs. Our results (Table 6), however, show that a majority of the SWRC model parameters cannot be estimated with high accuracy as a function of basic soil properties.…”

Section: Overall Performance Of the Parametric Ptfssupporting

confidence: 48%

“…The undisturbed samples were used to determine the bulk density, BD [29]. More details about this dataset are outlined in Haghverdi et al [5,18]. The water retention data of the repacked samples were measured using the HYPROP system at the Technical University of Braunschweig in Germany.…”

Section: Methodsmentioning

confidence: 99%

“…This is suggested given the high-resolution water retention and hydraulic conductivity data generated by the instrument, its relatively fast measurement cycle and its adaptation by researchers from different parts of the world [12][13][14][15][16][17]. In a recent study, Haghverdi et al [18] showed that artificial neural network-based PC-PTF successfully predicts the SWRC using high-resolution data collected via the HYPROP system.…”

Section: Introductionmentioning

confidence: 99%

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Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

Haghverdi

Öztürk

Durner

2020

Water

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A high-resolution soil water retention data set (81 repacked soil samples with 7729 observations) measured by the HYPROP system was used to develop and evaluate the performance of regression parametric pedotransfer functions (PTFs). A total of sixteen soil hydraulic models were evaluated including five unimodal water retention expressions of Brooks and Corey (BC model), Fredlund and Xing (FX model), Kosugi (K model), van Genuchten with four free parameters (VG model) and van Genuchten with five free parameters (VGm model). In addition, eleven bimodal, Peters–Durner–Iden (PDI) and bimodal-PDI variants of the original expressions were studied. Six modeling scenarios (S1 to S6) were examined with different combinations of the following input predictors: soil texture (percentages of sand, silt and clay), soil bulk density, organic matter content, percent of stable aggregates and saturated water content (θs). Although a majority of the model parameters showed low correlations with basic soil properties, most of the parametric PTFs provided reasonable water content estimations. The VGm parametric PTF with an RMSE of 0.034 cm3 cm−3 was the best PTF when all input predictors were considered. When averaged across modeling scenarios, the PDI variant of the K model with an RMSE of 0.045 cm3 cm−3 showed the highest performance. The best performance of all models occurred at S6 when θs was considered as an additional input predictor. The second-best performance for 11 out of the 16 models belonged to S1 with soil textural components as the only inputs. Our results do not recommend the development of parametric PTFs using bimodal variants because of their poor performance, which is attributed to their high number of free parameters.

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Section: Overall Performance Of the Parametric Ptfssupporting

confidence: 48%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

Haghverdi

Öztürk

Durner

2020

Water

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“…The soil texture was determined by the hydrometer method [52] and the PARIO soil particle analyzer (METER Group, Inc., Pullman, WA, USA) for the Turkish and Unites States samples, respectively. More details about the soil data set and the laboratory procedures are outlined in Haghverdi et al [1,16]. Table 1 summarizes the characteristics of the soils used in this study.…”

Section: Data Collection and Laboratory Analysesmentioning

confidence: 99%

“…The soil water retention curve (SWRC) is considered the most critical curve in soil physics and has numerous applications in the agro-hydrological areas. A considerable number of studies have been conducted on SWRC's related research topics including in situ and laboratory measurement techniques [1][2][3][4][5], mathematical parametrization [6][7][8][9][10][11][12][13][14][15], and indirect estimation via pedotransfer function (PTF) [1,[16][17][18][19][20][21][22][23]. The SWRC is often used to determine the soil hydraulic conductivity curve via capillary bundle models [24,25].…”

Section: Introductionmentioning

confidence: 99%

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: I. Direct Model Fit Using the Extended Evaporation and Dewpoint Methods

Haghverdi

Najarchi

Öztürk

et al. 2020

Water

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This study focuses on the reliable parametrization of the full Soil Water Retention Curve (SWRC) from saturation to oven-dryness using high resolution but limited range measured water retention data by the Hydraulic Property Analyzer (HYPROP) system. We studied the performance of five unimodal water retention models including the Brooks and Corey model (BC model), the Fredlund and Xing model (FX model), the Kosugi model (K model), the van Genuchten constrained model with four free parameters (VG model), and the van Genuchten unconstrained model with five free parameters (VGm model). In addition, eleven alternative expressions including Peters–Durner–Iden (PDI), bimodal, and bimodal-PDI variants of the original models were evaluated. We used a data set consisting of 94 soil samples from Turkey and the United States with high-resolution measured data (a total of 9264 measured water retention data pairs) mainly via the HYPROP system and supplemented for some samples with measured dry-end data using the WP4C instrument. Among unimodal expressions, the FX and the K models with the Mean Absolute Error (MAE) values equal to 0.005 cm3 cm−3 and 0.015 cm3 cm−3 have the highest and the lowest accuracy, respectively. Overall, the alternative variants provided a better fit than the unimodal expressions. The unimodal models, except for the FX model, fail to provide reliable dry-end estimations using HYPROP data (average MAE: 0.041 cm3 cm−3, average r: 0.52). Our results suggested that only models that account for the zero water content at the oven dryness and properly shift from the middle range to dry-end (i.e., the FX model and PDI variants) can adequately represent the full SWRC using typical data obtained via the HYPROP system.

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Prediction of Soil–Water Characteristic Curves in Bimodal Tropical Soils Using Artificial Neural Networks

dos Santos Pereira,

Silva Junior,

Mendes

et al. 2023

Geotech Geol Eng

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Measurement and estimation of the soil water retention curve using the evaporation method and the pseudo continuous pedotransfer function

Cited by 33 publications

References 30 publications

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: I. Direct Model Fit Using the Extended Evaporation and Dewpoint Methods

Prediction of Soil–Water Characteristic Curves in Bimodal Tropical Soils Using Artificial Neural Networks

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