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Yves Coquet - One of the best experts on this subject based on the ideXlab platform.
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Pedotransfer Functions for isoproturon sorption on soils and vadose zone materials
Pest management science, 2011Co-Authors: Jullien Moeys, Valérie Bergheaud, Yves CoquetAbstract:BACKGROUND: Sorption coefficients (the linear KD or the non-linear KF and NF) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 KD and 55 KF) was compiled, and Pedotransfer Functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. RESULTS: The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon KD. The Pedotransfer Function is KD = 1.7822 + 0.0162 OC1.5 − 0.1958 pH (KD in L kg−1 and OC in g kg−1). For low-OC soils (OC < 6.15 g kg−1), clay and pH are most influential. The Pedotransfer Function is then KD = 0.9980 + 0.0002 clay − 0.0990 pH (clay in g kg−1). Benchmarking KD estimations showed that Functions calibrated on more specific subsets of the data perform better on these subsets than Functions calibrated on larger subsets. CONCLUSION: Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific Pedotransfer Functions, (b) Pedotransfer Functions calibrated on a large dataset, (c) KOC values calculated on a large dataset or (d) KOC values taken from existing pesticide properties databases. Copyright © 2011 Society of Chemical Industry
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Pedotransfer Functions for isoproturon sorption on soils and vadose zone materials
Pest Management Science, 2011Co-Authors: Jullien Moeys, Valérie Bergheaud, Yves CoquetAbstract:Sorption coefficients (the linear KD or the non-linear KF and NF) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 KD and 55 KF) was compiled, and Pedotransfer Functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. RESULTS: The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon KD. The Pedotransfer Function is KD = 1.7822 + 0.0162 OC1.5 − 0.1958 pH (KD in L kg−1 and OC in g kg−1). For low-OC soils (OC < 6.15 g kg−1), clay and pH are most influential. The Pedotransfer Function is then KD = 0.9980 + 0.0002 clay − 0.0990 pH (clay in g kg−1). Benchmarking KD estimations showed that Functions calibrated on more specific subsets of the data perform better on these subsets than Functions calibrated on larger subsets. CONCLUSION: Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific Pedotransfer Functions, (b) Pedotransfer Functions calibrated on a large dataset, (c) KOC values calculated on a large dataset or (d) KOC values taken from existing pesticide properties databases.
Jullien Moeys - One of the best experts on this subject based on the ideXlab platform.
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Pedotransfer Functions for isoproturon sorption on soils and vadose zone materials
Pest management science, 2011Co-Authors: Jullien Moeys, Valérie Bergheaud, Yves CoquetAbstract:BACKGROUND: Sorption coefficients (the linear KD or the non-linear KF and NF) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 KD and 55 KF) was compiled, and Pedotransfer Functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. RESULTS: The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon KD. The Pedotransfer Function is KD = 1.7822 + 0.0162 OC1.5 − 0.1958 pH (KD in L kg−1 and OC in g kg−1). For low-OC soils (OC < 6.15 g kg−1), clay and pH are most influential. The Pedotransfer Function is then KD = 0.9980 + 0.0002 clay − 0.0990 pH (clay in g kg−1). Benchmarking KD estimations showed that Functions calibrated on more specific subsets of the data perform better on these subsets than Functions calibrated on larger subsets. CONCLUSION: Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific Pedotransfer Functions, (b) Pedotransfer Functions calibrated on a large dataset, (c) KOC values calculated on a large dataset or (d) KOC values taken from existing pesticide properties databases. Copyright © 2011 Society of Chemical Industry
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Pedotransfer Functions for isoproturon sorption on soils and vadose zone materials
Pest Management Science, 2011Co-Authors: Jullien Moeys, Valérie Bergheaud, Yves CoquetAbstract:Sorption coefficients (the linear KD or the non-linear KF and NF) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 KD and 55 KF) was compiled, and Pedotransfer Functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. RESULTS: The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon KD. The Pedotransfer Function is KD = 1.7822 + 0.0162 OC1.5 − 0.1958 pH (KD in L kg−1 and OC in g kg−1). For low-OC soils (OC < 6.15 g kg−1), clay and pH are most influential. The Pedotransfer Function is then KD = 0.9980 + 0.0002 clay − 0.0990 pH (clay in g kg−1). Benchmarking KD estimations showed that Functions calibrated on more specific subsets of the data perform better on these subsets than Functions calibrated on larger subsets. CONCLUSION: Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific Pedotransfer Functions, (b) Pedotransfer Functions calibrated on a large dataset, (c) KOC values calculated on a large dataset or (d) KOC values taken from existing pesticide properties databases.
K Auerswald - One of the best experts on this subject based on the ideXlab platform.
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regionalization of soil water retention curves in a highly variable soilscape i developing a new Pedotransfer Function
Geoderma, 1997Co-Authors: Andreas C Scheinost, W Sinowski, K AuerswaldAbstract:Geostatistically interpolated soil properties were combined with a Pedotransfer Function (PTF) to predict the three-dimensional variability of water retention curves (WRCs) in a highly variable soilscape. A new PTF had to be developed to account for the extreme variation in soil parameters: texture varying between gravel and clay, organic C content up to 81 g kg−1, and bulk density from 0.80 to 1.85 Mg m−3. A common procedure to generate such a PTF is first to parameterize the WRCs with a Function, and then to calculate regression equations, linking the Function's parameters with soil properties. This procedure could not be used, however, because of the overparametrization of possible Functions with respect to the eight measured data points of the WRCs. Therefore, the parameters of a Van Genuchten-type Function, θs, θr, α, and n, were substituted by linear equations relating these parameters with soil properties in a physically meaningful way: θs = f (porosity, clay),θr = f (clay, organic C), α = f (dg), and n = f (1δg). That is, the particle-size distribution parameters, dg and σg, were assumed to be related to the pore-size distribution parameters α and n. The substituted Van Genuchten Function was then fitted to all WRC data to estimate the slopes and intercepts of these relations. More than 99% of the WRCs' variation could be explained by this model. The suitability of the model as a PTF was tested with two additional data sets. It produced reliable predictions within the study area as well as when transferred to other soils. Compared with another PTF, the new PTF improved the prediction of WRCs by 60% within the study area. This improvement was mainly caused by accounting for skeletal soils and soils with low density and high organic matter content. Due to its wide range of validity and its inclusion of physically meaningful relations, this new PTF may be reliably applied to other soilscapes. Future efforts to improve the prediction of WRCs should concentrate on developing simple methods to measure the pore-size distribution.
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regionalization of soil water retention curves in a highly variable soilscape ii comparison of regionalization procedures using a Pedotransfer Function
Geoderma, 1997Co-Authors: W Sinowski, Andreas C Scheinost, K AuerswaldAbstract:As measuring soil water retention curves (WRCs) is time-consuming and costly, Pedotransfer Functions (PTFs) which predict WRCs from the fundamental soil properties bulk density (Db), texture, and organic C (Corg) are in common use. The regionalization of WRCs with a PTF can be performed in two different ways. (1) Interpolate first the fundamental properties, and apply then the PTF to the interpolated data to predict the WRCs. (2) Predict first the WRCs by applying the PTF onto the point-wise measurements of the fundamental data, and interpolate then the WRCs. Both procedures have been tested in a 1.5 km2 soilscape with a high variability in parent material and land use. The fundamental properties were measured at the 450 nodes of a rectangular 50 × 50 m grid. The WRCs were measured at seventeen irregularly distributed sites. A new PTF which had been adapted to the soilscape was used to predict the WRCs. Using procedure (1), the spatial variability of each fundamental property could be individually analyzed and accounted for in the regionalization process. Thus, the root of the mean squared differences (RMSD) between the predicted and the observed water contents was 16% lower for procedure (1) than for procedure (2). Considering the effect of land use by a residual variogram method reduced the standard deviation between predicted and observed values of Corg and Db by 115 and 20%, respectively, as determined by cross-validation. The residual method produced more plausible spatial patterns of the soil water content at −300 and −15,000 hPa. As a result of the improved spatial patterns and the decrease in the regionalization error, procedure (1) is clearly superior to procedure (2).
Wim Cornelis - One of the best experts on this subject based on the ideXlab platform.
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Introducing a Kriging-based Gaussian Process approach in Pedotransfer Functions: Evaluation for the prediction of soil water retention with temperate and tropical datasets
Journal of Hydrology, 2020Co-Authors: Jan De Pue, Yves-dady Botula, Phuong M. Nguyen, Marc Van Meirvenne, Wim CornelisAbstract:Abstract Data mining algorithms such as Artificial Neural Networks (ANN) and k-Nearest Neighbour (kNN) have proven their merits in Pedotransfer Function modelling. Kriging is a well-known algorithm for spatial interpolation, but in this study it is proposed as an alternative data mining technique. It was compared to kNN as a benchmark Pedotransfer Function to predict soil water retention for a wide range of datasets, containing soil data from both temperate and tropical regions. The performance of both methods was compared through Monte Carlo cross-validation and the precision of the predictions was assessed with an ensemble procedure. Across all datasets, a significant improvement in prediction bias, accuracy and precision was found with Kriging, compared to kNN. Moreover, it was demonstrated how predictions with Kriging are more robust and insensitive to non-correlated predictor variables, and how the optimized hyperparameters provide additional insight in the training dataset properties. Kriging was found to be a accurate, precise and robust data mining solution for Pedotransfer Function modelling.
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revisiting the pseudo continuous Pedotransfer Function concept impact of data quality and data mining method
Geoderma, 2014Co-Authors: Amir Haghverdi, H S Ozturk, Wim CornelisAbstract:Abstract The Pedotransfer Function (PTF) concept has been widely used in recent years as an indirect way to predict soil hydraulic properties, particularly the water retention curve (WRC). The pseudo continuous (PC) approach allows us to predict water content at any predefined matric head, resulting in an almost continuous WRC. When combined with powerful pattern recognition approaches, a PC-PTF can be trained to learn the shape of WRC from a discrete set of measured points, unlike traditional parametric PTFs which follow a predefined WRC shape dictated by the selected soil hydraulic equations. The purpose of this study was to investigate the impact of two elements on the performance of a PC-PTF: (i) data mining method (neural network, NN, versus support vector machine, SVM) and (ii) distribution and density of the provided water retention data in the training phase. Two datasets from Turkey and Belgium, consisting of mainly fine and coarse-textured soils, respectively, were employed. Multiple scenarios containing different combinations of measured water retention points in the training phase were defined. The lower root mean square error (RMSE) on average (0.044 cm3 cm− 3) attained with the NN-based PTF shows that it is a better option than SVM (RMSE of 0.052 cm3 cm− 3) for deriving PC-PTFs. The accuracy of PC-PTF was firmly dependent on the presence of measured water retention points in the entire range of WRC. Applying different scenarios revealed that a well distributed set of measured water retention points in the training phase could result in up to 0.03 cm3 cm− 3 reduction in RMSE values.
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Impact of soil hydraulic parameter uncertainty on soil moisture modeling
Water Resources Research, 2011Co-Authors: Lien Loosvelt, Valentijn R. N. Pauwels, Wim Cornelis, Gabrielle De Lannoy, Niko E. C. VerhoestAbstract:[1] For simulations in basins where soil information is limited to soil type maps, a methodology is presented to quantify the uncertainty of soil hydraulic parameters arising from within-soil-class variability and to assess the impact of this uncertainty on soil moisture modeling. Continuous Pedotransfer Functions were applied to samples with different texture within each soil class to construct discrete probability distributions of the soil hydraulic parameters. When propagating the parameter distributions through a hydrologic model, a wide range of simulated soil moisture was generated within a single soil class. The Pedotransfer Function was found to play a crucial role in assessing the uncertainty in the modeled soil moisture, and the geographic origin of the Pedotransfer Function (region specific versus nonregion specific) highly affected the range and shape of the probability distribution of the soil hydraulic parameters. Furthermore, the modeled soil moisture distribution was found to be non-Gaussian. An accurate uncertainty assessment therefore requires the characterization of its higher-order moments. As an extension of this research, we have shown that applying continuous region-specific Pedotransfer Functions to the central point of a soil class is a better alternative to standard (often nonregion-specific) class Pedotransfer Functions for determining an average set of soil hydraulic parameters.
J H M Wosten - One of the best experts on this subject based on the ideXlab platform.
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Pedotransfer Functions bridging the gap between available basic soil data and missing soil hydraulic characteristics
Journal of Hydrology, 2001Co-Authors: J H M Wosten, Ya. A. Pachepsky, W J RawlsAbstract:Water retention and hydraulic conductivity are crucial input parameters in any modelling study on water flow and solute transport in soils. Due to inherent temporal and spatial variability in these hydraulic characteristics, large numbers of samples are required to properly characterise areas of land. Hydraulic characteristics can be obtained from direct laboratory and field measurements. However, these measurements are time consuming which makes it costly to characterise an area of land. As an alternative, analysis of existing databases of measured soil hydraulic data may result in Pedotransfer Functions. In practise, these Functions often prove to be good predictors for missing soil hydraulic characteristics. Examples are presented of different equations describing hydraulic characteristics and of Pedotransfer Functions used to predict parameters in these equations. Grouping of data prior to Pedotransfer Function development is discussed as well as the use of different soil properties as predictors. In addition to regression analysis, new techniques such as artificial neural networks, group methods of data handling, and classification and regression trees are increasingly being used for Pedotransfer Function development. Actual development of Pedotransfer Functions is demonstrated by describing a practical case study. Examples are presented of Pedotransfer Function for predicting other than hydraulic characteristics. Accuracy and reliability of Pedotransfer Functions are demonstrated and discussed. In this respect, Functional evaluation of Pedotransfer Functions proves to be a good tool to assess the desired accuracy of a Pedotransfer Function for a specific application.
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Comparison of class and continuous Pedotransfer Functions to generate soil hydraulic characteristics
Geoderma, 1995Co-Authors: J H M Wosten, Peter Finke, M.j.w. JansenAbstract:Abstract Class Pedotransfer Functions were used to generate average hydraulic characteristics for distinct soil texture classes. Continuous Pedotransfer Functions were used to generate soil hydraulic characteristics from the actually measured median of the sand particle size, bulk density and percentages clay, silt and organic matter. Both approaches were used to predict the soil physical input data to calculate five different Functional aspects of soil behaviour. The Functional aspects were: number of workable days, number of days with adequate soil aeration, elapsed time until 10% breakthrough of chloride, amount of cadmium leached after one year and amount of Isoproturon leached after one year. Simulations of water and solute transport were made for 88 profiles which form a statistically representative set of profiles for cover sands in the northeastern part of the Netherlands. The calculated number of workable days did not depend on the type of Pedotransfer used. However, the differences between the class and continuous Pedotransfer Function approach were significant for the other four Functional aspects of soil behaviour. For adsorbing cadmium and adsorbing and degradable Isoproturon, differences between the two approaches were statistically significant because they were systematic. However, these differences were so small that they were irrelevant in practice. When to prefer which approach was ambiguous and depended on the Functional aspect under consideration. When differences were not significant or irrelevant in practice, the cheaper and easier to use class Pedotransfer Function approach is preferred over the continuous Pedotransfer Function approach.