The Experts below are selected from a list of 32115 Experts worldwide ranked by ideXlab platform
Feike J Leij - One of the best experts on this subject based on the ideXlab platform.
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Soil Water Retention i introduction of a shape index
Soil Science Society of America Journal, 2005Co-Authors: R Haverkamp, Carlos Fuentes, Feike J Leij, Antonella Sciortino, P J RossAbstract:Knowledge of Soil Water Retention is fundamental to quantify the flow of Water and dissolved substances in the subsurface. Water Retention is often quantified with models fitted to observed Retention points. Interpretation and conversion of parameters from different models is subjective and prone to error. We examined 461 Retention curves from the UNSODA database and 660 from the GRIZZLY database. Parameters of the Brooks-Corey (BC) and van Genuchten (vG) equations were fitted to the Retention data. The shape parameters in these functions (λ, m, and n) are closely correlated to Soil texture and may be predicted with so-called pedotransfer functions (PTFs). Among the scale parameters, the saturated Water content 0, proved to be a robust fitting parameter regardless of parameterization. Reliable optimization of the residual Water content 0, is more difficult; without any constraint it was negative for 54.4% of the GRIZZLY samples, and its value was strongly correlated to the shape parameters. The BC- and vG-shape parameters are often converted assuming λ = mn, which is incorrect when λ or mn is large (e.g., λ > 0.8). To facilitate the interpretation, conversion, and optimization of Retention parameters, we introduce a Water Retention shape index P. This index constitutes an integral measure of the slope of the Retention curve and characterizes the Retention behavior of a particular Soil with a single number. A value for the index can be estimated directly from Retention data. For the majority of the samples P ranged between 0 and 0.4; rarely did /' exceed 3, which is the maximum expected for fractal behavior. The value for P was related to Soil texture: fine-textured Soils tend to have smaller values than coarse-textured Soils. The shape index provides a benchmark for conversion and comparison of parameters.
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using neural networks to predict Soil Water Retention and Soil hydraulic conductivity
Soil & Tillage Research, 1998Co-Authors: Marcel G Schaap, Feike J LeijAbstract:Direct measurement of hydraulic properties is time consuming, costly, and sometimes unreliable because of Soil heterogeneity and experimental errors. Instead, hydraulic properties can be estimated from surrogate data such as Soil texture and bulk density with pedotransfer functions (PTFs). This paper describes neural network PTFs to predict Soil Water Retention, saturated and unsaturated hydraulic properties from limited or more extended sets of Soil properties. Accuracy of prediction generally increased if more input data are used but there was always a considerable difference between predictions and measurements. The neural networks were combined with the bootstrap method to generate uncertainty estimates of the predicted hydraulic properties.
Yuan Guo - One of the best experts on this subject based on the ideXlab platform.
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method for quick prediction of hydraulic conductivity and Soil Water Retention of unsaturated Soils
Transportation Research Record, 2018Co-Authors: Shaoyang Dong, Yuan GuoAbstract:Hydraulic conductivity and Soil-Water Retention are two critical Soil properties describing the fluid flow in unsaturated Soils. Existing experimental procedures tend to be time consuming and labor...
Marcel G Schaap - One of the best experts on this subject based on the ideXlab platform.
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using neural networks to predict Soil Water Retention and Soil hydraulic conductivity
Soil & Tillage Research, 1998Co-Authors: Marcel G Schaap, Feike J LeijAbstract:Direct measurement of hydraulic properties is time consuming, costly, and sometimes unreliable because of Soil heterogeneity and experimental errors. Instead, hydraulic properties can be estimated from surrogate data such as Soil texture and bulk density with pedotransfer functions (PTFs). This paper describes neural network PTFs to predict Soil Water Retention, saturated and unsaturated hydraulic properties from limited or more extended sets of Soil properties. Accuracy of prediction generally increased if more input data are used but there was always a considerable difference between predictions and measurements. The neural networks were combined with the bootstrap method to generate uncertainty estimates of the predicted hydraulic properties.
Zeyong Gao - One of the best experts on this subject based on the ideXlab platform.
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root induced changes to Soil Water Retention in permafrost regions of the qinghai tibet plateau china
Journal of Soils and Sediments, 2018Co-Authors: Zeyong Gao, Fujun Niu, Yibo Wang, Zhanju Lin, Jing Luo, Minghao LiuAbstract:Soil Water Retention plays a crucial role in regulating Soil moisture dynamics, Water circulation, plant growth, contaminant transport, and permafrost stability, and it is an issue of concern in Water-limited ecosystems. However, our understanding of the relationship between plant roots and Soil Water Retention is still relatively poor in the alpine grasslands of permafrost regions. To addresses this, our study evaluated the effect of plants on the Soil Water Retention in permafrost regions of the Qinghai-Tibet Plateau. Three alpine grassland sites were identified and characterized as alpine wet meadow (AWM), alpine meadow (AM), and alpine steppe (AS). Root biomass, Soil Water Retention, and Soil physico-chemical properties were examined in the top 0–50 cm of active layer in the three experimental sites in the hinterland of the Qinghai-Tibet Plateau (QTP). Pedotransfer functions (PTFs) and Retention Curve program (RETC) were employed to illustrate how the plant roots affect Soil Water Retention. Approximately 80, 65, and 60% of root biomass was distributed in the top 0–20 cm in the AWM, AM, and AS Soil, respectively. Soil Water Retention was enhanced with the presence of plant roots; thereinto, the highest values of field capacity were found in AWM Soil: on average, about 0.45 cm3 cm−3. Field capacity of AWM Soil was almost twice as high as that of AM Soil, and triple higher than that of AS Soil. Correlation and regression analysis showed that root-induced changes to Soil Water Retention were caused by altering the Soil organic matter and Soil structure. In addition, we evaluated the Retention Curve (RETC) program’s performance and found that the program underestimated Soil Water Retention if the effects of plant roots were not considered. A lack of alpine plants is associated with a decline in Soil physical conditions and Soil Water Retention in permafrost regions, and the function of plant roots should be considered when predicting hydrological processes.
Ya A Pachepsky - One of the best experts on this subject based on the ideXlab platform.
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software data news software to estimate 33 and 1500kpa Soil Water Retention using the non parametric k nearest neighbor technique
Environmental Modelling and Software, 2008Co-Authors: A Nemes, Ya A Pachepsky, W J Rawls, R T Roberts, Th M Van GenuchtenAbstract:A computer tool has been developed that uses a k-Nearest Neighbor (k-NN) lazy learning algorithm to estimate Soil Water Retention at -33 and -1500kPa matric potentials and its uncertainty. The user can customize the provided source data collection to accommodate specific local needs. Ad hoc calculations make this technique a competitive alternative to publish pedotransfer equations, as re-development of such equations is not needed when new data become available.
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sensitivity analysis of the nonparametric nearest neighbor technique to estimate Soil Water Retention
Vadose Zone Journal, 2006Co-Authors: Ya A Pachepsky, W J Rawls, A Nemes, Th M Van GenuchtenAbstract:A k-nearest neighbor (k-NN) nonparametric algorithm variant was earlier applied successfully to estimate Soil Water Retention. In this study, we tested the sensitivity of that k-NN variant to different data and algorithm options, such as: (i) estimations made to Soils with differing distribution of properties; (ii) the use of different sample weighting methods; (iii) the number of ensembles we developed; (iv) data density in the reference data set; (v) the presence of outliers in the reference data set; (vi) unequal weighting of input attributes; and (vii) the addition of locally specific data to the reference data set. We used a hierarchical set of input attributes and data set sizes to develop ensembles of predictions using multiple randomized subset selections. The k-NN technique performed comparably well as neural network models developed on the same data. Using .50 ensemble members did not improve the results any further. The k-NN technique showed little sensitivity to the choice of sample weighting methods and to suboptimal weighting of input attributes. Differences in data density in parts of the reference data set did not substantially impact estimation errors. Estimations substantially improved for locally specific data when some local samples were included in the reference data set, while estimations for other samples remained almost unaffected. The k-NN technique showsa large degree of stability and insensitivity to different settings and options, can easily adopt new data without the need to redevelop equations, and is an effective alternative to other techniques to estimate Soil Water Retention.
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effect of Soil organic carbon on Soil Water Retention
Geoderma, 2003Co-Authors: W J Rawls, Ya A Pachepsky, Jerry C Ritchie, T M Sobecki, H BloodworthAbstract:Abstract Reports about the relationship between Soil Water Retention and organic carbon content are contradictory. We hypothesized that this relationship is affected by both proportions of textural components and amount of organic carbon. To test the hypothesis, we used the U.S. National Soil Characterization database and the database from pilot studies on Soil quality as affected by long-term management. Regression trees and group method of data handling (GMDH) revealed a complex joint effect of texture and taxonomic order on Water Retention at −33 kPa. Adding information on taxonomic order and on taxonomic order and organic carbon content to the textural class brought 10% and 20% improvement in Water Retention estimation, respectively, as compared with estimation from the textural class alone. Using total clay, sand and silt along with organic carbon content and taxonomic order resulted in 25% improvement in accuracy over using textural classes. Similar but lower trends in accuracy were found for Water Retention at −1500 kPa and the slope of the Water Retention curve. At low organic carbon contents, the sensitivity of the Water Retention to changes in organic matter content was highest in sandy Soils. Increase in organic matter content led to increase of Water Retention in sandy Soils, and to a decrease in fine-textured Soils. At high organic carbon values, all Soils showed an increase in Water Retention. The largest increase was in sandy and silty Soils. Results are expressed as equations that can be used to evaluate effect of the carbon sequestration and management practices on Soil hydraulic properties.
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Soil Water Retention as related to topographic variables
Soil Science Society of America Journal, 2001Co-Authors: Ya A Pachepsky, Dennis Timlin, W J RawlsAbstract:Digital elevation models were proposed and used as a data source to estimate Soil properties. This study evaluated variability of texture and Water Retention of Soils for a gently sloping 3.7-ha field located in the long-term precision farming research site at the Beltsville Agricultural Research Center, MD. The specific objectives of this research were (i) to characterize variability of Water Retention across the hillslope, and (ii) determine and describe any correlations of Soil Water Retention with Soil texture and surface topography. Soil was sampled along four 30-m transects and in 39 points within the study area. Textural fraction contents, bulk density, and Water Retention at 0, 2.5, 5.0, 10, 33, 100, 500, and 1500 kPa were measured in samples taken from 4- to 10-cm depth. A 30-m digital elevation model (DEM) was constructed from aerial photography data. Slopes, profile curvatures, and tangential curvatures were computed in grid nodes and interpolated to the sampling locations. Regressions with spatially correlated errors were used to relate Water Retention and texture to computed topographic variables. Sand, silt, and clay contents depended on slope and curvatures. Soil Water Retention at 10 and 33 kPa correlated with sand and silt contents. The regression model relating Water Retention to the topographic variables explained more than 60% of variation in Soil Water content at 10 and 33 kPa, and only 20% of variation at 100 kPa. Increases in slope values and decreases in tangential curvature values, i.e., less concavity or more convexity across the slope, led to the decreases in Water Retention at 10 and 33 kPa. Results of this work show a potential for topographic variables to be used in interpretation of field-scale variability of Soil properties and, possibly, yield maps in precision agriculture.
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artificial neural networks to estimate Soil Water Retention from easily measurable data
Soil Science Society of America Journal, 1996Co-Authors: Ya A Pachepsky, Dennis Timlin, G VarallyayAbstract:Indirect estimation of Soil Water Retention from easily measurable data of Soil surveys is needed to extend the applicability of hydrological models. Artificial neural networks (ANN) are becoming a common tool for modeling complex input-output dependencies. The objective of this work was to compare the accuracy of ANN and statistical regressions for Water Retention estimation from texture and bulk density. We used data on Water contents at eight matric potentials for 130 Haplustoll and 100 Aquic Ustoll Soil samples. Although the differences were not always statistically significant, ANN predicted Water contents at selected matric potentials better than regression. The performances of ANN and regressions were comparable when van Genuchten's equation was fitted to data for each sample, and parameters of this equation were estimated from texture and bulk density. The precision of parameter estimations was lower than the precision of estimating Water contents at a given Soil Water potential with both ANN and regressions. Grouping samples by horizons improved the precision of the estimates, especially in subSoil. Because they can mimic natural many inputs-many outputs relationships, ANN may be useful in the estimation of Soil hydraulic properties from easily measurable Soil data.