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  • 1
    Language: English
    In: Soil Science Society of America Journal, 2015, Vol.79(4), p.1094(7)
    Description: The importance of saturated hydraulic conductivity () as a soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating () uses textural classes rather than specific textural fraction contents as a pedotransfer input. The objective of this work was to develop and evaluate a grouping-based pedotransfer procedure to estimate () for sample sizes used in laboratory measurements. A search of publications and reports resulted in the collection of 1245 data sets with coupled data on (), USDA textural class, and bulk density in the United States into a database called USKSAT. A separate database was assembled for the state of Florida that included 24,566 data sets. Data in each textural class were split into high and low bulk density groups using the splitting algorithm that created the most homogeneous groups. Sample diameters and lengths were 〈10 cm. Peaks of the semi-partial were well defined for loamy soils. The threshold bulk density separating high and low bulk density groups is 1.24 g cm.sup.-3 for clay soils, about 1.33 g cm.sup.-3 for loamy soils, and about 1.65 g cm.sup.-3 for sandy soils. The high bulk density groups included a much broader range of () values than the low bulk density groups for clays and loams but not sandy soils. Inspection of superimposed dependencies of ()on bulk density in the USKSAT database and in the Florida database showed their similarity. When geometric means were used as estimates of () within groups, the accuracy was not high and yet was comparable with estimates obtained from far more detailed soil information using sophisticated machine learning methods. Estimating () from textural class and bulk density may have the advantage of utility in data-poor environments and large-scale projects.
    Keywords: Soil Quality – Research ; Soil Density – Research
    ISSN: 0361-5995
    E-ISSN: 14350661
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  • 2
    Language: English
    In: Journal of Hydrology, 2015, Vol.528, p.127(11)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhydrol.2015.06.024 Byline: Behzad Ghanbarian, Vahid Taslimitehrani, Guozhu Dong, Yakov A. Pachepsky Abstract: * Sample dimensions effect on hydraulic properties prediction is addressed. * A novel data mining method called CPXR is introduced. * The proposed CPXR-based models are statistically evaluated. * The proposed models are compared with the multiple linear regression models. Article History: Received 11 March 2015; Revised 8 June 2015; Accepted 11 June 2015 Article Note: (miscellaneous) This manuscript was handled by Peter K. Kitanidis, Editor-in-Chief, with the assistance of J. Simunek, Associate Editor
    Keywords: Hydrogeology – Analysis ; Soil Moisture – Analysis
    ISSN: 0022-1694
    Source: Cengage Learning, Inc.
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  • 3
    Language: English
    In: Water Research, 01 October 2012, Vol.46(15), pp.4750-4760
    Description: This study assessed fecal coliform contamination in the Wachusett Reservoir Watershed in Massachusetts, USA using Soil and Water Assessment Tool (SWAT) because bacteria are one of the major water quality parameters of concern. The bacteria subroutine in SWAT, considering in-stream bacteria die-off only, was modified in this study to include solar radiation-associated die-off and the contribution of wildlife. The result of sensitivity analysis demonstrates that solar radiation is one of the most significant fate factors of fecal coliform. A water temperature-associated function to represent the contribution of beaver activity in the watershed to fecal contamination improved prediction accuracy. The modified SWAT model provides an improved estimate of bacteria from the watershed. Our approach will be useful for simulating bacterial concentrations to provide predictive and reliable information of fecal contamination thus facilitating the implementation of effective watershed management. ► Our modified SWAT model provided an improved estimate of fecal coliform in water. ► The result showed solar radiation was significant fate factors of fecal coliform. ► The model prediction was improved by incorporating wildlife activity in the module. ► Our approach is useful for simulating bacteria for effective watershed management.
    Keywords: Fecal Coliform ; SWAT Model ; Solar Intensity ; Wildlife ; Engineering
    ISSN: 0043-1354
    E-ISSN: 1879-2448
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  • 4
    Language: English
    In: Water Research, 2011, Vol.45(17), pp.5535-5544
    Description: The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash–Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies. ► Four models were applied to predict groundwater As concentrations. ► As contamination could not be explained by a linear model. ► The accuracies of nonlinear models are better than those of linear models. ► Principal Component-Artificial Neural Network (PC-ANN) was the superior model. ► The PC-ANN still needs to be validated by using new datasets.
    Keywords: Multiple Linear Regression ; Principal Component Regression ; Artificial Neural Network ; Principal Component-Artificial Neural Network ; Engineering
    ISSN: 0043-1354
    E-ISSN: 1879-2448
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  • 5
    Language: English
    In: Journal of Hydrology
    Description: Environmental models typically require a complete time series of meteorological inputs, thus reconstructing missing data is a key issue in the functionality of such physical models. The objective of this work was to develop a new technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using a two-step reconstruction method (RT + ANN) that employed artificial neural networks (ANN) with inputs only from stations that were found to be influential in bootstrap applications of regression trees (RT). The predictive performance of RT + ANN was also compared with those of stand-alone RT and ANN methods. In addition to statistical comparisons of the reconstructed precipitation time series, these resulting data in the Soil and Water Assessment Tool (SWAT) watershed model were used to perform an error propagation analysis in streamflow simulations. The RT provided a transparent visual representation of the similarity between the stations in their daily precipitation time series. Seven years of data from 39 weather stations showed that both RT and ANN provided the reconstruction accuracy comparable to (or better than) published earlier results of precipitation reconstruction. The RT + ANN method significantly improved accuracy and was more robust when compared to RT or ANN methods. This method also provided more accurate and robust SWAT streamflow predictions with reconstructed precipitation.
    Keywords: Artificial Neural Network ; Precipitation ; Reconstruction Missing Data ; Regression Tree ; Streamflow ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 6
    Language: English
    In: Journal of Environmental Management, Nov 15, 2012, Vol.110, p.1(7)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2012.05.012 Byline: Fatima Cardoso (a), Daniel Shelton (b), Ali Sadeghi (c), Adel Shirmohammadi (a), Yakov Pachepsky (b), Wayne Dulaney (c) Abstract: Vegetated filter strips (VFS) are commonly recommended as a best management practice to prevent manure-borne microorganisms from reaching surface water resources. However, relatively little is known about the efficacy of VFS in mitigating bacterial runoff from land-applied swine manure. A field lysimeter study was designed to evaluate the effect of surface soil hydrologic conditions and vegetation on the retention of swine manure-borne Escherichia coli and Salmonella under simulated rainfall conditions. Experimental plots (6.5 m x 3.9 m) were set on a 5% slope lysimeter with loamy topsoil, clay loam or loam subsoil and a controllable groundwater level. Three small flow-intercepting miniflumes were installed 4.5 m from the plot's top, while all remaining runoff was collected in a gutter at the bottom. Plots were divided into bare soil and grass vegetation and upper surface soil moisture before rainfall events was controlled by the subsurface groundwater level. Swine manure slurry inoculated with E. coli and Salmonella, and with added bromide tracer, was applied on the top of the plots and simultaneously initiated the simulated rainfall. Runoff was collected and analyzed every 5 min. No substantial differences between retention of E. coli and Salmonella were found. In initially wet soil surface conditions, there was limited infiltration both in bare and in vegetated plots; almost all bromide and about 30% of bacteria were recovered in runoff water. In initially dry soil surface conditions, there were substantial discrepancies between bare and vegetated plots. In bare plots, recoveries of runoff water, bromide and bacteria under dry conditions were comparable to wet conditions. However, in dry vegetated plots, from 50% to 75% of water was lost to infiltration, while bromide recoveries ranged from 14 to 36% and bacteria recovery was only 5%. Substantial intraplot heterogeneity was revealed by the data from miniflumes. GIS analysis of the plot microtopography showed that miniflumes located in the zones of flow convergence collected the majority of bacteria. Overall, the efficiency of VFS, with respect to the retention of swine manure bacteria, varied dramatically depending upon the hydrologic soil surface condition. Consequently, VFS recommendations should account for expected amounts of surface soil water saturation as well as the relative soil water storage capacity of the VFS. Author Affiliation: (a) University of Maryland, College of Agriculture and Natural Resources, College Park, MD, USA (b) USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD, USA (c) USDA-ARS Hydrology and Remote Sensing Laboratory, 10300 Baltimore Ave., Beltsville, MD 20705, USA Article History: Received 8 February 2011; Revised 13 March 2012; Accepted 15 May 2012
    Keywords: Runoff -- Analysis ; Bromine Compounds -- Analysis ; Groundwater -- Analysis ; Bacteria -- Analysis ; Soil Moisture -- Analysis ; Salmonella -- Analysis ; Loams -- Analysis ; Hydrology -- Analysis ; Water Resource Management -- Analysis ; Water Resources -- Analysis
    ISSN: 0301-4797
    Source: Cengage Learning, Inc.
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  • 7
    Language: English
    In: Journal of Hydrology, Jan 11, 2012, Vol.414-415, p.99(9)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhydrol.2011.10.018 Byline: Feng Pan (a)(c), Yakov A. Pachepsky (b), Andrey K. Guber (d), Brian J. McPherson (a)(c), Robert L. Hill (e) Keywords: Information content measures; Complexity measures; Streamflow patterns; Temporal and spatial scales Abstract: a* Higher complexity is observed in streamflows compared to precipitation. a* Temporal effects are very significant for streamflow information and complexity. a* Watershed area has only moderate effects on streamflow information and complexity relative to temporal effects. Author Affiliation: (a) Energy and Geoscience Institute, University of Utah, 423 Wakara Way Suite 300, Salt Lake City, UT 84108, USA (b) USDA-ARS Environmental Microbial and Food Safety Laboratory, 10300 Baltimore Ave., BARC-EAST Bldg. 173, Beltsville, MD 20705, USA (c) Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA (d) Department of Crop and Soil Sciences, Michigan State University, East Lancing, MI 48824, USA (e) Department of Environmental Science and Technology, University of Maryland, College Park, MD 20742, USA Article History: Received 26 April 2011; Revised 22 September 2011; Accepted 14 October 2011 Article Note: (miscellaneous) This manuscript was handled by Andras Bardossy, Editor-in-Chief, with the assistance of Erwin Zehe, Associate Editor
    Keywords: Food Safety ; Precipitation (Meteorology) ; Green Technology ; Streamflow
    ISSN: 0022-1694
    Source: Cengage Learning, Inc.
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  • 8
    Language: English
    In: Journal of Hydrology, October 2015, Vol.529, pp.805-815
    Description: To control algal blooms, the stressor–response relationships between water quality metrics, environmental variables, and algal growth need to be better understood and modeled. Machine-learning methods have been suggested as means to express the stressor–response relationships that are found when applying mechanistic water quality models. The objective of this work was to evaluate the efficiency of regression trees in the development of a stressor–response model for (Chl-a) concentrations, using the results from site-specific mechanistic water quality modeling. The 2-dimensional hydrodynamic and water quality model (CE-QUAL-W2) model was applied to simulate water quality using four-year observational data and additional scenarios of air temperature increases for the Yeongsan Reservoir in South Korea. Regression tree modeling was applied to the results of these simulations. Given the well-expressed seasonality in the simulated Chl-a dynamics, separate regression trees were developed for months from May to September. The regression trees provided a reasonably accurate representation of the stressor–response dependence generated by the CE-QUAL-W2 model. Different stressors were then selected as split variables for different months, and, in most cases, splits by the same stressor variable yielded the same correlation sign between the variable and the Chl-a concentration. Compared to physical variables, nutrient content appeared to better predict Chl-a responses. The highest Chl-a temperature sensitivities were found for May and June. Regression tree splits based on ammonium concentration resulted in a consistent trend of greater sensitivity in the groups of samples with higher ammonium concentrations. Regression tree models provided a transparent visual representation of the stressor–response relationships for Chl-a and its sensitivity. Overall, the representation of relationships using classification and regression tools can be considered a useful approach to assess the state of aquatic ecosystems and effectively determine significant stressor variables.
    Keywords: Stressor–Response ; Machine Learning ; Temperature Sensitivity ; Chlorophyll-a ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 9
    Language: English
    In: Journal of Hydrology, Feb 25, 2013, Vol.481, p.106(13)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhydrol.2012.12.024 Byline: Gonzalo Martinez (a)(b), Yakov A. Pachepsky (b), Harry Vereecken (c), Horst Hardelauf (c), Michael Herbst (c), Karl Vanderlinden (d) Keywords: Soil water content; Temporal stability; Simulations; Local controls; Saturated hydraulic conductivity Abstract: a* We simulated soil water flow in bare and grassed soil columns of three textures. a* Typical features of soil water temporal stability were recovered in simulations. a* Simulated duration and season affected the temporal stability of soil water contents. a* Spatio-temporal variations in soil water correlated with soil hydraulic conductivity. Author Affiliation: (a) Dept. of Agronomy, University of Cordoba, 14071 Cordoba, Spain (b) USDA-ARS- Environmental Microbial and Food Safety Lab, Beltsville, MD 20705, USA (c) Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Julich GmbH, 52428 Julich, Germany (d) IFAPA, Centro Las Torres-Tomejil, 41200 Alcala del Rio, Spain Article History: Received 15 December 2011; Revised 14 December 2012; Accepted 17 December 2012 Article Note: (miscellaneous) This manuscript was handled by Corrado Corradini, Editor-in-Chief, with the assistance of Axel Bronstert, Associate Editor
    Keywords: Hydrogeology -- Models ; Food Safety -- Models ; Soil Moisture -- Models ; Hydraulic Flow -- Models ; Water -- Models
    ISSN: 0022-1694
    Source: Cengage Learning, Inc.
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  • 10
    Language: English
    In: The Science of the Total Environment, Jan 1, 2016, Vol.539, p.583(9)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.scitotenv.2015.07.108 Byline: Ryan A. Blaustein, Robert L. Hill, Shirley A. Micallef, Daniel R. Shelton, Yakov A. Pachepsky Abstract: The rainfall-induced release of pathogens and microbial indicators from land-applied manure and their subsequent removal with runoff and infiltration precedes the impairment of surface and groundwater resources. It has been assumed that rainfall intensity and changes in intensity during rainfall do not affect microbial removal when expressed as a function of rainfall depth. The objective of this work was to test this assumption by measuring the removal of Escherichia coli, enterococci, total coliforms, and chloride ion from dairy manure applied in soil boxes containing fescue, under 3, 6, and 9cmh.sup.-1 of rainfall. Runoff and leachate were collected at increasing time intervals during rainfall, and post-rainfall soil samples were taken at 0, 2, 5, and 10cm depths. Three kinetic-based models were fitted to the data on manure-constituent removal with runoff. Rainfall intensity appeared to have positive effects on rainwater partitioning to runoff, and removal with this effluent type occurred in two stages. While rainfall intensity generally did not impact the parameters of runoff-removal models, it had significant, inverse effects on the numbers of bacteria remaining in soil after rainfall. As rainfall intensity and soil profile depth increased, the numbers of indicator bacteria tended to decrease. The cumulative removal of E. coli from manure exceeded that of enterococci, especially in the form of removal with infiltration. This work may be used to improve the parameterization of models for bacteria removal with runoff and to advance estimations of depths of bacteria removal with infiltration, both of which are critical to risk assessment of microbial fate and transport in the environment. Article History: Received 27 May 2015; Revised 30 June 2015; Accepted 23 July 2015 Article Note: (miscellaneous) Editor: D. Barcelo
    Keywords: Biological Indicators – Analysis ; Soil Microbiology – Analysis ; Bacteria – Analysis ; Groundwater – Analysis ; Rain – Analysis ; Rainwater – Analysis ; Escherichia Coli – Analysis
    ISSN: 0048-9697
    Source: Cengage Learning, Inc.
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