T.H. Skaggs

Agricultural Research Service, ERV, Texas, United States

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Publications (61)101 Total impact

  • Elia Scudiero · Dennis L. Corwin · Todd H. Skaggs
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    ABSTRACT: Water for irrigation is a major limitation to agricultural production in arid zones of the world. Irrigating with low leaching fractions in arid zones leads to soil salinization, thereby causing reduction in crop yield. Throughout his career, John Letey made unprecedented strides towards efficient agricultural water use to manage soil salinity, culminating in his work on leaching requirement. To support policies on water allocation, reliable regional-scale inventories of soil salinity are needed. Despite decades of research in soil mapping, no reliable and up-to-date salinity maps are available for large geographical regions, especially for the salinity ranges that are relevant to agricultural productivity (i.e., salinities less than 20 dS m-1, when measured as the electrical conductivity of the soil saturation extract). This study presents a salinity assessment model for the western San Joaquin Valley, California, USA using multi-year Landsat 7 ETM+ canopy reflectance. Highly detailed salinity maps for 22 fields comprising 542 ha were used for ground-truthing. Re-gridded to 30×30 m, the ground-truth data totaled over 5000 pixels with salinity values in the range 0 to 35.2 dS m-1. Multi-year maximum values of vegetation indices were used to model soil salinity. Soil type, meteorological data, and crop type were evaluated as covariates. All considered models were evaluated for their fit to the whole data set as well as their performance in a leave-one-field-out spatial cross-validation. The best performing model was a function of canopy reflectance, crop type (i.e., cropped or fallow), rainfall, and average minimum temperature, with R2=0.728 when evaluated against all data and R2=0.611 for the cross-validation predictions. Overall, reasonably accurate and precise high resolution, regional-scale remote sensing of soil salinity is possible, even over the critical range of 0 to 20 dS/m, where researchers and policy makers must focus to ameliorate loss of agricultural productivity and ecosystem health. https://scisoc.confex.com/scisoc/2015am/webprogram/Paper91449.html
    No preview · Conference Paper · Nov 2015
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    Elia Scudiero · Todd H. Skaggs · Dennis L. Corwin
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    ABSTRACT: Soil salinization is widely recognized to be a major threat to worldwide agriculture. Despite decades of research in soil mapping, no reliable and up-to-date salinity maps are available for large geographical regions, especially for the salinity ranges that are most relevant to agricultural productivity (i.e., salinities less than 20 dS m-1, when measured as the electrical conductivity of the soil saturation extract). This paper explores the potentials and limitations of assessing and mapping soil salinity via linear modeling of remote sensing vegetation indices. A case study is presented for western San Joaquin Valley, California, USA using multi-year Landsat 7 ETM+ canopy reflectance and the Canopy Response Salinity Index (CRSI). Highly detailed salinity maps for 22 fields comprising 542 ha were used for ground-truthing. Re-gridded to 30×30 m, the ground-truth data totaled over 5000 pixels with salinity values in the range 0 to 35.2 dS m-1. Multi-year maximum values of CRSI were used to model soil salinity. Soil type, meteorological data, and crop type were evaluated as covariates. All considered models were evaluated for their fit to the whole data set as well as their performance in a leave-one-field-out spatial cross-validation. The best performing model was a function of CRSI, crop type (i.e., cropped or fallow), rainfall, and average minimum temperature, with R2=0.728 when evaluated against all data and R2=0.611 for the cross-validation predictions. Broken out by salinity classes, the mean absolute errors (MAE) for the cross-validation predictions were (all units dS m-1): 2.94 for the 0–2 interval (non-saline), 2.12 for 2–4 (slightly saline), 2.35 for 4–8 (moderately saline), 3.23 for 8–16 (strongly saline), and 5.64 for >16 (extremely saline). On a per-field basis, the validation predictions had good agreement with the field average (R2=0.79, MAE=2.46 dS m-1), minimum (R2=0.76, MAE=2.25 dS m-1), and maximum (R2=0.76, MAE=3.09 dS m-1) observed salinity. Overall, reasonably accurate and precise high resolution, regional-scale remote sensing of soil salinity is possible, even over the critical range of 0 to 20 dS m-1, where researchers and policy makers must focus to prevent loss of agricultural productivity and ecosystem health.
    Full-text · Article · Nov 2015 · Remote Sensing of Environment
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    ABSTRACT: Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks including data latency, accurately prescribing irrigation intensity, and a lack of conservation of water volume for models using a prescribed soil moisture approach. In this study, we parameterize irrigation fluxes using satellite observations of evapotranspiration (ET) compared to ET from a suite of land surface models without irrigation. We then incorporate the irrigation flux into the Community Land Model (CLM) and use a systematic trial-and-error procedure to determine the ground- and surface-water withdrawals that are necessary to balance the new irrigation flux. The resulting CLM simulation with irrigation produces ET that matches the magnitude and seasonality of observed satellite ET well, with a mean difference of 6.3 mm month−1 and a correlation of 0.95. Differences between the new CLM ET values and satellite-observed ET values are always less than 30 mm month−1 and the differences show no pattern with respect to seasonality. The results reinforce the importance of accurately parameterizing anthropogenic hydrologic fluxes into land surface and climate models to assess environmental change under current and future climates and land management regimes.
    Full-text · Article · Oct 2015 · Geoscientific Model Development
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    T.H. Skaggs · M.H. Young · J.A. Vrugt
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    ABSTRACT: A significant portion of present-day soil and Earth science research is computational, involving complex data analysis pipelines, advanced mathematical and statistical models, and sophisticated computer codes. Opportunities for scientific progress are greatly diminished if reproducing and building on published research is difficult or impossible due to the complexity of these computational systems. Vadose Zone Journal (VZJ) is launching a Reproducible Research (RR) program in which code and data underlying a research article will be published alongside the article, thereby enabling readers to analyze data in a manner similar to that presented in the article and build on results in future research and applications. In this article, we discuss reproducible research, its background and use across other disciplines, its value to the scientific community, and its implementation in VZJ.
    Full-text · Article · Oct 2015 · Vadose Zone Journal
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    ABSTRACT: Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks including data latency, accurately prescribing irrigation intensity, and conservation of water volume for soil moisture approach. In this study, we parameterize irrigation fluxes using satellite observations of evapotranspiration (ET) against ET from a suite of land surface models without irrigation. We then apply this water flux into the Community Land Model (CLM) and use an iterative approach to estimate groundwater recharge and partition the water flux between groundwater and surface water. The ET simulated by CLM with irrigation matches the magnitude and seasonality of observed satellite ET well, with a mean difference of 6.3 mm month−1 and a correlation of 0.95. Differences between the new CLM ET values and observed ET values are always less than 30 mm month−1 and the differences show no pattern with respect to seasonality. The results reinforce the importance of accurately parameterizing anthropogenic hydrologic fluxes into land surface and climate models to assess environmental change under current and future climates and land management regimes.
    Full-text · Article · Apr 2015 · Geoscientific Model Development Discussions
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    ABSTRACT: Given a time-series of potential evapotranspiration and rainfall data, there are at least two approaches for estimating vertical percolation rates. One approach involves solving Richards' equation (RE) with a plant uptake model. An alternative approach involves applying a simple soil moisture accounting procedure (SMAP) based on a set of conceptual stores and conditional statements. It is often desirable to parameterize distributed vertical percolation models using regional soil texture maps. This can be achieved using pedotransfer functions when applying RE. However, robust soil texture based parameterizations for more simple SMAPs have not previously been available. This article presents a new SMAP designed to emulate the response of a one-dimensional homogenous RE model. Model parameters for 231 different soil textures are obtained by calibrating the SMAP model to 20 year time-series from equivalent RE model simulations. The results are then validated by comparing to an additional 13 years of simulated RE model data. The resulting work provides a new simple two parameter (% sand and % silt) SMAP, which provides consistent vertical percolation data as compared to RE based models. Results from the 231 numerical simulations are also found to be qualitatively consistent with intuitive ideas concerning soil texture and soil moisture dynamics. Vertical percolation rates are found to be highest in sandy soils. Sandy soils are found to provide less water for evapotranspiration. Surface runoff is found to be more important in soils with high clay content. This article is protected by copyright. All rights reserved.
    No preview · Article · Dec 2014
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    T. H. Skaggs · R. G. Anderson · D. L. Corwin · D. L. Suarez
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    ABSTRACT: Due to the diminishing availability of good quality water for irrigation, it is increasingly important that irrigation and salinity management tools be able to target submaximal crop yields and support the use of marginal quality waters. In this work, we present a steady-state irrigated systems modeling framework that accounts for reduced plant water uptake due to root zone salinity. Two explicit, closed-form analytical solutions for the root zone solute concentration profile are obtained, corresponding to two alternative functional forms of the uptake reduction function. The solutions express a general relationship between irrigation water salinity, irrigation rate, crop salt tolerance, crop transpiration, and (using standard approximations) crop yield. Example applications are illustrated, including the calculation of irrigation requirements for obtaining targeted submaximal yields, and the generation of crop-water production functions for varying irrigation waters, irrigation rates, and crops. Model predictions are shown to be mostly consistent with existing models and available experimental data. Yet the new solutions possess advantages over available alternatives, including: (i) the solutions were derived from a complete physical-mathematical description of the system, rather than based on an ad hoc formulation; (ii) the analytical solutions are explicit and can be evaluated without iterative techniques; (iii) the solutions permit consideration of two common functional forms of salinity induced reductions in crop water uptake, rather than being tied to one particular representation; and (iv) the utilized modeling framework is compatible with leading transient-state numerical models. This article is protected by copyright. All rights reserved.
    Full-text · Article · Dec 2014
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    Elia Scudiero · Dennis L Corwin · Todd H Skaggs
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    ABSTRACT: Despite decades of research in soil mapping, characterizing the spatial variability of soil salinity across broad regions remains a crucial challenge. This work explores the potential benefits of employing reflectance data from the six spectral bands (blue, 450-520 nm; green, 520-600 nm; red, 630-690 nm; near-infrared, 770-900 nm; infrared-1; 1550-1750 nm, and infrared-2, 2090-2350 nm) of the Landsat 7 (L7) satellite sensor (30x30 m resolution) for salinity assessment. Acquisitions of L7 throughout the western San Joaquin Valley, California (ca.15000 km2) were investigated over a seven-year period. Two salinity ground truth datasets were evaluated, across 23 fields farmed with various crops: 226 direct measurements (ca. 2x2 m resolution), from the 0-1.2 m soil profile; and ca. 6000 block-kriged estimations (30x30 m resolution), derived from geospatial electromagnetic induction measurements. The multi-year average of L7 data generally provided stronger correlations (up to R2=0.41), than those observed for each single year. Slightly stronger correlations (up to R2=0.43) were observed between salinity and the multi-year temporal variability of L7 reflectance (i.e., standard deviation at each map-cell over time). The strength of the correlations between L7 data and soil salinity varied according to changing meteorological conditions through the seven-year period and according to soil texture at a field by field basis. Additionally, selected salinity ranges (i.e., 0-2, 2-4, 4-8, 8-16, and >16 dS m-1) were characterized by significantly different values of the blue, green, red, and near-infrared bands. The results suggest that data fusion of the L7 multi-year reflectance with information on meteorological conditions, crop type, and soil texture could lead to a reliable salinity prediction model for the entire western San Joaquin Valley. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional scale maps of soil salinity.
    Full-text · Conference Paper · Nov 2014
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    Elia Scudiero · Todd H. Skaggs · Dennis L. Corwin
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    ABSTRACT: Despite decades of research in soil mapping, characterizing the spatial variability of soil salinity across large regions remains a crucial challenge. This work explores the potential use of Landsat 7 (L7) satellite reflectance data (30 × 30 m resolution) to facilitate salinity mapping. Reflectance data spanning a seven-year period (2007-2013) were obtained for western San Joaquin Valley, California (ca.1.5 × 106 ha), over five soil Orders (Entisols, Inceptisols, Mollisols, and Vertisols). Two ground-truth datasets were considered: 267 direct measurements of salinity (one per L7 pixel) from soil samples (ECe), and 4891 indirect salinity values ( ) estimated from the relationships of ECe with geospatial (on average 16 per L7 pixel) electromagnetic induction measurements. The ground-truth dataset was characterized by stronger relationship with the L7 reflectance, with the multi-year averages of the L7 data showing R2 up to 0.43. The correlations between L7 data and were significantly influenced by rainfall (stronger in dry years than in rainy years), soil properties (weaker in finer soils), and crop type (stronger when soil salinity was over crop stress tolerance threshold).The results suggest that a fusion of the L7 multi-year reflectance data with information on meteorological conditions, crop type, and soil texture could lead to a reliable salinity prediction model for the entire western San Joaquin Valley. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional-scale maps of soil salinity.
    Full-text · Article · Oct 2014
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    T. H. Skaggs · D. L. Suarez · D. L. Corwin
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    ABSTRACT: One strategy for maintaining irrigated agricultural productivity in the face of diminishing resource availability is to make greater use of marginal quality waters and lands. A key to sustaining systems using degraded irrigation waters is salinity management. Advanced simulation models and decision support tools can aid in the design and management of water reuse systems, but at present model predictions and related management recommendations contain significant uncertainty. Sensitivity analyses can help characterize and reduce uncertainties by revealing which parameter variations or uncertainties have the greatest impact on model outputs. In this work, the elementary effects method was used to obtain global sensitivity analyses of UNSATCHEM seasonal simulations of forage corn (Zea mays L.) production with differing irrigation rates and water compositions. Sensitivities were determined with respect to four model outcomes: crop yield, average root zone salinity, water leaching fraction, and salt leaching fraction. For a multiple-season, quasi-steady scenario, the sensitivity analysis found that overall the most important model parameters were the plant salt tolerance parameters, followed by the solute dispersivity. For a single-season scenario with irrigation scheduling based on soil water deficit, soil hydraulic parameters were the most important; the computed salt leaching fraction was also strongly affected by the initial ionic composition of the exchange phase because of its impact on mineral precipitation. In general, parameter sensitivities depend of the specifics of a given modeling scenario, and procedures for routine use of models for site-specific degraded irrigation water management should include site-specific uncertainty and sensitivity analyses. The elementary effects method used in this work is a useful approach for obtaining parameter sensitivity information at relatively low computational cost.
    Full-text · Article · Jun 2014 · Vadose Zone Journal
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    A.A. Siyal · M. Th. van Genuchten · T.H. Skaggs
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    ABSTRACT: Subsurface porous clay pipe irrigation is widely considered to be a very promising method for small-scale irrigation in arid regions. Unfortunately, salt accumulation at and near the soil surface using this method may affect germination of direct-seeded crops. Predicting salt movement and accumulation with clay pipe irrigation will allow producers to anticipate the need for leaching to control salinity in the soil root zone. The HYDRUS-2D model was used to simulate the accumulation of salt from a subsurface clay pipe irrigation system, installed at 30 cm depth, during the growing season of okra (Abelmoschus esculentus) irrigated with water having a salinity of 1.1 dS m−1. The loamy soil profile had an initial salinity of 2.3 dS m−1. Predicted electrical conductivity (ECe) values at the end of the growing season correlated significantly (R2 = 0.952) with measured saturated paste ECe data obtained at the end of the field experiments. Salinity was found to be relatively low around the pipes, but increased with distance away from the pipes. Measured and predicted soil salinity levels were especially higher above the clay pipes. Our results indicate that proper management of salt accumulation is vital for sustainable crop production whenever subsurface irrigation systems are being implemented.
    Full-text · Article · Oct 2013 · Agricultural Water Management
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    ABSTRACT: Contaminant transport processes in streams, rivers, and other surface water bodies can be analyzed or predict-ed using the advection-dispersion equation and related transport models. In part 1 of this two-part series we presented a large number of one-and multi-dimensional analytical solutions of the standard equilibrium advection-dispersion equa-tion (ADE) with and without terms accounting for zero-order production and first-order decay. The solutions are extend-ed in the current part 2 to advective-dispersive transport with simultaneous first-order mass exchange between the stream or river and zones with dead water (transient storage models), and to problems involving longitudinal advective-dispersive transport with simultaneous diffusion in fluvial sediments or near-stream subsurface regions comprising a hyporheic zone. Part 2 also provides solutions for one-dimensional advective-dispersive transport of contaminants sub-ject to consecutive decay chain reactions.
    Full-text · Article · Aug 2013 · Journal of Hydrology and Hydromechanics
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    ABSTRACT: Analytical solutions of the advection-dispersion equation and related models are indispensable for predicting or analyzing contaminant transport processes in streams and rivers, as well as in other surface water bodies. Many useful analytical solutions originated in disciplines other than surface-water hydrology, are scattered across the literature, and not always well known. In this two-part series we provide a discussion of the advection-dispersion equation and related models for predicting concentration distributions as a function of time and distance, and compile in one place a large number of analytical solutions. In the current part 1 we present a series of one-and multi-dimensional solutions of the standard equilibrium advection-dispersion equation with and without terms accounting for zero-order production and first-order decay. The solutions may prove useful for simplified analyses of contaminant transport in surface water, and for mathematical verification of more comprehensive numerical transport models. Part 2 provides solutions for advec-tive-dispersive transport with mass exchange into dead zones, diffusion in hyporheic zones, and consecutive decay chain reactions.
    Full-text · Article · Jun 2013 · Journal of Hydrology and Hydromechanics
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    ABSTRACT: Analytical solutions of the advection–dispersion solute transport equation remain useful for a large number of applications in science and engineering. In this paper we extend the Duhamel theorem, originally established for diffusion type problems, to the case of advective–dispersive transport subject to transient (time-dependent) boundary conditions. Generalized analytical formulas are established which relate the exact solutions to corresponding time-independent auxiliary solutions. Explicit analytical expressions were developed for the instantaneous pulse problem formulated from the generalized Dirac delta function for situations with first-type or third-type inlet boundary conditions of both finite and semi-infinite domains. The developed generalized equations were evaluated computationally against other specific solutions available from the literature. Results showed the consistency of our expressions.
    Full-text · Article · Apr 2013 · The Chemical Engineering Journal
  • T.H. Skaggs · D.L. Suarez · S. Goldberg
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    ABSTRACT: Advanced numerical simulation models can potentially help improve guidelines for irrigation and salinity management. Many simulation model parameters have considerable uncertainty, and ideally that uncertainty should be reflected in model predictions and recommendations. In this work, we investigate solute transport predication intervals that can be generated by propagating model parameter uncertainty using Monte Carlo techniques. Flow and transport is simulated with a standard numerical model, while soil parameters and their uncertainty are estimated with pedotransfer functions. Generalized global sensitivity coefficients are computed to determine the parameters having the greatest impact on transport prediction and uncertainty. Simulations are compared with Br transport measured under unsaturated conditions in large lysimeters packed with clayey soil materials. In a 48 cm tall, homogeneous soil profile, model prediction intervals provided a reasonably good description of a single, relatively "noisy" breakthrough curve. In replicated 180 cm tall, layered soil profiles, model structural errors limited the accuracy of the prediction intervals under one irrigation water treatment, whereas under another treatment the predictions tracked the time course of the data reasonably well but tended to overestimate solute concentrations. The width of the prediction intervals tended to be small relative to the range of transport variability that existed across replicated lysimeters, particularly at shallow depths. Additional work aimed at operational field testing of model prediction uncertainty is needed if advanced water management models are to reach their full potential.
    No preview · Article · Feb 2013 · Vadose Zone Journal
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    ABSTRACT: The advection–dispersion transport equation with first-order decay was solved analytically for multi-layered media using the classic integral transform technique (CITT). The solution procedure used an associated non-self-adjoint advection–diffusion eigenvalue problem that had the same form and coefficients as the original problem. The generalized solution of the eigenvalue problem for any numbers of layers was developed using mathematical induction, establishing recurrence formulas and a transcendental equation for determining the eigenvalues. The orthogonality property of the eigenfunctions was found using an integrating factor that transformed the non-self-adjoint advection–diffusion eigenvalue problem into a purely diffusive, self-adjoint problem. The performance of the closed-form analytical solution was evaluated by solving the advection–dispersion transport equation for two- and five-layer media test cases which have been previously reported in the literature. Additionally, a solution featuring first-order decay was developed. The analytical solution reproduced results from the literature, and it was found that the rate of convergence for the current solution was superior to that of previously published solutions.
    No preview · Article · Jan 2013 · International Journal of Heat and Mass Transfer
  • T.H. Skaggs · D.L. Suarez · S. Goldberg · P.J. Shouse
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    ABSTRACT: Recent studies suggest that standard guidelines for managing salinity in irrigated agriculture overestimate the leaching requirement. Transient-state, process-based model analyses offer the possibility of more efficient water and salinity management, but data are needed to evaluate the accuracy of various subcomponents of the models. In this study, tracer (Br) transport in twelve lysimeters identically packed with clayey soil materials was monitored at eight soil depths and in drainage waters. In the first phase of the experiment (the salinization phase), six of the lysimeters were irrigated with high EC waters (8.1 dS m(-1)) and six with low EC waters (0.4 dS m(-1)). In the second phase, all lysimeters were leached with low EC waters (0.4 dS m(-1)). Tracer transport was very different in the high and low EC irrigation treatments, with the high EC treatment exhibiting significant tailing in the breakthrough curves. Due to the replicated experimental design, it was possible to confirm that the differences between the experimental treatments were significant and not due to random deviation. Future research aimed at placing realistic confidence levels on model predictions will allow transient-state models to reach their full potential as water and salinity management tools. Published by Elsevier B.V.
    No preview · Article · Jul 2012 · Agricultural Water Management
  • Todd H. Skaggs
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    ABSTRACT: Critical path analysis (CPA) is a method for estimating macroscopic transport coefficients of heterogeneous materials that are highly disordered at the micro-scale. Developed originally to model conduction in semiconductors, numerous researchers have noted that CPA might also have relevance to flow and transport processes in porous media. However, the results of several numerical investigations of critical path analysis on pore network models raise questions about the applicability of CPA to porous media. Among other things, these studies found that (i) in well-connected 3D networks, CPA predictions were inaccurate and became worse when heterogeneity was increased; and (ii) CPA could not fully explain the transport properties of 2D networks. To better understand the applicability of CPA to porous media, we made numerical computations of permeability and electrical conductivity on 2D and 3D networks with differing pore-size distributions and geometries. A new CPA model for the relationship between the permeability and electrical conductivity was found to be in good agreement with numerical data, and to be a significant improvement over a classical CPA model. In sufficiently disordered 3D networks, the new CPA prediction was within ±20% of the true value, and was nearly optimal in terms of minimizing the squared prediction errors across differing network configurations. The agreement of CPA predictions with 2D network computations was similarly good, although 2D networks are in general not well-suited for evaluating CPA. Numerical transport coefficients derived for regular 3D networks of slit-shaped pores were found to be in better agreement with experimental data from rock samples than were coefficients derived for networks of cylindrical pores.
    No preview · Article · Oct 2011 · Advances in Water Resources
  • Todd H. Skaggs · Thomas J. Trout · Youri Rothfuss
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    ABSTRACT: Drip irrigation is more effective and less expensive if a large amount of soil can be wetted with each emitter without losing water or nutrients below the root zone. The distance that water spreads horizontally from a drip line and the volume of soil wetted are limiting factors that determine the spacing and number of drip lines and emitters, the frequency of irrigation, and thus the cost of irrigation. We used numerical simulations and field trials to investigate the effects of application rate, pulsed water application, and antecedent water content on the spreading of water from drip emitters. Simulation results showed that pulsing and lower application rates produced minor increases in horizontal spreading at the end of water application. The small increases were primarily due to longer irrigation times, however, and not to flow phenomena associated with pulsing or low application rates. Moreover, the small increases mostly disappeared after the infiltrated water had redistributed for a period of 24 h. Field trials confirmed the simulation findings, with no statistically significant difference in wetting being found among five water application treatments involving pulsed applications and varying application rates. The simulations showed that higher antecedent water content increases water spreading from drip irrigation systems, but the increases were greater in the vertical direction than in the horizontal, an undesirable outcome if crop roots are shallow or groundwater contamination is a concern. Overall, soil texture (hydraulic properties) and antecedent water content largely determine the spreading and distribution of a given water application, with pulsing and flow rate having very little impact.
    No preview · Article · Nov 2010 · Soil Science Society of America Journal
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    ABSTRACT: Spatial variability has a profound influence on a variety of landscape-scale agricultural issues including solute transport in the vadose zone, soil quality assessment, and site-specific crop management. Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (EC a) is a potential means of characterizing the spatial variability of any soil property that influences EC a including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Arguably the most significant step in the protocols for characterizing spatial variability with EC a -directed soil sampling is the statistical sampling design, which consists of two potential approaches: model-and design-based sampling strategies such as response surface sampling design (RSSD) and stratified random sampling design (SRSD), respectively. The primary objective of this study was to compare model-and design-based sampling strategies to evaluate if one sampling strategy outperformed the other or if both strategies were equal in performance. Using three different model validation tests, the regression equation estimated from the RSSD data produced accurate and unbiased predictions of the natural log salinity levels at the independently chosen SRSD sites. Design optimality scores (i.e., D-, V-, and G-optimality criteria) indicate that the use of the RSSD design should facilitate the estimation of a more accurate regression model, i.e., the RSSD approach should allow for better model discrimination, more precise parameter estimates, and smaller prediction variances. Even though a model-based sampling design, such as RSSD, has been less prevalent in the literature, it is concluded from the comparison that there is no reason to refrain from its use and in fact warrants equal consideration.
    Full-text · Article · Sep 2010 · Journal of Environmental & Engineering Geophysics