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  • 1
    Language: English
    In: Journal of Hydrology, April 2017, Vol.547, pp.39-53
    Description: Pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters in favor of expensive laboratory or field measurements. Rosetta (Schaap et al., 2001, denoted as Rosetta1) is one of many PTFs and is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method which allows the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity ( ), and their uncertainties. In this study, we present an improved set of hierarchical pedotransfer functions (Rosetta3) that unify the water retention and submodels into one. Parameter uncertainty of the fit of the VG curve to the original retention data is used in the ANN calibration procedure to reduce bias of parameters predicted by the new PTF. One thousand bootstrap replicas were used to calibrate the new models compared to 60 or 100 in Rosetta1, thus allowing the uni-variate and bi-variate probability distributions of predicted parameters to be quantified in greater detail. We determined the optimal weights for VG parameters and , the optimal number of hidden nodes in ANN, and the number of bootstrap replicas required for statistically stable estimates. Results show that matric potential-dependent bias was reduced significantly while root mean square error ( ) for water content were reduced modestly; for was increased by 0.9% (H3 ) to 3.3% (H5 ) in the new models on log scale of compared with the Rosetta1 model. It was found that estimated distributions of parameters were mildly non-Gaussian and could instead be described rather well with heavy-tailed α-stable distributions. On the other hand, arithmetic means had only a small estimation bias for most textures when compared with the mean-like “shift” parameter of the α-stable distributions. Arithmetic means and (co-)variances are therefore still recommended as summary statistics of the estimated distributions. However, it may be necessary to parameterize the distributions in different ways if the new estimates are used in stochastic analyses of vadose zone flow and transport. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code as well as additional documentation is available at: .
    Keywords: Pedotransfer Function ; Vadose Zone ; Hydraulic Parameter ; Neural Network ; Soil ; Water Content ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 2
    In: Geophysical Research Letters, 28 October 2018, Vol.45(20), pp.11,147-11,153
    Description: Periodic shifts in Earth's orbit alter incoming solar radiation and drive Quaternary climate cycles. However, unambiguous detection of these orbitally driven climatic changes in records of terrestrial sedimentation and pedogenesis remains poorly defined, limiting our understanding of climate change‐landscape feedbacks, impairing our interpretation of terrestrial paleoclimate proxies, and limiting linkages among pedogenesis, sedimentation, and paleoclimatic change. Using a meta‐analysis, we show that Quaternary soil ages preserved in the modern record have periodicities of 41 and 98 kyr, consistent with orbital cycles. Further, soil ages predominantly date to periods of low rates of climatic change following rapid climate shifts associated with glacial‐to‐interglacial transitions. Soil age appears linked to orbital cycles via climate‐modulated sediment deposition, which may largely constrain soil formation to distinct climate periods. These data demonstrate a record of widespread orbital cyclicity in sediment deposition and subsequent pedogenesis, providing a key insight into soil‐landscape evolution and terrestrial paleo‐environment changes. Over the past 2.6 million years, the Earth's climate has cycled at regular intervals in concert with orbital variations. Climate variations have driven changes in the rates of erosion and deposition of new sediment, but detection of these orbitally driven climate cycles has remained elusive in soil systems. We demonstrated that soils were preserved to the present at the same intervals as known orbital climate cycles using a meta‐analysis of soil chronosequences. We further tied dominant periods of soil formation to periods of relatively low rates of past climate change or periods of relatively stable, unchanging climate that enable soil formation. Our results provide a better understanding of how climate change impacts landscapes, which could greatly enhance our understanding of the impact of future climate change on soil resources and new insights into past environmental changes. A meta‐analysis of soil chronosequences was used to analyze Quaternary soil preservation Quaternary soil preservation occurred at periodicities of 41 and 98 kyr, aligning with obliquity and eccentricity orbital cycles Quaternary soils predominantly date to periods of low rates of climatic change following rapid glacial‐to‐interglacial transitions
    Keywords: Soil Chronosequences ; Quaternary Climate Cycles ; Landscape Evolution ; Paleoclimate ; Orbital Periodicity
    ISSN: 0094-8276
    E-ISSN: 1944-8007
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  • 3
    Language: English
    In: Vadose Zone Journal, 2010, Vol.9(3), p.573
    Keywords: Hydrogeology ; Hydrology ; Movement ; Nanoparticles ; Pore Water ; Porosity ; Research ; Scale Factor ; Soils ; Transport ; Unsaturated Zone;
    ISSN: Vadose Zone Journal
    E-ISSN: 1539-1663
    Source: CrossRef
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  • 4
    Language: English
    In: Water Resources Research, 12/2018, Vol.54(12), pp.9774-9790
    Description: A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global‐scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, , 1996, ), which explicitly assume a lognormal pore size distribution and apply the Young‐Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data‐poor to data‐rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi‐based PTFs outperformed two van Genuchten‐based PTFs calibrated on the same data. The new PTFs were applied to a 1 × 1 km global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes. We developed a set of hierarchical pedotransfer functions for the semiphysical Kosugi water retention model An evaluation using globally representative data demonstrated that the PTFs outperformed PTFs based on the van Genuchten retention model Global maps of hydraulic parameters, derived quantities, and associated uncertainties were produced at 1‐km resolution
    Keywords: Hydraulic Property ; Water Content ; Pressure Head ; Vadose Zone ; Pedotransfer ; Global Map;
    ISSN: Water Resources Research
    E-ISSN: 00431397
    E-ISSN: 19447973
    Source: American Geophysical Union (via CrossRef)
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  • 5
    Language: English
    In: Vadose Zone Journal, 2015, Vol.14(9), p.0
    Description: We applied spatial predictions of physical soil properties to a pedotransfer function to predict hydraulic properties at high resolution in a semiarid landscape. Estimated soil properties explained patterns of vegetation dynamics. A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high-resolution spatial representation of soil physical and hydraulic properties. We present a novel approach to predict hydraulic soil parameters by combining digital soil mapping techniques with pedotransfer functions (PTFs) and demonstrate that simple derived quantities are related to observed spatial patterns in ecosystem production during the North American Monsoon. Landsat reflectance and elevation data were used to predict physical soil properties at a 5-m spatial resolution for a semiarid landscape of 6265 ha using regression kriging. Resulting soil property maps were applied to the Rosetta PTF to predict saturated hydraulic conductivity and water retention parameters from which approximate water residence times were derived. Estimated idealized residence time for water lost to the deeper vadose zone and evapotranspiration corresponded to vegetation response. Antecedent precipitation was more important for explaining the relationships between modeled soil properties and vegetation response than the amount of monsoon precipitation. Increased spring precipitation before the monsoon produced stronger negative correlations with hydraulic conductivity and stronger positive correlations with plant available water. Modeled water residence times explained the patterns of vegetation and landscape morphology validating our approach as a method of producing functional spatial PTFs. Linking digital soil mapping with Rosetta led to predictions of hydraulic soil properties that were more closely related to vegetation dynamics than the data available in the Soil Survey Geographic (SSURGO) soil database.
    Keywords: Temperature Effects ; Atmospheric Precipitations ; Reflectance ; Residence Time ; Ecological Distribution ; Soils ; Evapotranspiration ; Mapping ; Monsoons ; Hydraulics ; Soil Surveys ; Vegetation ; Precipitation ; Soil Properties ; Permeability Coefficient ; Vadose Water ; Hydraulic Properties ; Monsoons ; North America ; Methods and Instruments ; Evaluation, Processing and Publication ; Msavi2, Modified Soil Adjusted Vegetation Index ; Nmse, Normalized Mean Square Error ; Ptf, Pedotransfer Function ; Ssurgo, Soil Survey Geographic Database ; Wrt, Water Retention Time;
    ISSN: Vadose Zone Journal
    E-ISSN: 1539-1663
    Source: CrossRef
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  • 6
    Language: English
    In: Soil Science Society of America Journal, 2018, Vol.82(6), p.1538
    Description: Understanding critical zone evolution and function requires an accurate assessment of the distributions of its soil physical and chemical properties. Two-dimensional (2D) digital soil mapping (DSM) provides a general understanding of soil characteristics across landscapes, but lacks the ability to predict soil properties with depth. Soil depth functions enable the reconstruction of soil properties with depth, potentially extending traditional DSM techniques to three dimensions (3D). We predicted the three-dimensional soil chemical and physical properties of a small forested subcatchment of the Catalina-Jemez Critical Zone Observatory using a combination of profile depth functions and traditional DSM techniques. The step function was used to reconstruct selected soil chemical and physical properties with depth for 24 described soil profiles. We compensated for uneven sampling depths by standardizing the profiles from 0.0 to 1.0, and splitting them into five equal standardized depth layers. Using available environmental covariates, step-wise regressions were used to predict soil properties across the catchment. R2 values for the predictive functions ranged from 0.20 to 0.97 (p=0.21 to 〈0.0001). Calcium and magnesium preferentially accumulated in channel drainages compared to potassium or sodium; this pattern corresponded with accumulation of clay in channel drainages. Parent material and sediment redistribution, driven by colluvial movement and hydrological flowpaths, were the main controls on the 3D soil chemical properties of the catchment. Combining depth functions with traditional DSM will provide more accurate assessments of soil spatial patterns across landscapes and with depth.
    Keywords: Soils ; Geochemistry Of Rocks, Soils, And Sediments ; Alkali Metals ; Alkaline Earth Metals ; Aluminum ; Arizona ; Basin And Range Province ; Calcium ; Carbon ; Catalina-Jemez Critical Zone Observatory ; Clastic Sediments ; Colluvium ; Critical Zone ; Depth ; Drainage Basins ; Forests ; Grain Size ; Magnesium ; Mapping ; Marshall Gulch ; Metals ; North America ; Parent Materials ; Pedogenesis ; Ph ; Pima County Arizona ; Potassium ; Regression Analysis ; Santa Catalina Mountains ; Sediments ; Silicon ; Size Distribution ; Sodium ; Soil Profiles ; Soils ; Southeastern Arizona ; Statistical Analysis ; Terrains ; Three-Dimensional Models ; Topography ; United States;
    ISSN: Soil Science Society of America Journal
    E-ISSN: 0361-5995
    E-ISSN: 14350661
    Source: CrossRef
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  • 7
    In: Water Resources Research, October 2016, Vol.52(10), pp.7631-7644
    Description: Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of ‐means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within‐cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site. Two new ways to parameterize vadose zone hydraulic properties based on soil texture are proposed and analyzed. One of these preserves heterogeneity with only a few adjustable parameters. The two approaches are compared through application to deep vadose zone experimental data.
    Keywords: Vadose Zone ; Inversion ; Pedotransfer ; Soil Texture ; Heterogeneity ; Hydraulic Properties
    ISSN: 0043-1397
    E-ISSN: 1944-7973
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  • 8
    Language: English
    In: Soil, March 30, 2017, Vol.3(1), p.67
    Description: Soils form as the result of a complex suite of biogeochemical and physical processes; however, effective modeling of soil property change and variability is still limited and does not yield widely applicable results. We suggest that predicting a distribution of probable values based upon the soil-forming state factors is more effective and applicable than predicting discrete values. Here we present a probabilistic approach for quantifying soil property variability through integrating energy and mass inputs over time. We analyzed changes in the distributions of soil texture and solum thickness as a function of increasing time and pedogenic energy (effective energy and mass transfer, EEMT) using soil chronosequence data compiled from the literature. Bivariate normal probability distributions of soil properties were parameterized using the chronosequence data; from the bivariate distributions, conditional univariate distributions based on the age and flux of matter and energy into the soil were calculated and probable ranges of each soil property determined. We tested the ability of this approach to predict the soil properties of the original soil chronosequence database and soil properties in complex terrain at several Critical Zone Observatories in the US. The presented probabilistic framework has the potential to greatly inform our understanding of soil evolution over geologic timescales. Considering soils probabilistically captures soil variability across multiple scales and explicitly quantifies uncertainty in soil property change with time.
    Keywords: Probability Distributions – Usage ; Soil Mechanics – Analysis
    ISSN: 21993971
    E-ISSN: 2199398X
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  • 9
    Language: English
    In: Journal of Hydrology, 01 October 2001, Vol.251(3-4), pp.186-201
    Description: In this study we used the Kozeny-Carman (K-C) equation as a semi-physical model for estimating the soil permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 micrometer pixel(-1). Permeabilities were predicted using two published ('Ahuja' and 'Berryman') and one generalized variant of the K-C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K-C model variants were optimized on measured permeabilities. The Ahuja and Berryman K-C models performed relatively poorly with R(2) values of 0.36 and 0.57, respectively, while the generalized model attained R(2) values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R(2) of 0.87. A fully empirical model that did not assume K-C concepts performed slightly worse (R(2) = 0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict permeability of soils in a semi-physical manner with the K-C equation. ; In the special issue: Pedotransfer functions in hydrology/edited by H.Elsenbeer. Paper presented at a meeting of the American Geophysical Union held Dec. 1999, San Francisco, California. ; p. 186-201.
    Keywords: Soils ; Conductivity ; Permeability ; Thin Sections ; Correlation ; Microscopic Methods ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 10
    Language: English
    In: Journal of Hydrology
    Description: Saturated hydraulic conductivity ( ) is a singular parameter in earth system science. not only governs the rate of flow of water under a hydraulic gradient as specified by the Darcy equation for saturated conditions, but also acts as a scaling factor in many unsaturated flow and transport applications that involve pore-size distribution models. Without knowledge of saturated hydraulic conductivity, it would be difficult to accurately describe the transport of water and dissolved or suspended constituents in soils and sediments, or calculate groundwater transport and recharge, and quantify the exchange between soils and the atmosphere. While the determination of is not especially difficult, it is expensive and (in many cases) infeasible to carry out field or lab experiments for large-scale applications. Pedotransfer functions (PTFs) are a class of largely data-driven empirical models that aim to estimate (and often other hydraulic quantities such as water retention characteristics) from easily available data. In this review, we first briefly discuss the history of the development of the concept of saturated hydraulic conductivity and its relation to the Kozeny-Carman (KC) equation. The KC equation serves as a central point in this review because it determines which soil variables affect saturated flow at the pore-scale, a domain which now can also be visited by computational fluid dynamics models. The KC equation also provides us with a structure in which we can classify the large number of PTFs that have been developed for estimating . Datasets and statistical techniques available for PTF development are discussed, and we also describe common metrics used to assess the accuracy and reliability of PTF estimates. The mutual agreement of two main classes (i.e., an effective porosity KC-based and soil texture-based) of PTFs is analyzed using a number of global maps of predicted . Finally, we discuss challenges and perspectives that might lead to PTFs with improved estimates of . In particular, we suggest establishing and utilizing large and completely independent databases to assess the accuracy and reliability of PTFs for global use, while also drawing in information from pedological and remote sensing sources.
    Keywords: Pedotransfer Function ; Saturated Hydraulic Conductivity ; Permeability ; Kozeny-Carman ; Vadose Zone ; Soil ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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