https://pubag.nal.usda.gov/download/10780/pdf

rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions

Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.

/pdf/soil-water-characteristic-estimates-by-texture-and-organic-56ntsbkyo4.pdf

Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

Soil moisture retrieval from AMSR-E

Author(s): Vrugt, JA; Gupta, HV; Bouten, W; Sorooshian, S | Abstract: Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers.

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A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrological model parameters

Mathematical models have become indispensable tools for studying vadose zone flow and transport processes. We reviewed the history of development, the main processes involved, and selected applications of HYDRUS and related models and software packages developed collaboratively by several groups in the United States, the Czech Republic, Israel, Belgium, and the Netherlands. Our main focus was on modeling tools developed jointly by the U.S. Salinity Laboratory of the USDA, Agricultural Research Service, and the University of California, Riverside. This collaboration during the past three decades has resulted in the development of a large number of numerical [e.g., SWMS_2D, HYDRUS-1D, HYDRUS-2D, HYDRUS (2D/3D), and HP1] as well as analytical (e.g., CXTFIT and STANMOD) computer tools for analyzing water flow and solute transport processes in soils and groundwater. The research also produced additional programs and databases (e.g., RETC, Rosetta, and UNSODA) for quantifying unsaturated soil hydraulic properties. All of the modeling tools, with the exception of HYDRUS-2D and HYDRUS (2D/3D), are in the public domain and can be downloaded freely from several websites.

Development and Applications of the HYDRUS and STANMOD Software Packages and Related Codes

The solution of many field-scale flow and transport problems requires estimates of unsaturated soil hydraulic properties. The objective of this study was to calibrate neural network models for prediction of water retention parameters and saturated hydraulic conductivity, K s , from basic soil properties. Twelve neural network models were developed to predict water retention parameters using a data set of 1209 samples containing sand, silt, and clay contents, bulk density, porosity, gravel content, and soil horizon as well as water retention data. A subset of 620 samples was used to develop 19 neural network models to predict K s . Prediction of water retention parameters and K s generally improved if more input data were used. In a more detailed investigation, four models with the following levels of input data were selected: (i) soil textural class, (ii) sand, silt, and clay contents, (iii) sand, silt, and clay contents and bulk density, and (iv) the previous variables and water content at a pressure head of 33 kPa. For water retention, the root mean square residuals decreased from 0.107 for the first to 0.060 m 3 m -3 for the fourth model while the root mean square residual K s decreased from 0.627 to 0.451 log(cm d -1 ). The neural network models performed better on our data set than four published pedotransfer functions for water retention (by 0.01-0.05 m 3 m -3 ) and better than six published functions for K s (by 0.1-0.9 order of magnitude). Use of the developed hierarchical neural network models is attractive because of improved accuracy and because it permits a considerable degree of flexibility toward available input data.

Neural network analysis for hierarchical prediction of soil hydraulic properties

Accurate process-based modeling of nonequilibrium water fl ow and solute transport remains a major challenge in vadose zone hydrology. Our objecƟ ve here was to describe a wide range of nonequilibrium fl ow and transport modeling approaches available within the latest version of the HYDRUS-1D soŌ ware package. The formulaƟ ons range from classical models simulaƟ ng uniform fl ow and transport, to relaƟ vely tradiƟ onal mobile-immobile water physical and two-site chemical nonequilibrium models, to more complex dual-permeability models that consider both physical and chemical nonequilibrium. The models are divided into three groups: (i) physical nonequilibrium transport models, (ii) chemical nonequilibrium transport models, and (iii) physical and chemical nonequilibrium transport models. Physical nonequilibrium models include the Mobile-Immobile Water Model, Dual-Porosity Model, Dual-Permeability Model, and Dual-Permeability Model with Immobile Water. Chemical nonequilibrium models include the One KineƟ c Site Model, the Two-Site Model, and the Two KineƟ c Sites Model. Finally, physical and chemical nonequilibrium transport models include the Dual-Porosity Model with One KineƟ c Site and the Dual-Permeability Model with Two-Site SorpƟ on. Example calculaƟ ons using the diff erent types of nonequilibrium models are presented. ImplicaƟ ons for the formulaƟ on of the inverse problem are also discussed. The many diff erent models that have been developed over the years for nonequilibrium fl ow and transport refl ect the mulƟ tude of oŌ en simultaneous processes that can govern nonequilibrium and preferenƟ al fl ow at the fi eld scale.

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Modeling Nonequilibrium Flow and Transport Processes Using HYDRUS

The Arya-Paris model is an indirect method to estimate the soil water characteristic from particle-size data. The scaling parameter, α, in the original model was assumed constant for all soil textures. In this study, α is defined as α i = (log N i /log n i ), ), where n i is the number of spherical particles in the ith particle-size fraction (determined by the fraction solid mass, w i , and mean particle radius, R i ) and N i is the number of spherical particles of radius R i required to trace the pore length generated by the same solid mass in a natural structure soil matrix. An estimate for log N i was obtained by either relating log N i to log n i using a logistic growth equation or by relating log N i linearly to log (w i /R 3 i ) based on the similarity principle. For any given texture, both approaches showed that α was not constant but decreased with increasing particle size, especially for the coarse fractions. In addition, α was also calculated as a single-value average for a given textural class. The three formulations of α were evaluated on 23 soils that represented a range in particle-size distribution, bulk density, and organic matter content. The average α consistently predicted higher pressure heads in the wet range and lower pressure heads in the dry range. The formulation based on the similarity principle resulted in bias similar to that of the constant a approach, whereas no bias was observed for the logistic growth equation. The logistic growth equation implicitly accounted for bias in experimental procedures, because it was fitted to log N i values computed from experimental soil water characteristic data. The formulation based on the similarity principle is independent of bias that might be inherent in experimental data.

Martinus Th. van Genuchten

Papers

Development and Applications of the HYDRUS and STANMOD Software Packages and Related Codes

Neural network analysis for hierarchical prediction of soil hydraulic properties

Modeling Nonequilibrium Flow and Transport Processes Using HYDRUS

Scaling Parameter to Predict the Soil Water Characteristic from Particle-Size Distribution Data

Macroscopic representation of structural geometry for simulating water and solute movement in dual-porosity media