Applying Polyacrylamide (PAM) to Reduce Seepage Loss of Water Through Unlined Canals

High molecular weight, linear, anionic polyacrylamide (PAM) is under investigation as a means of sealing unlined water delivery canals, thus potentially increasing the amount of water for downstream users. This study uses a two-layer conceptual model to explore the mechanism of reducing water loss from seepage

DOE-EPSCOR SPONSORED PROJECT FINAL REPORT

Concern over the quality of environmental management and restoration has motivated the model development for predicting water and solute transport in the vadose zone. Soil hydraulic properties are required inputs to subsurface models of water flow and contaminant transport in the vadose zone. Computer models are now routinely used in research and management to predict the movement of water and solutes into and through the vadose zone of soils. Such models can be used successfully only if reliable estimates of the soil hydraulic parameters are available. The hydraulic parameters considered in this project consist of the saturated hydraulic conductivity and four parameters of the water retention curves. To quantify hydraulic parameters for heterogeneous soils is both difficult and time consuming. The overall objective of this project was to better quantify soil hydraulic parameters which are critical in predicting water flows and contaminant transport in the vadose zone through a comprehensive and quantitative study to predict heterogeneous soil hydraulic properties and the associated uncertainties. Systematic and quantitative consideration of the parametric heterogeneity and uncertainty can properly address and further reduce predictive uncertainty for contamination characterization and environmental restoration at DOE-managed sites. We conducted a comprehensive study to assess soil hydraulic parameter heterogeneity and uncertainty. We have addressed a number of important issues related to the soil hydraulic property characterizations. The main focus centered on new methods to characterize anisotropy of unsaturated hydraulic property typical of layered soil formations, uncertainty updating method, and artificial neural network base pedo-transfer functions to predict hydraulic parameters from easily available data. The work also involved upscaling of hydraulic properties applicable to large scale flow and contaminant transport modeling in the vadose zone and geostatistical characterization of hydraulic parameter heterogeneity. The project also examined the validity of the some simple average schemes for unsaturated hydraulic properties widely used in previous studies. A new suite of pedo-transfer functions were developed to improve the predictability of hydraulic parameters. We also explored the concept of tension-dependent hydraulic conductivity anisotropy of unsaturated layered soils. This project strengthens collaboration between researchers at the Desert Research Institute in the EPSCoR State of Nevada and their colleagues at the Pacific Northwest National Laboratory. The results of numerical simulations of a field injection experiment at Hanford site in this project could be used to provide insights to the DOE mission of appropriate contamination characterization and environmental remediation

Effective hydraulic parameters for steady state vertical flow in heterogeneous soils,

[1] In hydroclimate and land-atmospheric interaction models, effective hydraulic properties are needed at large grid scales. In this study, the effective soil hydraulic parameters of the areally heterogeneous soil formation are derived by conceptualizing the heterogeneous soil formation as an equivalent homogeneous medium and assuming that the equivalent homogeneous soil will approximately discharge the same total amount of flux and produce same average pressure head profile in the formation. As compared to previous effective hydraulic property studies, a specific feature of this study is that the derived effective hydraulic parameters are mean-gradient-dependent (i.e., vary across depth). Although areal soil heterogeneity was formulated as parallel homogeneous stream tubes in this study, our results appear to be consistent with the previous findings of meangradient unsaturated hydraulic conductivit

Upscaling of soil hydraulic properties for steady state evaporation and infiltration

[1] Estimation of effective/average soil hydraulic properties for large land areas is an outstanding issue in hydrologic modeling. The goal of this study is to provide flowspecific rules and guidelines for upscaling soil hydraulic properties in an areally heterogeneous field. In this study, we examined the impact of areal heterogeneity of soil hydraulic parameters on soil ensemble behavior for steady state evaporation and infiltration. The specific objectives of this study are (1) to address the impact of averaging methods of shape parameters and parameter correlation on ensemble behavior of steady state flow in an areally heterogeneous field and (2) to investigate the effectiveness of the ''average parameters'' in terms of the degree of correlation between hydraulic property parameters for the steady state evaporation and infiltration in unsaturated soil. Using an analytical solution of Richards' equation, the ensemble characteristics and flow dynamics based on average hydraulic property parameters are studied for evaporation and infiltration. Using various flow and average scenarios, we illustrated the resulting differences among the various averaging schemes. For vertical evaporation and infiltration the use of a geometric mean value for the shape parameter a of Gardner-Russo model and Brooks-Corey model and arithmetic mean value for the saturated hydraulic conductivity K s simulates the ensemble flow behavior the best. The efficacy of the ''average parameters'' depends on the flow condition and the degree of correlation between the hydraulic property parameters. With the a and K s parameters perfectly correlated, the ''average parameters'' were found to be generally most effective. The correlation between the hydraulic conductivity K s and the parameter a results in an ensemble soil behavior more like a sand

Geostatistical and stochastic study of radionuclide transport in the unsaturated zone at Yucca Mountain

Motivation: Why Study of Unsaturated Zone? The unsaturated zone (UZ), where the proposed repository would be located, acts as a critical natural barrier by delaying the arrival of radionuclides at the saturated zone and by reducing radionuclide concentrations in groundwater through dispersion and dilution Quantitative prediction of radionuclide transport in the unsaturated zone becomes critical for performance assessment and design of the proposed repository of the Yucca Mountain Projec

Geostatistical and stochastic study of flow and tracer transport in the unsaturated zone at Yucca Mountain

Yucca Mountain has been proposed by the U.S. Department of Energy as the nation’s long-term, permanent geologic repository for spent nuclear fuel or high-level radioactive waste. The potential repository would be located in Yucca Mountain’s unsaturated zone (UZ), which acts as a critical natural barrier delaying arrival of radionuclides to the water table. Since radionuclide transport in groundwater can pose serious threats to human health and the environment, it is important to understand how much and how fast water and radionuclides travel through the UZ to groundwater. The UZ system consists of multiple hydrogeologic units whose hydraulic and geochemical properties exhibit systematic and random spatial variation, or heterogeneity, at multiple scales. Predictions of radionuclide transport under such complicated conditions are uncertain, and the uncertainty complicates decision making and risk analysis. This project aims at using geostatistical and stochastic methods to assess uncertainty of unsaturated flow and radionuclide transport in the UZ at Yucca Mountain. Focus of this study is parameter uncertainty of hydraulic and transport properties of the UZ. The parametric uncertainty arises since limited parameter measurements are unable to deterministically describe spatial variability of the parameters. In this project, matrix porosity, permeability and sorption coefficient of the reactive tracer (neptunium) of the UZ are treated as random variables. Corresponding propagation of parametric uncertainty is quantitatively measured using mean, variance, 5th and 95th percentiles of simulated state variables (e.g., saturation, capillary pressure, percolation flux, and travel time). These statistics are evaluated using a Monte Carlo method, in which a three-dimensional flow and transport model implemented using the TOUGH2 code is executed with multiple parameter realizations of the random model parameters

Regularized Artificial Neural Network Training for Biased Data of Soil Hydraulic Parameters

Abstract: Development and application of artificial neural network (ANN) pedotransfer functions for estimating soil hydraulic properties (SHP) have become popular in the last two decades. However, limited availability of SHP training data often constrains the full potential of improved SHP estimation with ANN in many practical situations. In many situations, SHP data are limited and could be biased by samples from a restricted portion of the data population. Artificial neural network pedotransfer functions developed under such situations are likely to yield biased estimates. We proposed a direct approach to minimize mean estimation errors (bias) in such situations and developed a regularized ANN algorithm. The new algorithm revised the ANN error function and its gradients with respect to neural network outputs. We applied the new algorithm to synthetically generated SHP data representing different data availability situations and found that the newly developed algorithms were effective in reducing bias. Training with both the new and conventional mean square error functions resulted in equally good results in test phases when ANN models were trained with randomly sampled unbiased data. However, when ANN was trained with and applied to SHP data with respectively different means (biased sample), the proposed regularized ANN was highly effective in minimizing the bias when compared with ANN with the conventional mean square error function