Colloid transport through soil and other porous media under transient flow conditions—A review

Understanding colloid transport in porous media under transient-flow conditions is crucial in understanding contaminant transport in soil or the vadose zone where flow conditions vary constantly. In this article, we provide a review of experimental studies, numerical approaches, and new technologies available to determine the transport of colloids in transient flow. Experiments indicate that soil structure and preferential flow are primary factors. In undisturbed soils with preferential flow pathways, macropores serve as main conduits for colloid transport. In homogeneously packed soil, the soil matrix often serves as filter. At the macroscale, transient flow facilitates colloid transport by frequently disturbing the force balance that retains colloids in the soil as indicated by the offset between colloid breakthrough peaks and discharge peaks. At the pore-scale and under saturated condition, straining, and attachment at solid–water interfaces are the main mechanisms for colloid retention. Variably saturated conditions add more complexity, such as immobile water zones, film straining, attachment to air–water interfaces, and air–water–solid contact lines. Filter ripening, size exclusion, ionic strength, and hydrophobicity are identified as the most influential factors. Our review indicates that microscale and continuum-scale models for colloid transport under transient-flow conditions are rare, compared to the numerous steady-state models. The few transient flow models that do exist are highly parameterized and suffer from a lack of a priori information of required pore-scale parameters. However, new techniques are becoming available to measure colloid transport in real-time and in a nondestructive way that might help to better understand transient flow colloid transport. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality

A 1-D modelling of streaming potential dependence on water content during drainage experiment in sand

The understanding of electrokinetics for unsaturated conditions is crucial for numerous of geophysical data interpretation. Nevertheless, the behaviour of the streaming potential coefficient C as a function of the water saturation Sw is still discussed. We propose here to model both the Richards' equation for hydrodynamics and the Poisson's equation for electrical potential for unsaturated conditions using 1-D finite element method. The equations are first presented and the numerical scheme is then detailed for the Poisson's equation. Then, computed streaming potentials (SPs) are compared to recently published SP measurements carried out during drainage experiment in a sand column. We show that the apparent measurement of DV / DP for the dipoles can provide the SP coefficient in these conditions. Two tests have been performed using existing models for the SP coefficient and a third one using a new relation. The results show that existing models of unsaturated SP coefficients C(Sw) provide poor results in terms of SP magnitude and behaviour. We demonstrate that the unsaturated SP coefficient can be until one order of magnitude larger than Csat, its value at saturation. We finally prove that the SP coefficient follows a non-monotonous behaviour with respect to water saturation. Key words: Electrical properties; Electromagnetic theory; Hydrogeophysics; Hydrology; Permeability and porosity; electrokinetic; streaming potential; self-potential; water content; water saturation; unsaturated condition; finite element modelin

Numerical Unsaturated Flow Model of Railway Drainage Systems

Substandard drainage assets are considered to be a major cause of flooding, earthwork failures, and deficient track geometry. Considering the deterioration of track materials due to cyclic loads and tamping forces, the impact of more frequent extreme rainfall events is likely to lead towards higher rates of hydraulic overloads in the drainage system, earthwork failures, and service disruptions. Therefore, the development of a numerical model could be able to describe the ageing track bed materials and provide an alternative tool for the simulation of the flow through the porous media used in the construction of railway tracks. In this paper the model HYDRUS is tested to simulate the drainage of trackbed materials under laboratory controlled conditions prior its application on actual railway drainage case studies

An unmixing algorithm for remotely sensed soil moisture

We present an unmixing method, based on genetic algorithm-soil-vegetation-atmosphere-transfer modeling to extract subgrid information of soil and vegetation from remotely sensed soil moisture (downscaled; e.g., soil hydraulic properties, area fractions of soil-vegetation combinations, and unmixed soil moisture time series) that most land surface models use. The unmixing method was evaluated using numerical experiments comprising mixed pixels with simple and complex soil-vegetation combinations, in idealized case studies (with or without uncertainty) and under actual field conditions (Walnut Creek (WC11) field, Soil Moisture Experiment 2005, Iowa). Additional validation experiments were conducted at an airborne-remote sensing footprint (Little Washita (LW21) site, Southern Great Plains 1997 hydrology campaign, Oklahoma) using Electronically Scanning Thin Array Radiometer (ESTAR). Results of the idealized experiments suggest that the unmixing method can extract optimal or near-optimal solutions to the inverse problem under different hydrologic and climatic conditions. Errors in soil moisture data and initial and boundary conditions can compound uncertainty in the solution. The solutions generated under actual field conditions (WC11 field) were able to match soil moisture observations. Analysis showed that typical soil moisture retention curves of cataloged dominant soils in WC11 field did not match well with the measurements, but those derived from actual field-scale soil moisture inversion matched better. The unmixing method performed well in replicating soil hydraulic behavior at the ESTAR footprint. Unlike in WC11 field, the typical soil moisture retention curves of cataloged soils in LW21 field matched better with the measurements. We envisaged that the unmixing method can provide quick and easy way of extracting subgrid soil moisture variability and soil-vegetation information in a pixel

Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02

Pixel-based effective soil hydraulic parameters are crucial inputs for large-scale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of near-surface RS soil moisture data to invert a physically based soil-water-atmosphere-plant (SWAP) model with a (multipopulated) modified-microGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of near-surface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for free-draining conditions. However, parameters derived under free-draining conditions generally produced too wet near-surface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texture-based data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soil-water-atmosphere-plant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of large-scale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our large-scale inverse modeling

Predicting Rice Yield Under Salinity Stress Using K/Na Ratio Variable in Plant Tissue

Estimation of yield reduction in crop caused by the salinity stress is mostly based on variations of soil electrical conductivity and the severity of water stress. Crop response curves to salinity were developed without considering ion toxicity and nutritional imbalance in the plant. The objective of this study was to explore the possibility of using the ratio of the concentration of potassium by sodium in rice leaf (leaf-K/Na) to predict yield under the salinity stress. The rice (Oryza sativa L.) yield under fresh and saline condition and the leaf-K/Na related database was created. Data were collected from consecutive three seasons of a field experiment in the Africa Rice Center experimental farm in Senegal (16° 11ʹ N, 16° 15ʹW). We studied the relationship between the relative yield (Yr), a ratio of yield under the salinity stress to the potential yield and the leaf-K/Na (x). Furthermore, we did regression analyses and F-test to determine the best fitting function. Results indicate that the exponential function [i.e. Yr = 100 exp (-b x)] was the best fitting model with the lowest root mean square error (9.683) and the highest R2 value (0.90). Example applications on independent data from published papers showed relatively good predictions, suggesting that the model can be used to predict rice yield in saline soils