Application of connectivity theory to model the impact of terrace failure on runoff in semi‐arid catchments

Terraces are a common feature of Mediterranean landscapes. In many places they are no longer maintained so that the number of intact terraces is in prolonged decline. The aim of this paper is to examine the effect of terrace removal and failure on hydrological connectivity and peak discharge in an agricultural catchment (475 ha) in south‐east Spain. The situation of 2006 is compared to that in 1956 and to a scenario without terraces (S2). The spatial distribution of concentrated flow was mapped after four storms in 2006. The degree of connectivity was quantified by means of connectivity functions and related to storm characteristics, land use and topography. For 1956, 2006 and scenario S2, connectivity functions and peak discharge to the river were determined for a storm with a return period of 8·2 years. The results show that the decrease in intact terraces has led to a strong increase in connectivity and discharge. The contributing area to the river system has increased by a factor 3·2 between 1956 and 2006. If all terraces were to be removed (scenario S2), the contributing area may further increase by a factor 6·0 compared to 2006. The spatial extent of concentrated flow and the degree of connectivity are related to storm magnitude as expressed by the erosivity index (EI₃₀). Although a large part of the concentrated flow (25–50%) occurs on dirt roads, it appears that croplands become a major source of runoff with increasing rainfall. The results suggest that connectivity theory can be used to improve rainfall–runoff models in semi‐arid areas. Copyright © 2009 John Wiley & Sons, Ltd.     

Generation of three dimensional flow fields for statistically anisotropic heterogeneous porous media

A methodology for generating three dimensional (3D) flow fields for statistically anisotropic heterogeneous porous media is presented and demonstrated. The simulated flow fields are shown to exhibit the input spatial correlation structure and observe mass continuity. Sample flow fields are presented in the form of cross sectional slices of the 3D formation. These cross sections demonstrate visually the characteristics of subsurface flow. The method was found to be faster than traditional techniques in terms of its computational requirements. Given this method, it is possible to generate the large number of realizations of a velocity field necessary to compute high order statistics in transport problems.     

HYDRO_GEN: A spatially distributed random field generator for correlated properties

This paper describes a new method for generating spatially-correlated random fields. Such fields are often encountered in hydrology and hydrogeology and in the earth sciences. The method is based on two observations: (i) spatially distributed attributes usually display a stationary correlation structure, and (ii) the screening effect of measurements leads to the sufficiency of a small search neighborhood when it comes to projecting measurements and data in space. The algorithm which was developed based on these principles is called HYDRO_GEN, and its features and properties are discussed in depth. HYDRO_GEN is found to be accurate and extremely fast. It is also versatile: it can simulate fields of different nature, starting from weakly stationary fields with a prescribed covariance and ending with fractal fields. The simulated fields can display statistical isotropy or anisotropy.     

Effects of pore-scale dispersion,degree of heterogeneity,sampling size,and source volume on the concentration moments of conservative solutes in heterogeneous formations

Pore-scale dispersion (PSD), aquifer heterogeneity, sampling volume, and source size influence solute concentrations of conservative tracers transported in heterogeneous porous formations. In this work, we developed a new set of analytical solutions for the concentration ensemble mean, variance, and coefficient of variation (CV), which consider the effects of all these factors. We developed these models as generalizations of the first-order solutions in the log-conductivity variance of point concentration proposed by [Fiori A, Dagan G. Concentration fluctuations in aquifer transport: a rigorous first-order solution and applications. J Contam Hydrol 2000;45(1–2):139–163]. Our first-order solutions compare well with numerical simulations for small and moderate formation heterogeneity and from small to large sampling and source volumes. However, their performance deteriorates for highly heterogeneous formations. Successively, we used our models to study the interplay among sampler size, source volume, and PSD. Our analysis shows a complex and important interaction among these factors. Additionally, we show that the relative importance of these factors is also a function of plume age, of aquifer heterogeneity, and of the measurement location with respect to the mean plume center of gravity. We found that the concentration moments are chiefly controlled by the sampling volume with pore-scale dispersion playing a minor role at short times and for small source volumes. However, the effect of the source volume cannot be neglected when it is larger than the sampling volume. A different behavior occurs for long periods, which may be relevant for old contaminations, or for small injection volumes. In these cases, PSD causes a significant dilution, which is reflected in the concentration statistics. Additionally, at the center of the mean plume, where high concentrations are most likely to occur, we found that sampling volume and PSD are attenuating mechanisms for both concentration ensemble mean and coefficient of variation, except at very large source and sampler sizes, where the coefficient of variation increases with sampler size and PSD. Formation heterogeneity causes a faster reduction of the ensemble mean concentrations and a larger uncertainty at the center of the mean plume. Therefore, our results highlight the importance of considering the combined effect of formation heterogeneity, exposure volume, PSD, source size, and measurement location in performing risk assessment.     

Modeling of transport in groundwater for environmental risk assessment

This paper presents the principles underlying a recently developed numerical technique for modeling transport in heterogeneous porous media. The method is then applied to derive the concentration mean and variance, the concentration CDF, exceedance probabilities and exposure time CDF, which are required by various regulatory agencies for risk and performance assessment calculations. The dependence of the various statistics on elapsed travel time, location in space, the dimension of the detection volume, natural variability and pore-scale dispersion is investigated and discussed.     

A Critical Analysis of Transverse Dispersivity Field Data

Transverse dispersion, or tracer spreading orthogonal to the mean flow direction, which is relevant e.g, for quantifying bio-degradation of contaminant plumes or mixing of reactive solutes, has been studied in the literature less than the longitudinal one. Inferring transverse dispersion coefficients from field experiments is a difficult and error-prone task, requiring a spatial resolution of solute plumes which is not easily achievable in applications. In absence of field data, it is a questionable common practice to set transverse dispersivities as a fraction of the longitudinal one, with the ratio 1/10 being the most prevalent. We collected estimates of field-scale transverse dispersivities from existing publications and explored possible scale relationships as guidance criteria for applications. Our investigation showed that a large number of estimates available in the literature are of low reliability and should be discarded from further analysis. The remaining reliable estimates are formation-specific, span three orders of magnitude and do not show any clear scale-dependence on the plume traveled distance. The ratios with the longitudinal dispersivity are also site specific and vary widely. The reliability of transverse dispersivities depends significantly on the type of field experiment and method of data analysis. In applications where transverse dispersion plays a significant role, inference of transverse dispersivities should be part of site characterization with the transverse dispersivity estimated as an independent parameter rather than related heuristically to longitudinal dispersivity.