A Conceptual Model of Soil Susceptibility to Macropore Flow

(shrinkage cracks, interaggregate voids, earthworm channels, and root holes) strongly infl u-ence water fl ow in structured soils and therefore patterns of solute displacement. Macropore fl ow increases the risk of leaching of surface-applied contaminants to groundwater, since infi ltrating water can be quickly channeled through only a very small fraction of the total pore volume, bypassing much of the adsorption and degradation capacity of the chemically and biologically reactive topsoil. Th e signifi cance of macropore fl ow has long been recog-nized (e.g., Th omas and Phillips, 1979), and in recent decades this has stimulated a major experimental research eff ort aimed at improving understanding of its causes, controlling factors, and consequences (Jarvis, 2007). Th is research eff ort has gone hand in hand with the development of many models that attempt to synthesize knowledge of the relevant processes in mathematical form (Gerke, 2006). Th ese models can be used as tools in research to test the limits of our understanding and generate new hypoth-eses, and as management tools to support policy development and decision making (Vanclooster et al., 2004).At well-investigated sites, input parameters to macropore fl ow models can be either directly measured or derived by calibra-tion against depth profi les of resident concentrations and solute breakthrough curves to further minimize prediction uncertainty (Larsbo and Jarvis, 2005). Many model users (e.g., agricultural advisors, water managers, and regulatory authorities), however, are also required to make predictions for less well characterized sites or at larger scales, for example, to map and quantify diff use pollution risks at farm, catchment, regional, and even national scales. Models that account for macropore fl ow must then be used predictively, without any direct measurements of input param-eters or site data for calibration, although it can be assumed that “soft” information in the form of soil and topographic maps and land use statistics will generally be available. Soil maps have long been used as a basis for catchment- and regional-scale vulnerabil-ity and risk assessments (Vanclooster et al., 2004). Th is approach to upscaling predictions from local-scale data is based on the assumption that soil mapping units (soil series or associations, characterized by benchmark pedons, i.e., typical sequences of recognized soil horizons in a profi le) are characterized by infor-mation relevant to solute transport and can be considered as macroscopically homogeneous structural units (e.g., Vogel and Roth, 2003; Vereecken et al., 2007). Th is assumption may be