Toward capturing hydrologically significant connectivity in spatial patterns

Abstract

Abstract. Many spatial fields exhibit connectivity features that have an important influence on hydrologic behavior. Examples include high-conductivity preferred flow paths in aquifers and saturated source areas in drainage lines. Connected features can be considered as arbitrarily shaped bands or pathways of connected pixels having similar (e.g., high) values. Connectivity is a property that is not captured by standard geostatistical approaches, which assume that spatial variation occurs in the most random possible way that is consistent with the spatial correlation, nor is it captured by indicator geostatistics. An alternative approach is to use connectivity functions. In this paper we apply connectivity functions to 13 observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns. It is shown that the connectivity functions are able to distinguish between connected and disconnected patterns. The importance of the connectivity in determining hydrologic behavior is explored using rainfall-runoff simulations and groundwater transport simulations. We propose the integral connectivity scale as a measure of the presence of hydrologic connectivity. Links between the connectivity functions and integral connectivity scale and simulated hydrologic behavior are demonstrated and explained from a hydrologic process perspective. Connectivity functions and the integral connectivity scale provide promising means for characterizing features that exist in observed spatial fields and that have an important influence on hydrologic behavior. Previously, this has not been possible within a statistical framework.

Advances in the use of observed spatial patterns of catchment hydrological response

“…These variograms are very similar to each other, which illustrates that the connectivity functions are able to distinguish between connected and disconnected patterns while variograms cannot. Western et al [113] provide a wider number of examples for the potential of the connectivity function approach. They discuss potential applications in hydrology such as in interpolation and stochastic simulation and for the derivation of bulk parameters to characterise hydrologically relevant spatial characteristics of patterns.…”

“…Ideally one would like to capture those features that are most relevant to the movement of water on the surface and in the sub-surface. Western et al [113] argued that connected features such as high conductivity preferred flow paths in aquifers and saturated source areas in drainage lines control the lateral movement of water, so it is the connectedness that needs to be represented by statistical measures of heterogeneity. They tested the utility of connectivity functions of Allard [3], Allard and Group [2], Gould and Tobochnik [44], and Stauffer and Aharony [93] on thirteen observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns.…”

Combined effect of heterogeneity, anisotropy and saturation on steady state flow and transport: Structure recognition and numerical simulation

“…Because of the dynamics of water in soil and across the atmosphere-soil interface, the hydrological systems may swoop between different states [e.g., Grayson et al, 1997;Western et al, 2001]. The switching from one state to another depends on climate and soil storage and on their interaction [e.g., Western et al, 2002].…”

Rural land management impacts on catchment scale flood risk

“…Connectivity has become a popular term in recent years with it being used to describe catchment processes in hydrology (Western et al, 2001), geomorphology (Brierly et al, 2006) and ecology (Pringle, 2003). However, there are problems with the use of this term, including no constrained definition and the difficulty of quantifying it (Bracken and Cloke, 2007;Michaelides and Chappell, 2009 as active areas which actually connect to the river network.…”

“…Binley et al, 1989); and (2) that upscaling is impossible and that modellers have to acknowledge that models, and particularly the parameters used are scale dependent (e.g. Beven, 1995;Bloschl, 2001). Armstrong and Martz (2008) found that reducing the spatial resolution of land cover data had a limited effect on hydrologic response at the outlet and that only an extreme shift to a homogeneous land cover changed the model output.…”