Development and Application of a Geostatistical Framework for Quantifying the Spatial Variability of River Channel Morphology and In-stream Habitat

Current research at the interface between geomorphology and ecology emphasizes linkages between geomorphic variability, habitat complexity, and the diversity and productivity of aquatic ecosystems. The goal of river restoration is to reestablish these connections by reintroducing the natural fluvial processes that create and maintain the habitat needed to sustain viable populations of critical species. Achieving this objective will require a novel, quantitative approach to the description of channel morphology and associated habitat conditions. Geostatistics provides a spatially explicit, stochastic framework for this characterization by summarizing variance at different scales, based on differences between pairs of observations separated by various lag distances. Although Euclidean distance is not a valid, in-water metric for the non-convex geometry of meandering channels, a more appropriate representation can be obtained by transforming measurement locations to a channel-centered coordinate system defined by streamwise s and normal n axes. I have developed an efficient transformation algorithm that allows for geostatistical modeling in the (s,n) space. Using field data from a pristine gravel-bed river in Wyoming and a recently restored reach of the Merced River in California, I illustrate how variogram model parameters are related to geomorphic context and ecologically relevant channel characteristics. More specifically, I show that spatial structure varies among reaches with different disturbance histories and demonstrate how cross-variograms of flow depth and velocity can be used to evaluate aquatic habitat. In addition to its utility as a tool for quantifying channel form and the evolution of simple restored reaches toward a more natural, complex state, this geostatistical approach could yield novel insight into a number of restoration-relevant issues including partial transport of mixed grain size sediment and the response of various organisms to different scales of morphologic and hydraulic variability. Extensions to spatial prediction, stochastic simulation, and decision-making under uncertainty are also discussed.