The challenge of modeling pool–riffle morphologies in channels with different densities of large woody debris and boulders

Studies on pool morphologies include reports of over 80% or 90% of pools being associated with structural controls and large obstructions that include boulders, bedrock outcrops and large woody debris (LWD). A Monte Carlo simulation approach and developmental computer model was created to predict pool formation, spacing and the percentage length covered by pools, riffles, scour holes and runs based on input data that include channel slope, width, the number of small and large boulders, and the number of 10–30 cm, 30–60 cm and >60 cm pieces of wood. The statistical‐empirical model is founded on the idea that boulders, bedrock outcrops and large woody debris provide a physical framework that then controls local water‐surface slopes, velocity patterns and the locations of pools and riffles. The spacing values of individual types and sizes of obstructions are modeled as log‐normal distributions with separate distributions for each obstruction type. Pools are assigned different probabilities of development depending on the obstruction type. Pool and riffle lengths used to create the subsequent morphology follow their own slope‐dependent, log‐normal trends. A minimum distance develops between successive pools because of the backwater and turbulent conditions needed for pool formation. The total number and spacing of pools, riffles and scour holes thus reflects the number and locations of obstructions and characteristics of the pool–riffle couplet. The simulation model accurately captures the number of pools in the modeled data range at 65% of all the verification field sites, and 86% of the verification field sites with a more limited range of width and slope characteristics. Lower levels of prediction capabilities are associated with modeled numbers of scour holes and log jams. The model accurately mimics some statistical attributes of pool spacing, and future versions of the model could be developed to improve overall predictive capabilities. Copyright © 2011 John Wiley & Sons, Ltd.