Nicholas Wallerstein

Influence of large woody debris on morphological evolution of incised, sand-bed channels

This paper documents the influence of Large Woody Debris (LWD) on the morphological evolution of unstable, degrading, sand-bed rivers in the Yazoo Basin, North Mississippi, USA. The study was performed as part of the Demonstration Erosion Control (DEC) project. Twenty-three river reaches were studied, with the aim of determining whether the presence of LWD was beneficial or detrimental to the recovery of stability in degrading, sand-bed river systems and to provide the geomorphic understanding necessary to underpin enhanced LWD management strategies. The results demonstrate that locations of LWD inputs, volumes of LWD stored in different reaches and number of jams per unit channel length are causally related to the morphological processes occurring during different stages of adjustment in these unstable, incised fluvial systems and may be explained using a Channel Evolution Model (CEM). The net impact of LWD jams on reach-scale sediment budgets was found, in general, to be positive: that is, jams trap more sediment than they mobilise. This suggests that LWD probably accelerates rather than retards recovery of a stable longitudinal profile and channel configuration following incision. Field typing of LWD jams, based on their impacts on the flow pattern, reveals that jam type is a function of the size of large, key elements in the jam in relation to the channel width. A Debris Jam Classification Scheme is proposed on this basis, with the spatial relationship between jam type and drainage basin area expressed using a dimensionless function of the ratio between channel width and average riparian tree height. The scheme features four jam types, Underflow, Dam, Deflector and Flow Parallel/Bar Head, each of which has a different morphological impact on local channel geometry. These jam types may be used to classify LWD jams as an aid in determining appropriate management strategies, according to their location within the drainage basin.

Modeling Probability of Blockage at Culvert Trash Screens Using Bayesian Approach

Trash screens are commonly installed at culvert entrances to prevent the ingress of debris that might otherwise become lodged within the structure. However, these can be a hazard in themselves if not cleared at an appropriate interval. There are currently no tools available to support making decisions regarding screen inspection requirements based upon potential site-by-site blockage risks. The analysis reported here was performed to address this issue. The parameter considered as key in the decision-making process was the probability of screen blockage. To determine this for any screen under consideration, a stochastic predictive model was developed using inspection records, obtained from 140 screens in Belfast, Northern Ireland, to relate blockage probabilities to seven potential drivers extracted from channel, land-use, meteorological, temporal, and social deprivation factors, employing a logistic regression approach. To allow for randomness in the data set, a Bayesian framework was adopted through which the uncertainty associated with any prediction could be reported using appropriate credible intervals. The predictive accuracy of the model was also assessed using appropriate measures and, despite documented uncertainties, was shown to be well within acceptable limits.