A NEW STEP-LENGTH-BASED MORPHODYNAMIC MODEL OF GRAVEL BED RIVER EVOLUTION

Quantifying gravel bed river morphodynamics over decadal to centennial timescales is integral to making informed stream management and restoration decisions. Shifts in discharge and sediment regime (due to factors such as climate and land use changes) over such timescales may drastically alter the trajectory of gravel bed stream evolution – with major implications for in-channel and riparian habitat. The longer-term timescales over which such processes may drive changes in stream morphology often render field-based studies inadequate. Scenario-based morphodynamic modeling has emerged as a viable means of quantifying gravel bed river evolution, yet current models fall short with regard to their ability to predict changes in stream dynamics over the timescales in question (decades to centuries) and at the spatial scales which drive aquatic habitat (individual channel bars), a problem due largely to the computational overhead required to calculate morphodynamics at these spatiotemporal scales. Though the computing power required to drive sediment transport has hindered previous modeling efforts, field-based research suggests a potential improvement: sediment typically moves downstream with characteristic step-lengths. Here we introduce a morphodynamic model which drives sediment transport using a step-length-based model, negating the need for frequent recalculation of sediment dynamics in the flow, and correspondingly reducing computational overhead. We apply this model to the gravel-bed River Feshie (UK), and observe that it accurately reproduces many bed morphologies observed during annual high-resolution (RTK-GPS, lidar) topographic surveys. We further utilize this step-length-based model to quantify the effects on channel bed morphology due to various discharge and sediment regimes. By utilizing simple step-length-based sediment transport distributions, the formation and preservation of bed morphologies can be accurately predicted with less computational overhead than offered in previous morphodynamic modeling efforts. The improvements in spatiotemporal scale gained using this new model may greatly aid in the management of gravel bed rivers and the preservation of physical habitat for aquatic species.