Linking temporal scales of suspended sediment transport in rivers: towards improving transferability of prediction

Suspended sediment (SS) transport in rivers is highly variable, making it challenging to develop predictive models that are applicable across timescales and rivers. Previous studies have identified catchment and hydro-meteorological variables controlling SS concentrations. However, due to the lack of long-term, high-frequency SS monitoring, it remains difficult to link SS transport dynamics during high-flow events with annual or decadal trends in SS transport. This study investigated how processes driving SS transport during high-flow events impact SS transport dynamics and trends observed over longer timescales. Suspended sediment samples from the River Aire (UK) (1989–2017) were used to (i) statistically identify factors driving SS transport over multiple timescales (high-flow events, intra- and inter-annual) and (ii) conceptualize SS transport as a fractal system to help link and interpret the effect of short-term events on long-term SS transport dynamics. Antecedent moisture conditions were a dominant factor controlling event-based SS transport, confirming results from previous studies. Findings also showed that extreme high-flow events (in SS concentration or discharge) mask factors controlling long-term trends. This cross-timescale effect was conceptualized as high fractal power, indicating that quantifying SS transport in the River Aire requires a multi-timescale approach. Characterizing the fractal power of a SS transport system presents a starting point in developing transferrable process-based approaches to quantify and predict SS transport, and develop management strategies. A classification system for SS transport dynamics in river systems in terms of fractal power could be developed which expresses the dominant processes underlying SS transport.