H204-04
Predicting Fine Particle Dynamics in Rivers: A Mobile-Immobile Model Framework Incorporating Both Baseflow and Stormflow Processes

Wednesday, 16 December 2020: 07:12
Virtual
Jennifer D Drummond1, Tomás Aquino2, Rob Davies-Colley3, Rebecca Stott3 and Stefan Krause4, (1)University of Birmingham, Birmingham, B15, United Kingdom, (2)University of Rennes, Dimenv, Rennes Cedex, France, (3)National Institute of Water and Atmospheric Research, Hamilton, New Zealand, (4)University of Birmingham, School of Geography, Earth and Environmental Sciences, Birmingham, B15, United Kingdom
Abstract:
Rivers transport fine particles and associated contaminants long distances downstream of diffuse and point sources, posing a risk for both ecological and human health. Particulate-associated contaminants include nutrients, fecal microbes, metals and biocides, and micro-plastics. During baseflow conditions, hyporheic exchange flow between surface water and the streambed transports fine particles into streambed sediments. These particles immobilize and accumulate during baseflow, particularly via hyporheic exchange and filtration within underlying sediments. However, accelerating flow conditions remobilize stored particles from streambed sediments during stormflow events. The extent of remobilization is dependent on both the increase in magnitude of the flow and the number of accumulated particles prior to the event within the streambed sediments. Therefore, in order to predict the persistence and fate of fine particles in streams it is important to consider both baseflow and stormflow processes within a model framework. We present a mobile-immobile model that incorporates fine particle transport, and immobilization during baseflow, and remobilization on the accelerating phase of stormflows with a comparison to observations in various experimental field campaigns. The model considers antecedent conditions of fine particle accumulation in the streambed sediments and the magnitude of the increased flow. Model outputs closely matched both timing and magnitude of E. coli observations through multiple high flow events in both artificial flood series, and natural flood events. Incorporating state-of-flow in the model framework provides realistic characterization of fine particle dynamics in streams and supports predictions of downstream transport and impacts.