Stochastic modeling of fine particulate organic carbon dynamics in rivers

The majority of particulate organic matter standing stock in streams is < 1 mm in diameter, and the mobile phase is primarily very fine particles. Such fine particles transport downstream in a series of deposition and resuspension events mediated by interactions with coarser bed sediment, yielding fine particle retention over a wide range of time scales. This retention controls the opportunity for biogeochemical processing of particulate organic carbon in streams. We present a conceptual model of particulate organic carbon transport in rivers categorized in three cyclic processes: (i) migration of fine particles from the water column to the underlying and surrounding sediments, (ii) fine particle transport and retention within the bed sediments, and (iii) resuspension of fine particles back to the water column. We developed a stochastic model to describe the transport and retention of fine suspended particles in rivers, including advective delivery of particles to the streambed, transport through pore waters, and reversible filtration within the streambed. We then apply this model to observations of fine particle transport in two small streams, and show that the stochastic mobile-immobile model supports improved interpretation of particulate organic carbon dynamics under base flow conditions. Analysis of in-stream solute and particle data shows that particles engage in multiple deposition and resuspension events during downstream transport, and that long-term retention in the streambed produces extended slow releases to the stream even during base flow conditions. We also show how multiscale stochastic modeling can be used to incorporate local observations of particle retention in predictions of whole-stream particle dynamics. Key Points Particulate organic carbon transports through deposition and resuspension POC transport is accounted for in a multiscale stochastic model framework A stochastic model allows upscaling of local observations and prediction