2022 CSDMS meeting-100
How does precipitation variability control bedload response across a mountainous channel network?
Erkan Istanbulluoglu, University of Washington Seattle Washington, United States. email@example.com
Jeff Keck, University of Washington Seattle Washington, United States.
Jessica Lundquist, University of Washington Seattle Washington, United States.
Stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport is nonlinear, discharge arising from precipitation resolved at 1-hr resolution may simulate bedload differently than discharge arising from daily total precipitation distributed evenly over 24-hrs. In this study, we quantify the bias introduced into a network-scale bedload transport model due to this simplification in forcing. Specifically, we examine the difference between bedload transport capacity driven by 1- vs 24-hr precipitation derived stream hydrographs at channel network locations varying from lowland pool-riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Bedload transport error is expressed as the ratio of cumulative transport capacity driven by 1-h to the 24-h hyetographs. We find that, depending on channel network location, cumulative error can range from 10-20% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the bankfull-excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. As bankfull-excess shear stress decreases in the upstream direction, the largest bedload transport capacity errors occur in upland channels. These findings have implications for flood-hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse-temporal-resolution climate data.