Hydrology, Cryosphere & Earth Surface Editors' Highlights

Untangling Sediment Transport Through River Networks

A stochastic sediment routing model for river networks is inverted to determine sediment source areas based on point observations of grain size and sediment flux at the basin outlet.

Source: Journal of Geophysical Research: Earth Surface


Sediment that is transported through a river network represents a complex integration of multiple sources and sizes of sediment input. Determining source areas and their downstream integration is frequently hampered by insufficient data, particularly for large river basins, but has relevance for managing natural and anthropogenic disturbances of watersheds (for example, the effects of dams and diversions). Schmitt et al. [2017] use a stochastic sediment routing model that randomly assigns input grain size to a network of sediment sources, which when run repeatedly produces a distribution of potential solutions for the size and flux of sediment carried by the river. Point observations of grain size and sediment flux at the basin outlet are then used to constrain admissible solutions within the range of uncertainty of the observations, allowing inversion of the model to determine associated source grain sizes and sediment fluxes through the network. The approach also identifies bottlenecks in the river network that regulate the flux of sediment through the system. The stochastic framework allows powerful leveraging of limited field observations that can inform management plans and structure subsequent validation efforts to better understand physical controls on network sediment flux and routing.

Citation: Schmitt, R. J. P., Bizzi, S., Castelletti, A. F., & Kondolf, G. M. [2017]. Stochastic modeling of sediment connectivity for reconstructing sand fluxes and origins in the unmonitored Se Kong, Se San, and Sre Pok tributaries of the Mekong River. Journal of Geophysical Research: Earth Surface, 122. https://doi.org/10.1002/2016JF004105

—John Buffington, Editor, JGR: Earth Surface

© 2018. The authors. CC BY-NC-ND 3.0