A Channel Network Model for Sediment Dynamics Over Watershed Management Time Scales. (29th May 2020)

• Record Type: Journal Article
• Authors: Beveridge, Claire; Istanbulluoglu, Erkan; Bandaragoda, Christina; Pfeiffer, Allison M.
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-29
• Subjects: watershed network model -- bedload transport -- suspended load transport; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001852
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: A mountain watershed network model is presented for use in decadal to centurial estimation of source‐to‐sink sediment dynamics. The model requires limited input parameters and can be effectively applied over spatial scales relevant to management of reservoirs, lakes, streams, and watersheds (1–100 km 2 ). The model operates over a connected stream network of Strahler‐ordered segments. The model is driven by streamflow from a physically based hydrology model and hillslope sediment supply from a stochastic mass wasting algorithm. For each daily time step, segment‐scale sediment mass balance is computed using bedload and suspended load transport equations. Sediment transport is partitioned between grain size fractions for bedload as gravel and sand, and for suspended load as sand and mud. Bedload and suspended load can deposit and re‐entrain at each segment. We demonstrated the model in the Elwha River Basin, upstream of the former Glines Canyon dam, over the dam's historic 84‐year lifespan. The model predicted the lifetime reservoir sedimentation volume within the uncertainty range of the measured volume (13.7–18.5 million m 3 ) for 25 of 28 model instances. Gravel, sand, and mud fraction volumes were predicted within measurement uncertainty ranges for 18 model instances. The network model improved the prediction of sediment yields compared to at‐a‐station sediment transport capacity relations. The network model also provided spatially and temporally distributed information that allowed for inquiry and understanding of the physical system beyond the sediment yields at the outlet. This work advances cross‐disciplinary and application‐oriented watershed sediment yield modeling approaches. Plain Language Summary: Predicting sediment processes in river systems is challenging. Most methods for doing so in water resource system planning and management are lacking in scientific advancement. However, approaches used in academic research for understanding river sediment processes are often not well suited for practical applications. Academic approaches are often siloed between disciplines and do not holistically address real‐world needs. In this study, we developed a new computer model for predicting sediment processes in river systems. We combined academic research approaches from various disciplines to address a practical need of predicting reservoir sediment accumulation in mountainous regions. We demonstrated the model in the Elwha River Basin, upstream of the former Glines Canyon dam, over the dam's historic 84‐year lifespan. Our model predicted the volume of sediment that accumulated behind the dam over its lifetime. We also predicted the reservoir sediment accumulation using a traditional approach called sediment rating curves to compare to our model performances. We demonstrated that our model predicted the reservoir sediment accumulation better than the rating curves did, and our model also provided more information about the broader river system than the curves. Overall, our work advances cross‐disciplinary and application‐oriented approaches to understanding river system sediment processes. Key Points: A river network bed and suspended load model was developed, which uses distributed hydrologic inputs and evolves sediment mass in streams The model provided improved predictions of reservoir sedimentation compared to sediment rating curve estimates The model enabled distributed sediment mass balance estimates with grain size partitioning, critical for stream habitat restoration