Reconstruction of sediment supply and transport behind the Elwha River dams
The Elwha River watershed in Washington State’s Olympic National Park is predominantly a steep mountainous landscape where dominant geomorphic processes include landslides, debris flows, and gullying. The removal of the two massive hydropower dams along the Elwha River in 2013 marked the largest global dam removal in history. Over the century-long lifespan of the dams, approximately 21 million cubic meters of sediment was impounded behind them. In this study, we test different approaches to reconstructing sediment supply and delivery to the Elwha dams throughout their lifespan. Available observational data that aid in this approach include DEM, channel morphology, meteorology, and streamflow and sediment discharge. We analyze the performance of three numerical modeling constructs — Cases I, II, and III — in predicting historic reservoir sediment yields in the Elwha River watershed. In Case I, we use a sediment rating curve approach and improve upon a published sediment rating curve (Curran et al., 2009) for the Elwha River using a modified, calibrated version of the Wilcock & Crowe (2003) sediment transport equation. In Cases II and III we use a network model that we have developed which connects processes of sediment supply from hillslopes with sediment detachment and transport at stream links of a channel network. The model is driven by streamflow and sediment forcings which can be of varying complexity. In Case II, the network model is driven by stochastic streamflow forcings that require simple input data and parameterization. In Case III, the network model is driven by physically-based model outputs of streamflow from a physically-based distributed model, DHSVM. Using these three cases, we strive to answer the questions: Is using a watershed network model warranted in predicting dam sedimentation? If so, what level of complexity is needed in the driving streamflow and sediment forcings? Is a rating curve approach adequate and robust enough for predicting dam sedimentation, particularly with expected changes in environmental conditions? Cases I, II, and III in this report require further calibration before these questions can fully be answered, however are valuable “proof-of-concept” models. We show that our uncalibrated modeling results along with the insights we have developed provide a promising path in moving forward to answer these questions.
- Research Organization:
- Hydro Research Foundation, Evergreen, MA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office
- DOE Contract Number:
- EE0006506
- OSTI ID:
- 1630752
- Report Number(s):
- DOE-HRF-6506-finalreport
- Country of Publication:
- United States
- Language:
- English
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