Background/Methods: I developed a river channel network model for estimating source-to-sink sediment dynamics in mountainous watersheds. The model is applicable over time (decadal to centurial) and space (1-100 km2) scales relevant to management of reservoirs, lakes, streams, and watersheds.
The model has three unique features: (1) it combines bedload and suspended load; (2) streamflow is simulated using a physically-based hydrology model (DHSVM); and (3) sediment inputs to streams are from a random mass wasting (i.e., landsliding) algorithm. Sediment mass balance is conducted each day for each stream segment in the watershed (see Figure 1 on left).
The model was demonstrated in the Elwha River Basin of Washington State, upstream of the former Glines Canyon dam (see Figure 2 on left). The removal of the two Elwha dams is the largest global dam removal yet in history.
Results: The model was run over the former Glines Canyon dam’s 84-year lifespan and compared to the volume of sediment that was measured in the reservoir just prior to dam removal. The model repeatedly predicted the lifetime reservoir sedimentation volume within the uncertainty range of the total measured volume. The model also predicted within the uncertainty range of the measured gravel, sand, and mud fractions in the reservoir. The network model sediment yields were improved compared to yields from sediment rating curves at the basin outlet, which are commonly used in practice. In addition, the network model provided sediment predictions distributed over time and space, which allow for inquiry and understanding of the watershed system beyond the sediment yields at the outlet.
This work advances cross-disciplinary and application-oriented watershed sediment yield modeling approaches, which are needed for better dam and sediment management under increasing environmental change.
More information: The publication is available here. The model code (written in Python programming language) is publicly available on GitHub.