USING UNIT STREAM POWER AT THE BASIN SCALE: AN APPROACH TO SUPPORTING RESILIENT URBAN STREAM CORRIDORS

Unit stream power (ω = Qs/w) describes the longitudinal distribution of energy expended by water on stream beds and banks, typically at the reach-scale. We present the use of basin-scale stream power estimates to support the management of urban streams. We developed the Resilience to Impacts Stream Corridor (RISC) tool to assess conditions in the drainage network receiving urban stormwater runoff. This tool relies on determining the distribution of unit stream power across a basin-scale geography to address:

• Where streams are likely to be sensitive to increased runoff
• Where opportunities exist to intervene in a way that can mitigate for either past or future channel incision that commonly accompanies such runoff increases
• What types of intervention, including stream corridor enhancement (e.g., grade control, large wood addition, floodplain grading, or riparian planting), would be needed for effective mitigation of additional runoff.

The local utility, through its municipal stormwater permit, is now obligated to assess and manage urban runoff volumes (in addition to stormwater quality), and to promulgate runoff control measures that “target natural surface or predevelopment hydrology” to minimize runoff volume impacts from new and re-developed parcels. However, much of the urban Tualatin basin was developed prior to the adoption of these rules. The RISC tool can be applied to both current and future developed portions of the basin.

Stream reaches exceeding entrainment thresholds of unit stream power were flagged for intervention. The relative exceedance of unit stream power above entrainment values was used to guide selection of enhancement strategies. Unit stream power was estimated for enhanced stream corridors by assuming that the full width of the geomorphic floodplain would be engaged through stream corridor enhancement. Using a combination of digital terrain models, land-use projections, and runoff regression equations, the RISC tool organized these spatially explicit computations in a python-scripted application to visualize results at the reach scale and allow for rapid re-examination of land development scenarios.