4.3 Article

Adaptive Management of Stream Channel Maintenance at Bridge Crossings in the Northern Tier Region, Pennsylvania1

Journal

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
Volume 45, Issue 5, Pages 1197-1208

Publisher

WILEY
DOI: 10.1111/j.1752-1688.2009.00352.x

Keywords

adaptive management; sediment transport; hydraulic structures; sustainability; bridge waterway; sediment mitigation

Funding

  1. Pennsylvania Department of Transportation's Northern Tier Waterways [020111-2]

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An adaptive management framework is applied to the problem of identifying mitigation measures for sediment deposition near bridge crossings in small streams in the Northern Tier region of northern Pennsylvania. The presence of the rigid bridge infrastructure introduces a challenge for applying adaptive management practices, because the integrity of the bridge structure itself has to be maintained regardless of the mitigation practices used in the stream channel near the bridge. In an effort to overcome the unacceptable risk that field-scale adaptive management experiments present to rigid bridge infrastructure, an adaptive management approach for laboratory-scale experimentation of mitigation methods at bridge crossings in the Northern Tier region is presented as a way to decrease the level of uncertainty about channel response to mitigation measures and increase the rate of learning about the effectiveness of these measures. Four cycles of adaptive management experiments are discussed to demonstrate that this approach results in fast and efficient learning about channel response to mitigation methods for the given conditions. The value of monitoring and of assessment of monitored data in the overall efficiency of the adaptive management approach is highlighted. Assessment of what was learned in the adaptive management experiment cycles presented here leads to new directions to continually improve management policies and practices in stream channels at bridge crossings in the Northern Tier region. The adaptive management process, rather than continuing with a normally risk-averse management approach, results in opportunities for learning new information about a system's response.

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