Evaluating concentrated flowpaths in riparian forest buffer contributing areas using LiDAR imagery and topographic metrics

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Abstract

Riparian forest (CP22) buffers are implemented in the Chesapeake Bay Watershed to trap pollutants in surface runoffthus minimizing the amount of pollutants entering the stream network. For these buffers to function effectively, overland flow must enter the riparian zones as dispersed sheet flow to facilitate slowing, filtering, and infiltrating of surface runoff. The occurrence of concentrated flowpaths, however, is prevalent across the watershed. Concentrated flowpaths limit buffer filtration capacity by channeling overland flow through or around buffers. In this study, two topographic metrics (topographic openness and flow accumulation) were used to evaluate the occurrence of concentrated flowpaths and to derive effective CP22 contributing areas in four Long-Term Agroecosystem Research (LTAR) watersheds within the Chesapeake Bay Watershed. The study watersheds include the Tuckahoe Creek watershed (TCW) located in Maryland, and the Spring Creek (SCW), Conewago Creek (CCW) and Mahantango Creek (MCW) watersheds located in Pennsylvania. Topographic openness identified detailed topographic variation and critical source areas in the lower relief areas while flow accumulation was better at identifying concentrated flowpaths in higher relief areas. Results also indicated that concentrated flowpaths are prevalent across all four watersheds, reducing CP22 effective contributing areas by 78% in the TCW, 54% in the SCW, 38% in the CCW and 22% in the MCW. Thus, to improve surface water quality within the Chesapeake BayWatershed, the implementation of riparian forest buffers should be done in such a way as to mitigate the effects of concentrated flowpaths that continue to short-circuit these buffers.

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Wallace, C. W., McCarty, G., Lee, S., Brooks, R. P., Veith, T. L., Kleinman, P. J. A., & Sadeghi, A. M. (2018). Evaluating concentrated flowpaths in riparian forest buffer contributing areas using LiDAR imagery and topographic metrics. Remote Sensing, 10(4). https://doi.org/10.3390/rs10040614

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