The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-02

AUTHORS

Melanie K. Vanderhoof, Hayley E. Distler, Megan W. Lang, Laurie C. Alexander

ABSTRACT

The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets. More... »

PAGES

63-86

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11273-017-9554-y

DOI

http://dx.doi.org/10.1007/s11273-017-9554-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085936637


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