Literature DB >> 36204687

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

Melanie K Vanderhoof1, Hayley E Distler1, Megan W Lang2, Laurie C Alexander3.   

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% to 60% of wetlands by count (21% to 93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50% to 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.

Entities:  

Keywords:  Accuracy; Landsat; Radarsat-2; SAR; Worldview-3; connectivity; depressions; forested wetlands; headwater streams; inundation; lidar; resolution; stream network

Year:  2017        PMID: 36204687      PMCID: PMC9534041          DOI: 10.1007/s11273-017-9554-y

Source DB:  PubMed          Journal:  Wetl Ecol Manag        ISSN: 0923-4861            Impact factor:   2.134


  7 in total

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Authors:  Melanie A Murphy; Jeffrey S Evans; Andrew Storfer
Journal:  Ecology       Date:  2010-01       Impact factor: 5.499

2.  Classification-algorithm evaluation: five performance measures based on confusion matrices.

Authors:  A D Forbes
Journal:  J Clin Monit       Date:  1995-05

3.  Water Quality Functions of Riparian Forest Buffers in Chesapeake Bay Watersheds

Authors: 
Journal:  Environ Manage       Date:  1997-09       Impact factor: 3.266

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Authors:  Matthew J Cohen; Irena F Creed; Laurie Alexander; Nandita B Basu; Aram J K Calhoun; Christopher Craft; Ellen D'Amico; Edward DeKeyser; Laurie Fowler; Heather E Golden; James W Jawitz; Peter Kalla; L Katherine Kirkman; Charles R Lane; Megan Lang; Scott G Leibowitz; David Bruce Lewis; John Marton; Daniel L McLaughlin; David M Mushet; Hadas Raanan-Kiperwas; Mark C Rains; Lora Smith; Susan C Walls
Journal:  Proc Natl Acad Sci U S A       Date:  2016-02-08       Impact factor: 12.779

5.  Gene selection and classification of microarray data using random forest.

Authors:  Ramón Díaz-Uriarte; Sara Alvarez de Andrés
Journal:  BMC Bioinformatics       Date:  2006-01-06       Impact factor: 3.169

6.  Patterns and drivers for wetland connections in the Prairie Pothole Region, United States.

Authors:  Melanie K Vanderhoof; Jay R Christensen; Laurie C Alexander
Journal:  Wetl Ecol Manag       Date:  2016-11-19       Impact factor: 1.379

  7 in total

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