Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations

Journal of the American Water Resources Association (JAWRA)
By: , and 

Links

Abstract

Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.

Publication type Article
Publication Subtype Journal Article
Title Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations
Series title Journal of the American Water Resources Association (JAWRA)
DOI 10.1111/1752-1688.12735
Volume 55
Issue 3
Year Published 2019
Language English
Publisher Wiley
Contributing office(s) WMA - Integrated Information and Dissemination Division
Description 19 p.
First page 559
Last page 577
Google Analytic Metrics Metrics page
Additional publication details