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Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation
Author(s) -
Tinoco Rafael O.,
San Juan Jorge E.,
Mullarney Julia C.
Publication year - 2020
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.4743
Subject(s) - vegetation (pathology) , environmental science , sediment , sediment transport , field (mathematics) , flow (mathematics) , erosion , range (aeronautics) , ecosystem , hydrology (agriculture) , geology , remote sensing , ecology , geomorphology , geotechnical engineering , engineering , mechanics , physics , medicine , mathematics , biology , pathology , aerospace engineering , pure mathematics
We present a critical analysis of experimental findings on vegetation–flow–sediment interactions obtained through both laboratory and field experiments on tidal and coastal environments. It is well established that aquatic vegetation provides a wide range of ecosystem services (e.g. protecting coastal communities from extreme events, reducing riverbank and coastal erosion, housing diverse ecosystems), and the effort to better understand such services has led to multiple approaches to reproduce the relevant physical processes through detailed laboratory experiments. State‐of‐the‐art measurement techniques allow researchers to measure velocity fields and sediment transport with high spatial and temporal resolution under well‐controlled flow conditions, yielding predictions for hydrodynamic and sediment transport scenarios that depend on simplified or bulk vegetation parameters. However, recent field studies have shown that some simplifications on the experimental setup (e.g. the use of rigid elements, a single diameter, a single element height, regular or staggered layout) can bias the outcome of the study, by either hiding or amplifying some of the relevant physical processes found in natural conditions. We discuss some observed cases of bias, including general practices that can lead to compromises associated with simplified assumptions. The analysis presented will identify potential pathways to move forward with laboratory and field measurements, which could better inform predictors to produce more robust, universal and accurate predictions on flow–vegetation–sediment interactions. © 2020 John Wiley & Sons, Ltd.