Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation
Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation
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DOI：
10.1002/esp.4743
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发表时间：
2020-01
影响因子：
3.3
通讯作者：
R. Tinoco;J. S. San Juan;J. Mullarney
中科院分区：
文献类型：
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作者：
R. Tinoco;J. S. San Juan;J. Mullarney
我们提出了一个关键的分析，通过实验室和现场实验获得的潮汐和沿海环境的植被-水流-沉积物相互作用的实验结果。众所周知，水生植被提供了广泛的生态系统服务（例如保护沿海社区免受极端事件的影响，减少河岸和海岸侵蚀，容纳各种生态系统），为了更好地了解这些服务，人们采取了多种方法，通过详细的实验室实验重现相关的物理过程。最先进的测量技术使研究人员能够在控制良好的水流条件下以高空间和时间分辨率测量速度场和泥沙输运，从而预测取决于简化或批量植被参数的水动力和泥沙输运情景。然而，最近的现场研究表明，实验装置的一些简化（例如使用刚性元件、单一直径、单一元件高度、规则或交错布局）可能会通过隐藏或放大一些相关的物理过程来影响研究结果。在自然条件下发现的过程。我们讨论了一些观察到的偏见的情况下，包括一般的做法，可能会导致与简化的假设相关的妥协。所提出的分析将确定推进实验室和实地测量的潜在途径，这可以更好地为预测者提供信息，以便对水流-植被-沉积物相互作用进行更可靠、更普遍和更准确的预测。© 2020约翰威利父子有限公司
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.