Doctoral Dissertation Research: The Effects of Watershed Dynamics and Flood Hydrology on Mangrove Sedimentation in Tropical Coastal Catchments

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。由于海平面上升和人类对地貌的改变，沿海流域遭受大洪水的风险越来越大。然而，需要更多的本地化洪水研究和建模，特别是在缺乏当地风暴影响和径流信息的地区。最近的文献集中在沿海洪水从海平面上升，少得多的关注上游的渠道流量或局部差异的流域内的洪水的贡献。缺乏足够洪水信息的地区往往是极端洪水可能对当地社区造成更严重和更具破坏性的地区。传统上，洪水管理在很大程度上依赖于仪器和建模方法，并排除了受这些事件影响的当地人关于洪水持续时间和范围的宝贵信息。这项调查提出了表征流域洪水程度的综合方法。本博士论文项目通过入户调查和保存的洪水景观证据，对一个典型的数据有限的沿海流域的极端洪水进行研究，以创建一个最近极端洪水事件的图形表示。这些数据也被用于当地校准的径流模型，以估计极端洪水的时间和强度。这项调查的结果有助于建立降雨和径流之间的关系的基线估计。本研究的另一个目的是了解最近的土地利用变化如何影响沿海洪水，这是通过分析卫星图像来评估，以突出在通道，海岸线和流域的主要变化。为了确定在更长的时间尺度上发生极端洪水的频率，调查人员正在从沿海红树林中提取沉积物岩心，并分析洪水沉积物的证据。这些方法结合起来，对过去的极端洪水进行了可靠的评估，并强调了使用多个数据源和当地信息来改善洪水规划和管理的重要性。此外，这些方法有助于减少洪水建模的不确定性，并提高对世界上任何数据有限地区洪水的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Coastal watersheds are increasingly at risk from major floods due to increased sea-level rise and human modification of the landscape. However, more localized flood research and modelling is needed, particularly in regions that lack information on local storm impacts and streamflow. Recent literature has focused on coastal flooding from sea-level rise, with much less attention to upstream contributions of channel flow or localized differences in flooding within the watershed. Often areas that lack sufficient flood information are where extreme floods are likely to be more severe and devastating to local communities. Traditionally, flood management has heavily relied on instrumental and modelling methods and has excluded valuable information on the duration and extent of flooding from locals affected by these events. This investigation advances integrated methods for characterizing the extent of flooding in watersheds. Research on this topic helps to inform management decisions pertaining to local flood risks and ecosystem management.This doctoral dissertation project investigates extreme floods in a representative data-limited coastal watershed by using household surveys and preserved evidence of flooding in the landscape to create a graphical representation of the most recent extreme flood event. These data are also used in a locally calibrated rainfall-runoff model to estimate the timing and intensity of extreme floods. The findings of this investigation help to establish baseline estimates on the relationship between rainfall and runoff. An additional objective of this research is to understand how more recent land-use changes affect coastal flooding, which is assessed by analyzing satellite imagery to highlight major changes in the channel, coastline, and watershed drainage area. To determine how often extreme floods occur on longer timescales, the investigators are extracting sediment cores from coastal mangroves and analyzing for evidence of flood deposits. Combined, these methods provide a robust assessment of past extreme floods and highlight the importance of using multiple data sources and local information to improve flood planning and management. Furthermore, these methods help to reduce uncertainty in flood modelling and provide an improved understanding of flooding in any data-limited region around the world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.