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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。由于海平面上升加剧和人类对景观的改造，沿海流域面临越来越大的洪水风险。然而，需要更多的局部洪水研究和建模，特别是在缺乏当地风暴影响和水流信息的地区。最近的文献主要关注海平面上升引起的沿海洪水，而很少关注河道流量的上游贡献或流域内洪水的局部差异。缺乏足够洪水信息的地区往往是极端洪水可能更加严重和对当地社区具有破坏性的地区。传统上，洪水管理严重依赖于工具和建模方法，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。这项研究提出了综合方法来表征流域洪水的程度。对该主题的研究有助于为有关当地洪水风险和生态系统管理的管理决策提供信息。本博士论文项目通过家庭调查和景观中保存的洪水证据来研究代表性数据有限的沿海流域的极端洪水，以创建最近极端洪水事件的图形表示。这些数据还用于当地校准的降雨径流模型，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关系的基线估计。本研究的另一个目标是了解最近的土地利用变化如何影响沿海洪水，通过分析卫星图像来评估，以突出河道、海岸线和流域流域的主要变化。为了确定极端洪水在更长的时间尺度上发生的频率，研究人员正在从沿海红树林提取沉积物岩心，并分析洪水沉积物的证据。综合起来，这些方法提供了对过去极端洪水的可靠评估，并强调了使用多种数据源和当地信息来改进洪水规划和管理的重要性。此外，这些方法有助于减少洪水模拟中的不确定性，并提供对世界上任何数据有限地区洪水的更好理解。该奖项反映了美国国家科学基金会的法定使命，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.