Understanding geomorphic response to hydrological events: filling the data gaps

了解地貌对水文事件的响应：填补数据空白

• 批准号: 2059933
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2059933
• 关键词: Understanding; geomorphic; response; hydrological; events

We will improve the management of river sediment and modelling of flood risk by developing and applying a 'sediments toolkit' for the characterisation of river bed grain size at local to catchment scales.-Flood intensity is increasing in the UK and many rivers are becoming more mobile, particularly Scotland's flashy and sediment-laden systems. Our understanding of how geomorphic systems respond and adjust to hydrological events comes principally from traditional morphological measurements, obtained from aerial photographs, LiDAR and maps. We know that the nature of the constituent sediments-especially size-is a critical element of geomorphic systems, controlling, for example, entrainment and mobility of sediment, channel conveyance during floods, and the ability to support ecologically and economically important fauna & flora. However, data on sediment properties is relatively sparse, principally because it is expensive and time-consuming to collect.SEPA have identified the lack of grain-size data at bar to catchment scales as a critical data gap, limiting their ability to predict future patterns of channel erosion/deposition and to model flood risk. Obtaining such data is therefore a key priority.-Despite the development of several image-based grain-sizing methods in recent years (including by Graham/Rice), uptake of these methods by practitioners has been partial owing to limited guidance on their selection and use, and a reluctance to adopt new/unproven technologies. If these obstacles are overcome, image-based sediment characterisation could significantly improve understanding and management of geomorphic systems.In this project, we will develop an integrated 'sediments toolkit' consisting of software, documentation and guidance for image-based sediment characterisation at multiple scales. It will be targeted at practitioners without specialist knowledge of image processing methods and released under an Open Source licence.Utilising the ability of small unmanned aircraft to obtain data over a wide range of spatial scales/resolutions, we will collect a heterogeneous dataset for a variety of river environments. Published and original (e.g. using structure-from-motion derived point clouds) algorithms will be tested to define a matrix of recommended methods for different scenarios, including: the nature of the environment being studied; the spatial and temporal resolution of the study (e.g. bar scale vs catchment scale); the nature of the data required (e.g. proportion of sand/gravel vs complete grain-size distribution); the accessibility of the site; and the resources (financial/human/technological) available.-The toolkit will have wide applicability to rivers in Scotland, the UK and beyond. We will use the River Dee, Aberdeenshire-where the 2015/16 floods caused significant erosion, destroying productive land and threatening infrastructure (e.g. Abergeldie Castle)-as a case study of how the incorporation of distributed grain-size data can improve river management. We will estimate sediment entrainment thresholds and identify potentialzones of sediment supply, erosion and deposition. Management strategies will be designed to reduce flood risk, mitigate threats to infrastructure and maintain hydromorphological integrity (as required by Water Framework Directive, WFD). We will then explore applications in other environments where grain size is of fundamental importance for understanding geomorphic adjustment, such as beaches.

我们将通过开发和应用“沉积物工具包”来改善河流沉积物的管理和洪水风险的建模，以描述当地到流域尺度的河床粒度。英国的洪水强度正在增加，许多河流变得更加移动的，特别是苏格兰的浮华和沉积物负载系统。我们对地貌系统如何响应和适应水文事件的理解主要来自传统的形态测量，从航空照片，激光雷达和地图中获得。我们知道，组成沉积物的性质，特别是大小，是地貌系统的一个关键因素，控制，例如，夹带和流动性的沉积物，在洪水期间的渠道运输，并支持生态和经济上重要的动物群和植物群的能力。然而，关于沉积物特性的数据相对较少，主要是因为收集这些数据既昂贵又耗时。国家环保总局已确定，从拦河坝到集水区尺度的粒度数据的缺乏是一个关键的数据缺口，这限制了他们预测未来河道侵蚀/沉积模式和模拟洪水风险的能力。因此，获得这些数据是一个关键的优先事项。尽管近年来开发了几种基于图像的粒度测量方法（包括Graham/Rice的方法），但由于对这些方法的选择和使用的指导有限，并且不愿意采用新的/未经证实的技术，从业人员对这些方法的采用一直是部分的。如果这些障碍被克服，基于图像的沉积物表征可以显着提高地貌systems.In这个项目中，我们将开发一个集成的“沉积物工具包”，包括软件，文档和指导基于图像的沉积物表征在多个尺度。它将针对没有图像处理方法专业知识的从业人员，并在开源许可证下发布。利用小型无人驾驶飞机在各种空间尺度/分辨率上获取数据的能力，我们将收集各种河流环境的异构数据集。已出版和原创（例如，使用从运动导出的点云结构）算法将进行测试，以确定不同情况下的推荐方法矩阵，包括：研究环境的性质;研究的空间和时间分辨率（例如，条形尺度与流域尺度）;所需数据的性质（例如砂/砾石的比例与完整的粒度分布）;场地的可达性;以及可用的资源（财务/人力/技术）。该工具包将广泛适用于苏格兰、英国及其他地区的河流。我们将使用阿伯丁郡的迪河-2015/16年的洪水造成了严重的侵蚀，破坏了生产性土地并威胁到基础设施（例如Abergeldie城堡）-作为分布式粒度数据的整合如何改善河流管理的案例研究。我们将估计泥沙夹带阈值，并确定潜在的泥沙供应区，侵蚀和沉积。将设计管理战略，以减少洪水风险，减轻对基础设施的威胁，并保持水文形态的完整性（根据水框架指令，WFD的要求）。然后，我们将探讨在其他环境中的应用，粒度是了解地貌调整，如海滩的根本重要性。