Susceptibility of catchments to INTense RAinfall and flooding (Project SINATRA)

集水区对强降雨和洪水的敏感性（SINATRA 项目）

• 批准号: NE/K008668/1
• 负责人: Thomas Coulthard
• 金额: $ 30.73万
• 依托单位: University of Hull
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FK008668%2F1
• 关键词: Susceptibility; catchments; INTense; RAinfall; flooding

Project SINATRA responds to the NERC call for research on flooding from intense rainfall (FFIR) with a programme of focused research designed to advance general scientific understanding of the processes determining the probability, incidence, and impacts of FFIR.Such extreme rainfall events may only last for a few hours at most, but can generate terrifying and destructive floods. Their impact can be affected by a wide range factors (or processes) such as the location and intensity of the rainfall, the shape and steepness of the catchment it falls on, how much sediment is moved by the water and the vulnerability of the communities in the flood's path. Furthermore, FFIR are by their nature rapid, making it very difficult for researchers to 'capture' measurements during events. The complexity, speed and lack of field measurements on FFIR make it difficult to create computer models to predict flooding and often we are uncertain as to their accuracy. To address these issues, NERC launched the FFIR research programme. It aims to reduce the risks from surface water and flash floods by improving our identification and prediction of the meteorological (weather), hydrological (flooding) and hydro-morphological (sediment and debris moved by floods) processes that lead to FFIR. A major requirement of the programme is identifying how particular catchments may be vulnerable to FFIR, due to factors such as catchment area, shape, geology and soil type as well as land-use. Additionally, the catchments most susceptible to FFIR are often small and ungauged.Project SINATRA will address these issues in three stages: Firstly increasing our understanding of what factors cause FFIR and gathering new, high resolution measurements of FFIR; Secondly using this new understanding and data to improve models of FFIR so we can predict where they may happen - nationwide and; Third to use these new findings and predictions to provide the Environment Agency and over professionals with information and software they can use to manage FFIR, reducing their damage and impact to communities. In more detail, we will:1. Enhance scientific understanding of the processes controlling FFIR, by-(a) assembling an archive of past FFIR events in Britain and their impacts, as a prerequisite for improving our ability to predict future occurrences of FFIR.(b) making real time observations of flooding during flood events as well as post-event surveys and historical event reconstruction, using fieldwork and crowd-sourcing methods.(c) characterising the physical drivers for UK summer flooding events by identifying the large-scale atmospheric conditions associated with FFIR events, and linking them to catchment type.2. Develop improved computer modelling capability to predict FFIR processes, by-(a) employing an integrated catchment/urban scale modelling approach to FFIR at high spatial and temporal scales, modelling rapid catchment response to flash floods and their impacts in urban areas.(b) scaling up to larger catchments by improving the representation of fast riverine and surface water flooding and hydromorphic change (including debris flow) in regional scale models of FFIR.(c) improving the representation of FFIR in the JULES land surface model by integrating river routing and fast runoff processes, and performing assimilation of soil moisture and river discharge into the model run.3. Translate these improvements in science into practical tools to inform the public more effectively, by-(a) developing tools to enable prediction of future FFIR impacts to support the Flood Forecasting Centre in issuing new 'impacts-based' warnings about their occurrence.(b) developing a FFIR analysis tool to assess risks associated with rare events in complex situations involving incomplete knowledge, analogous to those developed for safety assessment in radioactive waste management.In so doing SINATRA will achieve NERC's science goals for the FFIR programme.

SINATRA 项目响应 NERC 对强降雨洪水 (FFIR) 研究的号召，推出了一项重点研究计划，旨在促进对决定 FFIR 概率、发生率和影响的过程的一般科学理解。此类极端降雨事件最多只能持续几个小时，但可能会产生可怕的破坏性洪水。它们的影响可能受到多种因素（或过程）的影响，例如降雨的位置和强度、流域的形状和陡度、水移动的沉积物数量以及洪水路径中社区的脆弱性。此外，FFIR 本质上是快速的，这使得研究人员很难在事件期间“捕获”测量结果。 FFIR 的复杂性、速度和缺乏现场测量使得创建预测洪水的计算机模型变得困难，而且我们常常不确定其准确性。为了解决这些问题，NERC 启动了 FFIR 研究计划。它旨在通过改进对导致 FFIR 的气象（天气）、水文（洪水）和水文形态（洪水移动的沉积物和碎片）过程的识别和预测来减少地表水和山洪爆发的风险。该计划的一个主要要求是确定特定流域如何容易受到 FFIR 的影响，原因包括流域面积、形状、地质和土壤类型以及土地利用等因素。此外，最容易受到 FFIR 影响的流域通常较小且未测量。SINATRA 项目将分三个阶段解决这些问题：首先加深我们对导致 FFIR 的因素的了解，并收集新的高分辨率 FFIR 测量结果；其次，利用这种新的理解和数据来改进 FFIR 模型，以便我们可以预测它们可能发生的地方——全国范围内；第三，利用这些新发现和预测为环境局和专业人士提供可用于管理 FFIR 的信息和软件，减少其对社区的损害和影响。更详细地说，我们将：1。增强对控制 FFIR 过程的科学理解，方法是：(a) 收集英国过去的 FFIR 事件及其影响的档案，作为提高我们预测未来 FFIR 发生的能力的先决条件。(b) 使用实地工作和众包方法，对洪水事件期间的洪水以及事件后调查和历史事件重建进行实时观测。(c) 描述英国夏季的物理驱动因素 通过识别与 FFIR 事件相关的大范围大气条件并将其与流域类型联系起来来确定洪水事件。2．开发改进的计算机建模能力来预测 FFIR 过程，方法是：(a) 在高空间和时间尺度上对 FFIR 采用综合流域/城市尺度建模方法，对流域对山洪暴发的快速响应及其对城市地区的影响进行建模。(b) 通过改进区域尺度模型中快速河流和地表水泛滥和水貌变化（包括泥石流）的表示，扩大到更大的流域。 FFIR。(c)通过整合河流演进和快速径流过程，并将土壤湿度和河流流量同化到模型运行中，改进FFIR在JULES地表模型中的表示。3．将这些科学进步转化为实用工具，更有效地向公众提供信息，方法是：(a) 开发工具来预测未来 FFIR 影响，以支持洪水预报中心针对其发生发布新的“基于影响”的警告。(b) 开发 FFIR 分析工具来评估在涉及不完整知识的复杂情况下与罕见事件相关的风险，类似于为放射性废物管理中的安全评估而开发的工具。 将实现 NERC 的 FFIR 计划科学目标。

项目成果

Understanding the geomorphic impacts of Leaky Wooden Dams (LWDs) through utilising analog physical models, structure from motion photogrammetry and surface velocimetry.

通过利用模拟物理模型、运动摄影测量结构和表面测速来了解漏水木坝 (LWD) 的地貌影响。

• DOI: 10.5194/egusphere-egu22-10199
• 发表时间: 2022
• 作者: Carter C

The potential impact of green agendas on historic river landscapes: Numerical modelling of multiple weir removal in the Derwent Valley Mills world heritage site, UK

绿色议程对历史河流景观的潜在影响：英国德文特谷米尔斯世界遗产地多堰拆除的数值模拟

• DOI: 10.1016/j.geomorph.2017.05.009
• 发表时间: 2017
• 期刊: Geomorphology
• 影响因子: 3.9
• 作者: Howard A

The impact of different rainfall products on landscape modelling simulations

不同降雨产品对景观建模模拟的影响

• DOI: 10.1002/esp.4894
• 发表时间: 2020
• 期刊: Earth Surface Processes and Landforms
• 影响因子: 3.3
• 作者: Skinner C

Modelling long term basin scale sediment connectivity, driven by spatial land use changes

• DOI: 10.1016/j.geomorph.2016.05.027
• 发表时间: 2017-01
• 期刊: Geomorphology
• 影响因子: 3.9
• 作者: T. Coulthard;M. Wiel

Global sensitivity analysis of parameter uncertainty in landscape evolution models

• DOI: 10.5194/gmd-11-4873-2018
• 发表时间: 2017-10
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: C. Skinner;T. Coulthard;W. Schwanghart;M. J. Van De Wiel;G. Hancock