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/K00882X/1
• 负责人: Jim Freer
• 金额: $ 32.3万
• 依托单位: University of Bristol
• 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%2FK00882X%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项目响应了NERC对强降雨洪水（FFIR）研究的呼吁，其重点研究项目旨在促进对确定FFIR概率、发生率和影响的过程的一般科学理解。这种极端降雨事件可能最多只持续几个小时，但却会引发可怕的破坏性洪水。它们的影响可能受到一系列因素（或过程）的影响，例如降雨的位置和强度、降雨所处的流域的形状和陡峭程度、洪水移动了多少沉积物以及洪水路径上的社区的脆弱性。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建立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。它旨在通过改进我们对导致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，减少地表水和山洪暴发的风险。该方案的一项主要要求是确定由于集水区面积、形状、地质和土壤类型以及土地使用等因素，特定集水区如何容易受到FFIR的影响。此外，最易受FFIR影响的集水区通常很小且未测量。SINATRA项目将分三个阶段解决这些问题：首先，增加我们对导致FFIR的因素的理解，并收集新的、高分辨率的FFIR测量数据；其次，利用这种新的理解和数据来改进FFIR模型，这样我们就可以预测它们可能发生在全国和；第三，利用这些新的发现和预测，为环境署和其他专业人士提供管理FFIR的信息和软件，减少它们对社区的破坏和影响。更详细地说，我们将：1。加强对FFIR控制过程的科学理解，通过(a)收集英国过去FFIR事件及其影响的档案，作为提高我们预测未来FFIR发生能力的先决条件。(b)利用实地调查和众包的方法，在洪水期间进行实时的洪水观测，以及事后调查和历史事件重建。(c)通过识别与FFIR事件相关的大尺度大气条件，并将其与集水区类型2联系起来，表征英国夏季洪水事件的物理驱动因素。发展改进的计算机模拟能力，以预测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/城市尺度模拟方法，模拟流域对山洪暴发的快速反应及其对城市地区的影响。(b)通过改善FFIR区域尺度模型中快速河流和地表水洪水和水文形态变化（包括泥石流）的代表性，扩大到更大的集水区。(c)通过整合河流路径和快速径流过程，并将土壤水分和河流流量同化到模型运行中，改进FFIR在JULES陆面模型中的表示。将这些科学上的进步转化为实用工具，从而更有效地向公众提供信息，方法是：(a)开发能够预测未来FFIR影响的工具，以支持洪水预报中心就其发生发布新的“基于影响的”警告。(b)开发一种FFIR分析工具，以评估在涉及不完全知识的复杂情况下与罕见事件有关的风险，类似于为放射性废物管理安全评估所开发的工具。这样，SINATRA将实现NERC为FFIR项目制定的科学目标。

项目成果

Process consistency in models: The importance of system signatures, expert knowledge, and process complexity

• DOI: 10.1002/2014wr015484
• 发表时间: 2014-09
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: M. Hrachowitz;O. Fovet;L. Ruiz;T. Euser;S. Gharari;R. Nijzink;J. Freer;H. Savenije;C. Gascuel-Odoux

Satellite-supported flood forecasting in river networks: A real case study

• DOI: 10.1016/j.jhydrol.2015.01.084
• 发表时间: 2015-04-01
• 期刊: JOURNAL OF HYDROLOGY
• 影响因子: 6.4
• 作者: Garcia-Pintado, Javier;Mason, David C.;Bates, Paul D.
• 通讯作者: Bates, Paul D.

Improving the theoretical underpinnings of process‐based hydrologic models

• DOI: 10.1002/2015wr017910
• 发表时间: 2016-03
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: M. Clark;B. Schaefli;S. Schymanski;L. Samaniego;C. Luce;B. Jackson;J. Freer;J. Arnold;R. Moore-R.

Quantifying local rainfall dynamics and uncertain boundary conditions into a nested regional-local flood modeling system

将局部降雨动态和不确定边界条件量化到嵌套的区域-局部洪水建模系统中

• DOI: 10.1002/2016wr019903
• 发表时间: 2017
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Bermúdez M

Improving hydrologic model hypothesis testing by accounting for rainfall and discharge uncertainties

通过考虑降雨和流量的不确定性来改进水文模型假设检验

• 作者: Freer, J