The Dynamic Drivers of Flood Risk (DRIFT)

洪水风险的动态驱动因素 (DRIFT)

基本信息

批准号：
MR/V022008/1
负责人：
Louise Slater
金额：
$ 111.83万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FV022008%2F1
关键词：
Dynamic Drivers Flood Risk DRIFT

项目摘要

The cost of floods averages more than £2 billion annually in the UK and is feared to keep rising as the climate changes. However, there are critical gaps in our understanding of what drives flood nonstationarity, affecting stakeholders' ability to make planning decisions about appropriate levels of flood risk management, land use, or infrastructure required to protect populations.This project - the Dynamic Drivers of Flood Risk (DRIFT) - has been designed to address crucial challenges in understanding past and future changes in flood properties to improve decision-making support. The overarching aim is to develop a unified understanding of flood nonstationarity from the past into the future (1970-2070), transitioning seamlessly from short to long timescales. DRIFT will develop the first past-present-future prediction system allowing stakeholders to generate robust scenarios of drifting flood characteristics from the past into the future. Probabilistic models will be developed describing the influence of changing weather characteristics, climate, engineering structures, and land cover on flood properties, such as flood peaks, return periods, probabilities, durations and extent. Past trends in flood properties will be seamlessly linked with future climate forecasts, predictions and projections, over short- to long-term horizons. Future changes in flood properties will be estimated using a multi-model ensemble approach that seamlessly combines climate timescales (seasonal forecasts, decadal predictions, and multi-decadal projections) and land cover scenarios (such as urbanisation or afforestation), as well as management decisions.This past-present-future prediction system will be integrated within a decision support framework, providing simple and intuitive ways to display complex information on flood evolution. DRIFT's aim is to support stakeholders in making the best planning decisions to manage flood risks and achieve other co-benefits. A user-friendly decision support system will be co-developed to visualise changing flood characteristics under different scenarios. This tool will provide seamless information and visualisations of changing flood properties from the near to far future, for a range of climate and land cover scenarios.DRIFT has an iterative structure, with Phase I focusing on co-developing the models and decision support software with five key groups of UK flood stakeholders and project partners. Phase II subsequently expands to other regions of the world where observational streamflow data are available. The long-term aim is to develop robust decision-making support on the evolution of flood characteristics, providing direct benefits for societies globally.

在英国，地板的成本平均每年超过20亿英镑，并资助随着气候变化的速度而继续上升。但是，我们对驱动洪水不足的原因的理解存在关键差距，影响了利益相关者对保护人口所需的适当水平的洪水风险管理，土地使用或基础设施的计划决策的能力。该项目 - 洪水风险的动态驱动因素（漂移） - 旨在解决对过去和将来的洪水范围内的重要挑战，以应对洪水范围内的影响，以改善洪水范围的承诺。总体目的是将对过去的洪水非平稳性发展到未来（1970-2070），从短时间到长时间尺度无缝过渡。漂移将开发出第一个过去的未来预测系统，使利益相关者能够产生从过去的洪水特征到未来的强大场景。将开发概率模型，描述不断变化的天气特征，气候，工程结构和土地覆盖对洪水特性的影响，例如洪水峰，返回期，可能性，持续时间和程度。洪水特性的过去趋势将与未来的气候森林，预测和预测无缝地联系在一起。未来的洪水特性变化将使用多模型集合方法估算，该方法无缝结合气候时间尺度（季节性森林，际预测和多年预测）和土地覆盖方案（例如城市化或负担能力）（例如城市化或负担得起），以及在管理中的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内的范围内整合了框架，进化。 Drift的目的是支持利益相关者做出最佳计划决策，以管理洪水风险并实现其他共同利益。用户友好的决策支持系统将共同开发，以可视化不同情况下的洪水特征。该工具将提供无缝的信息和可视化，以使洪水从近乎遥远的未来变化，对于一系列的气候和土地覆盖方案。Drift具有迭代结构，I阶段的重点是与英国洪水利益相关者和项目伙伴共同开发模型和决策软件。 II阶段随后扩展到可用观测流数据的世界其他地区。长期的目的是在洪水特征的演变中建立强大的决策支持，从而为全球社会提供直接利益。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Large anomalies in future extreme precipitation sensitivity driven by atmospheric dynamics.

DOI：
10.1038/s41467-023-39039-7
发表时间：
2023-06-02
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Gu, Lei;Yin, Jiabo;Gentine, Pierre;Wang, Hui-Min;Slater, Louise J.;Sullivan, Sylvia C.;Chen, Jie;Zscheischler, Jakob;Guo, Shenglian
通讯作者：
Guo, Shenglian

Global Increases in Compound Flood-Hot Extreme Hazards Under Climate Warming

DOI：
10.1029/2022gl097726
发表时间：
2022-04-28
期刊：
GEOPHYSICAL RESEARCH LETTERS
影响因子：
5.2
作者：
Gu, Lei;Chen, Jie;Zhao, Tongtiegang
通讯作者：
Zhao, Tongtiegang

Elasticity curves describe streamflow sensitivity to precipitation across the entire flow distribution

弹性曲线描述了整个流量分布中水流对降水的敏感性

DOI：
10.5194/hess-2022-407
发表时间：
2023
期刊：
影响因子：
0
作者：
Anderson B
通讯作者：
Anderson B

Groundwater shapes North American river floods

DOI：
10.1088/1748-9326/acbecc
发表时间：
2023-03-01
期刊：
ENVIRONMENTAL RESEARCH LETTERS
影响因子：
6.7
作者：
Berghuijs, Wouter R.;Slater, Louise J.
通讯作者：
Slater, Louise J.

Greenhouse gas emissions drive global dryland expansion but not spatial patterns of change in aridification

温室气体排放推动全球旱地扩张，但并未推动干旱化的空间格局变化

DOI：
10.1175/jcli-d-22-0103.1
发表时间：
2022
期刊：
Journal of Climate
影响因子：
4.9
作者：
Shuyun Feng;Xihui Gu;Sijia Luo;Ruihan Liu;Aminjon Gulakhmadov;Louise J. Slater;Jianfeng Li;Xiang Zhang;Dongdong Kong
通讯作者：
Dongdong Kong