The Dynamic Drivers of Flood Risk (DRIFT)

洪水风险的动态驱动因素 (DRIFT)

• 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

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，每年洪水造成的损失平均超过20亿英镑，随着气候变化，人们担心这一数字还会继续上升。然而，我们对导致洪水非平稳性的原因的理解存在重大差距，影响利益相关者就洪水风险管理、土地使用或保护人口所需基础设施的适当水平做出规划决策的能力。该项目——洪水风险的动态驱动因素（DRIFT）——旨在解决理解过去和未来洪水特性变化的关键挑战，以改善决策支持。总体目标是从过去到未来（1970-2070）形成对洪水非平稳性的统一理解，从短时间尺度无缝过渡到长时间尺度。DRIFT将开发第一个过去-现在-未来预测系统，使利益相关者能够生成从过去到未来的漂移洪水特征的可靠情景。将开发概率模型，描述变化的天气特征、气候、工程结构和土地覆盖对洪水特性的影响，如洪峰、重现期、概率、持续时间和范围。洪水属性的过去趋势将与未来的气候预测、预测和预测无缝地联系在一起，从短期到长期。洪水特性的未来变化将使用一种多模式集合方法进行估计，该方法无缝地结合了气候时间尺度（季节预报、年代际预测和多年代际预测）和土地覆盖情景（如城市化或造林）以及管理决策。这个过去-现在-未来预测系统将集成在一个决策支持框架内，提供简单直观的方式来显示洪水演变的复杂信息。DRIFT的目标是支持利益相关者做出最佳规划决策，以管理洪水风险并实现其他协同效益。双方将共同开发一个方便使用者的决策支援系统，以直观显示不同情况下不断变化的洪水特征。该工具将为一系列气候和土地覆盖情景提供从近期到未来不断变化的洪水属性的无缝信息和可视化。DRIFT具有迭代结构，第一阶段侧重于与英国洪水利益相关者和项目合作伙伴的五个关键群体共同开发模型和决策支持软件。第二阶段随后扩展到世界上其他有观测流量数据的地区。长期目标是为洪水特征的演变提供强有力的决策支持，为全球社会提供直接利益。

项目成果

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
• 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