当前位置:
X-MOL 学术
›
Water Resour. Res.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
A 30 m Global Flood Inundation Model for Any Climate Scenario
Water Resources Research ( IF 4.6 ) Pub Date : 2024-08-21 , DOI: 10.1029/2023wr036460 Oliver E. J. Wing 1, 2 , Paul D. Bates 1, 2 , Niall D. Quinn 1 , James T. S. Savage 1 , Peter F. Uhe 1 , Anthony Cooper 1 , Thomas P. Collings 1 , Nans Addor 1, 3 , Natalie S. Lord 1, 2 , Simbi Hatchard 1 , Jannis M. Hoch 1 , Joe Bates 1 , Izzy Probyn 1 , Sam Himsworth 1 , Josué Rodríguez González 1 , Malcolm P. Brine 1 , Hamish Wilkinson 1 , Christopher C. Sampson 1 , Andrew M. Smith 1 , Jeffrey C. Neal 1, 2 , Ivan D. Haigh 1, 4
Water Resources Research ( IF 4.6 ) Pub Date : 2024-08-21 , DOI: 10.1029/2023wr036460 Oliver E. J. Wing 1, 2 , Paul D. Bates 1, 2 , Niall D. Quinn 1 , James T. S. Savage 1 , Peter F. Uhe 1 , Anthony Cooper 1 , Thomas P. Collings 1 , Nans Addor 1, 3 , Natalie S. Lord 1, 2 , Simbi Hatchard 1 , Jannis M. Hoch 1 , Joe Bates 1 , Izzy Probyn 1 , Sam Himsworth 1 , Josué Rodríguez González 1 , Malcolm P. Brine 1 , Hamish Wilkinson 1 , Christopher C. Sampson 1 , Andrew M. Smith 1 , Jeffrey C. Neal 1, 2 , Ivan D. Haigh 1, 4
Affiliation
Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1-2.6), we show end-of-century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range -6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5-8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness-for-purpose of this new-generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment.
中文翻译:
适用于任何气候情景的 30 m 全球洪水淹没模型
全球洪水测绘在过去十年中发展迅速,但以前的方法范围、功能和准确性有限。这些限制限制了现有模型框架可以回答的适用性和基础科学问题。利用最近可用的数据和建模方法,本文提出了一个新的全球~30 m分辨率的全球洪水图(GFM),完全覆盖了河流、雨水和沿海灾害,适用于任何重现期或气候情景,包括考虑不确定性。通过广泛汇编全球基准案例研究(从当地收集的事件水位到国家工程洪水图清单),我们对新的 GFM 进行了全面验证。对于洪水范围比较,我们证明 GFM 达到了~0.75 的关键成功指数。在更具歧视性的洪水水位测试中,GFM 与观测值的平均偏差约为 0.6 m。表明这种全局模型保真度水平的结果在文献中是前所未有的。在对未来变暖的乐观情景(SSP1-2.6)下,我们显示本世纪末全球洪水灾害(平均年淹没量)增加限制在 9%(可能范围 -6%–29%);在当前的灾害估计中,这可能处于-8%–12%的气候不确定性之内。相比之下,悲观情景 (SSP5-8.5) 危险变化从 2040 年代的背景噪音中出现,到 2100 年增加到 49%(可能范围为 7%–109%)。这项工作验证了这种新一代 GFM 用于影响分析,在政策制定、规划和商业风险评估方面具有各种有益的应用。
更新日期:2024-08-22
中文翻译:
适用于任何气候情景的 30 m 全球洪水淹没模型
全球洪水测绘在过去十年中发展迅速,但以前的方法范围、功能和准确性有限。这些限制限制了现有模型框架可以回答的适用性和基础科学问题。利用最近可用的数据和建模方法,本文提出了一个新的全球~30 m分辨率的全球洪水图(GFM),完全覆盖了河流、雨水和沿海灾害,适用于任何重现期或气候情景,包括考虑不确定性。通过广泛汇编全球基准案例研究(从当地收集的事件水位到国家工程洪水图清单),我们对新的 GFM 进行了全面验证。对于洪水范围比较,我们证明 GFM 达到了~0.75 的关键成功指数。在更具歧视性的洪水水位测试中,GFM 与观测值的平均偏差约为 0.6 m。表明这种全局模型保真度水平的结果在文献中是前所未有的。在对未来变暖的乐观情景(SSP1-2.6)下,我们显示本世纪末全球洪水灾害(平均年淹没量)增加限制在 9%(可能范围 -6%–29%);在当前的灾害估计中,这可能处于-8%–12%的气候不确定性之内。相比之下,悲观情景 (SSP5-8.5) 危险变化从 2040 年代的背景噪音中出现,到 2100 年增加到 49%(可能范围为 7%–109%)。这项工作验证了这种新一代 GFM 用于影响分析,在政策制定、规划和商业风险评估方面具有各种有益的应用。