干旱区生态环境知识资源中心(Arid): Toward Global Stochastic River Flood Modeling

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DOI	10.1029/2020WR027692
	Toward Global Stochastic River Flood Modeling
	Wing, Oliver E. J.; Quinn, Niall; Bates, Paul D.; Neal, Jeffrey C.; Smith, Andrew M.; Sampson, Christopher C.; Coxon, Gemma; Yamazaki, Dai; Sutanudjaja, Edwin H.; Alfieri, Lorenzo
通讯作者	Wing, OEJ
来源期刊	WATER RESOURCES RESEARCH
ISSN	0043-1397
EISSN	1944-7973
出版年	2020
卷号	56 期号:8
英文摘要	Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge-based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site-to-site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental-scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high-magnitude events. However, local-scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall-runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation- and observation-generated flood events produce near-identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points Large-scale flood hazard models typically neglect to represent the spatial dependence of real flood events Relative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge-driven flood event sets At the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data
英文关键词	flooding flood risk hydrological modelling hydraulic modelling spatial dependence
类型	Article
语种	英语
开放获取类型	Other Gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000582701700055
WOS关键词	SPATIAL DEPENDENCE ; HYDROLOGICAL MODEL ; FREQUENCY-ANALYSIS ; UNGAUGED BASINS ; HAZARD ; PRECIPITATION ; RISK ; PROBABILITY ; UNCERTAINTY ; FRAMEWORK
WOS类目	Environmental Sciences ; Limnology ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327149
作者单位	[Wing, Oliver E. J.; Bates, Paul D.; Neal, Jeffrey C.; Coxon, Gemma] Univ Bristol, Sch Geog Sci, Bristol, Avon, England; [Wing, Oliver E. J.; Quinn, Niall; Bates, Paul D.; Neal, Jeffrey C.; Smith, Andrew M.; Sampson, Christopher C.] Fathom, Bristol, Avon, England; [Yamazaki, Dai] Univ Tokyo, Inst Ind Sci, Tokyo, Japan; [Sutanudjaja, Edwin H.] Univ Utrecht, Dept Phys Geog, Utrecht, Netherlands; [Alfieri, Lorenzo] CIMA Res Fdn, Savona, Italy; [Alfieri, Lorenzo] European Commiss Joint Res Ctr, Ispra, Italy
推荐引用方式 GB/T 7714	Wing, Oliver E. J.,Quinn, Niall,Bates, Paul D.,et al. Toward Global Stochastic River Flood Modeling[J],2020,56(8).
APA	Wing, Oliver E. J..,Quinn, Niall.,Bates, Paul D..,Neal, Jeffrey C..,Smith, Andrew M..,...&Alfieri, Lorenzo.(2020).Toward Global Stochastic River Flood Modeling.WATER RESOURCES RESEARCH,56(8).
MLA	Wing, Oliver E. J.,et al."Toward Global Stochastic River Flood Modeling".WATER RESOURCES RESEARCH 56.8(2020).