The influence of climate model uncertainty on fluvial flood hazard estimation
| Autor: | Annie Visser, Lila Collet, Lindsay Catherine Beevers, Claire Maravat, Gordon Aitken |
|---|---|
| Přispěvatelé: | Institute for Infrastructure and Environment, Heriot-Watt University [Edinburgh] (HWU), Hydrosystèmes continentaux anthropisés : ressources, risques, restauration (UR HYCAR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences [SDE.MCG]Environmental Sciences/Global Changes 0208 environmental biotechnology Climate change Probabilistic 02 engineering and technology 01 natural sciences Flood inundation Natural hazard Earth and Planetary Sciences (miscellaneous) Uncertainty quantification Uncertainty analysis 0105 earth and related environmental sciences Water Science and Technology Flood myth business.industry Environmental resource management Probabilistic logic 020801 environmental engineering Latin hypercube sampling 13. Climate action Environmental science Climate model business |
| Zdroj: | Natural Hazards Natural Hazards, Springer Verlag, 2020, 104, pp.2489-2510. ⟨10.1007/s11069-020-04282-4⟩ |
| ISSN: | 0921-030X 1573-0840 |
| Popis: | Floods are the most common and widely distributed natural hazard, threatening life and property worldwide. Governments worldwide are facing significant challenges associated with flood hazard, specifically: increasing urbanization; against the background of uncertainty associated with increasing climate variability under climate change. Thus, flood hazard assessments need to consider climate change uncertainties explicitly. This paper explores the role of climate change uncertainty through uncertainty analysis in flood modelling through a probabilistic framework using a Monte Carlo approach and is demonstrated for case study catchment. Different input, structure and parameter uncertainties were investigated to understand how important the role of a non-stationary climate may be on future extreme flood events. Results suggest that inflow uncertainties are the most influential in order to capture the range of uncertainty in inundation extent, more important than hydraulic model parameter uncertainty, and thus, the influence of non-stationarity of climate on inundation extent is critical to capture. Topographic controls are shown to create tipping points in the inundation–flow relationship, and these may be useful and important to quantify for future planning and policy. Full Monte Carlo analysis within the probabilistic framework is computationally expensive, and there is a need to explore more time-efficient strategies which may result in a similar estimate of the full uncertainty. Simple uncertainty quantification techniques such as Latin hypercube sampling approaches were tested to reduce computational burden. |
| Databáze: | OpenAIRE |
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