{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:57:36Z","timestamp":1772261856358,"version":"3.50.1"},"reference-count":79,"publisher":"Copernicus GmbH","issue":"5","license":[{"start":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T00:00:00Z","timestamp":1525305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Hazards Earth Syst. Sci."],"abstract":"<jats:p>Abstract. Flood loss modelling is a\u00a0crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a\u00a0risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a\u00a0model rating framework to support ensemble construction, based on a\u00a0probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a\u00a0stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a\u00a0pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.<\/jats:p>","DOI":"10.5194\/nhess-18-1297-2018","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T01:13:00Z","timestamp":1525309980000},"page":"1297-1314","source":"Crossref","is-referenced-by-count":47,"title":["Multi-model ensembles for assessment of flood losses and associated uncertainty"],"prefix":"10.5194","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2807-3119","authenticated-orcid":false,"given":"Rui","family":"Figueiredo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3173-7019","authenticated-orcid":false,"given":"Kai","family":"Schr\u00f6ter","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Weiss-Motz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6283-2732","authenticated-orcid":false,"given":"Mario L. V.","family":"Martina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6274-3625","authenticated-orcid":false,"given":"Heidi","family":"Kreibich","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2018,5,3]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Apel,\u00a0H., Aronica,\u00a0G.\u00a0T., Kreibich,\u00a0H., and Thieken,\u00a0A.\u00a0H.: Flood risk analyses\u00a0\u2013 how detailed do we need to be?, Nat. Hazards, 49, 79\u201398, https:\/\/doi.org\/10.1007\/s11069-008-9277-8, 2009.","DOI":"10.1007\/s11069-008-9277-8"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Br\u00f6cker,\u00a0J.: Evaluating raw ensembles with the continuous ranked probability score, Q.\u00a0J. Roy. Meteor. 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