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Discussion papers | Copyright
https://doi.org/10.5194/nhess-2017-342
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Oct 2017

Research article | 10 Oct 2017

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Natural Hazards and Earth System Sciences (NHESS) and is expected to appear here in due course.

Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

Francesca Carisi1, Kai Schröter2, Alessio Domeneghetti1, Heidi Kreibich2, and Attilio Castellarin1 Francesca Carisi et al.
  • 1University of Bologna, DICAM, Water Resources, Bologna, Italy
  • 2Hydrology Section, German Research Centre for Geosciences, GFZ, Potsdam, Germany

Abstract. Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 52km2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth. However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

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Francesca Carisi et al.
Francesca Carisi et al.
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By analysing a comprehensive loss dataset of affected private households after a recent river flood event in Northern Italy, we tackle the problem of flood damage estimation in Emilia-Romagna (Italy). We develop empirical uni- and multi-variable loss models for residential sector. Outcomes highligh that the latter seem to outperform the first ones and, in addition, results show a higher accuracy of uni-variable models based on local data compared to literature ones derived for different context.
By analysing a comprehensive loss dataset of affected private households after a recent river...
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