0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Evaluating the effect of climate change on snow load on structures

 Evaluating the effect of climate change on snow load on structures
Auteur(s): ORCID, , ,
Présenté pendant IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019, publié dans , pp. 666-673
DOI: 10.2749/guimaraes.2019.0666
Prix: € 25,00 incl. TVA pour document PDF  
AJOUTER AU PANIER
Télécharger l'aperçu (fichier PDF) 0.87 MB

As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue fo...
Lire plus

Détails bibliographiques

Auteur(s): ORCID (University of Pisa, Pisa, Italy)
(University of Pisa, Pisa, Italy)
(University of Pisa, Pisa, Italy)
(Federal Waterways Engineering and Research Institute, Karlsruhe, Germany)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019
Publié dans:
Page(s): 666-673 Nombre total de pages (du PDF): 8
Page(s): 666-673
Nombre total de pages (du PDF): 8
DOI: 10.2749/guimaraes.2019.0666
Abstrait:

As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue for the resilience of infrastructures and buildings and is a key challenge for adaptation planning. In this paper, a suitable procedure for the estimation of future trends of climatic actions is presented starting from the output of regional climate models and taking into account the uncertainty in the model itself. In particular, the influence of climate change on ground snow loads is discussed in detail and the typical uncertainty range is determined applying an innovative algorithm for weather generation. Considering different greenhouse gasses emission scenarios, some results are presented for the Italian Mediterranean region proving the ability of the method to define factors of change for climate extremes also allowing a sound estimate of the uncertainty range associated with different models.