On the significance of the climate-dataset time resolution in characterising wind-driven rain and simultaneous wind pressure. Part I: scalar approach

Pérez-Bella, J.M. (Universidad de Zaragoza) ; Domínguez-Hernández, J. (Universidad de Zaragoza) ; Cano-Suñén, E. (Universidad de Zaragoza) ; Del Coz-Díaz, J.J. ; Alonso-Martínez, M.

Resumen: The joint action of wind-driven rain and wind pressure is the main cause of water penetration in building facades, which causes various habitability and durability problems. The most widespread characterisation of both climate factors is based on exposure indices calculated in free-field conditions from records of precipitation—wind speed (scalar indices), adding wind direction for directional indices. The time resolution of this climate dataset defines the calculation effort and the accuracy obtained, and average daily, monthly, or annual records are typical due to their greater availability. This paper investigates the influence of this time resolution on the accuracy of these scalar exposure indices (driving rain index, hereafter aDRI, and driving-rain wind pressure, hereafter DRWP) by assessing the nature and magnitude of errors associated with different averaged records. For this purpose, 10-min, hourly, daily, monthly and annual meteorological data collected over 15 years in 6 Spanish weather stations at locations characterised by different environments and topography are analysed. In addition, relationships capable of adjusting indices of any time resolution to an accuracy similar to that reached through 10-min records are proposed. In general, the value of driving-rain wind pressure exhibits greater sensitivity than the driving rain index at this time resolution, incorporating significant errors even with daily datasets. In turn, the use of monthly and annual records should be reconsidered, given their high uncertainty. The results demonstrate how the daily datasets for aDRI indices and hourly datasets for DRWP values are sufficient to characterise these exposures with errors of less than 11%.

Idioma: Inglés

DOI: 10.1007/s00477-017-1479-8

Año: 2017

Publicado en: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 32 (2017), 1783 - 1797
ISSN: 1436-3240

Factor impacto JCR: 2.668 (2017)

Categ. JCR: STATISTICS & PROBABILITY rank: 9 / 123 = 0.073 (2017) - Q1 - T1
Categ. JCR: ENGINEERING, CIVIL rank: 22 / 127 = 0.173 (2017) - Q1 - T1
Categ. JCR: WATER RESOURCES rank: 19 / 90 = 0.211 (2017) - Q1 - T1
Categ. JCR: ENGINEERING, ENVIRONMENTAL rank: 22 / 50 = 0.44 (2017) - Q2 - T2
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 87 / 241 = 0.361 (2017) - Q2 - T2

Factor impacto SCIMAGO: 1.096 - Environmental Science (miscellaneous) (Q1) - Environmental Engineering (Q1) - Water Science and Technology (Q1) - Safety, Risk, Reliability and Quality (Q1) - Environmental Chemistry (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/BIA2012-31609
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/FC-15-GRUPIN14-004

Tipo y forma: Article (PostPrint)

Área (Departamento): Area Ingeniería Construcción (Dpto. Ingeniería Mecánica)