Influence of special report on emissions scenarios and the representative concentration pathways scenarios on the preservation of churches with a deficient microclimate

Abstract

Climate change will significantly impact all over the world. Many studies related to architecture have quantified its impact on energy consumption and thermal comfort, among others. However, there are few studies related to heritage preservation that analyse its impact. Given the relation between indoor and outdoor microclimate, this work analyses the influence of climate change on the variation of indoor relative humidity and temperature levels to preserve heritage elements. For this purpose, the changes caused by both the Special Report on Emissions Scenarios (SRES) and the Representative Concentration Pathways (RCP) scenarios in the preservation level of a church with a deficient microclimate in the Mediterranean region were analysed. Monitorings were conducted with an interval of hourly acquisition for a year and neural networks were used to predict future time series (for the years 2050 and 2100). The results showed the significant impact of climate change, particularly with the RCP 8.5 scenario. The zones of high temperatures and relative humidity obtained the greatest percentage of hours in the current scenario. In this sense, 57.45% of the annual hours in the current scenario were grouped in these zones. However, the climate change scenarios obtained the following values ​​in 2100: 67.16% in B1, 75.29% in A1B, 76.48% in A2, 58.93% in RCP 2.6, 68.53% in RCP 4.5 and 81.10% in RCP 8.5. Thus, these results imply not only a greater degradation of heritage elements, but also a change in conservation strategies aimed at optimizing interior microclimates. •Analysis of the indoor microclimate of a church in the Mediterranean region. •Analysis of the affection of the indoor microclimate throughout the 21st century due to climate change. •Greater negative impact on preservation with the RCP 8.5 scenario. •Prediction of the time series through neural networks.