Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems

Data driven quantification of the temporal scope of building LCAs

In the construction sector, LCAs typically apply an approach based on fixed or partially fixed building lifespans/service lives/reference study period. The temporal scopes applied in building LCAs are hence typically not reflecting that the timeframes buildings can provide the service they are intended to provide, are (highly) dependent on numerous factors e.g.: building location, materials used to construct the building, energy supply and the use of the building. Inaccurate estimation of the temporal scope of a building LCA will lead to incorrect quantification of the environmental impacts of buildings. Incorrect quantification of the environmental performance of buildings may, in the worst case, derange/decelerate the development within the building sector towards more sustainable buildings. In this paper, a data set consisting of 20999 Danish buildings, demolished between 2009 and 2015, is analyzed. A multiple linear regression model is derived and used to quantify the temporal scope (often referred to as the reference study period) of building LCAs in an attempt to improve the accuracy of sustainability assessment of buildings, taking several influencing factors into account. The results obtained from the derived model are subsequently compared with several fixed/partially fixed building lifespan/service life/reference study period quantification approaches The regression model proved to estimate the lifespan with lower errors (compared to observed values) than the prevailing approach relying on a single fixed value for all building locations, uses and building materials. The application of model based site, use, and/or material specific etc. temporal scope quantification in LCA is new and provides a mean to reduce the uncertainty of LCA results; however, the approach needs to be formalized