Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections

Vincent Pons (1, 2) , Rasmus Benestad (3) , Edvard Sivertsen (4) , Tone Merete Muthanna (2) , Jean-Luc Bertrand-Krajewski (1)

1 DEEP - Déchets Eaux Environnement Pollutions
2 NTNU - Norwegian University of Science and Technology [Trondheim]
3 Norwegian Meteorological Institute
4 SINTEF Trondheim

A strategy to evaluate the suitability of different multiplicative random cascades to produce rainfall time series, taking into account climate change, inputs for green infrastructures models. The multiplicative random cascades reproduce a (multi)fractal distribution of precipitation through an iterative and multiplicative random process. In the current study, the initial model, a flexible cascade that deviates from multifractal scale invariance, was improved with (i) a temperature dependency and (ii) an additional function to reproduce the temporal structure of rainfall. The structure of the models with depth and temperature dependency was found to be applicable in eight locations studied across Norway and France. The resulting time series from both reference period and projection based on RCP 8.5 were applied to two green roofs with different properties. The different models led to a slight change in the performance of green roofs, but this was not significant compared to the range of outcomes due to ensemble uncertainty in climate modelling and the stochastic uncertainty due to the nature of the process. The hydrological dampening effect of the green infrastructure was found to decrease in most of the Norwegian cities due to an increase in precipitation, especially Bergen (Norway), while slightly increasing in Marseille (France) due to decrease in rainfall event frequency.

Domaines: Ingénierie de l'environnement

Vincent Pons, Rasmus Benestad, Edvard Sivertsen, Tone Merete Muthanna, Jean-Luc Bertrand-Krajewski. Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections. Hydrology and Earth System Sciences, 2022, 26, pp.2855 - 2874. ⟨10.5194/hess-26-2855-2022⟩. ⟨hal-03717452⟩