Abstract

Abstract. The need to prepare cities for climate change adaptation requests the urban modeller community to implement sustainable adaptation strategies within their models to be tested against specific city morphologies and scenarios. Greening city roofs is part of these strategies. In this context, the GREENROOF module for TEB (town energy balance) has been developed to model the interactions between buildings and green roof systems at the scale of the city. This module, which combines the ISBA model (Interaction between Soil Biosphere and Atmosphere) and TEB, allows for one to describe an extensive green roof composed of four functional layers (vegetation – grasses or sedums; substrate; retention/drainage layers; and artificial roof layers) and to model vegetation-atmosphere fluxes of heat, water and momentum, as well as the hydrological fluxes throughout the substrate and the drainage layers, and the thermal fluxes throughout the natural and artificial layers of the green roof. TEB-GREENROOF (SURFEX v7.3) should therefore be able to represent the impact of climate forcings on the functioning of green roof vegetation and, conversely, the influence of the green roof on the local climate. An evaluation of GREENROOF is performed for a case study located in Nancy (France) which consists of an instrumented extensive green roof with sedums and substrate and drainage layers that are typical of this kind of construction. After calibration of the drainage layer hydrological characteristics, model results show good dynamics for the substrate water content and the drainage at the green roof base, with nevertheless a tendency to underestimate the water content and overestimate the drainage. This does not impact too much the green roof temperatures, which present a good agreement with observations. Nonetheless GREENROOF tends to overestimate the soil temperatures and their amplitudes, but this effect is less important in the drainage layer. These results are encouraging with regard to modelling the impact of green roofs on thermal indoor comfort and energy consumption at the scale of cities, for which GREENROOF will be running with the building energy version of TEB – TEB-BEM. Moreover, with the green roof studied for GREENROOF evaluation being a type of extensive green roof widespread in cities, the type of hydrological characteristics highlighted for the case study will be used as the standard configuration to model extensive green roof impacts at the scale of cities.

Highlights

  • The evolution of the substrate water content and the green roof drainage over the entirety of the time series available are presented in Fig. 7 for the organic matter (OM) ensemble calibrations and the two OM subsets to illustrate their discrepancies in relation to that of their statistical scores (Table 5) calculated over the evaluation period running from 10 September to 29 November 2011

  • This is illustrated by better R (0.86 compared to 0.71), mean bias error (MBE) (−0.05 compared to −0.16 m3 m−3) and root-mean-square error (RMSE) values (0.06 compared to 0.16 m3 m−3), despite a higher standard deviation (SD) (0.05 m3 m−3) than subset 1 and observations (0.02 m3 m−3 for both)

  • A parameterization called GREENROOF for simulating extensive green roofs across the cities has been developed within TEB, which is consistent with the modular architecture of SURFEX

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Summary

Introduction

Many experiments conducted on green roofs have highlighted their potential to reduce roof runoff entering the storm water systems through retention and evapotranspiration. This has been demonstrated at the site scale (Berghage et al, 2009 and Voyde et al, 2010, are two good examples) as well as at the city (Mentens et al, 2006) and landscape (Oberndorfer et al, 2007) scales. The implementation of green roofs as opposed to urban forests and street trees may represent a more realistic and efficient greening strategy at the heart of cities, where the building fraction is high (and the ground-base surface available for greening scarce) and the initial evapotranspiration potential low

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