The cooling efficacy of green roofs in mitigating the urban heat island effect within dense cities is largely attributed to evapotranspiration (ET) processes. Hence, accurate understanding and quantification of ET are crucial for optimizing this cooling effect. ET can be estimated indirectly through the quantity $C_n^2$ which corresponds to the fluctuations of air refractive index $n$. $C_n^2$ is measured with the help of scintillometer, and also depends on local meteorological conditions (air temperature, wind, humidity...). Hence, it inherits the extreme variability across scales of these underlying geophysical fields. In this poster, we study the variability across scales of a 2 months long $C_n^2$ time series with 10 min time steps. It was collected with the help of a large aperture scintillometer during December 2019 and January 2020 over a wavy and vegetated green roof covering an area of 1 ha, known as the Blue Green Wave, which is located in Ecole nationale des ponts et chaussées campus near Paris (France). Universal Multifractals will be used. They are a physically based and mathematically robust framework which enables to characterize and simulate extreme variability across space-time scales with the help of only two parameters with physical interpretation: the mean intermittency codimension $C_1$ and multifractality index $0\le \alpha \leq2$. An additional one, which is needed for non-conservative fields such as ET is the non-conservativeness parameter $H$. A good scaling behaviour was found on scales ranging from 10 min to 85h. It was found that it is necessary to introduce an additional parameter denoted $a$. It corresponds to the power to which the assumed conservative underlying field should be raised before implementing a fractional integration to account for non conservativeness to retrieve the studied field. It appeared that $C_1=0.1,\alpha=1.8$, which is typical for atmospheric fields. $H$ is approximately equal to 0.44 in our case, which deviates from traditional scaling laws due to the intricate composition of the fluxes and requires further investigations. The retrieved scaling laws, which should be further confirmed with the help of other time series and spatial data, improve our understanding of the underlying physical processes generating variability and space-time dynamics in ET, which paves the way for future applications.