A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban storage heat flux variability explored using satellite, meteorological and geodata
The storage heat flux (Δ Q S ) is the net flow of heat stored within a volume that may include the air, trees, buildings and ground. Given the difficulty of measurement of this important and large flux in urban areas, we explore the use of Earth Observation (EO) data. EO surface temperatures are used with ground-based meteorological forcing, urban morphology, land cover and land use information to estimate spatial variations of Δ Q S in urban areas using the Element Surface Temperature Method (ESTM). First, we evaluate ESTM for four "simpler" surfaces. These have good agreement with observed values. ESTM coupled to SUEWS (an urban land surface model) is applied to three European cities (Basel, Heraklion, London), allowing EO data to enhance the exploration of the spatial variability in Δ Q S . The impervious surfaces (paved and buildings) contribute most to Δ Q S . Building wall area seems to explain variation of Δ Q S most consistently. As the paved fraction increases up to 0.4, there is a clear increase in Δ Q S . With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of Δ Q S .
Urban storage heat flux variability explored using satellite, meteorological and geodata
The storage heat flux (Δ Q S ) is the net flow of heat stored within a volume that may include the air, trees, buildings and ground. Given the difficulty of measurement of this important and large flux in urban areas, we explore the use of Earth Observation (EO) data. EO surface temperatures are used with ground-based meteorological forcing, urban morphology, land cover and land use information to estimate spatial variations of Δ Q S in urban areas using the Element Surface Temperature Method (ESTM). First, we evaluate ESTM for four "simpler" surfaces. These have good agreement with observed values. ESTM coupled to SUEWS (an urban land surface model) is applied to three European cities (Basel, Heraklion, London), allowing EO data to enhance the exploration of the spatial variability in Δ Q S . The impervious surfaces (paved and buildings) contribute most to Δ Q S . Building wall area seems to explain variation of Δ Q S most consistently. As the paved fraction increases up to 0.4, there is a clear increase in Δ Q S . With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of Δ Q S .
Urban storage heat flux variability explored using satellite, meteorological and geodata
Lindberg, F. (author) / Olofson, K. F. G. (author) / Sun, T. (author) / Grimmond, C. S. B. (author) / Feigenwinter, C. (author)
2020-01-01
Article (Journal)
Electronic Resource
English
DDC:
720
Geodata for the urban environment
| British Library Conference Proceedings | 2009
80th Geodata Convention (INTERGEO)
Online Contents | 1996
DEFINING AFRICA'S FUNDAMENTAL GEODATA
Online Contents | 2008
Online Contents | 1993