Urban storage heat flux variability explored using satellite, meteorological and geodata

Abstract

The storage heat flux (ΔQ 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 ΔQ 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 ΔQ S. The impervious surfaces (paved and buildings) contribute most to ΔQ S. Building wall area seems to explain variation of ΔQ S most consistently. As the paved fraction increases up to 0.4, there is a clear increase in ΔQ S. With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of ΔQ S .

Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis

“…This result proposes that the amount of heat stored in a material can be determined using multiple surface temperature measurements and thermal properties of the storage material. This is a modification of the element surface temperature method and thermal mass schemes presented for a range of analyses [19,20,25].…”

“…The TEB uses either the residual of the surface energy balance or the hysteresis model to formulate urban heat storage based on modeled fluxes [14,17,18]. Lastly, a fourth method can be found scattered throughout the literature and is based on a conduction and material storage model, referred to as both the element surface temperature method (ESTM) and the thermal mass scheme (TMS) [19][20][21][22].…”

“…One widely recognized satellite estimate of heat storage was computed using the residual method, which first computes every flux in the surface energy balance [24]. The element surface temperature method (ESTM) has gained popularity recently, perhaps due to increased computational power and numerical model capabilities [19,25]. However, the infrequency of satellite observations is still a clear hindrance on the ability to accurately characterize models associated with urban heat storage, as many of the satellite methods are unable to produce the full diurnal cycle of ∆Q s .…”

“…The development of the thermal variability scheme is similar to that of the thermal mass scheme and element surface temperature methods, which both use the variability of satellite-derived land surface temperature as an input to estimate heat storage. [19,38]. The TVS, as with other satellite routines, requires each satellite pixel to behave as a control volume element, which allows the surface energy balance to characterize each pixel as a unique portion of an urban area, allowing for partition of energy into components [39,40].…”

Impact of Building Structure on Heat Storage Flux Estimation: An Observational Case Study in Beijing

“…However, to the best of our knowledge, there still few studies to investigate how Q S varies by urban thermal anisotropy, as well as the impact of Q S anisotropy on the UHI effect. Although Lindberg et al [22] simulated 3-D urban Q S , they did not consider the impact of building structure and thermal anisotropy on Q S substantively because they used the same T s values for roof and walls in the daytime.…”