Finding common ground: A methodology for city-scale subsurface thermal modelling
Finding common ground: A methodology for city-scale subsurface thermal modelling
复制标题
DOI：
10.1016/j.uclim.2023.101513
复制
发表时间：
2023-04-02
期刊：
影响因子：
6.4
通讯作者：
Soga，K.
中科院分区：
文献类型：
--
作者：
Kreitmair，M. J.;Makasis，N.;Soga，K.
城市地下人为基础设施的存在可以增加地下的温度，这种现象被称为地下城市热岛效应。大型城市规模的数值地下温度模型占两个热通量来自人造结构以及水文地质变化往往是计算上的禁止，由于所需的建模尺度大不相同。我们提出了一种新的和可扩展的方法来确定城市中心地下的热气候在整体规模，分组体积的地面到原型根据常见的热行为，使用监督机器学习算法确定。我们采用这种方法，在英国伦敦中心的两个行政区，结果显示出良好的协议与一个行政区的高分辨率模型。我们进一步证明了该方法的可扩展性，通过建模的行政区顺序，其中第二个行政区的建模几乎没有额外的计算成本在0.10和0.23摄氏度之间的局部深度平均温度的准确性损失。这种方法是确定城市地下大规模地下气候的重要一步，为各种应用提供了见解，例如更好地了解地热能潜力。
The presence of anthropogenic infrastructure within the ground under cities can act to increase the temperature of the subsurface in a phenomenon known as the subsurface urban heat island effect. Large city-scale numerical subsurface temperature models accounting for both the heat fluxes stemming from human-made structures as well as hydrogeological variation are often computationally prohibitive due to the vastly different scales of modelling required. We present a novel and scalable methodology for the determination of the subsurface thermal climate beneath urban centres at a holistic scale, grouping volumes of ground into archetypes according to common thermal behaviours, identified using supervised machine learning algorithms. We apply this methodology to two boroughs in the centre of London, UK, with the results showing good agreement with a higher-resolution model for one of the boroughs. We further demonstrate the scalability of the approach by modelling the boroughs sequentially, where the second borough was modelled at little to no additional computation cost at a loss of accuracy in the local depth-averaged temperature between 0.10 and 0.23 degrees Celsius. This methodology is an important step towards determining large-scale subsurface climate beneath cities, providing insights for various applications, such as an improved understanding of geothermal energy potential.