Anthropogenic heat and moisture release from cities is an important component of the urban energy balance, and contributes directly to the urban heat island effect in both the surface canopy and boundary layers. Estimates of anthropogenic heat release at local and meso scales have been reported in the climate literature for many midlatitude cities, but few tropical cities. Here we use a top-down inventory approach to estimate the sensible component of anthropogenic heat release at meso-scale for the world’s 27 ‘megacities,’ most of which are located in tropical and subtropical regions of Asia and Africa. In our estimates, we account for three major sectors of urban anthropogenic activity: vehicle fuel consumption; building and industrial energy use; and human metabolism. We source these data from the “Metabolism of Megacities” dataset housed at the University of Toronto. The anthropogenic estimates are spatially and temporally coarse, but provide the first baseline approximations of waste heat in low/middle income cities. We compare the heat emissions and their sector-based origins across all megacities for 2001 and 2010, and convey the influences of macroclimate, industrial activity, population density, and per capita energy use on sensible heat release from urban environments.

The fast urbanization in Guangzhou Metro area (with population more than 10 million) has significantly modified local and regional meteorological conditions. As one of the largest industrialized regions in China, Guangzhou has also experienced increased levels of air pollutants. However the current trend of population increase and urban expansion is expected to continue in the future. It is therefore imperative to understand and project effects of urbanization on weather, regional climate and air quality using regional models such as the Weather Research and Forecasting (WRF) model coupled to urban canopy models. However, fine-scale gridded urban canopy parameters (UCPs) are needed to drive the model but yet difficult to obtain in cities where the detailed urban morphological data do not exist. In this study, we developed a new approach to derive UCP database of Guangzhou from Google-earth imagery, which are freely available at high resolution (0.61 meter) and frequently updated. Two images at the same place with different view angles of buildings were used to identify the building span and height and to calculate urban morphology parameters (e.g., mean building height, building plan area fraction, building plan area density). Frontal area index was calculated under 8 wind directions with 45-degree intervals. The sky view factor was calculated with 32 slices number of 1-m resolution and no limit rang of extended distance. Special efforts were undertaken to ensure consistency between various datasets of UCPs, land-use and land-cover (LULC), urban fraction, and plan area fraction in WRF. Numerous high-resolution WRF numerical experiments were conducted using various sources of LULC and UCP data to reveal the impacts of new UCPs on regional weather and air quality.

Accurate simulation of the city energy balance requires studying the thermal behaviour of buildings and therefore it requires knowing many details: at least the buildings geometries, their envelope materials and surroundings ground covering. For instance, information on the shape and location of a building are useful to assess the performance of solar panels; information on the materiality of the facades make it possible to estimate solar gains through the windows and therefore to perform building energy balance simulations; etc.

Consequently, several recent research works have been aiming to integer building scale data into urban scale simulations (microclimate, energy consumption of buildings at the city scale, energy production, etc.). Among those researches, we can quote the WUDAPT project (Ching, 2012) aiming to define a worldwide building database based on the LCZ classification of urban forms (Stewart & Oke, 2009).

The main issue in integrating this type of information to urban scale simulations is the lack of precision of the available data for buildings (Ching et al., 2009). If a limited number of buildings can be very precisely described (through existing Building Information Model (BIM) for instance (Ferries & Bonhomme, 2014), but also through historical studies or architectural inventories), the data at the city scale remains broadly heterogeneous.

In this paper, we will present how we used those localized descriptions of buildings to enrich existing urban database in the context of the MapUCE project (a French research program that aims to integrate quantitative data from urban microclimate, climate and energy in urban policies).

Our working method, called GENIUS (GENerator of Interactive Urban blockS), was to perform a literature review combined with interviews of urban planners to characterize a typology of urban forms in the whole French territory, and to associate it with a wide database. This typology structures the information according to four main themes: (1) type of urban form (pavilions, towers, etc.), (2) buildings use (housing, office building, etc.), (3) buildings location and (4) date of construction. For each combination of (1), (2), (3) and (4), our database provides information regarding: (a) building materiality (envelope materials for walls, roofs and windows), (b) buildings morphology (compactness, number of floors, etc.), (c) integration to the urban fabric (contiguity, alignment, size of plots, vegetation, etc.) and (d) technical features (heating, cooling and ventilation systems, shadings devices, etc.).

This database will allow refining the description of French cities in order to perform interdisciplinary and accurate simulations of energy use, renewable energy and urban microclimate.

Bibliography:

- Ching, J. (2012). WUDAPT: Conceptual framework for an international community urban morphology database to support meso-urban and climate models. Urban Climate News, (n°45).

- Ching, J., Taha, H., Williams, D., Brown, M., Burian, S., Chen, F., McPherson, T. (2009). National urban database and access portal tool. Bulletin of the American Meteorological Society, 90(8), 1157–1168.

- Ferries, B., & Bonhomme, M. (2014). La maquette numérique, un moyen d’augmenter la densité informationnelle d’un territoire ? (Building Information Model, a way to increase informational density of a territory?) [in French] In the proceedings of the 6th Seminar of Numerical Architecture Conception - SCAN'14. Luxembourg.

- Stewart, I., & Oke, T. (2009). Classifying Urban Climate Field Sites by « Local Climate Zones ». International Association for Urban Climate, (34).

Heatwaves have the highest mortality rates out of any natural disasters and they are becoming hotter, longer and more frequent with global warming. We use a case study of the devastating heatwave that affected Melbourne, Australia in 2009 during which there was an unprecedented three days above 43C, 374 excess deaths and 714 cases of people being admitted to hospital due to heat related illness. We assess the ability of the Weather Research Forecasting model (WRF) to simulate the near-surface temperatures and human thermal comfort during this heatwave. We compare a regular non-urban parameterised WRF run, WRF coupled with an urban land surface model and WRF coupled to a high-resolution urban surface data set. These simulations are then analysed with respect to high-resolution observational meteorological data available for Melbourne. This comparison is a crucial first step to modelling the effectiveness of urban heat mitigation techniques with the aim of improving human thermal comfort, and hence, saving lives during future more extreme heatwaves. Melbourne serves as an example of the viability for the partial mitigation of heatwaves through intelligent urban design. The techniques generated during this study can then be applied to other cities, globally.

To understand the urban climate, predict the effect of urbanization or attend to improve the impact of some human activities, it is necessary to have a good understanding of the role of the urban surface. Indeed it has been demonstrated that surface forms affect urban microclimate (Givoni 1989, Oke 1981, 1988) and therefore changes the consumer behaviour of residents especially the building energy consumption (Santamouris, 2001, Ohashi et al., 2007).

The urban territory is continuously changing: high-rise buildings densification, new road infrastructures, increase of impervious surfaces, consumption of agricultural and natural areas… The result is new border, new shapes and new morphology for the urban geometries. In this context, monitoring urban changes became a challenge for urban planners and decision makers.

Geographical Information System (GIS) applications are increasingly being used to compute a set of indicators such as the Sky View Factor, the mean building height, the compactness ratio… All of theses indicators are used to study and monitor the urban structure (Long et al 2003, Bocher et al 2009). Besides, in the late 1990s, a large number of GIS-based tools have been developed by taking advantage of data organisation, spatial analysis and visualisation (eg. cartography). These three functions coincide with the focus of an indicator that needs to organize data, to quantify and to communicate.

If this diversity is valuable, on the other hand it can also act as a disincentive for the scientists and urban stakeholders communities. These tools are often build to answer a particular subject (mono-thematic approach). Moreover, most of them are based on proprietary softwares which limits their distribution, the possibility to examine their implementation (algorithm) since the main software is required to run the tool (black box) (Steiniger and Bocher, 2009, Steiniger and Hay, 2009). Last but not least, the definitions used to compute an indicator may differ according to the authors.

This situation is in sharp contrast with the needs of the scientific community to share results and experiences, and to experiment with new methods. Moreover, it is inconsistent with number of laws and regulations relating to the protection of the environment that promote common indicators.

To fill this gap, we propose a new open source spatial database, called H2GIS (http://www.h2gis.org/), to manipulate and process geographic and alphanumeric data (Gouge et al, 2014). H2GIS is a spatial extension of the Relational Database Management System (RDBMS) H2 Database Engine (http://www.h2database.com/) in the spirit of PostGIS (http://postgis.net/). It adds support for managing spatial features and operations on the new Geometry type of H2. H2GIS is fully compliant with the OGC’s Simple Features for SQL (SFSQL) 1.2.1 standards (Herring 2010, 2011).

In this paper we show how the spatial RDBMS H2GIS should be an ideal framework to model the urban data (store and distinguish spatial relationships), create a generic set of spatial urban indicators and used them with massive data (scalable, multi-core processing).

As an illustration, H2GIS is used in the MApUCE project which aims to integrate in urban policies and most relevant legal documents quantitative data from urban microclimate, climate and energy. Based on literature review, we offer an open spatial analysis toolbox to study the urban surface.

As the urban climate and weather depend strongly on the details of the local environment, an accurate description of local environment is essential. For urban areas this includes morphological data on the built-up environment like building height, building density, and frontal area. However, also the amount of vegetation in the urban canyon is critical.

This study combines data from a variety of sources to create a high spatial resolution (~100m) collection of urban environment properties, representing the whole of the Netherlands in maps. The data sources include the Dutch cadastre and the Dutch statistical office (vector data) and height- and terrain maps, and aerial photographs (raster data). Combining this model with urban temperature time series, an urban heat island (UHI) climatology for the Netherlands on a neighbourhood scale is derived.

The climatological data is made available on an interactive website, assisting urban planners with assessing and mitigating adverse effects of extreme warm weather events. For instance, 15 percent of the elderly population (age 65 and above) is exposed to a large UHI (95th percentile surpasses 4.5K), potentially leading to health issues. The urban data collection can also be used to perform high-resolution weather forecasts with numerical weather prediction models, for example WRF.

Like many European cities, Dublin has seen a decline in development for the past number of years owning to suppressed economic activities. However as documented in national and international news, there is now a significant housing crisis in Dublin as economic recovery begins to take hold and concentrate in the city. This has led to high demand for both housing and office space with virtually no new supply in the past 7 years. In turn, there is increased pressure for the Dublin local authorities to begin development of new residential and commercial units to ease the rising demand. In response to this the government of Ireland recently announced a €1.5 billion investment in direct provision for some 35,000 additional social housing units with development to begin as early as 2016. As with historic developments, this will likely have a significant impact on the near-surface atmosphere, depending on the final extent of sealed urban fabric. The pathways for future developments in Dublin have already been simulated using the MOLAND model in which four scenarios of building density are utilized broadly classified as “high” “medium” “low” and “business as usual development” (Brennan et al, 2009). However consideration has not yet been given to the impact each pathway will have on the thermo-hydrological characteristics of the city. Here, we undertake to simulate the impact each individual development pathway will have the on the urban surface energy and water balance of Dublin city. We employ the Surface Urban Energy and Water Balance Scheme (SUEWS) model to carry out our assessment of the impact on the urban energy (UEB) and urban water (UWB) balance. We model the period 2010-2012 in Dublin and evaluate our simulations against 3 urban flux observation platforms to ensure the modeled UEB and UWB are reasonable – the model is forced using climatically normal forcing data. We then developed Local Climate Zone maps for present day Dublin and for each development pathway scenario (2026) and, using this for land cover, model the diurnal, seasonal and annual energy and water budget for Dublin for each development scenarios. Subsequently we provide an assessment of the each development path on the UEB and UWB. Our primary aim was to aid local authority planners in pursing the development pathway which would have the least impact on canopy-layer climate while still achieving the required 35,000 units by 2026. Arising from our simulations, we provide guidance of the local scale development types that should be pursued to achieve both aims.