Copernicus for urban resilience in Europe

Abstract

The urban community faces a significant obstacle in effectively utilising Earth Observation (EO) intelligence, particularly the Copernicus EO program of the European Union, to address the multifaceted aspects of urban sustainability and bolster urban resilience in the face of climate change challenges. In this context, here we present the efforts of the CURE project, which received funding under the European Union's Horizon 2020 Research and Innovation Framework Programme, to leverage the Copernicus Core Services (CCS) in supporting urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, demonstrating that CCS can facilitate urban planning and management strategies to improve the resilience of cities. With a strong emphasis on stakeholder engagement, CURE has identified eleven cross-cutting applications between CCS that correspond to the major dimensions of urban sustainability and align with user needs. These applications have been integrated into a cloud-based platform known as DIAS (Data and Information Access Services), which is capable of delivering reliable, usable and relevant intelligence to support the development of downstream services towards enhancing resilience planning of cities throughout Europe.

Figure 1

Baseline design of the CURE system. It includes the set-up of hardware components, such as processing units and storage, as well as the software design and interaction between those components, the information provided by the platform (Information as a Service—InaaS) and the deployed applications in a cloud (WEkEO—DIAS) environment. The CURE Prototype is built on two sub services, (i) the CURE MASTER and (ii) the Application Nodes. The CURE Master monitors and orchestrates tasks (Airflow) in the Application Nodes based on their execution state and process chain; offers an Application Programming Interface (API) based access; as well as Graphical User Interface (RESTful APIs well-defined machine-to-machine communication over the web) for the submission, scheduling and monitoring of individual jobs.

Figure 2

Surface temperature maps for Berlin as resulted by AP01. (a) Daytime (30 May 2019, at 11.26 local time); and (b) night-time (16 April 2019, 22.32 local time) urban surface temperature maps at 100 × 100 m spatial resolution for the city of Berlin and (c,d) the respective 1 × 1 km spatial resolution products. Maps created with QGIS software, version 3.16 (www.qgis.org).

Figure 3

Spatial distribution of mean daily CO₂ emissions, in g CO₂ m⁻² d⁻¹, for the “front-runner” city Heraklion, for Working Days in the spring season (March–April-May) in 2019. Highest emissions are linked to traffic (road network) and densely built-up areas (buildings, human metabolism). Vegetated areas act as a sink for CO₂ during the vegetation period. Map created with QGIS software, version 3.16 (www.qgis.org).

Figure 4

Simple vulnerability analysis for the city of Heraklion: exposure of buildings to flood hazard by land use type (Urban Atlas categories) of the building block. Map created with QGIS software, version 3.16 (www.qgis.org) and graph with Microsoft Excel 2016.

Figure 5

Total annual mean concentrations in μg/m³ for the centre of Sofia city: NO₂ (left column) and PM2.5 (right column). The maps in the bottom row are produced using Copernicus data only, whereas the maps in the top row are produced using downscaled emissions combined with local data. Maps created with QGIS software, version 3.16 (www.qgis.org).

Figure 6

Daily mean WBGT in San Sebastian for 23 July 2019. This map shows a time-average of the heat stress situation and takes also the night-time into account, when the urban heat island is at its strongest. The variability in the WBGT temperatures is typically around 2 to 4 °C. Forested areas are the coolest locations on the map and the bigger they are the larger the cooling effect. Water areas show less cooling in these maps, as they often keep a high temperature during the night, enhancing the urban heat island problem. Impervious areas show, as expected, the highest WBGT values. Map created with QGIS software, version 3.16 (www.qgis.org).

Figure 7

Green roofs priority in San Sebastian. It is obvious that the roofs, which obtain a higher prioritisation, are located in the peripheral areas of the city, which correspond to buildings with a larger roof surface, generally flatter roofs and of lesser age. On the other hand, the zone criteria allow differences to be established in those areas where the LST and imperviousness is high and the NDVI is low. These prioritised zones, therefore, constitute useful information for managing the different risks faced by the city; however, it should be borne in mind that these are not absolute priority values and that they can be modified by decision-makers according to the problems they need to address. Map created with QGIS software, version 3.16 (www.qgis.org).