City-descriptive input data for urban climate models: Model requirements, data sources and challenges Abstract 1) Introduction 1.1 Brief overview of urban atmospheric modelling 1.2 Scale issues: mesoscale and microscale 1.3 Coverage issues: from city-scale to global modelling 1.4 Fit for purpose 2) Land use and land cover classes 2.1 Description of the parameters and their relevance 2.2 Methodologies to gather land cover data 2.2.1. Remote sensing methods 2.2.2. From vector topographical databases and land registries 2.2.3. Data fusion 3) Morphological parameters 3.1 Description of the parameters and their relevance 3.2 Links between morphological parameters 3.3 Methodologies to gather morphological parameters 3.3.1 Data from remote sensing 3.3.2 GIS treatment of 2.5D cadaster vector data of individual buildings 3.3.4 Crowdsourcing or deep learning methods 4) Architectural parameters 4.1 Description of the parameters and their relevance 4.2 Developing comprehensive architectural databases 4.3 Methodologies to gather architectural information 4.3.1 Identification of representative archetypes 4.3.2 Remote sensing and image processing 4.3.3 Crowdsourcing 5) Socioeconomic data and building use 5.1 Description of the parameters and their relevance 5.2 Methodologies to gather uses, socioeconomic and anthropogenic heat parameters 5.2.1 From inventories 5.2.2 Crowdsourcing 6) Urban vegetation 6.1 Description of the parameters and their relevance 6.2 Methodologies to collect vegetation parameters at mesoscale 28 6.3 Methodologies to collect vegetation parameters at microscale 29 7) Discussion 30 7.1 Licensing issues 30 7.2 Cataloguing issues 31 7.3 Data quality 7.4 Open data 31 7.5 Research challenges for the next decade 32 7.6 From data of various origins to Urban Climate Services 33 8 Conclusions 33 Appendix 1: Overview of several global land cover data sets with an urban description 34 Acknowledgements 36 References 36

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Information gaps and asymmetries are common in the housing market and this is frequently the case with the risks of natural processes, especially in coastal areas where the amenity dimension may dominate the risk aspect. Flood risk disclosure through maps is a policy instrument aimed at addressing this situation. We assess its effectiveness by identifying whether such maps induce a price differential for single family coastal dwellings in three Finnish cities, and by estimating the discount per square meter for various flooding probabilities (return times). The estimations indicate a significant price drop after the information disclosure for properties located in floodprone areas as indicated by the maps. In the case of sea flooding information in Helsinki, the price effect is sensitive to the communicated probability of flooding. Overall, the discussed policy instrument appears to have functioned as intended, correcting information gaps and asymmetries related to flood risk. The identified effect is spatially selective; it caused a short-term localized shock in market prices in conjunction with some reorientation of demand from risky coastal properties towards ones that represent a similar level of coastal amenity, but are less risky in terms of flooding. This hints at the potential for incorporating the shocks associated with flood events or risk information into broader-scoped urban modelling and simulation. Similarly, the reasonable accuracy with which the housing market processes the additional information shows a potential for wider use of the disclosure of nonobvious risks in real estate markets. In the case of adapting to climate change risks, additional uncertainties may make the disclosure instrument less effective, if used as a single tool.