Green infrastructure has a role to play in climate change adaptation strategies in cities. Alternative urban spaces should be designed considering new requirements in terms of urban microclimate and thermal comfort. Pervious pavements such as green parking lots can contribute to this goal through solar evaporative cooling. However, the cooling benefits of such systems remain under debate during dry and warm periods. The aim of this study was to compare experimentally the thermal behavior of different parking lot types (PLTs) with vegetated urban soil. Four parking lots were instrumented, with temperature probes buried at different depths. Underground temperatures were measured during summer 2019, and the hottest days of the period were analyzed. Results show that the less mineral used in the surface coating, the less it warms up. The temperature difference at the upper layer can reach 10 °C between mineral and non-mineral PLTs. PLTs can be grouped into three types: (i) high surface temperature during daytime and nighttime, important heat transfer toward the sublayers, and low time shift (asphalt system); (ii) high (resp. low) surface temperature during daytime (resp. nighttime), weak heat transfer toward the sublayers, and important time shift (paved stone system); and (iii) low surface temperature during daytime and nighttime, weak heat transfer toward the sublayers, and important time shift (vegetation and substrate system, wood chips system, vegetated urban soil). The results of this study underline that pervious pavements demonstrate thermal benefits under warm and dry summer conditions compared to conventional parking lot solutions. The results also indicate that the hygrothermal properties of urban materials are crucial for urban heat island mitigation.

Information describing the elements of urban landscapes is required as input data to study numerous physical processes (e.g., climate, noise, air pollution). However, the accessibility and quality of urban data is heterogeneous across the world. As an example, a major open-source geographical data project (OpenStreetMap) demonstrates incomplete data regarding key urban properties such as building height. The present study implements and evaluates a statistical approach that models the missing values of building height in OpenStreetMap. A random forest method is applied to estimate building height based on a building’s closest environment. A total of 62 geographical indicators are calculated with the GeoClimate tool and used as independent variables. A training dataset of 14 French communes is selected, and the reference building height is provided by the BDTopo IGN. An optimized random forest algorithm is proposed, and outputs are compared with an evaluation dataset. At building scale for all cities, at least 50 % of the buildings have their height estimated with an error of less than 4 m (the cities' median building heights range from 4.5 to 18 m). Two communes (Paris and Meudon) demonstrate building height results that deviate from the main trend due to their specific urban fabrics. Putting aside these two communes, when building height is averaged at a regular grid scale (100 m×100 m), the median absolute error is 1.6 m, and at least 75 % of the cells of any city have an error lower than 3.2 m. This level of magnitude is quite reasonable when compared to the accuracy of the reference data (at least 50 % of the buildings have a height uncertainty equal to 5 m). This work offers insights about the estimation of missing urban data using statistical methods and contributes to the use of open-source datasets based on open-source software. The software used to produce the data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2021b), and the dataset can be freely accessed at https://doi.org/10.5281/zenodo.6855063 (Bernard et al., 2021).

Since 2012, Local Climate Zones (LCZ) have been used for numerous studies related to urban environment. In 2015, this use amplified because a method to map urban areas in LCZs was introduced by the World Urban Database and Access Portal Tools (WUDAPT). However in 2017, the first HUMan INfluence EXperiment showed that these maps often have poor or low quality. Since the maps are used in different applications such as urban modelling and land use/land cover change studies, it is of the utmost importance to improve mapping accuracies and a second experiment was launched. In HUMINEX 2.0, the focus lies on providing guidelines on the use of the mapping protocol based on the results of both HUMINEX 1.0 and 2.0. The results showed that: (1) it is important to follow the mapping protocol as strictly as possible, (2) a reasonable amount of time should be spent on the mapping procedure, (3) all users should perform a driving test, and (4) training area sets should be stored in the WUDAPT database for other users.

Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO^® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).

Environmental issues such as human thermal discomfort or air quality are getting particularly important for policy makers and city planners.However, meteorological variables within the Urban Boundary Layer show a very strong spatial and temporal heterogeneity, and therefore, numerical models aiming to properly predict this variability are often complex and computationally heavy. These characteristics can represent a major barrier to the needed dialogue with policy makers and city planners.This contribution proposes a model to predict the spatial variability of the night-time Urban Heat Island Intensity (UHII) based on input data that can be simply understood by stakeholders.Relatively simple methods, combining linear models and variables mainly derived from Local Climate Zones, can approximate the results of computationally heavier numerical models with an acceptable normalized residual mean square error (nrmse) and a good spatial representation.In order to account for multiple scales of the phenomenon, we built aggregation variables according to wind direction and strength and variables relative to the shape of the urban sprawl. We improved the nrmse compared to [Garde et al 2020] who worked on the same data.The spatial correlation of the residuals is also explored through spatial regression models.The loss of precision is counter balanced by the gain in explainability and rapidity of use. Once the parameters of the model are estimated, one can explore the impact of a major urban renovation project with almost no delay as long as the geographical information is available.To illustrate the method, we based our training and evaluation on numerical simulations performed with the SURFEX/MesoNH models for 42 French cities.