Urban ecosystems assessment: An integrated approach to maintenance of habitats and their biodiversity

Natural habitats and their biodiversity are usually associated with protected areas, incompatible with direct anthropogenic influence. Is there a biodiversity in urban environment, what is the role of peri-urban areas to the provision of species richness and is their potential being properly utilized? These are current issues that deserve the attention of decision-makers because the human's need of natural environment in cities is expressed more intensely than in any previous period in history. Green and blue infrastructure elements, being part of the larger system of urban ecosystems, provide an essential and proven benefits to the city dwellers, like health improvement, opportunities for nature-based daily outdoor recreation, strengthening sense of place etc. The main objective of this research is to assess this part of the landscape elements in urban and peri-urban environment, which are most supportive to the maintenance of habitats and their biodiversity. Selected Functional urban area with center city of Burgas is choosen for a case study. The urban ecosystems are assessed in GIS environment with unified indicator (based on City Biodiversity Index approach) according to 5 criteria: hemeroby index, share of protected areas, fragmentation index, presence of water and species richness. The assessment is performed on two spatial levels: within Functional urban area by Urban Atlas spatial units and within urban core – by grid cells (local climate zones). The final higher scores identify areas that provide the greatest extent the maintenance of habitats and their biodiversity. The results could support the urban planning and help to optimize the link between the natural elements within the Functional urban areas, providing ecological, economic and social benefits to the regions through the enhancement of the urban ecosystem’s functions and their services.

Modifizierung des Niederschlags über urbanen Gebieten am Beispiel Berlin

<p>Vielzählige Beschreibungen vorwiegend aus dem nordamerikanischen Raum weisen auf einen Einfluss urbaner Gebiete auf den Niederschlag hin. Dabei sind die zugrundeliegenden Ursachen nicht hinlänglich geklärt.</p> <p>Eingebettet in das BMBF-ClimXtreme-Projekt wird im Rahmen der Studie der Einfluss urbaner Gebiete auf den Niederschlag am Beispiel Berlins untersucht. Dazu werden sowohl 5-Minuten/1km-Radardaten (YW-Produkt des Deutschen Wetterdienstes) als auch das mesoskalige Weather Research and Forecasting Model (WRF) verwendet. Rückgrat der Analysen ist ein Algorithmus zur Identifikation und zum Tracking hochreichender konvektiver Zellen. Indem der Algorithmus auf die Radardaten angewendet wurde, wurden Zelltracks für den Großraum Berlin im Zeitraum 2001 bis 2020 ermittelt.</p> <p>Entsprechend wurden Situationen identifiziert, in denen a.) Zellen über dem Stadtgebiet entstehen, während im Umland keine hochreichende Konvektion vorhanden ist, b.) Zellen bei der Überquerung Berlins über dem Stadtgebiet eine Verstärkung der Niederschlagsintensität und/oder Größenzunahme zeigen und c.) Zellen, die nach Überquerung des Stadtgebiets im Lee der eine verstärkte Niederschlagsintensität und/oder Größenzunahme zeigen.</p> <p>Für die einzelnen Fälle werden die atmosphärischen Bedingungen / mögliche Ursachen, wie Zirkulation, Stabilität, Urbane Wärmeinsel und Aerosolgehalt aus Messdaten (z.B. Stationen) und Modellen (ERA5) ermittelt. Weiterhin wird versucht entsprechende Effekte mit konvektionsauflösenden WRF-Simulationen (ERA5 als Input) nachzubilden. Hierbei wird der Einfluss der Stadtstruktur durch “Local Climate Zones” beschrieben, welche im Rahmen von Sensitivitätsanalysen variiert werden. Für entsprechende Vergleiche mit den Radardaten wird der Zellidentifizierungs- und trackingalgorithmus auch auf die WRF-Simulationen angewendet. </p>

Evidences of horizontal urban heat advection in London using 6 years of data from a citizen weather station network

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering 6 years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio- temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ) – a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by ∼1.0 ◦C to ∼2.0 ◦C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ◦C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to ∼2.0 ◦C on average and negatively by down to ∼-1.0 ◦C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS for future urban design.

Air Humidity Characteristics in “Local Climate Zones” of Novi Sad (Serbia) Based on Long-Term Data

This study aims to investigate spatial and temporal dynamics and relationship between air temperature and five air humidity parameters (relative humidity, water vapor pressure, absolute humidity, specific humidity, and vapor pressure deficit) in Novi Sad, Serbia, based on two-year data (Dec 2015–Dec 2017). The analysis includes different urban areas of Novi Sad, which are delineated in five built (urban) types of local climate zones (LCZ) (LCZ 2, LCZ 5, LCZ 6, LCZ 8, and LCZ 9), and one land cover (natural) local climate zone (LCZ A) located outside the urban area. Temporal analysis included annual, seasonal, and monthly dynamics of air temperature and air humidity parameters, as well as their patterns during the extreme periods (heat and cold wave). The results showed that urban dry island (UDI) occurs in densely urbanized LCZ 2 from February to October, unlike other urban LCZs. The analysis of the air humidity dynamics during the heat wave shows that UDI intensity is most pronounced during the daytime, but also in the evening (approximately until midnight) in LCZ 2. However, lower UDI intensity is observed in the afternoon, in other urban LCZs (LCZ 6, LCZ 8, and LCZ 9) and occasionally in the later afternoon in LCZ 5. Regression analysis confirms the relationship between air temperature and each of the analyzed air humidity parameters.

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.

Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies.

The Utilization of Land Surface Temperature Information as an Input for Coastal City

Numerous studies have shown that there is a positive correlation between the increase of urban built-up areas with elevated Surface Urban Heat Island (UHI) temperature. It can be considered that SUHI is a by-product of urbanisation. The study found that SUHI in Makassar City is seasonal dependent. High surface temperature tends to occur in the dry season within the urban centre, expanding to the South-Eastern. Furthermore, by combining land surface temperature and Local Climate Zone (LCZ) classification scheme, 16 out of 17 local climate zones were identified, excluding LCZ 7 (light built) within the observation year. In detailed, the combination of LCZ 3 class (compact low rise) and LCZ 10 class (industrial), occupied more than 80 % of the total built-up category with a surface temperature range of 11° C and 16° C respectively. Furthermore, the result indicates a homogenous surface temperature within LCZ 3 with a lower SD of 1.40° C compared to LCZ 10 of 1.95° C. Also, the study explored the correlation of various urban and non-urban indices using artificial neural network. Based on the model used, the indices showed poor correlation with LCZ 3 but adversely correlates to LCZ 10. A final loss value of 0.222 in LCZ 10 was obtained. In contrast, LCZ 3 resulted in high final loss value of 146.554. The result indicated that there are other variables which should be considered in exploring SUHI correlation within LCZ 3 (compact low rise) in Makassar City. In contrast, LCZ 10 (industrial) correlate positively with three urban indices, consisting of NDBI (43.94), BI (37.79), and NDBal (34.77). In brief, the result indicated that SUHI phenomenon in LCZ 3 was poorly represented by the model, whereas the level of city development can be predicted better using LCZ 10 (industrial) areas.