Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.the Strategic Priority Research Program of the Chinese Academy of Sciencesthe National Natural Science Foundation of ChinaPeer Reviewe

Analysis of Temperature extremes in Canadian Cities using CMIP6 Data

An ever-growing Canadian urban population could be severely impacted by increase in temperature. Canada’s mean temperature is projected to increase by 6-8°C towards the end of the 21st century. The consequence of rising temperatures is an increased likelihood of extreme temperature events like heatwaves and wildfires. The thesis aims to assess changes in extreme temperature in large Canadian urban areas. The research will help in developing mitigation measures like urban planning, which help cope with changing temperature extremes. Predicting urban temperature change will require rigorous assessment of climate models, to account for the uncertainty in projecting temperature in large urban agglomerates. CMIP6 ensemble of models, provide an opportunity for assessment of urban-based projections. The models however, would need to be of fine resolution to fully capture its variability since urban temperature is heavily influenced by local urban features that contribute to Urban heat island (UHI). Historical maximum and minimum temperature trends are analyzed for eighteen urban areas in the Canada with population greater than 250,000 and use twelve CMIP6 models of fine resolution (<1°), and four tier-one emission scenarios to assess maximum, minimum, and mean temperature trends in future. An efficient observation dataset, Serially based station data (SCDNA), was used as a reference observation dataset and a novel bias-correction technique, the Semi-Parametric Quantile Mapping method (SPQM), was used to bias-correct future temperature data. Extreme temperature events were analyzed with the help of eight selected indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), across all the emission scenarios for all the cities in the study. The indices were computed for the entire future time-period (2021-2100) and for three time-slices, T1 (2021-2050), T2 (2040-2070) and T3 (2070-2100) to assess temporal variability. The magnitude, frequency, and duration of the occurrence of extreme events can be analyzed effectively using the ETCCDI indices, classified as absolute, threshold, and duration Indices and percentile indices. The historical temperature trends in Canadian cities were found to be related with urban features like elevation and population-growth but not strongly linked with urban area. Other features of UHI were deemed essential to understand the transitioning of historical and future temperature trends in Canada. Four emission scenarios predict increasing mean temperatures in all Canadian cities, except for the optimistic emission scenario (SSP1-2.6), which shows a marginal decreasing trend in the last quarter of the 21st century. Uneven changes are noted in all the projected indices, for example, in the annual maxima of daily maximum temperature (TXx), i.e., an increase of 0.5 °C and 0.6 °C per decade over the T1 and T2 respectively, and 0.99°C for T3 for the SSP5-8.5. Results show faster rates of warming across Canadian cities especially for the higher emission scenarios (SSP3-7.0 and SSP5-8.5). Spatial trends of extreme temperature indices correlate with temperature trends in individual climate zones in Canada, and the cities associated with a zone, expectedly experience similar trends. Cities in the Prairies and the Great Lakes zones, experience the highest increasing trends over the absolute and threshold indices in the higher emission scenarios, whereas the cities in the Canadian coasts experience higher increasing trends in the percentile indices. Lower emission scenarios also point towards increasing spatial trends in all Canadian cities. The coastal cities also experience the highest trends for the warm-spell duration index (WSDI) and a decreasing trend in the cold-spell duration index (CSDI). Spatial patterns of duration indices in the Canadian coastal cities point towards hotter summers, and milder winters, whereas the cities in the Canadian prairies, the Great Lakes, and Quebec will experience hotter summers with longer durations of extremely hot weather, in addition to persistence of harsher winters. Temperature projections have several applications, for example, in civil engineering applications, where temperature has a great role in the estimation and assessment of concrete and reinforcement deterioration. Another field of research is urban-based mortality studies, a consequence of the increasing frequency and duration of extreme temperature events. Heat-wave analysis, estimated through extreme temperature indices, forms the basis for estimating mortality rates from heat waves and other extreme temperature events. Climate models and CMIP6 models have systematic errors in their development and hence can only predict temperature projections with a limited degree of confidence. An extension of the work in this thesis could be the use of various model performance indicators, that quantitatively assess the performance of temperature projections made by CMIP6 models in Canadian cities. The future temperature projections and estimations of heat waves provide a scientific basis for a better understanding of the temperature patterns and temperature-related extreme events in Canadian cities and thus help facilitate climate change adaptation

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

The Relationship Between Water Temperature and Proximity to Surface Urban Heat Islands within the Lower Chesapeake Bay Watershed for the Summer of 2019

Surface urban heat islands (SUHIs) are land surfaces with high concentrations of impervious surfaces like roofs, roads, sidewalks and other infrastructures that trap, absorb, and re-emit heat throughout the day/night and typically present higher temperatures than their surrounding rural areas. In this study, I evaluate how presence of and distance to SUHIs are associated with water temperature in the lower Chesapeake Bay watershed for the summer of 2019. When heavy precipitation events occur, flooding creates stormwater runoff, which is exposed to the hotter temperatures in urban areas. This introduces thermal pollution to nearby rivers and streams disrupting aquatic ecosystems. The hypothesis for this research is that the closer water temperature points are to SUHIs, the warmer the temperature measured will be. To assess this, I processed Landsat 8 and 9 scenes in order to derive land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference build-up index (NDBI). I also processed land cover from the national land cover dataset (NLCD) and a digital elevation model (DEM) from which I derived flow direction (FD) and flow accumulation (FA). I used water temperatures measured by water quality stations from 25 sources as well. If areas with a surface temperature were half a standard deviation above the agricultural land cover LST average, they were defined as SUHIs, following Kaplan et al. (2018). The other datasets were used to extract other factors that can impact temperature or the relationship between distance to SUHIs and temperature. In addition, I also processed local climate zones (LCZs) to validate the identified SUHIs. To extract distance from SUHI areas and the water temperature datapoints, I used ArcGIS’s Euclidean Distance and Direction Distance tools. These were calculated for various cases, including; no-distance (contained within SUHI), omni-directional distance, and upstream/downstream distance. Some of these methodological attempts were more successful 2 than others. Overall results do not show a strong relationship between warmer water temperatures and proximity to SUHIs; therefore, in general terms, the hypothesis is not supported. However, there are some noteworthy findings; a) there are warmer water temperatures near urban centers where most of the SUHIs are located; b) elevation has the strongest influence and highest significance on water temperature with the trends of the variables explored (i.e., at higher elevations, the water temperature is cooler while at lower elevations, the water temperature is warmer); and, c) Euclidean distance to SUHIs and NDVI are other significant factors. With more time and resources, I would include more data on environmental confounding factors and use improved methods to calculate various distance measures, which would likely help tease out more specific relationships between water temperature and SUHIs as well as to interpret their correlations

Anthropogenic and natural drivers of a strong winter urban heat island in a typical Arctic city

The Arctic has rapidly urbanized in recent decades with 2 million people currently living in more than a hundred cities north of 65∘&thinsp;N. These cities have a harsh but sensitive climate and warming here is the principle driver of destructive thawing, water leakages, air pollution and other detrimental environmental impacts. This study reports on the urban temperature anomaly in a typical Arctic city. This persistent warm anomaly reaches up to 11&thinsp;K in winter with the wintertime mean urban temperature being 1.9&thinsp;K higher on average in the city center than in the surrounding natural landscape. An urban temperature anomaly, also known as an urban heat island (UHI), was found using remote sensing and in situ temperature data. High-resolution (1&thinsp;km) model experiments run with and without an urban surface parameterization helped to identify the leading physical and geographical factors supporting a strong temperature anomaly in a cold climate. The statistical analysis and modeling suggest that at least 50&thinsp;% of this warm anomaly is caused by the UHI effect, driven mostly by direct anthropogenic heating, while the rest is created by natural microclimatic variability over the undulating relief of the area. The current UHI effect can be as large as the projected, and already amplified, warming for the region in the 21st century. In contrast to earlier reports, this study found that the wintertime UHI in the Arctic should be largely attributed to direct anthropogenic heating. This is a strong argument in support of energy efficiency measures, urban climate change mitigation policy and against high-density urban development in polar settlements. The complex pattern of thermal conditions, as revealed in this study, challenges urban planners to account for the observed microclimatic diversity in perspective sustainable development solutions.</p

Characterization of Surface Urban Heat Island in the Greater Toronto Area Using Thermal Infrared Satellite Imagery

For the past decades, there have been increasing concerns about urban environmental degradation, especially under the circumstance of urbanization. This thesis compares the trends between air temperature and surface temperature, and characterizes spatial distribution and connection with relevant urban characteristics, in the Greater Toronto Area (GTA) of Ontario in the context of surface urban heat island (SUHI). The trends in annual and seasonal temperature were investigated in the GTA from 1984 to 2014. The Mann-Kendall test is used to assess the significance of the trends and the Theil-Sen slope estimator is used to identify their magnitude. Statistically significant increasing trends for annual mean temperatures are observed mainly at the urban and suburban stations. The temperature variation is consistent with the pace of urbanization, however, the choice of the stations is vital in the estimation of the UHI intensity which can overestimate or underestimate the prediction. A local scale investigation was continued by applying Landsat and ASTER thermal-band images in order to characterize SUHI intensity in the study area. Results show that strong SUHI phenomenon is mainly observed at downtown Toronto and industrial areas. As the enhancement of urbanization, tracking and monitoring of SUHI is imperative to understand the potential impact of the increased heat waves

Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS

Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime