The urban heat island (UHI) effect is a phenomenon that results in cities being warmer than the surrounding rural areas, due to a large amount of impervious surfaces. The purpose of this study is to evaluate the effectiveness of green roofs to mitigate the effects of solar energy on a building in the southern United States. In order to test the green roofs, temperatures were monitored inside and on top of unconditioned model buildings with green and with traditional roofs. Over the course of the study, the data collected showed that green roofs provided a significant benefit for the buildings by reducing daily high temperatures during summer and daily low temperatures during winter, while also reducing temperature fluctuation. The findings of this study suggest that a green roof will reduce indoor temperature and rooftop temperature, while providing several other benefits for city inhabitants such as reduced air temperature.

The urban heat island (UHI) is a problem that is likely to be exacerbated by ongoing climate change, but it is often claimed that urban trees can mitigate it and hence adapt our cities to climate change. Many researchers have attempted to quantify the cooling effects of trees using modelling approaches. However, the major disadvantage of most of the models is that they consider all vegetation to act as a single saturated layer and that their effect is merely proportional to its surface cover. Therefore, they fail to take into account potential differences between tree species and the effect of different environmental and growing conditions. To address this issue four different studies were conducted in Manchester, UK from February, 2010 to December, 2012. The studies compared the growth and cooling abilities of several commonly planted urban tree species, and investigated a single species planted in a range of growing conditions: investigating the effect of urban soil compaction and aeration and also the effect of urbanization and simulated climate change in the rooting zone. Overall, our studies showed that species selection and growing conditions can substantially alter the evapotranspirational cooling provided by urban trees. Fast growing species such as Pyrus calleryana, with their dense and wide canopy can provide cooling up to 2.2 kW tree-1, 3-4 times that of Sorbus arnoldiana, which have a thinner and narrower canopy and a moderate growth rate. P. calleryana was also investigated under three contrasting growth conditions: in cut-out pits in pavements; in grass verges; and in pits filled with Amsterdam soil. Trees in the less compacted Amsterdam soil had grown almost twice as fast as those in pavements and also had better leaf physiological performance. Together with a longer growing season, and better uptake of soil nutrients and moisture, trees grown in Amsterdam soil provided evapotranspirational cooling of up to 7kW, 5 times higher than those grown in pavements. Another experiment in which P. calleryana trees were planted in 3 standard planting techniques with non-compacted load bearing soils and with or without permeable slabs showed that optimum cooling is not only dependent on preventing soil compaction but also on ensuring that the covering materials are permeable to oxygen. Trees in the open pits provided up-to 1 kW of cooling, compared to around 350 and 650 W by the small and large covered pits respectively. Our final experiment showed that urbanization can increase tree growth by 20-30%; however, despite being under more water stressed conditions trees grown in simulated climate change plots had 40% higher sap flux density, and hence cooling potential. The study suggested that at least with P. calleryana, transpirational cooling benefit might be enhanced in places like Manchester with increased soil temperature in future, but potentially at the expense of photosynthesis and carbon gain. Together these studies show that evaporative cooling of trees depends strongly on both species and growing conditions. If incorporated into regional and local energy exchange models our results can help us to quantify the magnitude and effectiveness of greenspaces in the city in adapting them to climate change.

No description available.

A combination of the urban heat island effect and a rising temperature baseline resulting from global climate change inequitably impacts socially vulnerable populations residing in urban areas. This dissertation examines distributional inequity of exposure to urban heat by socially disadvantaged groups and minorities in the context of climate justice. Using Cutter’s hazards-of-place model, variables indicative of social vulnerability and biophysical vulnerability are statistically tested for their associations. Biophysical vulnerability is conceptualized utilizing a urban heat risk index calculated from summer 2010 LANDSAT imagery to measure land surface temperature , structural density through the normalized difference built-up index, and vegetation abundance through the normalized difference vegetation index. A cross-section of twenty geographically distributed metropolitan statistical areas (MSAs) in the U.S. are examined using census derived variables at the tract level. The results of bivariate correlation analysis, ordinary least squares regression, and spatial autoregression analysis indicate consistent and significant associations between greater social disadvantage and higher urban heat levels. Multilevel modeling is used to examine the relationship of MSA-level segregation with tract-level minority status and social disadvantage to higher levels of urban heat. Segregation has a significant but varied relationship with the variables, indicating that there are inconsistent associations with urban heat due to differing urban ecologies. Urban heat and social vulnerability present a varying landscape of thermal inequity in different urban areas, associated in many cases with residential segregation.

Urbanisation alters the hydrologic response of a catchment, resulting in increased runoff rates and volumes, and loss of infiltration and base flow. Quantification of uncertainties is important in hydrologic designs of urban infrastructure. Major sources of uncertainty in the Intensity Duration Frequency (IDF) relationships are due to insufficient quantity and quality of data leading to parameter uncertainty and, in the case of projections of future IDF relationships under climate change, uncertainty arising from use of multiple General Circulation Models (GCMs) and scenarios. The work presented in the thesis presents methodologies to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCMs using a Bayesian approach. High uncertainties in GEV parameters and return levels are observed at shorter durations for Bangalore City. Twenty six GCMs from the CMIP5 datasets, along with four RCP scenarios are considered for studying the effects of climate change. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. Disaggregation of precipitation extremes from larger time scales to smaller time scales when the extremes are modeled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations. A Bayesian hierarchical model is used to obtain spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. Applicability of the methodology is demonstrated with data from 19 telemetric rain gauge stations in Bangalore City, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate while for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. In this work, variation in the dependence structure of extreme precipitation within an urban area and its surrounding non-urban areas at various durations is studied. The Berlin City, Germany, with surrounding non-urban area is considered to demonstrate the methodology. For this case study, the hourly precipitation shows independence within the city even at small distances, whereas the daily precipitation shows a high degree of dependence. This dependence structure of the daily precipitation gets masked as more and more surrounding non-urban areas are included in the analysis. The geographical covariates are seen to be predominant within the city and the climatological covariates prevail when non-urban areas are added. These results suggest the importance of quantification of dependence structure of spatial precipitation at the sub-daily timescales, as well as the need to more precisely model spatial extremes within the urban areas. The work presented in this thesis thus contributes to quantification of uncertainty in precipitation extremes through developing methodologies for generating probabilistic future IDF relationships under climate change, spatial mapping of probabilistic return levels and modeling dependence structure of extreme precipitation in urban areas at fine resolutions.

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No. of bitstreams: 2 Dissertação - Rubens Villar Siqueira - 2015.pdf: 4882241 bytes, checksum: 3f8cb0b344dec7efd60e3c7564ed2c56 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-12-03 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Climate analysis, whether at global, regional or local level, it has been the subject of research in various fields of earth sciences. Among the climatic parameters, temperature and precipitation have gained importance in recent decades because of significant changes in their magnitudes. Thus, this work performs a detailed analysis of the temperature for the Greater Goiânia, using satellite images to generate surface temperature for the study area, at first, through an analysis between the years 1997 and 2008 and after in about twenty years, periodically every four years, for the years 1997, 2001, 2005, 2009 and 2014. The elaborate maps, besides showing the spatial variation of urban heat islands, show that there was significant changes to the minimum temperature, maximum and average. Between the period 1997 and 2008, the minimum decrease about 1.4°C and maximum jump of 31.2°C to 36.0°C. Test results for the five periods between 1997 and 2014, show that the year 2014 is presented as the hottest in the years studied. Through the resulting maps of this analysis, it can see that the range of temperatures, the difference between the maximum and minimum, grow with the years. An estimated temperature of satellite validation model was performed by direct comparison between the surface temperature and the data of GOIÂNIA weather station belonging to INMET, with differences of 0.7°C to 1.9°C between the temperatures demonstrating the applicability of satellite images to estimate temperatures in areas that do not have a dense meteorological network. The last analysis performed is forecast monthly temperatures for the period between the years 2040-2047, using the method of Holt-Winters. The model used for predicting allowed the computation of the seasonality of the minimum monthly temperatures, average and maximum for the historical period between the years 1970 to 2015. The predicted temperatures renew the expectation of increased minimum temperatures, average and maximum presented by the analysis of Historic data. As shown, in addition to the monthly increases in temperature, the occurrence of these will be situated in the highest classes of about 1.0° C warmer. We can see that, too, after 2000, all temperatures rise significantly, where their amplitudes between the minimum and maximum are located at a higher level than in previous years. / A análise do clima, seja em escala global, regional ou local, tem sido objeto de pesquisa em diversas áreas das ciências da terra. Dentre os parâmetros climáticos, a temperatura e a precipitação ganharam importância nas últimas décadas devido as alterações significativas em suas magnitudes. Desta forma, este trabalho executa uma análise particularizada da temperatura para a Região Metropolitana de Goiânia, utilizando imagens de satélites a fim de gerar a temperatura de superfície para a área de estudo, em um primeiro momento, por meio de uma análise entre os anos de 1997 e 2008 e após em cerca de vinte anos, periodicamente a cada quatro anos, para os anos de 1997, 2001, 2005, 2009 e 2014. Os mapas elaborados, além de mostrarem a variação espacial das ilhas de calor urbano, demonstram que houve variações significativas para as temperaturas mínimas, máximas e médias. Entre o período de 1997 e 2008, as mínimas decrescem aproximadamente em 1,4°C e as máximas saltam de 31,2°C para 36,0°C. Os resultados da análise para os cinco períodos entre 1997 e 2014, demonstram que o ano de 2014 se apresentou como o mais quente entre os anos estudados. Por meio dos mapas resultantes desta análise, é possível notar que a amplitude das temperaturas, diferença entre as máximas e mínimas, crescem com o decorrer dos anos. Um modelo de validação das temperaturas estimadas por satélite foi executado por meio da comparação direta entre a temperatura de superfície e os dados da estação meteorológica GOIÂNIA, pertencente ao INMET, apresentando diferenças de 0,7°C a 1,9°C entre as temperaturas, demonstrando a aplicabilidade de imagens de satélite para estimativa de temperaturas em áreas que não dispõem de uma rede meteorológica adensada. A última análise executada trata da previsão de temperaturas mensais para o período entre os anos de 2040 a 2047, utilizando o método de Holt-Winters. O modelo adotado para a previsão permitiu a computação da sazonalidade das temperaturas mensais mínimas, médias e máximas para o período histórico entre os anos de 1970 a 2015. As temperaturas previstas reafirmam a expectativa do aumento das temperaturas mínimas, médias e máximas apresentadas pela análise dos dados históricos. Conforme demonstrado, além dos aumentos nas temperaturas mensais, a ocorrência destas se situará em regiões mais altas, com cerca de 1,0°C mais quentes. Podemos notar que, também, após o ano 2000, todas as temperaturas se elevam de forma significativa, onde suas amplitudes entre as mínimas e máximas se situam em um patamar mais elevado que nos anos anteriores.

This study critically examines the (UHI) effect in urban and suburban neighbourhoods of Quito, Ecuador, over a 19-year period, focusing on the interplay between atmospheric pollution and urban/ suburban temperature. Utilizing Empirical Dynamic Modeling(EDM) and Convergent Cross-Mapping (CCM), this study dives into the nonlinear dynamics of environmental factors, a method that traditional linear models fail to address effectively.The results unveil a consistent and strong positive correlation across various neighbourhoods, with temperature fluctuations indicating a typical UHI effect. This is most noticeable in urbanized areas where the temperature is significantly higher due to dense infrastructure and reduced greenery, a pattern that diminishes as one moves towards the outskirts. Specifically, pollutants like PM2.5 exhibit a non-uniform positive correlation, suggesting their collective increase or decrease across different regions, whereas CO shows a very slight and inconsistent inverse relationship across locations. The causal analysis further substantiates a significant interaction between PM2.5 concentrations and temperature, with the data revealing a reciprocal predictive capacity between these variables. The CCM analysis, through its graphical representation of predictive skills, confirms the causal effect of PM2.5 on urban temperature, marking an essential contribution to understanding the UHI effect and its implications for urban environmental dynamics. This study provides a comprehensive overview of the UHI phenomenon, highlighting the intricate relationship between urbanization, atmospheric pollution, and climate. The findings emphasize the necessity for urban planning and policy to consider these complex interactions to mitigate the effects of climate change on urban environments.

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / Environmental Sciences / Ph. D. (Environmental Management)

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / College of Agriculture and Environmental Sciences / Ph. D. (Environmental Management)