Seasonal and daily effects of the sea on the surface urban heat island intensity: A case study of cities in the Caspian Sea Plain

Firozjaei, Mohammad Karimi; Seddighi, Amir Saied; Mijani, Naeim; Kazemi, Yasin; Amiraslani, Farshad

doi:10.1016/j.uclim.2023.101603

Cited by 12 publications

(5 citation statements)

References 47 publications

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“…La interacción de las UHI en una ciudad tropical costera puede ser diferente a la de las ciudades en zona de clima templado, afectándola gravemente. Sin embargo, hay una carencia de estudios sobre UHI centradas en las ciudades tropicales costeras (Chew et al, 2021) A nivel internacional se han realizado algunos estudios en ciudades costeras como Grecia (Giannaros y Melas, 2012), Oman (Charabi yyBakhit, 2011), en el Mar Caspian (Firozjaei et al, 2023), Estambul (Dihkan et al, 2015), China (X. Xu et al, 2023) y en el Mar Mediterráneo (Kassomenos et al, 2022), sin embargo, estos estudios aún son emergentes en México y el Golfo de México.…”

Section: Islas De Calor Urbano (Uhi)unclassified

Análisis espaciotemporal de islas de calor aplicado en la ciudad costera de San Francisco de Campeche, México

Canul-Turriza,

Aké-Turriza,

May-Tzuc

et al. 2024

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The urbanization of the city of San Francisco de Campeche influences the formation of urban heat islands due to construction materials, buildings and structures, human activities, lack of vegetation, and transportation infrastructure. Heat islands have negative consequences such as increased energy consumption and heat stress for the population, contributing to climate change due to increased greenhouse gas emissions caused by additional energy demand. Cities such as Sydney, Beijing, Nanjing, Moscow, and Hong Kong are implementing urban planning strategies that promote urban vegetation, the use of reflective building materials, the improvement of public transport, and the promotion of energy efficiency in buildings. Landsat satellite images were used to analyze population growth and urban sprawl to identify heat islands, and a vegetation index analysis was also made. Regarding the analyses, it was recognized that the temperature increased by approximately 6°C between 1990 and 2022. There has also been a decrease in vegetation due to the urban sprawl and housing growth, quadrupling the Normalized Difference Vegetation Index (NDVI) in the 0-0.25 class for the same period. Finally, mitigation measures are proposed to counteract the effects caused by heat islands in the city.

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Section: Islas De Calor Urbano (Uhi)unclassified

Análisis espaciotemporal de islas de calor aplicado en la ciudad costera de San Francisco de Campeche, México

Canul-Turriza,

Aké-Turriza,

May-Tzuc

et al. 2024

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“…However, this spatial spillover has rarely been considered in modeling seasonal LST variations. Another source of LST autocorrelation is where large natural areas can influence neighboring areas by spreading additional cooling or warming effects [14,16,[44][45][46]. Investigating the cooling effect of river bodies, Cai et al [36] observe that the relationship between building form and seasonal LST becomes stronger as the distance to the closest river corridors increases.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies use indicators like percent water bodies or NDWI, with some reporting that water content reduces LST in winter [13,20,27,28], while others find no significant effect in winter [30,47]. Other studies examine the distance to water bodies and mostly focus on their cooling effect in summer [14,29,36,45,72]. Limited research addresses the effect of water proximity on winter LST, finding no significant effect [29,44].…”

Section: Effects Of Proximity To Green and Blue Spaces On Seasonal Lstmentioning

confidence: 99%

“…For example, urban forests and woods are beneficial for urban cooling, owing to active evapotranspiration and shading, but their impacts in cold months are less understood. Similarly, urban water bodies, such as lakes and ponds, have surface temperatures lower than those of their surroundings in summer, but the thermal impact of large stretching rivers is less understood [14][15][16]. The seasonal effects of conserving and restoring urban blue spaces need to be better understood.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Urban Morphology on Seasonal Land Surface Temperatures: Comparing Grid- and Block-Based Approaches

Jeon,

Park,

Guldmann

2023

IJGI

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Climate change is expected to result in increased occurrences of extreme weather events such as heat waves and cold spells. Urban planning responses are crucial for improving the capacity of cities and communities to deal with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, built-environment factors are identified that moderate or exacerbate seasonal land surface temperatures (LSTs). The focus is on Seoul, South Korea, as a case study, and seasonal LST values are calculated at both the grid (100 m × 100 m) and street block levels, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for large forests and water bodies. The quadrant analysis reveals a spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. Spatial regression analyses demonstrate that higher vegetation density and proximity to water bodies play key roles in moderating LSTs, leading to cooler summers and warmer winters. Building characteristics have a constant impact on LSTs across all seasons: horizontal expansion increases the LST, while vertical expansion reduces the LST. These findings are consistent for both grid- and block-level analyses. This study emphasizes the flexible role of the natural environment in moderating temperatures.

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“…Nowadays, climate change and its negative effects have become one of the biggest challenges on Earth [1][2][3][4]. This phenomenon, with its complexities, is recognized as one of the critical and highly significant issues that have far-reaching impacts on economies, the environment, public health, and the quality of human life [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

Alsubhi,

Qureshi,

Siddiqui

2023

Remote Sensing

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Dust storms are one of the major causes of the destruction of natural ecosystems and human infrastructure worldwide. Therefore, the identification and mapping of susceptible regions to dust storm formation (SRDSFs) is of great importance. Determining SRDSFs by considering the concept of risk in the decision-making process and the kind of manager’s attitude and planning can be very valuable in dedicating financial resources and time to identifying and controlling the negative impacts of SRDSFs. The purpose of this study was to present a new risk-based method in decision making to create SRDSF maps of pessimistic and optimistic scenarios. To achieve the purpose of this research, effective criteria obtained from various sources were used, including simulated surface data, satellite products, and soil data of Saudi Arabia. These effective criteria included vegetation cover, soil moisture, soil erodibility, wind speed, precipitation, and absolute air humidity. For this purpose, the ordered weighted averaging (OWA) model was employed to generate existing SRDSF maps in different scenarios. The results showed that the wind speed and precipitation criteria had the highest and lowest impact in identifying dust centers, respectively. The areas identified as SRDSFs in very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 85,950, 168,275, 255,225, 410,000, and 596,500 km2, respectively. The overall accuracy of very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 84.1, 83.3, 81.6, 78.2, and 73.2%, respectively. The very pessimistic scenario can identify the SRDSFs in the study area with higher accuracy. The overall accuracy of the results of these scenarios compared to the dust sources obtained from the previous studies were 92.7, 94.2, 95.1, 88.4, and 79.7% respectively. The dust sources identified in the previous studies have a higher agreement with the results of the neutral scenario. The proposed method has high flexibility in producing a wide range of SRDSF maps in very pessimistic to very optimistic scenarios. The results of the pessimistic scenarios are suitable for risk-averse managers with limited financial resources and time, and the results of the optimistic scenarios are suitable for risk-taking managers with sufficient financial resources and time.

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Seasonal and daily effects of the sea on the surface urban heat island intensity: A case study of cities in the Caspian Sea Plain

Cited by 12 publications

References 47 publications

Análisis espaciotemporal de islas de calor aplicado en la ciudad costera de San Francisco de Campeche, México

Análisis espaciotemporal de islas de calor aplicado en la ciudad costera de San Francisco de Campeche, México

Impacts of Urban Morphology on Seasonal Land Surface Temperatures: Comparing Grid- and Block-Based Approaches

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

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