scholarly journals Estimasi Dampak Urban Heat Island terhadap Laju Evapotranspirasi: Studi Kasus di Kota Palembang

2021 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Ari Sugiarto ◽  
Budi Indra Setiawan ◽  
Chusnul Arif ◽  
Satyanto Krido Saptomo
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Meteorological Station ◽  
Temperature Data ◽  
Water Evaporation ◽  
Heat Island ◽  
National Agency ◽  
Evapotranspiration Rate ◽  
Urban Heat ◽  
The Difference

A review of air temperature in the Palembang city by reviewing data from the National Agency for Meteorology, Climatology, and Geophysics/BMKG (Kenten Climatology Station and the SMB II Meteorological Station) shows a difference in air temperature can indicate the occurrence of Urban Heat Island (UHI). The difference in air temperature affects the evapotranspiration rate (ET) because air temperature very influencing water evaporation. ET rate estimation with air temperature data is the first step to prove this hypothesis. Hargreaves and Samani, Blaney and Criddle, Linacre, and Kharuffa models is the ET model that using air temperature as the variable was used to estimate the ET rate. Air temperature data used in the period 2011-2020 by reviewing data from the Kenten Climatology Station and the SMB II Meteorological Station. The results of this study of air temperature data from the Kenten Climatology Station and the SMB II Meteorology Station showed a difference in air temperature with the minimum ∆T of 0.42 oC, the maximum of 0.43 oC, and the daily average of 0.41 oC. This difference in air temperature has an impact on the difference in the ET rate with the average ∆ET of the Hargreaves and Samani model of 0.05 mm/day, the Blaney and Criddle model of 0.05 mm/day, the Linacre model of 0.06 mm/day, and the Kharuffa model of 0.14 mm/day. The results of this study predicted that an increase in air temperature causes an increase in the ET rate of ± 10-30%.

scholarly journals Historical and future temporal trends in the summer Urban Heat Island of Athens (Greece) 

2021 ◽  
Author(s):  
Tim van der Schriek ◽  
Konstantinos V. Varotsos ◽  
Dimitra Founda ◽  
Christos Giannakopoulos
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Temperature ◽  
Temporal Trends ◽  
Temperature Data ◽  
Climate Change Scenarios ◽  
Heat Island ◽  
Hot Days ◽  
The Future ◽  
Urban Heat

<p>Historical changes, spanning 1971–2016, in the Athens Urban Heat Island (UHI) over summer were assessed by contrasting two air temperature records from established meteorological stations in urban and rural settings. When contrasting two 20-year historical periods (1976–1995 and 1996–2015), there is a significant difference in summer UHI regimes. The stronger UHI-intensity of the second period (1996–2015) is likely linked to increased pollution and heat input. Observations suggest that the Athens summer UHI characteristics even fluctuate on multi-annual basis. Specifically, the reduction in air pollution during the Greek Economic Recession (2008-2016) probable subtly changed the UHI regime, through lowering the frequencies of extremely hot days (T<sub>max</sub> > 37 °C) and nights (T<sub>min</sub> > 26 °C).</p><p>Subsequently, we examined the future temporal trends of two different UHIs in Athens (Greece) under three climate change scenarios. A five-member regional climate model (RCM) sub-ensemble from EURO-CORDEX with a horizontal resolution of 0.11° (~12 × 12 km) simulated air temperature data, spanning the period 1976–2100, for the two station sites. Three future emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) were implanted in the simulations after 2005. The observed daily maximum and minimum air temperature data (T<sub>max</sub> and T<sub>min</sub>) from two historical UHI regimes (1976–1995 and 1996–2015, respectively) were used, separately, to bias-adjust the model simulations thus creating two sets of results.</p><p>This novel approach allowed us to assess future temperature developments in Athens under two different UHI intensity regimes. We found that the future frequency of days with T<sub>max</sub> > 37 °C in Athens was only different from rural background values under the intense UHI regime. There is a large increase in the future frequency of nights with T<sub>min</sub> > 26 °C in Athens under all UHI regimes and climate scenarios; these events remain comparatively rare at the rural site.</p><p>This study shows a large urban amplification of the frequency of extremely hot days and nights which is likely forced by increasing air pollution and heat input. Consequently, local mitigation policies aimed at decreasing urban atmospheric pollution are expected to be also effective in reducing urban temperatures during extreme heat events in Athens under all future climate change scenarios. Such policies therefore have multiple benefits, including: reducing electricity (energy) needs, improving living quality and decreasing heat- and pollution related illnesses/deaths.</p><p> </p>

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

2021 ◽  
Author(s):  
Yonghong Hu ◽  
Gensuo Jia ◽  
Jinlong Ai ◽  
Yong Zhang ◽  
Meiting Hou ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Urban Climate ◽  
Global Scale ◽  
Heat Island ◽  
Climate Effect ◽  
Urban Heat ◽  
The Difference ◽  
Temperature Records ◽  
Dynamic Time ◽  
Selection Of

Abstract Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and the key issues are how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing and validated by remote sensing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distribution information. The analysis for station pairs with high similarity could provide appropriate classification for typical urban stations (FT, SY, HD, TZ, CY, CP, MTG, BJ, SJS, DX, FS) and typical rural stations (ZT, SDZ, XYL) in Beijing. We also found that some traditional rural stations can’t represent temperature variation in rural surface because of their surrounding environments highly modified by urbanization process in last decades, and they may underestimate the urban climate effect by 1.24℃. DTW algorithm is simple in analysis and application for temperature sequences, and has good potentials in improving urban heat island estimation in regional or global scale by selecting more appropriate temperature records.

scholarly journals Spatiotemporal Changes in the Built Environment Characteristics and Urban Heat Island Effect in a Medium-Sized City, Chiayi City, Taiwan

2020 ◽  
Vol 12 (1) ◽  
pp. 365 ◽  
Author(s):  
Jou-Man Huang ◽  
Heui-Yung Chang ◽  
Yu-Su Wang
Keyword(s):  
Population Density ◽  
Built Environment ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Area Ratio ◽  
Heat Island ◽  
Downtown Area ◽  
Green Area ◽  
Spatiotemporal Changes ◽  
Urban Heat

This study took Chiayi City—a tropical, medium-sized city—as an example to investigate the urban heat island (UHI) effect using mobile transects and built environment characteristics in 2018. The findings were compared to those from a study in 1999 to explore the spatiotemporal changes in the built environment characteristics and UHI phenomenon. The result for the UHI intensity (UHII) during the day was approximately 4.1 °C and at midnight was approximately 2.5 °C. Compared with the survey in 1999, the UHII during the day increased by approximately 1.3 °C, and the UHII at midnight decreased by approximately 1.2 °C. The trend of the spatial distribution of the increasing artificial area ratio (AAR) proved the importance of urban land use expansion on UHI. The results of the air temperature survey were incorporated with the nesting space in GIS to explore the role of built environment characteristics in UHI effects. The higher the population density (PD) and artificial area ratio (AAR) were, the closer the proximity was to the downtown area. The green area ratio (GAR) was less than 0.2 in the downtown area and increased closer to the rural areas. The built environment factors were analyzed in detail and correlated with the UHI effect. The air temperature in the daytime increased with the population density (PD) and artificial area ratio (AAR), but decreased with the green area ratio (GAR) (r = ±0.3–0.4). The result showed good agreement with previous studies.

Reflective Parking Lots for Microscale Urban Heat Island Mitigation

2020 ◽  
Vol 2674 (8) ◽  
pp. 663-671
Author(s):  
Sushobhan Sen ◽  
Juan Pablo Ricardo Mendèz-Ruiz Fernandèz ◽  
Jeffery Roesler
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Significant Proportion ◽  
Climate Index ◽  
Heat Island ◽  
Universal Thermal Climate Index ◽  
Parking Lots ◽  
Parking Lot ◽  
Thermal Climate ◽  
Urban Heat

Paved surfaces, especially parking lots, occupy a significant proportion of the horizontal surface area in cities. The low albedo of many of these parking lots contribute to the urban heat island (UHI) and affect the local microclimate around them. The albedo of six parking lots in Champaign-Urbana, U.S., was measured using a ground-based albedometer and was found to vary between 0.18 and 0.28, with a statistically significant variation in albedo at different points within each parking lot. The numerical model ENVI-met was then employed to model the microclimate around one of these lots to examine the potential of increasing its albedo to mitigate UHI. The higher albedo decreased the air temperature over the parking lot by about 1°C. Furthermore, the Universal Thermal Climate Index (UTCI), which combines the effects of air temperature, reflected radiation, wind speed, clothing, metabolism, and humidity, demonstrated that increasing the albedo of the parking lot could improve overall pedestrian thermal comfort and even eliminate it during several hours of the day, and thus mitigate the UHI effect.

scholarly journals Spatial and Temporal Structure of the Urban Heat Island in Seoul

2005 ◽  
Vol 44 (5) ◽  
pp. 591-605 ◽  
Author(s):  
Yeon-Hee Kim ◽  
Jong-Jin Baik
Keyword(s):  
Urban Heat Island ◽  
Temporal Structure ◽  
Temperature Data ◽  
Total Variance ◽  
Anthropogenic Heat ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Near Surface ◽  
Average Maximum ◽  
Urban Heat

Abstract The spatial and temporal structure of the urban heat island in Seoul, Korea, is investigated using near-surface temperature data measured at 31 automatic weather stations (AWSs) in the Seoul metropolitan area for the 1-yr period from March 2001 to February 2002. The urban heat island in Seoul deviates considerably from an idealized, concentric heat island structure, mainly because of the location of the main commercial and industrial sectors and the local topography. Relatively warm regions extend in the east–west direction and relatively cold regions are located near the northern and southern mountains. Several warm cores are observed whose intensity, size, and location are found to vary seasonally and diurnally. Similar to previous studies, the urban heat island in Seoul is stronger in the nighttime than in the daytime and decreases with increasing wind speed and cloud cover, but it is least developed in summer. The average maximum urban heat island intensity is 2.2°C over the 1-yr period and it is 3.4°C at 0300 local standard time (LST) and 0.6°C at 1500 LST. The reversed urban heat island is occasionally observed in the afternoon, but its intensity is very weak. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in the Seoul urban heat island. In the analysis using temperature data that are averaged for each hour of the 1-yr period, the first EOF explains 80.6% of the total variance and is a major diurnal mode. The second EOF, whose horizontal structure is positive in the eastern part of Seoul and is negative in the western part, explains 16.0% of the total variance. This mode is related to the land use type and the diurnal pattern of anthropogenic heat release. In the analysis using temperature data at 0300 LST, the leading four modes explain 72.4% of the total variance. The first EOF reflects that the weakest urban heat island intensity is in summer. It is found that the urban heat island in Seoul is stronger on weekdays than weekends.

scholarly journals Changes in the Water Temperature of Rivers Impacted by the Urban Heat Island: Case Study of Suceava City

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1343 ◽  
Author(s):  
Andrei-Emil Briciu ◽  
Dumitru Mihăilă ◽  
Adrian Graur ◽  
Dinu Iulian Oprea ◽  
Alin Prisăcariu ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Water Temperature ◽  
Urban Heat Island ◽  
River Water ◽  
Air Temperature ◽  
Stream Water ◽  
Heat Island ◽  
Urban Heat ◽  
The City ◽  
River Water Temperature

Cities alter the thermal regime of urban rivers in very variable ways which are not yet deciphered for the territory of Romania. The urban heat island of Suceava city was measured in 2019 and its impact on Suceava River was assessed using hourly and daily values from a network of 12 water and air monitoring stations. In 2019, Suceava River water temperature was 11.54 °C upstream of Suceava city (Mihoveni) and 11.97 °C downstream (Tişăuţi)—a 3.7% increase in the water temperature downstream. After the stream water passes through the city, the diurnal thermal profile of Suceava River water temperature shows steeper slopes and earlier moments of the maximum and minimum temperatures than upstream because of the urban heat island. In an average day, an increase of water temperature with a maximum of 0.99 °C occurred downstream, partly explained by the 2.46 °C corresponding difference between the urban floodplain and the surrounding area. The stream water diurnal cycle has been shifted towards a variation specific to that of the local air temperature. The heat exchange between Suceava River and Suceava city is bidirectional. The stream water diurnal thermal cycle is statistically more significant downstream due to the heat transfer from the city into the river. This transfer occurs partly through urban tributaries which are 1.94 °C warmer than Suceava River upstream of Suceava city. The wavelet coherence analyses and ANCOVA (analysis of covariance) prove that there are significant (0.95 confidence level) causal relationships between the changes in Suceava River water temperature downstream and the fluctuations of the urban air temperature. The complex bidirectional heat transfer and the changes in the diurnal thermal profiles are important to be analysed in other urban systems in order to decipher in more detail the observed causal relationships.

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