Urban Heat Island Modeling for Tropical Climates

By Ansar Khan, Soumendu Chatterjee and Yupeng Wang

Urban Heat Island Modeling for Tropical Climates takes into account the different urban physics in tropical environments, presenting a way of UHI scaling for tropical cities. Topics include measuring, modeling and proper mitigation strategies, which account for the surface energy balance of tropics. Tropical cities are more susceptible to the effects of projected global warming because of conditions in tropical climates and the rapid growth of so many cities in this zone. The need for research on measuring, modeling and mitigation of UHI effects in tropical cities is of growing importance.

This book walks through the basics of Urban Heat Islands, including causes, measurement and analysis then expands upon issues as well as the novel techniques that can be used to address issues specific to the region.

• Reviews topics related to understanding the fundamentals of modeling and impacts of urban heat islands
• Covers many techniques, from remote sensing, to numerical modeling and then applying them to urban climate studies in general, and in tropical cities
• Describes the scaling of urban heat islands based on long-term seasonal thermal parameters as feature-based classification systems using a probabilistic and fuzzy logic approach, unlike local climate zones (LCZs)

• Language: English
• Publisher: Elsevier Science
• Release date: Nov 17, 2020
• ISBN: 9780128225585

Ansar Khan

Dr. Khan is an Assistant Professor of Geography at Lalbaba College, Howrah, India. His research incorporates simulation and numerical modeling of global climatic events including urban climate and microclimatic variation using the state-of-the-art Weather Research and Forecasting (WRF) model and Regional Climate Model (RegCM). A key focus of his research is searching for appropriate mitigation strategies and technologies to decrease the overheating of tropical urban areas while also decreasing energy consumption and protecting health. He has commendable expertise in handling data and implementing models in many software platforms. His research contribution has been recognized by some leading researchers in the domain of urban climate research.

Book preview

Urban Heat Island Modeling for Tropical Climates

Ansar Khan

Department of Geography, Lalbaba College, University of Calcutta, India

Soumendu Chatterjee

Department of Geography, Presidency University, India

Yupeng Weng

School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, China

Table of Contents

Cover image

Title page

Copyright

Dedication

Foreword

Acknowledgments

Summary

Abbreviations and Acronyms

1. Context and background of urban heat island

1.1. Introduction

1.2. Describing the review of literature

1.3. Indian scenario

1.4. Case study: tropical Kolkata

1.5. Conceptual framework of urban heat island

1.6. Purpose of this book

1.7. Research premises

1.8. Book design and methodological overview

1.9. Limitations

1.10. Organization of the book

1.11. Conclusions

2. Characterizing thermal fields and evaluating UHI effects

2.1. Introduction

2.2. Approaches to LST modeling

2.3. Database and image preprocessing

2.4. Retrieval of land surface temperature

2.5. Spatial pattern of thermal fields over time

2.6. Dynamics of thermal fields and urban heat island evolution

2.7. Conclusions

3. UHI drivers and mapping the urban thermal environment

3.1. Introduction

3.2. Urban heat island drivers and preparation of spatial database

3.3. Factor-based urban heat island modeling

3.4. Model evaluation, assessment, and performance

3.5. Appraisal of the final urban heat island map

3.6. Contribution of built-up areas to urban heat island effects

3.7. Conclusions

4. Scaling UHI and microclimate environment

4.1. Introduction

4.2. Urban heat island and microclimate environment

4.3. Beta distribution and threshold selection for fuzzy logic application

4.4. Fractal net evolution approach for scaling urban heat island

4.5. Scaling of urban heat island and microclimate

4.6. Mean center and standard deviational ellipse of urban heat island

4.7. Building of local climate zone for urban heat island validation

4.8. Thermal heterogeneity of local climate zone classes

4.9. Conclusions

5. WRF/UCM simulation for city-scale UHI modeling

5.1. Introduction

5.2. Weather research and forecasting/urban canopy model simulations for urban heat island modeling

5.3. City scale urban heat island simulation

5.4. Conclusions

6. Simulating microscale thermal interactions using ENVI-met climate model

6.1. Introduction

6.2. Surface atmosphere thermal interactions

6.3. Microthermal simulation

6.4. Simulation setup

6.5. Model evaluation and validation

6.6. Conclusions

7. Future research for tropical UHI

7.1. Introductions

7.2. Tropical perspectives of urban heat island modeling and mitigation

7.3. Needs for tropical urban heat island research

7.4. Future tasks for tropical urban heat island investigations

7.5. Conclusions

Glossary terms

Appendices

Author index

Subject index

Copyright

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Dedication

Dedicated to our parents, who are the first teacher in life.

Foreword

Currently, over half of the world's population lives in urban areas. By midcentury, it is estimated that this number grows to 70%. Urbanization causes urban heat island (UHI) where the city is warmer than suburban rural areas. As the urban areas grow in both size and density, the intensity of the UHI becomes more pronounced. In winter, UHIs would help to slightly decrease heating in buildings. However, in summer, heat islands significantly increase building cooling loads, cause discomfort both indoor and outdoor, worsen ambient air quality, and increase morbidity and mortality. On top of that, with global warming, the urban dwellers experience the cumulative effects of both UHI and increasing temperatures because of global warming. Understanding regional impacts of UHI is crucial to developing region-specific mitigation measure to counter the cumulative effects of UHI and global warming.

Cities can also be the best laboratories for researching the impacts of global climate change since much of the climate change risk to human is concentrated in urban areas, particularly the cities in tropical regions that are likely to see the strongest impacts from climate change. However, research on the augmentation of rapid climate change by local urban warming remains weak in tropics. Much of the climate agencies overlooked the role of tropical urban areas as both a forcing factor and key stakeholder in managing climate risk and crisis.

The tropical cities are more susceptible to the effects of the projected global warming because they are already located in the hot and humid climates and growing at rapid rates. The significance of research on modeling of UHI effects in tropical cities has increased significantly over the past 15 years. The Intergovernmental Panel on Climate Change (IPCC, 2018) specifically names Kolkata and Karachi in a new report that these two cities could expect annual conditions equivalent to their 2015 heat waves if the global warming temperatures cross the 2°C mark. The temperature rise in some Indian metropolitan areas in the past 150 years is estimated in Delhi by 1°C, Mumbai 0.7°C, Kolkata 1.2°C, and Chennai 0.6°C.

A key difficulty in untangling the tropical urban warming from global warming–induced climate change are the computational and parametric challenges associated with representing urban areas in high-resolution climate models without modeling the urban areas themselves, a technique not without problems. The insufficiency of data sets (necessary for model parameterization) that are captured for the fast-expanding tropical cities (as most of cities in tropics are located in the developing countries) is a major hindrance in conducting the model-based research. Moreover, the rapid and haphazard process of urban expansion in the developing countries and the fast-changing surface characters by means of concretization adds further uncertainty and challenges in such modeling. Hence, the dynamics of land use and land cover (LULC) in the cities of developing countries needs to be fully characterized while modeling the UHI effects.

The tropical cities have different surface energy balance from those cities of temperate regions. Firstly, the predominance of direct solar radiation in net all-wave radiation because of high angle of the sun over the tropics induces excess heating of the urban structures in contrast to the midlatitudes. Secondly, during the dry season, the tropical areas receive greater amount of net radiation and the latent heat transfer remains considerably higher than the sensible heat transfer, while the conditions reverse during the rainy season. This needs to be addressed through modeling strategies.

Urban Heat Island Modelling for Tropical Climates is a full synthesis of modern scientific and applied research on UHI phenomena in tropical urban climate from its formation to mitigation. This book begins with an outline of constituents of urban thermal environment and their relation to surface energy balance. It develops a comprehensive concept of tropical UHI and related microthermal interaction in various building environments. It explains the physical principles governing the formation of distinct urban microclimates, and it then illustrates how this knowledge can be applied to mitigate the undesirable consequences of swift and haphazard urban development and help create more sustainable and resilient cities. With urban climate science, now a fully fledged growing field, this timely book fulfills the need to bring together the disparate parts of urban climate research in tropical cities into a coherent framework. It is a valuable resource for students, researchers, and policy-makers in the fields of urban climate, urban architecture and planning, environmental engineering, urban design, and redevelopment.

The key features and contents in the book that will be most valuable to the readers include the following:

✓ Discusses various measurement techniques that are employed, i.e., in situ micrometeorological station for monitoring, thermal remote sensing for mapping, scaling urban thermal field, mesoscale urban model simulation for city-scale UHI modeling and forecast.

✓ Scales UHI on the basis of long-term seasonal thermal parameters using probabilistic and high-level machine learning techniques.

✓ Examines the commonly used UHI mitigation strategies in tropical context. The absence of mitigation strategies for tropical cities has shown a dilemma in implementing climate-sensitive and water-sensitive urban planning (i.e., considering energy demand, thermal comfort, air pollution, urban building architecture, etc.), hence identifying test resilience and effectiveness of different UHI mitigation strategies through microclimate simulations of the effects of different mitigation strategies—cool pavement, cool roof, green and cool city—for the three different urban morphologies of open low-rise, compact low-rise, and mid-rise residential areas in tropics.

✓ Analyzes the effectiveness of the mitigation strategies in improving thermal discomfort (indoor and outdoor) in different building environments and the role of vegetation in determining surface energy balance in tropical cities.

I enjoyed reading this book. I hope you do too.

Hashem Akbari, Heat Island Group, United States

Acknowledgments

The authors wish to acknowledge and thank all those bodies and individuals who so generously contributed in any way to the formation of this book. Every effort has been made to trace all the copyrighted materials. If any have been inadvertently overlooked, the publisher will be pleased to make the necessary arrangements at the first opportunity.

Special mention goes to Prof. Hashem Akbari, Heat Islands Group, United States. He has supported in every possible way since the beginning of our book. His fruitful comments and insightful suggestions have been a crucial formative influence on the present shape of the book. His critical and careful reading of our writing has saved from a lot of errors. Without his guidance and encouragement, our book would have never come out in the present form. We have seen in him an unpretentious and devoted knowledge. Furthermore, it has been a memorable and enjoyable experience for the work with him.

Similar profound gratitude goes to Prof. Manju Mohan, Indian Institute of Technology, Delhi, who has been truly a dedicated mentor. We are particularly indebted to Prof. Chandana Mitra, Auburn University, Alabama, for her consistent support in our field work and for her support when so generously hosting us in providing information. We have very fond memories of our time there.

We convey our truthful thank to Prof. Hiroyuki Kusaka and Dr. Doan Quang Van of University of Tsukuba, Japan; Dr. Iain Douglas Stewart from University of Toronto, Canada; and Dr. Saad Saleem Bhatti of University of Liverpool, United Kingdom for their valuable comments and suggestions, which have enriched quality of the book substantially.

Our book has been an amazing experience, and we thank Prof. Walter Leal Filho, Hamburg University of Applied Sciences, Germany, not only for his tremendous academic support but also for giving us so many wonderful opportunities.

We are also immensely appreciative to Dr. Ali Gholizadeh Touchaei, Concordia University, Canada, especially for sharing his software expertise so willingly and for being so dedicated to his role as secondary supervisor.

Our beloved student, Mr. Apurba Dinda, Presidency University, Kolkata, has extended his all possible support in a very special way, and we gained a lot from Mr. Dinda, through his personal and scholarly interactions and his suggestions at various points of our book journey.

We are also appreciative to academic colleagues Dr. Sk Mithun, Haldia Government College, Haldia; Dr. Subrata Jana, Belda College, Belda; Mr. Nityananda Sar, North-eastern Hill University, Shillong; and Mr. Samiran Khorat, University of Calcutta, Kolkata, for their support and patience during final editing of the book.

Thanks also go to our parents and teachers, who have encouraged and helped at every stage of personal and academic life, and longed to see this achievement come true. They are the most important people in our world, and we dedicate this book them.

Last, but not least, we deeply appreciate the good humor, forbearance, and patience of our family members who may have felt some degree of neglect as we focused so much of energy, time, and attention on this labor of love.

Ansar Khan

Soumendu Chatterjee

Yupeng Wang

Summary

The study of urban heat island (UHI) has a very long history of about 187 years, and researches had been carried out primarily in temperate climate. However, the nature, pattern, and trend of UHI phenomena in tropical climate are substantially different from temperate climate in terms of surface energy balance, rate and process of urbanization, massive land use and land cover (LULC) conversion, building geometry, and other spatiotemporal driving factors.Thus, the modification and alteration of urban environment through urbanization and LULC conversion can have an exaggerated impact on urban climate. A regional climate phenomenon known as the UHI effects, measured by the near-surface air temperature differences between urban areas and adjacent rural surrounds, is a climate consequence of rapid and haphazard urbanization which can be evaluated by descriptive or empirical approaches. This is a first tropical UHI study for the 300-year historic megapolis of Kolkata, whose urban climate has been changed dramatically in the past 20 years. Hence, an effort has been made to quantify the UHI and its microclimate variation for the Kolkata metropolitan area (KMA) using in situ observed data, thermal and optical remote sensing, mesoscale cross-atmospheric weather research and forecasting (WRF) model coupled with urban canopy model (WRF/UCM) system and ENVI-met microclimate model. The available studies on UHI effects in KMA are awfully trivial and insufficient, while most of UHI researches mainly cover the temperate continental realms rather than the tropical cities. As we believe, this is a substantial contribution for UHI research in KMA, and results could be adopted for UHI mitigations ensure sustainability of cities and urban society. Chapter 1 comprises review and evaluation of scientific UHI literatures, background and statement of the UHI research problem, development of conceptual framework, purposes of study, significance of the study, research questions and assumption, limitation and scope of the study, and organization and overview of full works. Chapter 2 characterizes thermal fields and evolution of UHI phenomena in KMA region using satellite remote sensing (SRS) data. Chapter 3 maps the UHI drivers and urban thermal heterogeneity environment using satellite data with machine learning algorithms. Chapter 4 scales the UHI and microclimate environment using long-term thermal parameters following fractal network evolution approach (FNEA). Chapter 5 simulates the city-scale UHI phenomena under cold synoptic conditions using mesoscale cross-atmospheric WRF/UCM system to understand the diurnal UHI phenomena. Chapter 6 simulates the microthermal interactions in urban typical building environments with five mitigation models (base, cool pavements, cool roof, green, and cool city) designed to combat the UHI effects. Finally, Chapter 7 looks into summary of findings and its justification and integrity. It also highlights the major contributions, drawbacks, and future line of UHI research in tropical cities.

Abbreviations and Acronyms

AHP   Analytical hierarchy process

AUC   Area under curve

AVHHR   Advanced very-high-resolution radiometer

BAEM   Built-up area extraction method

BEP   Building environment parameterization

CAD   Computer-aided design

CBD   Central business district

CFD   Computational fluid dynamics

CI   Consistency index

CR   Consistency ratio

DFPS   Double-window flexible pace search

DMSP   Defence Meteorological Satellite Programme

DNs   Digital numbers

EMB   Eastern metropolitan bypass

ETM   Enhanced thematic mapper

FLAASH   Fast line-of-sight atmospheric analysis of hypercubes

FN   False negative

FNEA   Fractal net evolution approach

FP   False positive

GCPs   Ground control points

GE   Google Earth

GFS   Global forecast system

GIS   Geographic information system

GPS   Global positioning system

GRBF   Gaussian radial basis function

HMC   Howrah Municipal Corporation

IDW   Inverse distance weight

ILWIS   Integrated land and water information system

IMW   Improved monowindow

IST   Indian standard time

KC   Kappa coefficient

KLR   Kernel logistic regression

KMA   Kolkata metropolitan area

KMC   Kolkata Municipal Corporation

LCZ   Local climate zone

LiDAR   Laser-illuminated detection and ranging

LSI   Landscape shape index

LST   Land surface temperature

LULC   Land use and land cover

MLA   Maximum likelihood algorithm

MODIS   Moderate-resolution imaging spectroradiometer

MSS   Multispectral scanner

MWA   Monowindow algorithm

NASA   National Aeronautics and Space Administration

NATMO   National Atlas and Thematic Mapping Organization

NDBI   Normalized difference built-up index

NDVI   Normalized difference vegetation index

NEERI   National Environmental Engineering Institute

NIR   Near infrared

NLL   Negative log likelihood

NOAA   National Oceanic and Atmospheric Administration

NSR   Net solar radiation

NTM   NDVI thresholds method

NWP   Numerical weather prediction

OA   Overall accuracy

OCSVM   One-class support vector machine

OLI   Operational land imager

OLS   Operational land satellite

OSM   OpenStreetMap

PCA   Principal component analysis

PD   Patch density

PDF   Probability density function

PET   Physiologically equivalent temperature

RMSE   Root mean square error

ROC   Receiver operating characteristic

RS   Remote sensing

RVFS   Rapid visual field survey

SAA   Spatial agglomeration analysis

SAGA   System for automated geoscientific analyses

SLEA   Stepwise land-class elimination

SMCE   Spatial multicriteria evaluation

SRS   Satellite remote sensing

SVF   Sky view factor

SVM   Support vector machine

TIR   Thermal infrared

TIRS   Thermal infrared sensor

TM   Thematic mapper

TN   True negative

ToA   Top of atmosphere

TP   True positive

TRP   Thermal radiative power

UAs   Urban agglomerations

UBL   Urban boundary layer

UCE   Urban canyon effect

UCL   Urban canopy layer

UCM   Urban canopy model

UHIER   Urban heat island effect ratio

UHII   Urban heat island intensity

UHIs   Urban heat islands

USGS   United States Geological Survey

UTM   Universal Transverse Mercator

WMO   World Meteorological Organization

WRF   Weather research and forecasting

Mathematical Notations

   Anthropogenic energy release

   Advection

   Atmospheric transmittance

   At-satellite observed radiance

   Brightness temperature

   Coefficient of determination

   Covariance matrix of class

   DN value of target pixel

   Elements of matrix

   Fractional vegetation cover

   Fuzzy membership function

   Geometrical factor

   Ground emissivity

   Ground radiance in thermal band

   Heat storage

   Kernel functions

   Lagrange multipliers

   Largest or principal eigen

   Maximum DN value

   Maximum LST

   Mean center

   Mean effective temperature of the atmosphere

   Mean vector

   Minimum LST

   Net advection

   Net solar radiation

   Perimeter of image object

   Rural–urban temperature difference

   Soil heat flux

   Standard deviation

   Surface roughness term

   Turbulent fluxes of latent heat

   Turbulent fluxes of sensible heat

   Upwelling atmospheric radiance

   Vector of model parameters

   Wall height

   Air density

   At-sensor spectral radiance

   Downwelling atmospheric radiance

   Saturation mixing ratio

   Getis-Ord general G index

   Land surface temperature

   Projection factor

   Heat from buildings

   Human metabolic heat

   Heat from vehicle emissions

   Segmentation function

   Temperature parameters

   Wall view factors

1: Context and background of urban heat island

Abstract

Modification and alteration of the urban environment through land use and land cover conversions resulting from expansion of the impervious surfaces can have exaggerated impacts on the urban climate. The regional climate phenomenon known as urban heat island (UHI) effect, a measure of the near-surface air temperature differential between urban areas and their rural surrounds, has recently drawn substantial attention of the urban climatologists, particularly in the context of tropical cities. An effort has been made in this book to quantify the UHI effects and microclimate variations within the tropical metropolis of Kolkata, India, along with analyses of factors and mechanism of UHI development, and evaluation of strategies to ameliorate the effects of extra heat accumulation in urban atmosphere. This research uses thermal remote sensing images to capture land surface temperature, the weather research and forecasting (WRF) model coupled with urban canopy model (WRF/UCM) system to simulate meso-scale cross-atmospheric conditions for diurnal cycles, and the ENVI-met microclimate modeling system to evaluate the available strategies for UHI mitigation. There is a plethora of literature on UHI, the majority of which is devoted to the studies in temperate continental realms rather than in tropical cities, while such accounts on the Kolkata metropolitan area (KMA) are awfully trivial and insufficient. This is the first documentary study for the 300-year-old historic megapolis of Kolkata, India, where urban climate has been reported to have undergone significant changes during the past 20  years. This chapter critically reviews and systematically evaluates majority of the scientific contributions toward understanding and mitigating UHI phenomena and also includes the background, significance, conceptual framework, objectives, and overview of the whole work. This study examines some major aspects of the UHI phenomena in the KMA region aiming at advancing the knowledge required for thermal management of tropical cities.

Keywords

Kolkata metropolitan area; Land use land cover; Microclimate variation; Tropical climates; Urban heat island; Urbanization

1.1. Introduction

In recent decades, the global human community is experiencing two formidable challenges, one resulting from environmental processes and the other from socioeconomic processes: (a) the global climate change that has become more adverse and is leading to occurrences of extreme weather events, e.g., heat wave in urban areas, severe weather hazards, flood, and so on, and (b) the unprecedented growth of population along with the increased rate of urbanization (Stocker, Qin, Plattner, Tignor, Allen et al., 2013). The impacts of global climate change and its signatures have been received by every parcel of the earth-atmospheric system. According to United Nations Development Programme (UNDP, 2007), about 60% of the increase in global population by 2035 is expected to take place in the global cities. Thus, the collective effects of climate change (regional or global) and accelerated urbanization, accompanied with infrastructural development, might enhance the vulnerability of urban environment and compromise human comfort of the urban dwellers. It is, therefore, urgent to understand the projected effects of climate dilemma and rapid development on the urban environmental problems (Chen, Kusaka, Bornstein, Ching, Grimmond et al., 2011). Urbanization may have offered many advantages to facilitate human lives, but at the same time, this process has also played multiple roles in prompting urban climate change. Accordingly, the danger of global climate change and its effects, particularly in augmenting urban heat island (UHI) effects, have been a growing concern for the climatologists (Doan & Kusaka, 2015). A large number of scientific studies have been carried out across the globe at different geographical scales (global, regional, or local) to understand the nature, and trend of temperature change in urban areas during the recent past and comprehensive inferences could be drawn about such thermal evolution at the city scale (Khan & Chatterjee, 2016). The UHI is characterized by an island of warmer near-surface air temperature centered on urbanized landscapes and surrounded by progressively cooler air over suburban or rural areas. A number of studies have claimed that the nature, pattern, intensity, and persistence of UHI are directly related to city size, building structure, urban surface, anthropogenic heat release, topography, city orientation, and local meteorological conditions (Oke, 1982). In this regard, an urban built environment with lower albedo absorbs about 80% of the total incoming solar radiation that creates the warmer condition in urban areas compared with their rural surroundings (Touchaei & Wang, 2015).

Researches have been carried out on UHI in different climate areas to explore the mechanism of UHI phenomena and impact of urban development on UHI effects (Doan & Kusaka, 2015). The most of the studies have focused on the urban modification of local climate, i.e., UHI effects as the fallout of land use and land cover (LULC) change in urban scale. It implies that temperature tends to be greater in urban core than its suburban natural surroundings at nighttime (Arnfield, 2003; Landsberg, 1981; Oke & Cleugh, 1987; Parker, 2010). In urban areas, main drivers contributing to UHI formation have been reported by Chen, Yang, and Zhu (2014). They have identified that the physics of surface–atmosphere interactions have undergone changes due to replacement of natural surfaces by urban cover. This may have led to alteration of urban albedo due to (1) different thermal conductivity and thermal emissivity of urban materials than naturals, (2) release of anthropogenic heat from different human activities, (3) decrease of surface evapotranspiration in urban surface due to concretization, and (4) changes in flow character and attributes of the near-surface atmospheric processes due to low sky view factor (SVF), complex urban geometrical structures, complex street patterns, and tall building architectures. The relative contributions of UHI drivers that have triggered changes in the physics of urban atmosphere including rapid expansion of urban land, LULC change, and anthropogenic heat release to the UHI cannot be distinguished from the available meteorological data. Thus, modeling studies are gradually gaining academic importance in quantifying the impact of the haphazard urbanization process on urban climate. Model-based simulations are often used in discerning the shape, pattern, and persistence of UHI phenomena and associated microclimate variation over the geospatial and morphological units of cities.

Since the 1960s, the dawn of resolution-specific (coarse and fine) earth-monitoring satellites, satellite remote sensing (SRS) has been extensively utilized to retrieve land surface temperature (LST) data for the purpose of quantification and modeling of UHI in cities. However, thermal infrared SRS data have brought substantial progress in UHI research (Li & Yin, 2013; Liu & Zhang, 2011). Compared with the conventional in situ observed meteorological data, thermal SRS data are more effective in detecting and monitoring the dynamics of thermal heterogeneity of urban environment. The results from studies based on analyses of thermal remote sensing (TRS) data also confirm that atmospheric conditions and the building environment, i.e., building geometry, building density, building materials, paved materials, and abundance of green surface are the major contributors to the UHI phenomena (Weng, 2009).

The advancement in sciences related to numerical weather prediction (NWP) has created opportunities for employing a large number of cross-scale atmospheric modeling systems coupled with human response systems to simulate and predict the urban climates from regional to building scales (Chen et al., 2011; Kusaka & Kimura, 2004; Kusaka, Kondo, Kikegawa, & Kimura, 2001). In this regard, the weather research and forecasting (WRF) model coupled with urban canopy model (WRF/UCM) is a useful tool for quantifying the urban climate and UHI effects. It executes with a grid spacing of 0.5–1  km vis-a-vis at the meso-scale (105  m) conventional for NWP models. The WRF/UCM precisely captures the elements of urban climate such as wind flow, air temperature, and moisture content in the boundary layer and their combined effects on meso-scale motions in the urban atmosphere (Adachi, Kimura, Kusaka, Duda, Yamagata et al., 2014; Kusaka, Kondo, Kikegawa, & Kimura et al., 2001).

The applicability of NWP models in urban microclimate studies vary largely with physical structures and model resolutions (Taleghani, Kleerekoper, Tenpierik, & van den Dobbelsteen, 2015). It is imperative and crucial for a UHI study to generalize the physics and the scale of the microclimate under investigation. Introduction of the ENVI-met model has brought opportunities for capturing different climatological processes operative at the building level and storing data outputs on meteorological variables within a daily cycle (Bruse & Fleer, 1998). The ENVI-met is a nonstationary and nonhydrostatic model that prognoses all the exchanges such as wind circulation, turbulence, and fluxes of solar radiation, air temperature, and air humidity in an urban microclimate environment (Wang & Akbari, 2015). The urban physics is an important component of this microclimate model that simulates the urban microclimate on a well-sustained physical basis (laws of thermodynamics and fluid dynamics). It efficiently combines complex and diverse processes and interactions that vary from human body to city scale. Thus, mitigation strategies such as urban ventilation, material alteration, improvement of thermal comfort, and building energy demand are being widely researched using the model in different UHI environments (Santamouris, 2014a; Sun & Augenbroe, 2014; Mirzaei & Haghighat, 2012). The mitigation studies have also been conducted on urban areas by different scientific communities such as building scientists, architects, urban climatologists, meteorologists, and geographers.

1.2. Describing the review of literature

The urban areas set up a different feedback mechanism in the earth-atmospheric system that modifies the urban climate across cities. The study of urban climate has a long history that could produce well-developed stream literatures on UHI. In 1818, the first scientific measurements of temperature were recorded by Luke Howard for London and areas surrounding the city. This historic study has concluded that an urban area is distinctly warmer than its rural surrounds. Since Howard's temperature observations, UHI research in hundreds of cities across the globe have been published in thousands of scholarly documents. The studies cover major urban areas in the continental realms of Europe, North America, and Asia and have been unrivaled in making significant contributions to urban climatology of great geographical interests. Such observations and interpretations stimulated that urban meteorology is no more a less trodden compared with other disciplines, and now it is not lacking the regular and coherent form of science. The current UHI studies consistently confirm that the cities create its own distinct climate in urban areas being impacted by the changes in LULC and urban hydrology, and the resultant environmental impacts are gradually minimized away from the urban core to the surrounding rural areas.

Understanding the UHI effect and its feedback demands an integrated approach engaging different fields of scientific disciplines. Accordingly, UHI literature is immensely heterogeneous for being composed of contributions that came from researchers in