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Counteracting Urban Heat Island Effects in a Global Climate Change Scenario

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Nguyễn Gia Hào

Academic year: 2023

Chia sẻ "Counteracting Urban Heat Island Effects in a Global Climate Change Scenario"

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Greenhouse Effect - The trapping and accumulation of heat in the lower atmosphere near the planet's surface. Member States - The EU-27 countries are divided into new Member States (NMS) and old Member States (OMS) according to the date of their accession to the European Union (EU).

Contents

The Urban Heat Island: Evidence, Measures and Tools

Pilot Actions in European Cities

Editor Bios and Contributors

Editor Bios

Contributors

Denis Maragno Department of Design and Planning in Complex Environments, IUAV University of Venice, Venice, Italy. Davide Martinucci Department of Design and Planning in Complex Environments, IUAV University of Venice, Venice, Italy.

Panel A depicts the current state of Olga Hospital (also with green roofs for each building with a flat roof), Panel B the park scenario, in panel C a building is replaced by a small pond (shallow water) and in panel C the no. . These building structures are part of the development outline plan and are not mandatory for the potential.

Fig. 2.1   Process chain for measurement project planning,
Fig. 2.1 Process chain for measurement project planning,

List of Tables

Thermal comfort indices refer to a person with summer clothes (0.5 clo) and light activity (80 W above . basal metabolism) (Google Earth-RayMan). Thermal comfort indices refer to a person with summer clothes (0.5 clo) and a low level of activity (80 W above basal metabolism).

Table 3.8   Information regarding the selected
Table 3.8 Information regarding the selected

Planning and Climate Change: Concepts, Approaches, Design

Introduction

Climate Change and the City: A Complex Relationship From Sustainability to Climate Change: Towards a New

Currently, the international scientific community recognizes climate change as a major challenge for the development and sustainability of the twenty-first century (UNDP OECD 2009; World Bank 2012; UN-Habitat 2011a, b), for the revitalization of urban areas, and recognizes two main aspects: (i) the difficulties in achieving common consensuses to reduce greenhouse gas (GHG) emissions in international negotiations and (ii) a growing international consensus on the urgent need to formulate climate change adaptation strategies at national, regional and local levels (Musco and Magni 2014). The year 2020 is not a suitable time frame for solving problems related to the effects of climate change.

Towards Urban Adaptation

What territorial and urban planning needs to do is pay more attention to the physical and social realities of the places, go beyond looking at the individual events and embrace the extreme complexity of each territory and city. It seems obvious that all these issues require a reconsideration of the form and use of the territory and the city through the integrated improvement of environmental components, to counteract the effects of the CC while rethinking the contemporary city through to look for a sustainable balance.

Conclusions: Building Urban Adaptation – The Main Role of Planners

Contribution of working group III to the fourth assessment report of the Intergovernmental Panel on Climate Change. Contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change.

The Urban Heat Island: Evidence, Measures and Tools

General Introduction

The work package WP 3 collects technical and scientific definitions and state of the art on the urban heat island phenomenon and further presents strategies to simulate future scenarios using modeling systems. In the following, a wide range of different tools and studies carried out by the project partners in the course of the activities in work package WP3 are presented.

Overview of Models and Tools

Boundary conditions from ENSEMBLE model RT2B (http://ensembles-eu.met-office.com) and REMO regional climate model (http://www.remo-rcm.de). Boundary conditions from GCM CNRM-CM5 (http://www.enes.org/models/ . earthsystem-models/cnrm-cerfacs/cnrm-cm5).

Case Studies

  • Projections of Climate Trends for Urban Areas in Central Europe Using WRF
    • Introduction
    • Data and Methods
    • Results
    • Conclusion
  • Human-Biometeorological Assessment of Changing Conditions in the Region of Stuttgart in the Twenty-First
    • Data and Methods
    • Results
  • Urban Climate Modelling with SURFEX/TEB at the Hungarian Meteorological Service
    • Introduction
    • Methodology
    • Results
    • Summary
  • Regional Climate Modelling Considering the Effect of Urbanization on Climate Change in Central Europe
    • Introduction
    • Background of Modelling for Europe
    • Urban Parameterization and Experimental Setup
    • Results
    • Conclusions
  • Statistical Downscaling Techniques Applied
    • Introduction
    • Data and Methods
    • Results
    • Conclusion

Based on the results, SURFEX captures the main characteristics of urban climatology: temperature exceedance i. For the region of Central Europe, we investigate the impact of the urban environment using its introduction into the regional climate model.

Fig. 1.1  WRF nested domain with 7 km horizontal resolution, showing USGS 2006 classified  land use, projected on a LAT/LON grid with the coordinate system WGS 84 Zone 32 N
Fig. 1.1 WRF nested domain with 7 km horizontal resolution, showing USGS 2006 classified land use, projected on a LAT/LON grid with the coordinate system WGS 84 Zone 32 N

Urban Heat Island Gold Standard and Urban Heat Island Atlas

Gold Standard for UHI Measurements and

Introduction of The Central-European Urban Heat Island Atlas

Introduction

These statements are provided as recommendations and suggestions during the study and investigation of urban climate. Finally, these standardized approaches and guidelines are useful in making recommendations for the future deployment of urban climate networks.

Concepts

Any city operating measurement sites for UHI detection is strongly encouraged to adopt the recommendations of the Gold Standard into their measurement and evaluation systems to achieve better coverage of UHI phenomena over the city. Due to the relatively high cost and difficulty of locating equipment for fixed meteorological monitoring stations, their deployment and maintenance ultimately results in sparse data coverage for urban areas.

Fig. 2.1  Process chain for measurement project planning, management and service
Fig. 2.1 Process chain for measurement project planning, management and service

Planning a Representative Urban Climate Station Network

If the location of the sensor can be moved, it should be placed where it will sample from a single LCZ. It is recommended to avoid transitional areas when placing meteorological instruments” (Stewart and Oke, 2012, p. 1891).

Exposure of Instruments

  • Temperature
  • Humidity
  • Wind Speed and Direction
  • Precipitation
  • Solar Radiation

The ratio of these two easily distinguishable parts depends on the buildings and the width of the street. Measuring precipitation (such as rain or snow) is very sensitive to changes in airflow in the vicinity of the measurement.

Fig. 2.2  Suitable location of air temperature and humidity sensors in urban environments (Based  on WMO Guide, 2008)
Fig. 2.2 Suitable location of air temperature and humidity sensors in urban environments (Based on WMO Guide, 2008)

Measurement Programs in Urban Environments

Site Description for METADATA

The natural relief of the landscape can be ignored if it is far enough away (>1 km). If the answer is no, the relief is a natural feature of the area and is not taken into account.

Table 2.2  Accuracy requirements for surface meteorological measurements
Table 2.2 Accuracy requirements for surface meteorological measurements

Data Transmission and Data Management

An example of visualization files of the surroundings of the Twarda observation site is shown in fig. Calibration of equipment and instruments during intercomparison periods is essential to ensure the quality of the data.

Table 2.3  Example of a documentary file for the urban station shown on Fig. 2.10
Table 2.3 Example of a documentary file for the urban station shown on Fig. 2.10

The Central-European Urban Heat Island Atlas

  • UHI Atlas and its database
  • High Resolution (<0.5 km) Raster Data and Vector Data
    • Corine Land Cover
    • Land Cover/Land Use for Cities Included in Project (Urban Atlas) Format: vector data (1 : 10.000)
    • Digital Elevation Data SRTM Format: raster data (3 arc sec)

The European Urban Atlas provides reliable, high-resolution, comparable land use maps for 305 major urban zones and their surroundings (over 100,000 inhabitants as defined by the Urban Audit) for the reference year 2006. Temporal coverage: different examples for spring, summer and autumn season Spatial coverage: Budapest, Vienna, Ljubljana, Prague, Stuttgart, Venice, Warszawa Brief description of Landsat images.

Fig. 2.12  Print screen of UHI atlas
Fig. 2.12 Print screen of UHI atlas
  • MODIS LST Images
  • VIIRS Night Scene Images
  • Air Temperature (2 m Above the Ground)

Where to find the data layers: http://earthobservatory.nasa.gov Credit: NASA's Earth Observatory, NASA. Temporal coverage: selected periods for different seasons in 2011 Spatial coverage in UHI atlas: Central Europe.

The Data from the Partners

Air temperature is calculated based on MODIS land surface temperature (LST) by dr. The calculation method is presented in the paper Estimation of mean daily air temperature from MODIS LST in alpine areas written by Columbi et al.

Summary

The UHI Atlas is an example for the presentation of urban heat island phenomena and influencing factors. Simulation of the average urban heat island using 2D surface parameters: empirical modelling, verification and extension.

Urban Heat Island Phenomenon and Related Mitigation Measures in Central Europe

Introduction

Furthermore, the intensity of urban heat islands is believed to increase proportionally to the size and population of the urban area (Oke 1972). To develop such alternative models, certain characteristics of the urban environment are hypothesized to influence UHI and the urban microclimate variance.

The Urban Heat Island in Central Europe

Thus, a large set of data was collected and analyzed regarding the extent of the UHI effect in multiple cities in Central Europe. Statistical relationships between the values ​​of these variables and the extent of microclimatic variance provide the basis for simple empirically based models.

Short-Term Analyses of the Observations

In Warsaw, for example, the UHI intensity level varies from about 1 K during the day to almost 7 K at night, while in Stuttgart the levels are quite stable, ranging from 1 K to 2 K. UHI pattern differences are also evident. in cumulative frequency distribution curves of Fig.

Table 3.3  Overview for the data sets used for the analysis Reference week
Table 3.3 Overview for the data sets used for the analysis Reference week

Long-Term Analyses of the Observations

Modelling Efforts

3.8, 3.9 and 3.10 show the average hourly temperature during a reference summer day in Vienna, Padua and Warsaw for the base case and three mitigation scenarios. Figure and 3.13 show the corresponding temperature changes between the base case and applicable mitigation scenarios during a reference summer day.

Fig. 3.7  Long-term UHI intensity trend over a period of 30 years3  Methodologies for UHI Analysis
Fig. 3.7 Long-term UHI intensity trend over a period of 30 years3 Methodologies for UHI Analysis

A Systematic Framework for the Representation of Urban Variables

To derive the specific values ​​of the U2O variables for the selected urban areas, we used data provided by the city of Vienna in the form of a Digital Elevation Model (DEM). Once U2Os and their respective variables are derived, the existence and magnitude of the correlations between urban microclimate variance and the U2O variables are investigated.

Table 3.7  Variables to capture the surface and material properties of an U2O Surface/material properties Definition
Table 3.7  Variables to capture the surface and material properties of an U2O Surface/material properties Definition

Conclusion

Two decades of urban climate research: a review of turbulence, energy and water exchange, and the urban heat island. Canyon geometry and nocturnal urban heat island comparison of a scale model and field observations.

Relevance of Thermal Indices

Introduction

Before establishing mitigation and adaptation measures to counter the urban heat island, urban planners and officials need to understand the spatial and temporal dimensions of a city's meteorological and climatic conditions (Matzarakis et al. 2008; Ketterer and Matzarakis 2014a, b). Thus, there is a demand for the assessment and quantification of adaptation measures that improve urban climate, i.e. street morphology, different types of vegetation (Hwang et al.

Methods and Data

  • Physiologically Equivalent Temperature
  • Universal Thermal Climate Index

Thereby, the heat balance in the human body is maintained with a work metabolism of 80 W (light activity, added to the basic metabolism) and a heat resistance of clothing 0.9 clo) (Höppe 1999). The rating classes for PET (Table 4.1) are only valid for the assumed values ​​of internal heat production (80 W) and thermal resistance of the garment (0.9) (Matzarakis and Mayer 1997).

Fig. 4.1  Flowchart of the human-biometeorological assessment of the thermal environment A
Fig. 4.1 Flowchart of the human-biometeorological assessment of the thermal environment A

Exemplary Results

There are five stations in Stuttgart and all of them have been selected for the analysis (Table 4.3). In particular, the daily minimum values ​​are higher for the urban stations, compared to the Hohenheim suburban site or the Echterdingen national reference station.

Table 4.3  Location and altitude of the measurement stations Measurement station
Table 4.3 Location and altitude of the measurement stations Measurement station

Discussion and Conclusion

The physiological equivalent temperature – a universal index for biometeorological assessment of the thermal environment. Human-biometeorological quantification of the urban heat island in a city with complex topography - the case of Stuttgart.

Chapter 5

  • Introduction
  • Development of the UHI Project’s DSS
  • UHI’s Decision Support System .1 Structure
    • The Interface
    • Project Logical Framework
    • The Consultation Tool
    • The DSS
  • DSS Input
    • WP5 Mitigation and Adaptation Strategies
  • Main Acronyms

The following paragraphs describe the operation of the DSS selected within the project for UHI. The DSS link can be found on the main page of the UHI website (Fig. 5.2.

Open Access This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link to the Creative Commons license is provided and any changes made are indicated. The images or other third-party material in this chapter are included in the work's Creative Commons license, unless otherwise indicated in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the licensee to duplicate, adapt or reproduce the material.

Pilot Actions in European Cities

UHI in the Metropolitan Cluster of Bologna- Modena: Mitigation and Adaptation Strategies

Implementing Solutions for Climate Change in Urban Context

Measures to reduce the heat island effect in this regard are an excellent example of an action that fits the perspective of adaptation to climate change and its mitigation. In this chapter we want to draw a picture of the relevance of the heat island phenomenon in relation to climate change and illustrate the potential of urban planning interventions.

The Metropolitan Cluster Of Bologna-Modena .1 Urban and Environmental Framework

  • Pilot Area Identification Methodology and Description
  • UHI Phenomenon in the Urban Area of Modena and Application of Models to Simulate Mitigation
  • Experimental Environmental Quality Index to Assess the UHI’s Mitigation Actions in a Building Lot
  • Adaptation Strategy to Heat Risk: Assessment
    • Alert System
    • Emilia-Romagna Regional Government Coordination Actions Emilia-Romagna Regional Government coordinates actions to assist people groups
    • Proposed Pilot Action: Preliminary Assessment of a Possible Development of the Heat Risk Alert System Based on the Use
  • Conclusions

The Emilia-Romagna region decided to select the pilot area "Villaggio Artigiano" (craft village) in Modena, as the administration was preparing a municipal operational plan (POC, prescribed by the regional planning law). The proposed pilot action is aimed at verifying a possible improvement of the heat warning system currently operating in the Emilia-Romagna region.

Fig. 6.1  Hierarchy of urban centres – Road and rail network
Fig. 6.1 Hierarchy of urban centres – Road and rail network

Next Steps

Consequently, a new calculation methodology has been defined, which will be tested in the redevelopment of the village, which is able to measure the environmental impacts and the achievement of the planning objectives and to estimate the cost-benefit ratio in the redevelopment of urban plots. Identifying indices to measure the many environmental impacts of urban transformations is the challenge for urban planners.

Appendices Appendix A

Hình ảnh

Fig. 1.16  Urban and suburban land-surface categories at 2 km × 2 km resolution
Fig. 1.22  Ensemble Mean (EM) of seasonal changes of minimum temperature projected at  Bologna station (CCAReg model), scenario A1B, 2021–2050 with respect to 1961–1990
Fig. 1.21  Climate change projections of winter minimum temperature-Bologna station, scenario  A1B, 2021–2050
Fig. 1.23  Ensemble Mean (EM) of seasonal changes of maximum temperature projected at  Bologna station (CCAReg model), scenario A1B, 2021–2050 with respect to 1961–1990
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