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Cited by 24Urban Heat Island MitigationClimate Change and Health ImpactsClimate Change and Environmental Impact

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A GIS-based framework to assess heatwave vulnerability and impact scenarios in urban systems

Open access
D’Ambrosio, Valeria, Di Martino, Ferdinando, Miraglia, Vittorio·2023·Scientific Reports
DOI 10.1038/s41598-023-39820-0PMC PMC10421934
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Abstract

In this work, we propose a GIS-based platform aimed at the analysis of heatwave scenarios risks produced in urbanised environments, applied to assess vulnerability and impact heatwave scenarios. Our framework implements a hierarchical model that represents a good trade-off between forecast accuracy and portability in different urban fabrics, apart from the spatial scale of the data, using topographic and remote sensing spatial data provided by institutional agencies. The framework has been applied to two study areas: the dense city of Naples (Italy) and the intermediately populated city of Avellino (Italy) in order to evaluate its accuracy performances and portability in different urban fabrics. Our framework can be used by urban planners and decision makers as a tool to locate potential risk zones where it is necessary to implement climate-resilient solutions.

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Crossref · 10.1038/s41598-023-39820-0

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Figures

Figure 1 Scheme of the process applied in15 to evaluate the impact scenarios to climate phenomena.
Figure 1 Scheme of the process applied in15 to evaluate the impact scenarios to climate phenomena.CC BY · PubMed Central
Figure 2 Schema of the indicators used in15 to evaluate the residential buildings and open spaces heatwave vulnerability.
Figure 2 Schema of the indicators used in15 to evaluate the residential buildings and open spaces heatwave vulnerability.CC BY · PubMed Central
Figure 3 Model for assessing impact scenarios for climatic phenomena.
Figure 3 Model for assessing impact scenarios for climatic phenomena.CC BY · PubMed Central
Figure 4 Risk/Impact scenarios evaluating by different exposition.
Figure 4 Risk/Impact scenarios evaluating by different exposition.CC BY · PubMed Central
Figure 5 Residential building subsystem vulnerability intermediate indicators: Thermal lag (a); Thermal decrement factor (b); Building volume (c); Solar exposure of building envelope (d).
Figure 5 Residential building subsystem vulnerability intermediate indicators: Thermal lag (a); Thermal decrement factor (b); Building volume (c); Solar exposure of building envelope (d).CC BY · PubMed Central
Figure 6 Open spaces subsystem vulnerability indicators: Albedo (a); Sky view Factor (b); NDVI (c); Solar exposure of open space (d).
Figure 6 Open spaces subsystem vulnerability indicators: Albedo (a); Sky view Factor (b); NDVI (c); Solar exposure of open space (d).CC BY · PubMed Central
Figure 7 Urban system intrinsic vulnerabilities: residential building vulnerability (a); open spaces vulnerability (b).
Figure 7 Urban system intrinsic vulnerabilities: residential building vulnerability (a); open spaces vulnerability (b).CC BY · PubMed Central
Figure 8 Exposure to heatwave phenomena: Population density (PD) (a); Disadvantaged population (DP) (b); Fuel poverty (FP) (c); frequency histogram (d).
Figure 8 Exposure to heatwave phenomena: Population density (PD) (a); Disadvantaged population (DP) (b); Fuel poverty (FP) (c); frequency histogram (d).CC BY · PubMed Central
Figure 9 Heatwave hazard scenarios: short-term hazard (S) (a); medium-term hazard (M) (b); long-term hazard (L) (c); frequency histogram (d).
Figure 9 Heatwave hazard scenarios: short-term hazard (S) (a); medium-term hazard (M) (b); long-term hazard (L) (c); frequency histogram (d).CC BY · PubMed Central
Figure 10 Impact scenarios by population density: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).
Figure 10 Impact scenarios by population density: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).CC BY · PubMed Central
Figure 11 Impact scenarios by disadvantaged population: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).
Figure 11 Impact scenarios by disadvantaged population: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).CC BY · PubMed Central
Figure 12 Impact scenarios by fuel poverty: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).
Figure 12 Impact scenarios by fuel poverty: short-term impact scenario (S) (a); medium-term impact scenario (M) (b); long-term impact scenario (L) (c); frequency histogram (d).CC BY · PubMed Central
Figure 13 Buildings subsystem vulnerability of Avellino (a); frequency histogram (b).
Figure 13 Buildings subsystem vulnerability of Avellino (a); frequency histogram (b).CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 17

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  2. Kumar, D., Alam, M. & Sanjayan, J. Building adaptation to extreme heatwaves. In: Engineering for Extremes. Springer Tracts in Civil Engineering, (eds Stewart, M. G. & Rosowsky, D. V.) (Springer, Cham, 2022). https://doi.org/10.1007/978-3-030-85018-0_9 · DOI
  3. Macnee, R. G. D. & Tokai, A. Heatwave vulnerability and exposure mapping for Osaka City. Environ. Syst. Decis. Jpn. 36, 368–376. https://doi.org/10.1007/s10669-016-9607-4 (2016). · DOI
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  11. D’Ambrosio, V. & Di Martino, F. The METROPOLIS research. Experimental models and decision-making processes for the adaptive environmental design in climate change article history. UPLanD J. Urban Plan. Landsc. Environ. Des. 1(1), 187–217. https://doi.org/10.6092/2531-9906/5038 (2016). · DOI
  12. Huang, S. et al. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 32, 1–6. https://doi.org/10.1007/s11676-020-01155-1 (2021). · DOI
  13. Spano D., Mereu V., Bacciu V., Marras S., Trabucco A., Adinolf M., Barbato G., Bosello F., Breil M., Chiriacò M. V., Coppini G., Essenfelder A., Galluccio G., Lovato T., Marzi S., Masina S., Mercogliano P., Mysiak J., Noce S., Pal J., Reder A., Rianna G., Rizzo A., Santini M., Sini E., Staccione A., Villani V., Zavatarelli M. (2020), “Analisi del rischio. I cambiamenti climatici in Italia”. https://doi.org/10.25424/CMCC/ANALISI_DEL_RISCHIO · DOI
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Metadata

Authors
D’Ambrosio, V., Di Martino, F., & Miraglia, V.
Affiliations
University of Naples Federico II (IT)
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2023-08-11
Volume
13
Issue
1
Pages
13073-13073
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-023-39820-0
PMC
PMC10421934
OpenAlex
W4385761506
Cited by
24
References
3
Citations by year
2026: 10 · 2025: 9 · 2024: 4 · 2023: 1
FWCI
2.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
Keywords
heat waves, urban heat, heatwave vulnerability, hierarchical models, urban climate risk, climate-resilient urban planning, risk zoning, Naples, Avellino, remote sensing data, topographic data

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