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Spatial patterns in urban water consumption: The role of local climate zones and temperature dynamics

  • Mohammad Maleki
  • , Amirbahador Damroodi
  • , Mahsa Mostaghim
  • , Amir Reza Bakhshi Lomer
  • , Samira Sadat Saleh
  • , Junye Wang
  • , Nabi Moradpour
  • , Iain D. Stewart
  • , Kanglin (Connie) Chen
  • , Fatemeh Kazemi
  • Kharazmi University
  • Tarbiat Modarres University
  • Pooyesh Qom Higher Education Institute
  • Transport for London
  • University of North Texas
  • University of Tehran
  • University of Toronto
  • Harvard University
  • Edith Cowan University

Research output: Contribution to journalJournal Articlepeer-review

5 Citations (Scopus)

Abstract

Urban Water Consumption (UWC) is a major challenge in arid regions, intensified by urbanization, population growth, and resource scarcity, prompting debates on relocating Iran's capital to address resource scarcity and sustainability. This study analyzed the relationship between Local Climate Zones (LCZ), Land Surface Temperature (LST), and water usage in Tehran (2015–2019) to inform urban water management. UWC data was spatially matched to urban areas to calculate per capita consumption. An LCZ map for the base year 2017 was generated using the Random Forest (RF) algorithm, achieving an accuracy of 88.88 %. LST data for the five years was derived using the single-channel algorithm. LCZ2 of dense midrise buildings exhibited the largest area, while LCZG of water had the smallest area. Annual per capita UWC showed a consistent upward trend, with 2019 experiencing the most significant increase. The highest UWC was in LCZG and LCZ2, respectively, while LCZ7 of low dense single buildings recorded the lowest. Most of the city's area had neighbourhoods with an average LST ranging between 30 °C and 35 °C throughout the study period. The correlation between population density, LST, and UWC was 10 % to 17 %. Modelling accuracy, measured by Root Mean Square Error (RMSE), ranged from 1.4 to 9.9. This research highlights the need for climate-sensitive urban design and sustainable water management, providing a foundation for policies to address water scarcity in vulnerable urban areas. Additionally, analyzing annual population dynamics and improving UWC modeling will help better reflect future urban water consumption patterns.

Original languageEnglish
Article number106438
JournalSustainable Cities and Society
Volume127
DOIs
Publication statusPublished - 1 Jun. 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  3. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  4. SDG 13 - Climate Action
    SDG 13 Climate Action
  5. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Cities
  • Local climate zones
  • Machine learning
  • Temperature dynamics
  • Urban water consumption

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