Spatial patterns in urban water consumption: The role of local climate zones and temperature dynamics
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".