تعیین روند زمانی و مکانی و نقطه تغییر بارش و دمای بیشینه تهران

Extended Abstract

Introduction

In recent decades, climate change has become one of the major challenges for urban management in megacities such as Tehran. Rising temperatures, declining precipitation, temporal fluctuations in climatic patterns, and the expansion of urban heat islands are among the visible consequences of climate change at the urban scale. Tehran, because of its high population density, unique geographical location, and rapid physical development, is highly vulnerable to climatic variations. Particularly in the cold seasons, fluctuations in temperature and precipitation can significantly impact water resources, energy consumption, and service infrastructure.

Despite numerous global studies and some national research efforts, a precise and integrated examination of trends and homogeneity in Tehran’s climatic variables (especially at the seasonal scale) has received limited attention. Furthermore, analyzing trends and the statistical structure of these data can play a critical role in climate policy-making, drought management, and water resource planning.

This study aims to analyze trends and assess the homogeneity of maximum temperature and precipitation during the cold seasons (autumn and winter) at four synoptic stations in Tehran from 1994 to 2023. The main focus is to identify statistically significant trends and structural breakpoints in the time series of climatic variables to better understand the behavior of Tehran’s urban climate in recent decades.

Accordingly, the research addresses the following questions: Is there a statistically significant trend in maximum temperature and precipitation during autumn and winter in Tehran? Are the time series of these variables homogeneous, or have they experienced structural breaks over time? Which stations have undergone the most significant statistical changes, and what are the implications of these changes for Tehran’s climate management?

Methodology

This study employs an applied, quantitative, and analytical approach, utilizing daily maximum temperature and precipitation data from four synoptic stations Geophysics, Shemiran, Mehrabad, and Chitgar in Tehran. The data were collected for the autumn and winter seasons over 30 years (1994–2023). Following rigorous quality control procedures and removal of missing or erroneous data, the datasets were organized into seasonal time series. To analyze trends, the non-parametric Mann-Kendall test was applied, a widely accepted and robust method for detecting monotonic trends in climatic time series. Additionally, Sen’s slope estimator was used to quantify the annual rate of change, providing a reliable measure of trend magnitude.

To assess homogeneity and detect structural breaks in the time series, four statistical tests were employed: Pettitt, Standard Normal Homogeneity Test (SNHT), Buishand, and Von Neumann tests. Pettitt and Von Neumann tests are non-parametric tests that can identify sudden changes and non-random structures without requiring assumptions of normality. Conversely, SNHT and Buishand tests assume normality and are designed to detect shifts in the mean and variance. These methods were applied separately for each variable, season, and station.

Results and discussion

Results from the Mann-Kendall test revealed a statistically significant increasing trend in maximum temperature across most stations during both autumn and winter. Notably, Shemiran and Geophysics stations exhibited Sen’s slope values of up to 0.09 °C per year, with average temperature increases exceeding 2 °C in some seasons after detected breakpoints. The test statistics (Z values) were significant at the 95% confidence level, confirming the robustness of these trends. Conversely, autumn precipitation showed no significant trends at any of the stations. However, winter precipitation exhibited a significant declining trend at Mehrabad and Chitgar stations, with Sen’s slopes of -1.85 and -2.27 mm per year, respectively. These findings indicate a gradual decrease in winter rainfall, posing a threat to seasonal water resources in parts of Tehran.

Homogeneity tests confirmed the presence of significant structural breaks in temperature data at several stations. Pettitt and SNHT tests identified change points predominantly in the 2000s (the 1380s in the solar Hijri calendar) at Shemiran, Geophysics, and Chitgar stations. Buishand and Von Neumann tests also revealed non-random structures and instability in the series. For precipitation, structural breaks were detected only in winter data at Mehrabad and Chitgar stations, whereas autumn precipitation remained homogeneous.

These findings align with national studies such as those by Bazgir et al. (2019), Alipour and Malkian (2019), Mortazipour (2020), and Baharvandi (2021), who emphasized climatic data inhomogeneity and increasing temperature trends. They also correspond with international research in similar urban contexts (e.g., Faquseh & Grossi, 2024; Samantaray et al., 2025), confirming seasonal temperature rises and altered precipitation behavior.

Conclusion

This study demonstrates that over the last three decades, Tehran has experienced a significant increase in maximum temperatures during autumn and winter, accompanied by a notable decline in winter precipitation at certain stations. The occurrence of statistical structural breaks in both temperature and precipitation series indicates significant climatic instability at the urban scale, primarily during the 2000s (1380s in the Iranian calendar). These climatic changes have critical implications for water resource management, energy consumption, urban planning, and climate resilience strategies in Tehran. Identifying these trends enables policymakers to develop data-driven, proactive, and adaptive strategies to mitigate risks and enhance the city’s climate adaptability.





Funding

There is no funding support.



Authors’ Contribution

All of the authors approved the content of

The manuscript and agreed on all aspects of

the work.



Conflict of Interest

Authors declared no conflict of interest.



Acknowledgments

We are grateful to all the scientific consultants of this paper.