نوع مقاله : مقاله پژوهشی

موضوعات

• اقلیم شهری

عنوان مقاله English

Trend Detection and Forecasting of LST in Tabriz City using the Non-parametric Mann-Kendall and NNAR

نویسندگان English

کلیدواژه‌ها English

Extended Abstract

1. Introduction

Urbanization entails the expansion and development of urban areas, often resulting in the conversion of natural land cover into artificial surfaces such as asphalt and concrete. This transformation significantly impacts the surface and atmospheric conditions of these regions. As urban areas expand, the resulting changes increasingly alter these environments' thermal properties and energy exchange processes. Concurrently, the global urban population is rising at an unprecedented rate. Projections suggest that by 2050, approximately 68% of the world’s population will reside in urban areas, with much of this growth concentrated in developing countries. This demographic shift is poised to exacerbate the challenges of urbanization, including escalating energy demands, worsening air pollution, and increased vulnerability to heat waves. One of the critical consequences of these factors is the formation and intensification of Urban Heat Islands. UHI refers to urban areas that experience significantly higher temperatures than their rural and natural counterparts due to increased anthropogenic heat emissions, reduced vegetation cover, and the prevalence of heat-retaining surface materials. The UHI phenomenon has profound implications for public health, energy consumption, and urban sustainability. Among the key parameters for studying UHIs is Land Surface Temperature, which is crucial for understanding the thermal characteristics of urban environments. Analyzing LST trends is essential for evaluating the impacts of urbanization and assessing the effectiveness of UHI mitigation strategies. High-resolution LST data offer critical insights into the spatial and temporal dynamics of UHIs, enabling policymakers and urban planners to implement effective adaptive strategies for managing extreme thermal conditions in urban areas. Furthermore, long-term LST time series analyses are fundamental for understanding climate change and its effects across different spatial and temporal scales.

2. Materials and methods

The LST data were extracted from the MODIS satellite images for both day and night. These images were processed using the GEE platform and obtained for each summer season from 2002 to 2023 (minimum, average, and maximum). The Mann-Kendall test was used to assess the linear trend in the LST. An autoregressive neural network was employed to forecast the trend by 2030.

3. Results and Discussion

The results of the anomaly analysis showed that during the daytime, the highest positive departure and the highest negative departure from the overall mean were recorded in 2006 (4.74°C) and 2023 (4.29°C), respectively. During the nighttime, the highest positive and negative departures from the overall mean were observed in 2006 (2.8°C) and 2009 (-2.77°C), respectively. Based on the trend analysis, the trends of the minimum (0.031°C), average (0.037°C), and maximum (0.065°C) LSTs during the daytime are significant. At night, only the trend of the minimum LST (0.034°C) is significant. Additionally, the rate of increase in the maximum daytime LST is higher than the minimum nighttime LST. The forecasting findings indicate that the model performed better during the nighttime than the daytime. Furthermore, the maximum daytime LST and the minimum nighttime LST in the summer of 2030 are expected to deviate by 1.12°C and 1.28°C from the overall mean of the period, respectively.

The present study's findings are consistent with those of previous research, including the work of Zolfaghari et al. (2023). They observed a significant increase in temperature in the provinces of Qom, Semnan, and Isfahan, as well as a significant decrease in temperature in Yazd. Additionally, our results align with the findings of Halabian (2017) and Choubari and Najafi (2017), who concluded that both maximum and minimum temperatures in Iran have shown an increasing trend. However, our findings diverge from those of Choubari and Najafi (2017) and Ghasemi (2017) regarding the steeper increase in minimum temperatures compared to maximum temperatures, noting that their study focused on the Iranian Plateau. Furthermore, Frempong et al. (2022), in their study in Ghana, found that the overall increase in minimum temperatures is more pronounced than that of maximum temperatures, which contrasts with the present study's findings. In contrast, Rao et al. (2014) reported findings that are consistent with our study, documenting an increase in mean minimum temperatures across India.

4. Conclusion

The projected rise in temperatures will lead to several significant consequences, including a decline in urban quality of life, heightened levels of pollution, and increased demand for cooling energy during the summer months. In this regard, the present study's findings, which analyze land surface temperature trends and project future scenarios, offer valuable insights for urban planners and policymakers. These insights can aid in developing mitigative and adaptive strategies to effectively address the challenges posed by climate change.

5. Acknowledgement & Funding

We would like to thank the anonymous reviewers for their valuable comments on our paper.

6. Conflict of Interest

The authors declare no conflict of interest.