نیوار

نیوار

Linear Signs and Kendall Lines: Detecting Local Climate Warming through Combined Statistical Approaches

نوع مقاله : مقاله پژوهشی

نویسندگان
Hedieh Ahmadpari 1
Vitaly Khaustov 2
Ata Amini 3
1 resources, hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia
2 Candidate of Technical Sciences, Associate Professor at the Department of Engineering Hydrology of the RSHU, Saint Petersburg, Russia
3 Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
10.30467/nivar.2025.537834.1347
چکیده
Climate variability poses major challenges for semi-arid regions, where rising temperatures directly affect ecosystems, water resources, and human livelihoods. Understanding temperature dynamics is therefore critical for regional climate adaptation and resilience planning. This study aims to detect, quantify, and interpret monthly and annual temperature trends to support climate adaptation in semi-arid regions exposed to increasing climate variability. To achieve this, long-term temperature data recorded at a representative synoptic station from 1998 to 2022 were analyzed using a combined set of statistical tools. The statistical tools used included the Mann–Kendall test for trend detection, Sen's slope estimator to measure the rate of change, Pearson correlation for identifying linear relationships, and linear regression for modeling the trends. The integrated methodology allowed for cross-validation between parametric and nonparametric approaches, ensuring robustness and consistency of the results. Findings revealed statistically significant warming patterns during January, July, August, September, October, November, and in the annual average, with Z-values ranging from 2.39 to 3.55, all exceeding the critical threshold of ±1.96 at the 95% confidence level. No significant trends were observed in March, June, and December, while February, April, and May showed weak negative trends, also statistically non-significant. These results highlighted the uneven nature of warming and confirmed the value of combined statistical methods in detecting subtle climate signals. The approach and findings can be adapted for temperature trend analysis in other semi-arid or data-limited regions, contributing to global climate resilience planning and regional climate diagnostics.
کلیدواژه‌ها
Temperature trends
semi-arid climate
Mann Kendall test
climate variability
statistical trend analysis
موضوعات

عنوان مقاله English

Linear Signs and Kendall Lines: Detecting Local Climate Warming through Combined Statistical Approaches

نویسندگان English

Hedieh Ahmadpari 1
Vitaly Khaustov 2
Ata Amini 3
1 PhD candidate, Hydrology of land, water resources, hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia, h.ahmadpari@stud.rshu.ru
2 Candidate of Technical Sciences, Associate Professor at the Department of Engineering Hydrology of the RSHU, Saint Petersburg, Russia
3 Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
چکیده English

Climate variability poses major challenges for semi-arid regions, where rising temperatures directly affect ecosystems, water resources, and human livelihoods. Understanding temperature dynamics is therefore critical for regional climate adaptation and resilience planning. This study aims to detect, quantify, and interpret monthly and annual temperature trends to support climate adaptation in semi-arid regions exposed to increasing climate variability. To achieve this, long-term temperature data recorded at a representative synoptic station from 1998 to 2022 were analyzed using a combined set of statistical tools. The statistical tools used included the Mann–Kendall test for trend detection, Sen's slope estimator to measure the rate of change, Pearson correlation for identifying linear relationships, and linear regression for modeling the trends. The integrated methodology allowed for cross-validation between parametric and nonparametric approaches, ensuring robustness and consistency of the results. Findings revealed statistically significant warming patterns during January, July, August, September, October, November, and in the annual average, with Z-values ranging from 2.39 to 3.55, all exceeding the critical threshold of ±1.96 at the 95% confidence level. No significant trends were observed in March, June, and December, while February, April, and May showed weak negative trends, also statistically non-significant. These results highlighted the uneven nature of warming and confirmed the value of combined statistical methods in detecting subtle climate signals. The approach and findings can be adapted for temperature trend analysis in other semi-arid or data-limited regions, contributing to global climate resilience planning and regional climate diagnostics.

کلیدواژه‌ها English

Temperature trends
semi-arid climate
Mann Kendall test
climate variability
statistical trend analysis
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  • تاریخ دریافت 07 مرداد 1404
  • تاریخ بازنگری 13 شهریور 1404
  • تاریخ پذیرش 20 مهر 1404
  • تاریخ انتشار 01 مهر 1404