Data-Driven Statistical Analysis of Sunshine Hours Trends in Lorestan Province, Iran

Understanding trends in sunshine hours is crucial for evaluating regional climate dynamics, solar energy potential, and agricultural productivity. This study analyzed the temporal trends in monthly and annual sunshine hours in the Darreh Dozdan River Basin, located in Lorestan Province, Iran, using a ۲۵-year dataset (۱۹۹۸–۲۰۲۲). The analysis employed Mann–Kendall test, Sen’s slope estimator, Pearson correlation coefficient, and linear regression to identify significant changes in sunshine hours. The analysis showed a significant decreasing trend in May (Z = –۲.۶۲; Sen’s slope = –۱.۹۲ hours; r = –۰.۵۴; regression slope = –۲.۱۹ hours) and a significant increasing trend in September (Z = ۲.۳۳; Sen’s slope = +۰.۹۱ hours; r = ۰.۴۵; regression slope = +۰.۹۳ hours) over the study period. The annual series showed no significant change (Z = –۰.۲۳; Sen’s slope = +۰.۲۶ hours over the study period; r = –۰.۰۹; regression slope = –۱.۴۹), while other months displayed weak and insignificant trends. Overall, the consistency among all four methods confirmed the robustness of the decreasing trend in May and the increasing trend in September. The findings offer a valuable basis for future studies on regional climate variability and its environmental impacts.

کلیدواژه ها:

Sunshine Hours ، Time-series analysis ، Trend Analysis ، data-driven approach