Understanding the intricacies of regional climate change is essential for effective adaptation, yet trend analyses are often constrained by spatial aggregation biases. This study investigates the spatiotemporal evolution of maximum temperature in Lorestan Province, western Iran, over a 20-year period (2001–2020) by integrating conventional univariate trend detection with a novel multivariate approach based on Principal Component Analysis (PCA). Station-scale analysis using the modified Mann-Kendall test revealed a pervasive but largely non-significant positive annual warming trend across all eight stations, with total increases ranging from 0.77 °C to 1.25 °C. Crucially, monthly-scale disaggregation exposed a distinct seasonal asymmetry: significant warming was concentrated in February and the summer months (July–September), while transitional periods exhibited minimal change, demonstrating that annual aggregates conceal critical seasonal extremes. PCA revealed exceptional spatial coherence, with the first principal component explaining 99.9% of the total variance and factor loadings exceeding 0.999 across all stations, indicating a highly synchronized thermal regime governed by regional-scale drivers. However, the multivariate Mann-Kendall test found no significant region-wide trend (Z = 0.062, p = 0.951), highlighting that locally significant warming signals are diluted when spatially integrated, thereby cautioning against generalized regional policy. These findings underscore the necessity of local-scale trend assessments for climate adaptation, particularly in agriculture and water resources. The pronounced warming in February threatens plant chilling requirements, while intensifying summer heat elevates evapotranspiration and energy demands, necessitating targeted, site-specific management strategies rather than reliance on provincial averages for climate resilience planning in semiarid environments.
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Nazeri Tahroudi,M , Zeynali,M and Mohammadrezapour,O . (2026). Integrating Univariate and Multivariate Trend Detection with Principal Component Analysis. Water Harvesting Research, 9(1), 124-139. doi: 10.22077/jwhr.2026.12018.1216
MLA
Nazeri Tahroudi,M , , Zeynali,M , and Mohammadrezapour,O . "Integrating Univariate and Multivariate Trend Detection with Principal Component Analysis", Water Harvesting Research, 9, 1, 2026, 124-139. doi: 10.22077/jwhr.2026.12018.1216
HARVARD
Nazeri Tahroudi M, Zeynali M, Mohammadrezapour O. (2026). 'Integrating Univariate and Multivariate Trend Detection with Principal Component Analysis', Water Harvesting Research, 9(1), pp. 124-139. doi: 10.22077/jwhr.2026.12018.1216
CHICAGO
M Nazeri Tahroudi, M Zeynali and O Mohammadrezapour, "Integrating Univariate and Multivariate Trend Detection with Principal Component Analysis," Water Harvesting Research, 9 1 (2026): 124-139, doi: 10.22077/jwhr.2026.12018.1216
VANCOUVER
Nazeri Tahroudi M, Zeynali M, Mohammadrezapour O. Integrating Univariate and Multivariate Trend Detection with Principal Component Analysis. Water Harvest Res. 2026;9(1):124-139. doi: 10.22077/jwhr.2026.12018.1216
