Entropy‐Based Analysis of Evaporation Variability: Assessing Climatic Parameters Using Wavelet Coherence and GeoDetector

Evaporation is a pivotal process in the hydrological cycle, particularly in water‐scarce regions under climate change pressures. This study presents an integrated framework combining permutation entropy (PE), GeoDetector and wavelet coherence (WTC) to analyse evaporation variability across Iran's diverse climatic zones. Daily meteorological observations (1995–2024) were aggregated to monthly values for preprocessing, from which long‐term station‐wise climatic summaries were derived for GeoDetector analysis, while WTC was applied to the monthly series and PE was computed using the original daily evaporation data. For WTC and GeoDetector analyses, these daily observations were aggregated into monthly values, whereas PE was computed from the original daily evaporation time series to preserve high‐frequency variability. GeoDetector results indicate that minimum temperature (26%), mean temperature (21%) and sunshine duration (12%) are the strongest contributors to evaporation variability, followed by humidity (16%), while precipitation (5%) and wind speed (4%) have smaller effects. The combined interaction of temperature‐related variables further amplifies their influence (up to 47% explanatory power). WTC analysis reveals significant coherence between evaporation and both temperature and sunshine duration at 6–24 month and annual scales, whereas wind speed and humidity show weaker or intermittent associations. These findings provide quantitative evidence of the climatic controls on evaporation and demonstrate the effectiveness of entropy‐based methods in capturing nonlinear and multi‐scale variability. Such insights are essential for improving hydrological models, predicting water availability and guiding adaptive water‐resource management under climate change.