Analysis of Inherent Memory in Hydroclimatic Time Series: Implications for Statistical Tests and Long-Term Data Generation

This study delves into the pivotal importance of intrinsic memory within time series data, focusing on hydroclimatic variables and their far-reaching implications for statistical tests and the generation of long-term data. Relying solely on assumptions proves inadequate for accurate analysis; thus, this study underscores the importance of collecting data through long-duration observations, harnessing the power of big data analytics. The spatial distribution of autocorrelation structures in global observed/reference data is thoroughly examined for various hydrometeorological variables, including diurnal temperature range, precipitation, temperature, vapor pressure, wet day frequency, and potential evapotranspiration, drawing from extensive datasets. Our findings reveal that most regions across the world exhibit significant autocorrelation in all these variables, showcasing the potential of big data in enhancing our understanding of hydroclimatic patterns.