Calculation of Standardized Precipitation Index

The Standardized Precipitation Index (SPI) serves as a normalized measure for precipitation assessment, with extensive applications across meteorological, agricultural, and ecological environmental domains. The SPI computation algorithm fundamentally relies on statistical distribution characteristics of annual precipitation data. By evaluating spatiotemporal variations in precipitation patterns, this methodology effectively quantifies drought and moisture conditions, providing scientific foundations for decision-making and management in related fields. The core implementation involves fitting long-term precipitation records to gamma probability distributions, then transforming the cumulative probabilities to standard normal Z-scores. Key computational steps include: 1) Data preprocessing and quality control of historical precipitation series, 2) Parameter estimation for gamma distribution fitting using maximum likelihood methods, 3) Probability conversion through statistical normalization techniques. This standardized approach enables cross-regional comparisons and temporal analysis of precipitation anomalies. Consequently, the SPI has gained significant attention in recent years and evolved into an essential tool for investigating precipitation variability, water resource management, and climate impact assessments. The index's algorithmic consistency allows for automated calculation pipelines in operational monitoring systems, making it particularly valuable for early warning systems and climate adaptation strategies.