Influence of lunar phases and meteorological factors on rainfall in Karachi City, Pakistan

Abstract

Climate change is responsible for unpredictable weather patterns in Southeast Asia, especially in Pakistan. To better understand the influence of lunar phases and meteorological factors on extreme rainfall events in Karachi City, 42 years of rainfall data and 12 years of sea level data were analyzed. The study used wavelet transform coherence to assess the influence of lunar phases and sea level fluctuations on extreme rainfall. Additionally, three machine learning algorithms - random forest, decision tree, and extreme gradient boosting - were applied first to meteorological data (Case A) and then to combined meteorological and lunar data (Case B) to evaluate their influence on extreme rainfall. Humidity in case A, and cloud cover in case B were the most influential meteorological factors along with west winds, and north winds in both cases, while the waning crescent, waning gibbous, and waxing gibbous lunar phases were the most dominant lunar phases in Case B. The coefficient of determination (R2) in random forest increased from 0.871 in Case A, to 0.893 in Case B and proved to be the most accurate algorithm in both cases. A rainfall threshold of 50 mm, above which urban pluvial flooding occurred, was consistently observed during the waxing gibbous, waning crescent, and waning gibbous lunar phases. The study improves our understanding of lunar influenced extreme rainfall temporal variations values of machine learning models for rainfall analysis and extreme events projection and provide essential information to develop efficient adaptation and mitigation policies in pluvial flood-prone urban areas.