Analysis of the Influence of Temperature and Humidity on Rainfall in Sindh Province by Vector Autoregression (VAR)

Abstract

The effects of climate change are relatively local, although it is a worldwide problem. Since climate change has already occurred, it has had a wide variety of effects in almost all regions of the country and has also had an impact on many economic sectors. Rainfall, temperature, cloud cover, wind speed, humidity, and heavy sunlight are the main climatic variables. In order to understand the subsequent changes of these climatic variables, the behaviour of these variables should be studied. which also helps to implement significant policies. Investigating the behaviour of the climatic factors in the past, present, and future is a major problem. Primary goal of this research project was to create an adequate vector autoregression (VAR) model that could forecast monthly temperature, humidity, and rainfall at three meteorological stations in Sindh Province, Pakistan. The Kwiatkowski–Phillips-Schmidt–Shin, Phillips–Perron, and Augmented Dickey–Fuller tests have all verified the stationarity of time series variables. Order of the VAR model was determined by applying Schwarz information criteria, Hannan-Quinn information criteria, Akaike information criteria, final prediction error, and likelihood ratio test. Ordinary least squares approach was the method utilized to estimate the parameters of the model. It was determined that the optimal models for this study were VAR (8) and VAR (7). The structural analyses were performed using the forecast error variance decomposition and impulse response function. These structural studies show that, in the future, humidity, temperature, and rainfall will all be endogenous. The research indicates that humidity and temperature both favour rainfall. When temperature and humidity are both high, rainfall is greatest, and when they are both low, it is minimum. This study suggests that the correlation between temperature and relative humidity holds negligible influence over changes in rainfall patterns.