Spatial-temporal factors affecting monthly rainfall in some Central Asian countries assuming a Weibull regression model

Climate change has been observed worldwide in the last years. Among the different effects of climate change, rain precipitation is one of the effects that most challenge the population of all countries in the world. The main goal of this study is to introduce a data analysis of monthly rainfall data related to five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tadjikistan, Turkmenistan and Uzbekistan) for a long period of time to discover the behavior of rain precipitation in these countries for each month of the year in the last decades and possible link with climate change. Since climate data are positive real values, Weibull regression models were fitted for the rain precipitation data (precipitation sums by climate station and year) under a classical inference approach in presence of some spatial factors as latitude and longitude of the climate stations in each country, temporal factors (linear year effects), altitude of the climate station and categorical factors (countries). The obtained results show that some factors have different effects in the monthly rainfall of the assumed countries during the follow-up assumed period, possibly linked to the climate change observed in the last decades worldwide.

Automated summary

This study aims to analyze long-term rainfall data in five Central Asian countries to understand the behavior of monthly rainfall and its possible link with climate change. The results indicate that certain factors have varying effects on monthly rainfall in these countries, potentially related to global climate change.