Study the seasonal trend analysis and probability distribution functions of rainfall for atmospheric region of Pakistan

Autor: Muhammad Yonus, Bulbul Jan, Hamza Khan, Faisal Nawaz, Muhammad Ali
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: MethodsX, Vol 10, Iss , Pp 102058- (2023)
Druh dokumentu: article
ISSN: 2215-0161
DOI: 10.1016/j.mex.2023.102058
Popis: It is known that the rainfall is one of the major aspects of the climate change and hydrological processes. Its impact on the resources of water has always been considered very essential. In the countries like Pakistan having severe weather conditions the effect of rainfall can be more crucial. Due to the geographical differences, the impact of sudden rainfall can be more disastrous in Pakistan as in 2011 and 2022 only the few days’ extreme rainfall in summer monsoon disturbed many regions of Pakistan especially southern region of Pakistan and lost hundreds of lives and homes. In addition, there are two methods applied for this study (1) trend analysis and (2) probability distribution functions. Our results revealed that only three cities i.e. Chitral, Skardu and Gilgit having increasing as well as the station Sialkot have increasing trend pattern in only summer rainfall, and other all have shown decreasing trend overall. Furthermore, key extreme weather events over Pakistan have changed in frequency and intensity during the past decades due to significant increase in global warming. The methods and results will be useful to study the historical rainfall data for projecting future rainfall variations impact on hydrological processes due to climate change. • The Data, methods and results of this study can be useful to government officials for protective measures and future developments. • Detecting trend analysis was applied to point out how strong the trend in the rainfall is and whether it is increasing or decreasing. • Additionally, the probability distribution was applied to indicate changed in frequency and intensity during the past decades due to significant variations in rainfall data.
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