The impact of climate on water, environment and biodiversity is a conventional fact and can be observed throughout the world, especially each south Asian region like Pakistan. The Indus River region of Pakistan is going through phenomenal changes due to climate variations: persistent heat waves, recurring sultry cyclones, frequent floods and prolonged droughts. Floods are one of the important reasons for the life loss, damages of the globe. Pakistan faces large amounts of catastrophic flooding from the past 30 or 35 years, due to the influences of their variability, the dynamics and uncertainty owing to monsoon rainfalls and snow/glacier melts or combination of both. To study the impact of the dynamics and variability a profound mathematical analysis of local temperature and rainfall change projections, in unexpected variations of river flow is essential. This paper provides the realistic approaches by utilizing trend analysis using the techniques of least square estimation and nonparametric Mann-Kendall test and also incorporates multiple linear regression technique (MLR) based on step-wise method. Trend analysis is an important tool used to enumerate and elucidate the dynamics of the hydroclimatic stochastic system. MLR is a mathematical modelling method, used to explain the interrelations of the phenomena and explore their variation relative to the other dependent variable. These methods employed twenty-seven year data of Indus River flows (seven stations), precipitation (four cites) and temperature (three cites). The results show decline of rainfall and rising trend of temperature found in most of the cities, whereas, good explanation of the river flow establishes with MLR in upper stations. The results of this paper may help to understand the space-time dynamics and variability of river flow phenomena. Moreover, It will be useful for agriculture, hydropower generation and water management sectors in planning the future scenarios and forecasting of leading catastrophic occasions.
Indus River, Trend, Dynamics, Temperature, Rainfall, MLR
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