Stochastic River Flow Modelling and Forecasting of Upper Indus Basin Pages 630-636

Stochastic River Flow Modelling and Forecasting of Upper Indus Basin
Pages 630-636
Hamza Khan and Syed Ahmad Hassan

DOI: http://dx.doi.org/10.6000/1927-5129.2015.11.84

Published: 18 December 2015

Abstract: Upper Indus Basin (UIB) region has faced seasonal and sometimes unpredictable disastrous flow in their tributaries and contributing one of the world’s largest Indus River System. As these streams emerged from high mountains of Hindukush, Karakorum and Himalaya ranges, and formed as a lifeline for the local population of which90% is accommodate by Indus River system source. A little change in the regional climate may cause floods and outburst flows in the river and affects the lives, regional ecosystem and long part of the Karakoram highway. On the other hand, the shortage of water in Pakistan can create an alarming condition in future because a huge amount of eastern glaciers is shrinking. According to UN-ICC 2011 report Pakistan is in top of the four risky countries which adversely affected by climate change and especially worst hit by bi-catastrophes in 2010.During summer, intensifications of temperature may dissimilar for different locations/altitudes but it affects the glaciated areas. Moreover, the summer river flow and precipitation in previous winter and spring seasons has significant correlation shows their influence in the UIB region. Consequently, it may also be responsible for fluctuation in the seasonal/regular flows of UIB Rivers. To study these variations this paper analyses two types of data, mean monthly and 4-times moving average of monthly for long-term forecast. They belong to two different rivers Ghizer-Gilgit at Gilgit and Ghizer-Gilgit-Hunza at Alam stations. Both types of data illustrate a strong seasonal cycle. Therefore, seasonal autoregressive integrated moving average (SARIMA) models of time series method have been used. The five selected SARIMA models explore 90% and more river flow forecast. Moreover, the result with 4-times moving average is more accurate than simple data.

Keywords: Upper Indus Basin, SARIMA, Time series, Monsoon, Stochastic.