<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Pakistan’s agricultural economy is reliant on the Indus River’s irrigation system, which is fed by the water coming from the great Himalayas-Karakoram Glacier Mountains. Because of hilly terrain areas, the climatic variations have an intense effect on the river flow, especially during the winter and monsoon months. Consequently, significant variations, which are observed annually, result in flooding situations in the monsoon months and reduced flows in the winter season. Thousands of people have lost their lives and massive property destruction has taken place due to disastrous floods that occurred during 2010 and 2016. Past studies have focused on proper water resources and the management of extreme events such as floods and droughts; however, modelling and forecasting based on the various climatic factors and stochastic variations are rare. This paper attempts to forecast Indus River flows using multiple linear regression (MLR), the stochastic time series, the seasonal autoregressive integrated moving average (SARIMA), and its reduced heteroscedasticity model, i.e., SARIMA-GARCH (generalized autoregressive conditional heteroscedasticity) methods at the Kalabagh station. The results show that MLR is best over the short-term; SARIMA is better over the long-term, and SARIMA-GARCH may be superior for a very long-term forecast.</p>
</abstract>

Pakistan’s agricultural economy is reliant on the Indus River’s irrigation system, which is fed by the water coming from the great Himalayas-Karakoram Glacier Mountains. Because of hilly terrain areas, the climatic variations have an intense effect on the river flow, especially during the winter and monsoon months. Consequently, significant variations, which are observed annually, result in flooding situations in the monsoon months and reduced flows in the winter season. Thousands of people have lost their lives and massive property destruction has taken place due to disastrous floods that occurred during 2010 and 2016. Past studies have focused on proper water resources and the management of extreme events such as floods and droughts; however, modelling and forecasting based on the various climatic factors and stochastic variations are rare. This paper attempts to forecast Indus River flows using multiple linear regression (MLR), the stochastic time series, the seasonal autoregressive integrated moving average (SARIMA), and its reduced heteroscedasticity model, i.e., SARIMA-GARCH (generalized autoregressive conditional heteroscedasticity) methods at the Kalabagh station. The results show that MLR is best over the short-term; SARIMA is better over the long-term, and SARIMA-GARCH may be superior for a very long-term forecast.

The Impact of Climate Change on Stochastic Variations of the Hydrology of the Flow of the Indus River

Slovak Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Pakistan’s agricultural economy is reliant on the Indus River’s irrigation system, which is fed by the water coming from the great Himalayas-Karakoram Glacier...

The Impact of Climate Change on Stochastic Variations of the Hydrology of the Flow of the Indus River

Online veröffentlicht: 13. Apr. 2022

Seitenbereich: 33 - 41

DOI: https://doi.org/10.2478/sjce-2022-0004

Schlüsselwörter
Multipleregression, Flood, Time series, SARIMA, Temperature, Rainfall

© 2022 Muhammad Yonus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The Impact of Climate Change on Stochastic Variations of the Hydrology of the Flow of the Indus River

Online veröffentlicht: 13. Apr. 2022

Seitenbereich: 33 - 41

DOI: https://doi.org/10.2478/sjce-2022-0004

SchlüsselwörterMultipleregression, Flood, Time series, SARIMA, Temperature, Rainfall

© 2022 Muhammad Yonus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Multipleregression, Flood, Time series, SARIMA, Temperature, Rainfall