Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri

Generated runoff data have been used in the past for planning and management of water resources. However, in Malaysia, runoff data is usually unavailable for long term forecasting. If the runoff data is available, the record is too short to give any statistical significance. This long term record is...

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Main Author:	Muhammad Ashri, Aisar Ashra
Format:	Thesis
Language:	English
Published:	2015
Online Access:	http://ir.uitm.edu.my/id/eprint/17662/ http://ir.uitm.edu.my/id/eprint/17662/2/TM_AISAR%20ASHRA%20MUHAMMAD%20ASHRI%20EC%2015_5.pdf

id	uitm-17662
recordtype	eprints
spelling	uitm-176622018-10-16T06:56:18Z http://ir.uitm.edu.my/id/eprint/17662/ Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri Muhammad Ashri, Aisar Ashra Generated runoff data have been used in the past for planning and management of water resources. However, in Malaysia, runoff data is usually unavailable for long term forecasting. If the runoff data is available, the record is too short to give any statistical significance. This long term record is needed in order to estimate the long term forecasting of the future events such as flood and drought. This study intended to use stochastic rainfall-runoff model in simulation of synthetic monthly stream flow data. The main objective of this research is to generate the synthetic runoff data that preserved the statistical properties of historical data. The Lag-one Markov Chain is adopted to generate synthetic rainfall data at four selected study areas in Malaysia. Then, the parameter from the synthetic rainfall is used as an input to the stochastic rainfall-runoff model. A stochastic rainfall-runoff model has been developed to simulate monthly sequences of runoff for the selected study areas. In this method, runoff was generated using ARMAX model. The generated sequence is then used for determination of monthly risks and exceedance probability. The comparison of results indicates that the model developed satisfactorily preserves the monthly stochastic and statistical properties of the historical data sequences. Hence, the model was found able to generate monthly runoff data for the Segamat, Maran, Kuala Pilah and Besut. The generated data can be used to simulate the unavailable historical records and the same approach may also be used for other sites in Malaysia. 2015 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/17662/2/TM_AISAR%20ASHRA%20MUHAMMAD%20ASHRI%20EC%2015_5.pdf Muhammad Ashri, Aisar Ashra (2015) Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
description	Generated runoff data have been used in the past for planning and management of water resources. However, in Malaysia, runoff data is usually unavailable for long term forecasting. If the runoff data is available, the record is too short to give any statistical significance. This long term record is needed in order to estimate the long term forecasting of the future events such as flood and drought. This study intended to use stochastic rainfall-runoff model in simulation of synthetic monthly stream flow data. The main objective of this research is to generate the synthetic runoff data that preserved the statistical properties of historical data. The Lag-one Markov Chain is adopted to generate synthetic rainfall data at four selected study areas in Malaysia. Then, the parameter from the synthetic rainfall is used as an input to the stochastic rainfall-runoff model. A stochastic rainfall-runoff model has been developed to simulate monthly sequences of runoff for the selected study areas. In this method, runoff was generated using ARMAX model. The generated sequence is then used for determination of monthly risks and exceedance probability. The comparison of results indicates that the model developed satisfactorily preserves the monthly stochastic and statistical properties of the historical data sequences. Hence, the model was found able to generate monthly runoff data for the Segamat, Maran, Kuala Pilah and Besut. The generated data can be used to simulate the unavailable historical records and the same approach may also be used for other sites in Malaysia.
format	Thesis
author	Muhammad Ashri, Aisar Ashra
spellingShingle	Muhammad Ashri, Aisar Ashra Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
author_facet	Muhammad Ashri, Aisar Ashra
author_sort	Muhammad Ashri, Aisar Ashra
title	Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
title_short	Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
title_full	Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
title_fullStr	Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
title_full_unstemmed	Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri
title_sort	flood risk using stochastic rainfall-runoff model /aisar ashra muhammad ashri
publishDate	2015
url	http://ir.uitm.edu.my/id/eprint/17662/ http://ir.uitm.edu.my/id/eprint/17662/2/TM_AISAR%20ASHRA%20MUHAMMAD%20ASHRI%20EC%2015_5.pdf
first_indexed	2023-09-18T22:58:47Z
last_indexed	2023-09-18T22:58:47Z
_version_	1777418032933502976

Flood risk using stochastic rainfall-runoff model /Aisar Ashra Muhammad Ashri

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