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Title: Generation of daily rainfall time series using a hybrid stochastic model
Author: Alajarmeh, Ramiaah Mohammad Saleh
ISNI:       0000 0004 5372 1768
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2014
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Rainfall occurrence and intensity are the most important drivers of the surface runoff process. The knowledge of the occurrence and intensity of rainfall events is a crucial concern for water resources planners and designers. Stochastic rainfall generators are considered a robust tool that can generate the rainfall intensity of any time length at the interested locations. The validity of the stochastic daily rainfall generators for the Middle East in general and Jordan in specific has not been well researched. The aim of the present research is to estimate daily rainfall time series in different climates with particular focus on semiarid and arid regions. As an important result of the present research, a hybrid single-site stochastic daily rainfall generator (the hybrid model) has been developed using both a two-state first-order Markov chain based model and enhancing the simulation of long dry series using a modified serial approach. Long-term daily rainfall time series from rainfall stations under two different precipitation-hydrological regimes; namely, a frontal dominated precipitation-hydrological regime (Northern Ireland) and a semiarid to arid regime driven predominantly by convective rainfall (Jordan) have been used in testing and evaluation of the hybrid model, in addition to the two original approaches (Markov chain based model and Serial approach represented by LARS-WG). Standard statistical analysis and tests have been used to evaluate the performance of the single-site stochastic daily rainfall generators in both regimes. These single-site stochastic daily rainfall generators then have been assessed at multiple sites using a network of daily rainfall stations within the Lough Neagh basin in Northern Ireland and the Mujib basin in Jordan in order to evaluate their ability to correlate the generated time series of the neighbouring stations. According to the present research results of the comparative performance of the single-site stochastic daily rainfall generators, the hybrid model performance in both regimes was superior.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available