Long-Term Stochastic Modeling of Monthly Streamflow in River Nile

Synthetic time series created from historical streamflow data are thought of as substitute events with a similar likelihood of recurrence to the real event. This technique has the potential to greatly reduce the uncertainty surrounding measured streamflow. The goal of this study is to create a synth...

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Bibliographic Details
Published in:Sustainability 2023-01, Vol.15 (3), p.2170
Main Authors: Abdelaziz, Shokry, Mahmoud Ahmed, Ahmed Mohamed, Eltahan, Abdelhamid Mohamed, Abd Elhamid, Ahmed Medhat Ismail
Format: Article
Language:English
Subjects:
Analysis
Climate change
Distribution (Probability theory)
Distribution functions
Evaluation
Fourier transforms
Hydrologic data
Hydrology
Markov analysis
Markov chains
Markov processes
Methods
Quality assessment
Rain
Rivers
Seasonal variations
Simulation
Statistical analysis
Statistics
Stochastic models
Stochasticity
Stream discharge
Stream flow
Streamflow
Substitutes
Time series
Water shortages
Wavelet transforms
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Summary:Synthetic time series created from historical streamflow data are thought of as substitute events with a similar likelihood of recurrence to the real event. This technique has the potential to greatly reduce the uncertainty surrounding measured streamflow. The goal of this study is to create a synthetic streamflow model using a combination of Markov chain and Fourier transform techniques based on long-term historical data for the Nile River. First, the Markov chain’s auto-regression is applied, in which the data’s trend and seasonality are discovered and eliminated before applying the Pearson III distribution function. The Pearson III distribution function is substituted by a discrete Fourier transform (DFT) technique in the second approach. The applicability of the two techniques to simulate the streamflow between 1900 and 1999 is evaluated. The ability of the generated series to maintain the four most important statistical properties of the samples of monthly flows, i.e., the mean, standard deviation, autocorrelation lag coefficient, and cumulative distribution, was used to assess the quality of the series. The results reveal that the two techniques, with small differences in accuracy, reflect the monthly variation in streamflow well in terms of the three mentioned parameters. According to the coefficient of determination (R2) and normalized root mean square error (NRMSE) statistics, the discrete Fourier transform (DFT) approach is somewhat superior for simulating the monthly predicted discharge.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15032170