Modeling Change of Flowrate and Sediment Transport on a Changing Climate using Hilbert Huang Transform and Uncertainty Analysis
Date Issued
2015
Date
2015
Author(s)
Chu, Yi
Abstract
The Hilbert-Huang transform (HHT), a data analysis method for dynamic and nonlinear timeseries, is applied to our analysis of flow rates and temperatures of rivers in northern Taiwan. HHT consists of two independent analytical methods: empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). EMD will decompose the time series data into several independent intrinsic mode functions (IMFs) and then derive the trend from the whole data span. As the EMD suffers from the problem of mode mixing, a new developed noise-assisted method called ensemble empirical mode decomposition (EEMD) will be adopted. Next, Hilbert transform turns the derived IMFs into time-frequency-energy functions, designated as Hilbert spectrum. An energy weighted measurement equation is adopted to calculate the hidden scales in the IMFs. The resulting time scales can range from a few months to decades and a long term trend. Furthermore, we combine the Weibull formula and HHT to estimate the occurrences number of extreme flow events per year. Results of frequency analysis can provide the change in extreme flow event occurrences under climate change. Meanwhile, uncertainty embedded in the flow rate data is also concerned. By using two kinds of Pont Estimate Methods and Monte Carlo simulation, one can obtain not only the derived IMFs and trend, but also uncertainty bands of the model predictions. The result of PEM show a little difference in the last few IMFs but give similar results as the Monte Carlo simulation. On the other hand, we simulate the particle movement in this area with the stochastic jump diffusion particle tracking model (SJD-PTM). Mechanism of resuspension is considered by the model of pickup probability. Two kinds of experimental data are tested here. It found that only the smaller and finer particles present a clear view of particle trajectories. Particles with larger diameter cannot be resuspended until the arrival of extreme events. Applications to the field data can be divided into long term simulation and an event based simulation (short period). Both include temporal velocity variation in the mean drift and frequency change in the Poisson process. Simulations with SJD-PTM will come out the ensemble means and variances of the particle trajectory. This result can be used to estimate the possible time for particles to reach the reservoir.
Subjects
Hilbert Huang Transform
Frequency analysis
uncertainty analysis, particle tracking model
sediment transport
SDGs
Type
thesis
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