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The Hilbert-Huang Transform (HHT) is a recently developed technique used to analyze nonstationary data. Hydrologic and environmental series are, in the main, analyzed by using techniques developed for stationary data. This has led to problems of interpretation of the results. Environmental and hydrologic series are quite often nonstationary. This book analyzes these data by using methods based on the Hilbert-Huang transform. These results are compared to the results from the traditional methods such as those based on Fourier transform and other classical statistical tests.
PREFACE 1. INTRODUCTION 2. HILBERT-HUANG TRANSFORM (HHT) SPECTRAL ANALYSIS 2.1. Introduction 2.2. Conventional Spectral Analysis Methods 2.3. Empirical Mode Decomposition 2.4. Hilbert-Huang Spectra 2.5. Relationship Between HHT and Fourier Spectra 2.6. Volatility of Time Series 2.7. Degree of Stationarity of Time Series 2.8. Stationarity Tests 2.9. Concluding Comments 3. HILBERT-HUANG SPECTRA OF SIMULATED DATA 3.1. Introduction 3.2. Synthetic Data Analysis 3.3. Simulation of Nonstationary Random Processes 3.4. Confidence Intervals for Marginal Hilbert Spectrum 3.5. Concluding Comments 4. RAINFALL DATA ANALYSIS 4.1. Introduction and Data Used 4.2. HCN rainfall data 4.3. NCDC rainfall data 4.4. Concluding Comments 5. STREAMFLOW DATA ANALYSIS 5.1. Introduction and Data Used 5.2. USGS Streamflow Data 5.3. Analysis of Warta, Godavari and Krishna Rivers Flow Data 5.4. Concluding Comments 6. TEMPERATURE DATA ANALYSIS 6.1. Introduction and Data Used 6.2. European Long-term Monthly Temperature Time 6.3. HCN and NCDC Monthly Temperature Time Series 6.4. Concluding Comments 7. WIND DATA ANALYSIS 7.1. Introduction and Data Used 7.2. Hourly Wind Speed Data 7.3. Daily Average Wind Speed Data 7.4. Daily Peak Wind Speed Data 7.5. Concluding Comments 8. LAKE TEMPERATURE DATA ANALYSIS 8.1. Introduction and Data Used 8.2. Lake Temperature Spatial Series Analysis 9. CONCLUSIONS REFERENCES INDEX
I believe Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series will satisfy researchers in any discipline who analyze nonstationary and/or nonlinear time series. The book does not claim to be a final word on the merits of the HHT, but it does extend empirical claims regarding the potential effectiveness of the HHT. There are no exercises, although the book could be used in a teaching setting. Overall, I was glad to read the book and believe the HHT is well worth continued study as a potentially effective tool in the challenging area of nonstationary and non-linear time series analysis. Tom Burr, Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
