Description: Nonstationarities in Hydrologic and Environmental Time Series by A.R. Rao, K.H. Hamed, Huey-Long Chen Conventionally, time series have been studied either in the time domain or the frequency domain. On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Most of the time series analysis methods applied today rely heavily on the key assumptions of linearity, Gaussianity and stationarity. Natural time series, including hydrologic, climatic and environmental time series, which satisfy these assumptions seem to be the exception rather than the rule. Nevertheless, most time series analysis is performed using standard methods after relaxing the required conditions one way or another, in the hope that the departure from these assumptions is not large enough to affect the result of the analysis. A large amount of data is available today after almost a century of intensive data collection of various natural time series. In addition to a few older data series such as sunspot numbers, sea surface temperatures, and so on, data obtained through dating techniques (tree-ring data, ice core data, geological and marine deposits) are available. With the advent of powerful computers, the use of simplified methods can no longer be justified, especially with the limited success of those methods in explaining the inherent variability in natural time series.This study presents a number of techniques that have been discussed in the literature during the 1980s and 1990s concerning the investigation of stationarity, linearity and Gaussianity of hydrologic and environmental times series. These techniques cover different approaches for assessing nonstationarity, ranging from time domain analysis, to frequency domain analysis, to the combined time-frequency and time-scale analyses, to segmentation analysis, in addition to formal statistical tests of linearity and Gaussianity. Author Biography A. Ramachandra Rao (Ph.D., Indian Statistical Institute) is a Professor in the Division of Theoretical Statistics and Mathematics of the Indian Statistical Institute at Kolkata. He was a Visiting assistant professor in the University of Minnesota for one year. He published more than 25 research papers in Graph Theory, is a joint author of a book on Linear Algebra at the honours level and edited the Proceedings of three Conferences on Graph Theory. His major interests are Graph Theory and its Applications to Social Sciences and Linear Algebra. Table of Contents 1. Introduction.- 2. Data Used in the Book.- 2.1. Hydrologic and Climatic Data.- 2.2. Synthetic and Observed Environmental Data.- 2.3. Observed Data.- 3. Time Domain Analysis.- 3.1. Introduction.- 3.2. Visual Inspection of Time Series.- 3.3. Statistical Tests of Significance.- 3.4. Testing Autocorrelated Data.- 3.5. Application of Trend Tests to Hydrologic Data.- 3.6. Conclusions.- 4. Frequency Domain Analysis.- 4.1. Introduction.- 4.2. Conventional Spectral Analysis.- 4.3. Multi-Taper Method (MTM) of Spectral Analysis.- 4.4. Maximum Entropy Spectral Analysis.- 4.5. Spectral Analysis of Hydrologic and Climatic Data.- 4.6. Discussion of Results.- 4.7. Conclusions.- 5. Time-Frequency Analysis.- 5.1. Introduction.- 5.2. Evolutionary Spectral Analysis.- 5.3. Evolution of Line Components in Hydrologic and Climatic Data.- 5.4. Evolution of Continuous Spectra in Hydrologic and Climatic Data.- 5.5. Conclusions.- 6. Time-Scale Analysis.- 6.1. Introduction.- 6.2. Wavelet Analysis.- 6.3. Wavelet Trend Analysis.- 6.4. Identification of Dominant Scales.- 6.5. Time-Scale Distribution.- 6.6. Behavior of Hydrologic and Climatic Time Series at Different Scales.- 6.7. Conclusions.- 7. Segmentation of Non-Stationary Time Series.- 7.1. Introduction.- 7.2. Tests based on AR Models.- 7.3. A test based on wavelet analysis.- 7.4. Segmentation algorithm.- 7.5. Variations of test statistics with the AR order p.- 7.6. Sensitivity of test statistics for detecting change points.- 7.7. Performances of algorithms with and without boundary optimization.- 7.8. Conclusions about the segmentation algorithm.- 8. Estimation of Turbulent Kinetic Energy Dissipation.- 8.1. Introduction.- 8.2. Multi-taper Spectral Estimation.- 8.3. Batchelor Curve Fitting.- 8.4. Comparison of Spectral Estimation Methods.- 8.5.Batchelor Curve Fitting to Synthetic Series.- 8.6. Conclusions on Batchelor curve fitting.- 9. Segmentation of Observed Data.- 9.1. Introduction.- 9.2. Temperature Gradient Profiles.- 9.3. Conclusions on Segmentation of Temperature Gradient Profiles.- 9.4. Hydrologic Series.- 9.5. Conclusions on Segmentation of Hydrologic Series.- 10. Linearity and Gaussianity Analysis.- 10.1. Introduction.- 10.2. Tests for Gaussianity and Linearity (Hinich, 1982).- 10.3. Testing for Stationary Segments.- 10.4. Conclusions about Testing the Hydrologic Series.- 11. Bayesian Detection of Shifts in Hydrologic Time Series.- 11.1. Introduction.- 11.2. Data Used in this Chapter.- 11.3. A Bayesian Method to Detect Shifts in Data.- 11.4. Discussion of Results.- 11.5. Conclusions.- 12. References.- 13. Index. Review From the reviews:"The authors consider a number of modern statistical tests of nonstationarity, including trend analysis, multitaper method and maximum entropy spectral analysis, evolutionary spectral analysis, wavelet analysis, and series segmentation through change point detection. … this book is well organized and easy to read … . A clear distinction is made between processes with discrete, continuous, and mixed spectra … . Nonstationarities in Hydrologic and Environmental Time Series addresses a number of important issues and ideas … ." (Adam Monahan, Bulletin of the American Meteorological Society, March, 2005) Promotional Springer Book Archives Long Description Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the time representation of a signal is poorly localized in frequency , i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain . On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. In studying stationary or conditionally stationary processes with mixed spectra , the separate use of time domain and frequency domain analyses is sufficient to reveal the structure of the process . Results discussed in the previous chapters suggest that the time series analyzed in this book are conditionally stationary processes with mixed spectra. Additionally, there is some indication of nonstationarity, especially in longer time series. Review Quote From the reviews:"The authors consider a number of modern statistical tests of nonstationarity, including trend analysis, multitaper method and maximum entropy spectral analysis, evolutionary spectral analysis, wavelet analysis, and series segmentation through change point detection. … this book is well organized and easy to read … . A clear distinction is made between processes with discrete, continuous, and mixed spectra … . Nonstationarities in Hydrologic and Environmental Time Series addresses a number of important issues and ideas … ." (Adam Monahan, Bulletin of the American Meteorological Society, March, 2005) Details ISBN1402012977 Short Title NONSTATIONARITIES IN HYDROLOGI Series Water Science and Technology Library Language English ISBN-10 1402012977 ISBN-13 9781402012976 Media Book Format Hardcover Series Number 45 Year 2003 Publication Date 2003-07-31 Country of Publication United States Birth 1939 Pages 365 Edition 2003rd Imprint Springer-Verlag New York Inc. Place of Publication New York, NY DOI 10.1023/b129762;10.1007/978-94-010-0117-5 AU Release Date 2003-07-31 NZ Release Date 2003-07-31 US Release Date 2003-07-31 UK Release Date 2003-07-31 Author Huey-Long Chen Publisher Springer-Verlag New York Inc. Edition Description 2003 ed. Alternative 9789401039796 DEWEY 551.460151955 Illustrations XXVII, 365 p. Audience Undergraduate We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96290098;
Price: 315.84 AUD
Location: Melbourne
End Time: 2025-01-14T09:20:51.000Z
Shipping Cost: 14.27 AUD
Product Images
Item Specifics
Restocking fee: No
Return shipping will be paid by: Buyer
Returns Accepted: Returns Accepted
Item must be returned within: 30 Days
ISBN-13: 9781402012976
Book Title: Nonstationarities in Hydrologic and Environmental Time Series
Item Height: 235mm
Item Width: 155mm
Author: K.H. Hamed, Huey-Long Chen, A.R. Rao
Format: Hardcover
Language: English
Topic: Engineering & Technology, Computer Science, Science, Mathematics
Publisher: Springer-Verlag New York Inc.
Publication Year: 2003
Type: Textbook
Item Weight: 834g
Number of Pages: 365 Pages
