Nonstationarities in hydrologic and environmental time series:
"This book presents a number of new techniques that have been discussed in the literature during the last two decades concerning the investigation of stationarity, linearity and Gaussianity of hydrologic and environmental times series. These techniques cover different approaches for assessing n...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston
Kluwer Acad. Publ.
2003
|
| Schriftenreihe: | Water science and technology library
45 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "This book presents a number of new techniques that have been discussed in the literature during the last two decades 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. It is hoped that this endeavor would facilitate further research into this important area."--BOOK JACKET. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXVI, 362 S. graph. Darst. |
| ISBN: | 1402012977 |
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Datensatz im Suchindex
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| adam_text | Titel: Nonstationarities in hydrologic and environmental time series
Autor: Rao, Adiseshappa Ramachandra
Jahr: 2003
TABLE OF CONTENTS
1. Introduction..........................2
2. Data Used in the Book
2.1. Hydrologic and Climatic Data...................................... 4
2.2. Synthetic and Observed Environmental Data............................5
2.2.1. Synthetic Data Sampling from Batchelor Spectrum................5
2.2.2. Details of Data Generated by Sampling from the Batchelor Spectrum . 18
2.2.3. Synthetic Data from AR Model............................... 22
2.3. Observed Data................................................. 25
2.3.1. Measured Temperature Gradient Profiles.......................25
3. Time Domain Analysis
3.1. Introduction....................................................27
3.2. Visual Inspection of Time Series....................................28
3.3. Statistical Tests of Significance..................................... 29
3.3.1. Parametric Tests........................................... 29
3.3.2. Non-parametric Tests.......................................31
3.4. Testing Autocorrelated Data.......................................32
3.5. Application of Trend Tests to Hydrologic Data........................35
3.5.1. Visual Inspection of Data....................................35
3.5.2. Statistical Trend Tests...................................... 36
3.5.3. Sub-period Trend Analysis................................. 46
3.6. Conclusions................................................... 54
4. Frequency Domain Analysis
4.1. Introduction....................................................56
4.2. Conventional Spectral Analysis..................................... 57
4.3. Multi-Taper Method (MTM) of Spectral Analysis.......................59
4.4. Maximum Entropy Spectral Analysis.................................63
4.5. Spectral Analysis of Hydrologic and Climatic Data......................64
4.5.1. Results from MEM Analysis...................................64
4.5.2. Results from MTM Analysis...................................76
4.6. Discussion of Results.............................................99
4.7. Conclusions.................................................... 113
5. Time-Frequency Analysis
5.1. Introduction....................................................115
5.2. Evolutionary Spectral Analysis.....................................116
5.3. Evolution of Line Components in Hydrologic and Climatic Data...........120
5.4. Evolution of Continuous Spectra in Hydrologic and Climatic Data.........130
5.5. Conclusions....................................................155
6. Time-Scale Analysis
6.1. Introduction.................................................... 160
6.2. Wavelet Analysis.................................................160
6.3. Wavelet Trend Analysis...........................................162
6.4. Identification of Dominant Scales....................................178
6.5 Time-Scale Distribution............................................179
6.6. Behavior of Hydrologic and Climatic Time Series at Different Scales........180
6.7. Conclusions.....................................................209
7. Segmentation of Non-Stationary Time Series
7.1. Introduction....................................................213
7.2. Tests based on AR Models.........................................215
7.2.1. Test 1 (de Souza and Thomson, 1982)..........................215
7.2.2. Test 2 (lmberger and hey, 1991)..............................217
7.2.3. Test 3 (Davis, Huang and Yao, 1995)..........................217
7.2.4. Test4 (Tsay, 1988)........................................220
7.3. A test based on wavelet analysis....................................222
7.4. Segmentation algorithm.......................................... 225
7.5. Variations of test statistics with the AR order/?........................228
7.6. Sensitivity of test statistics for detecting change points..................231
7.6.1. Detection results for synthetic series from model 2.1.2............232
7.6.2. Detection results for synthetic series from model 2.1.3............233
7.6.3. Detection results for synthetic series from model 2.1.4.............238
7.6.4. Detection results for synthetic series from model 2.1.5.............244
7.6.5. Conclusions on performances of tests 1-5.......................245
7.7. Performances of algorithms with and without boundary optimization...... 246
7.7.1. Detection of non-stationary segment.......................... 246
7.7.2. Detection of multi-segment series.............................247
7.8. Conclusions about the segmentation algorithm.........................249
8. Estimation of Turbulent Kinetic Energy Dissipation
8.1. Introduction....................................................253
8.2. Multi-taper Spectral Estimation.....................................256
8.3. Batchelor Curve Fitting...........................................257
8.4. Comparison of Spectral Estimation Methods...........................259
8.5. Batchelor Curve Fitting to Synthetic Series............................262
8.5.1. Batchelor curve fitting using the first error function...............263
8.5.2. Batchelor curve fitting using the second error function............ 268
8.5.3. Batchelor curve fitting using the third error function...............269
8.6. Conclusions on Batchelor curve fitting..............................276
9. Segmentation of Observed Data
9.1. Introduction....................................................277
9.2. Temperature Gradient Profiles......................................277
9.2.1. Ratios of Unresolved, Bad-Fit and Good-Fit Segments.............277
9.2.2. Estimated Values of £ and xt from Resolved Spectra............281
9.2.3. Estimated Values of £ andxr from Profiles in the Same Lake......281
9.2.4. Estimated Values of £ andxr from Different Lakes............. 282
9.3. Conclusions on Segmentation of Temperature Gradient Profiles..........282
9.4. Hydrologic Series...............................................301
9.4.1. Stationary Segments from Hydrologic Series....................301
9.4.2. Change Points in Hydrologic Series...........................308
9.5. Conclusions on Segmentation of Hydrologic Series.....................311
10. Linearity and Gaussianity Analysis
10.1. Introduction..................................................312
10.2. Tests for Gaussianity and Linearity (Hinich, 1982)................... 312
10.3. Testing for Stationary Segments.................................. 315
10.3.1. Testing Temperature Gradient Profiles.......................316
10.3.2. Testing Hydrologic Series.................................316
10.4. Conclusions about Testing the Hydrologic Series.....................316
11. Bayesian Detection of Shifts in Hydrologic Time Series
11.1. Introduction..................................................320
11.2. Data Used in this Chapter........................................321
11.3. A Bayesian Method to Detect Shifts in Data..........................322
11.3.1. Theory.................................................322
11.3.1.1. Parameters of the distribution and the change point n .......328
11.3.1.2. The Unconditional Posterior Distributions of 8, u and y.....330
11.3.1.3. The Conditional Posterior Distributions of Pj, of and 9j......332
11.3.2. Computation Sequences....................................333
11.4. Discussion of Results...........................................335
11.4.1.The Posterior Distribution of the Change point n!...............335
11.4.2. The Unconditional Posterior Distributions of 5, u and y.........335
11.4.3. The Conditional Posterior Distributions of Pj ,af and9j........336
11.5. Conclusions..................................................346
12. References....................................................347
13. Index........................................................359
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| spelling | Rao, Adiseshappa Ramachandra Verfasser aut Nonstationarities in hydrologic and environmental time series by A. Ramachandra Rao, Khaled H. Hamed, and Huey-Long Chen Boston Kluwer Acad. Publ. 2003 XXVI, 362 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Water science and technology library 45 Includes bibliographical references and index "This book presents a number of new techniques that have been discussed in the literature during the last two decades 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. It is hoped that this endeavor would facilitate further research into this important area."--BOOK JACKET. Mathematik Hydrology Mathematics Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Hydrologie (DE-588)4026309-5 gnd rswk-swf Hydrologie (DE-588)4026309-5 s Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Hamed, Khaled H. Sonstige oth Chen, Huey-Long Sonstige oth Water science and technology library 45 (DE-604)BV000625426 45 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010300432&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Nonstationarities in hydrologic and environmental time series |
| title_auth | Nonstationarities in hydrologic and environmental time series |
| title_exact_search | Nonstationarities in hydrologic and environmental time series |
| title_full | Nonstationarities in hydrologic and environmental time series by A. Ramachandra Rao, Khaled H. Hamed, and Huey-Long Chen |
| title_fullStr | Nonstationarities in hydrologic and environmental time series by A. Ramachandra Rao, Khaled H. Hamed, and Huey-Long Chen |
| title_full_unstemmed | Nonstationarities in hydrologic and environmental time series by A. Ramachandra Rao, Khaled H. Hamed, and Huey-Long Chen |
| title_short | Nonstationarities in hydrologic and environmental time series |
| title_sort | nonstationarities in hydrologic and environmental time series |
| topic | Mathematik Hydrology Mathematics Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd Hydrologie (DE-588)4026309-5 gnd |
| topic_facet | Mathematik Hydrology Mathematics Time series analysis Zeitreihenanalyse Hydrologie |
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