Keywords:
Climatic changes.
;
Hydrology.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (329 pages)
Edition:
1st ed.
ISBN:
9783642148637
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=645931
Language:
English
Note:
Intro -- Preface -- Contents -- Acronyms -- Contributors -- Part I Extremes, Uncertainty, and Reconstruction -- 1 The Statistics of Return Intervals, Maxima, and Centennial Events Under the Influence of Long-Term Correlations -- Jan F. Eichner, Jan W. Kantelhardt, Armin Bunde, and Shlomo Havlin -- 1.1 Introduction -- 1.2 Statistics of Return Intervals -- 1.2.1 Mean Return Interval and Standard Deviation -- 1.2.2 Stretched Exponential and Finite-Size Effects for Large Return Intervals -- 1.2.3 Power-Law Regime and Discretization Effects for Small Return Intervals -- 1.2.4 Long-Term Correlations of the Return Intervals -- 1.2.5 Conditional Return Interval Distributions -- 1.2.6 Conditional Return Periods -- 1.3 Statistics of Maxima -- 1.3.1 Extreme Value Statistics for i.i.d. Data -- 1.3.2 Effect of Long-Term Persistence on the Distribution of the Maxima -- 1.3.3 Effect of Long-Term Persistence on the Correlations of the Maxima -- 1.3.4 Conditional Mean Maxima -- 1.3.5 Conditional Maxima Distributions -- 1.4 Centennial Events -- 1.5 Conclusion -- References -- 2 The Bootstrap in Climate Risk Analysis -- Manfred Mudelsee -- 2.1 Introduction -- 2.2 Method -- 2.2.1 Kernel Risk Estimation -- 2.2.2 Bootstrap Confidence Band Construction -- 2.3 Data -- 2.4 Results -- 2.5 Conclusion -- References -- 3 Confidence Intervals for Flood Return Level Estimates Assuming Long-Range Dependence -- Henning W. Rust, Malaak Kallache, Hans Joachim Schellnhuber, and Jürgen P. Kropp -- 3.1 Introduction -- 3.2 Basic Theory -- 3.2.1 The Generalized Extreme Value Distribution -- 3.2.2 GEV Parameter Estimation -- 3.3 Effects of Dependence on Confidence Intervals -- 3.4 Bootstrapping the Estimators Variance -- 3.4.1 Motivation of the Central Idea -- 3.4.2 Modelling the Distribution -- 3.4.3 Modelling the ACF.
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3.4.4 Combining Distribution and Autocorrelation -- 3.4.5 Generating Bootstrap Ensembles -- 3.5 Comparison of the Bootstrap Approaches -- 3.5.1 Monte Carlo Reference Ensemble -- 3.5.2 The Bootstrap Ensembles -- 3.5.3 Ensemble Variability and Dependence on Ensemble Size -- 3.6 Case Study -- 3.6.1 Extreme Value Analysis -- 3.6.2 Modelling the ACF of the Daily Series -- 3.6.3 Modelling the ACF of the Maxima Series -- 3.6.4 Combining Distribution and ACF -- 3.6.5 Calculating the Confidence Limit -- 3.7 Discussion -- 3.8 Conclusion -- References -- 4 Regional Determination of Historical Heavy Rain for Reconstruction of Extreme Flood Events -- Paul Dostal, Florian Imbery, Katrin Bürger, and Jochen Seidel -- 4.1 Introduction -- 4.2 Methodological Concept and Data Overview -- 4.2.1 Meteorological Data from October 1824 -- 4.2.2 Methods -- 4.3 Results -- 4.3.1 The Extreme Weather Situation of October 1824 -- 4.3.2 Calculation of the 1824 Precipitation -- 4.3.3 Modelling the Area Precipitation of 1824 -- 4.3.4 Modelled Neckar Discharges for the Flood Eventof October 1824 -- 4.4 Conclusion -- References -- 5 Development of Regional Flood Frequency Relationships for Gauged and Ungauged Catchments Using L-Moments -- Rakesh Kumar and Chandranath Chatterjee -- 5.1 Introduction -- 5.2 Regional Flood Frequency Analysis -- 5.2.1 At-Site Flood Frequency Analysis -- 5.2.2 At-Site and Regional Flood Frequency Analysis -- 5.2.3 Regional Flood Frequency Analysis -- 5.3 L-Moment Approach -- 5.3.1 Probability Weighted Moments and L-Moments -- 5.3.2 Screening of Data Using Discordancy Measure Test -- 5.3.3 Test of Regional Homogeneity -- 5.3.4 Identification of Robust Regional Frequency Distribution -- 5.4 Study Area and Data Availability -- 5.5 Analysis and Discussion of Results -- 5.5.1 Screening of Data Using Discordancy Measure Test.
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5.5.2 Test of Regional Homogeneity -- 5.5.3 Identification of Robust Regional Frequency Distribution -- 5.5.4 Regional Flood Frequency Relationship for Gauged Catchments -- 5.5.5 Regional Flood Frequency Relationship for Ungauged Catchments -- 5.6 Conclusion -- References -- Part II Extremes, Trends, and Changes -- 6 Intense Precipitation and High Floods -- Observations and Projections -- Zbigniew W. Kundzewicz -- 6.1 Introduction -- 6.2 Observations of Intense Precipitation -- 6.3 Observations of River Flow -- 6.4 Projections of Intense Precipitation and High River Flow -- 6.5 Conclusion -- References -- 7 About Trend Detection in River Floods -- Maciej Radziejewski -- 7.1 Introduction -- 7.2 Methods -- 7.2.1 Testing of Significance -- 7.2.2 Resampling -- 7.2.3 Tests for Changes -- 7.3 Trends in Time Series of River Flows -- 7.4 Trends in Seasonal Maxima -- 7.5 Seasonal Peaks Over Threshold -- 7.6 Most Extreme Flows -- 7.7 Conclusion -- References -- 8 Extreme Value Analysis Considering Trends: Application to Discharge Data of the Danube River Basin -- Malaak Kallache, Henning W. Rust, Holger Lange, and Jürgen P. Kropp -- 8.1 Introduction -- 8.2 Method -- 8.2.1 Choice of the Extreme Values -- 8.2.2 Point Processes -- 8.2.3 Test for Trend -- 8.2.4 Return-Level Estimation -- 8.3 Results -- 8.4 Conclusion -- References -- 9 Extreme Value and Trend Analysis Based on Statistical Modelling of Precipitation Time Series -- Silke Trömel and Christian-D. Schönwiese -- 9.1 Introduction -- 9.2 Components -- 9.3 The Distance Function and the Model Selection Criterion -- 9.4 Application to a German Station Network -- 9.4.1 General Remarks -- 9.4.2 Example: Eisenbach--Bubenbach -- 9.4.3 Probability Assessment of Extreme Values -- 9.4.4 Changes in the Expected Value -- 9.5 Conclusions -- References.
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10 A Review on the Pettitt Test -- Diego Rybski and Jörg Neumann -- 10.1 Introduction -- 10.2 Data -- 10.3 Methods -- 10.4 Results -- 10.4.1 River Runoff Records -- 10.4.2 Simulations -- 10.4.3 Reasoning -- 10.5 Conclusion -- References -- Part III Long-Term Phenomena and Nonlinear Properties -- 11 Detrended Fluctuation Studies of Long-Term Persistence and Multifractality of Precipitation and River Runoff Records -- Diego Rybski, Armin Bunde, Shlomo Havlin, Jan W. Kantelhardt, and Eva Koscielny-Bunde -- 11.1 Introduction -- 11.2 Data -- 11.3 Correlation Analysis -- 11.3.1 General -- 11.3.2 Standard Fluctuation Analysis (FA) -- 11.3.3 The Detrended Fluctuation Analysis (DFA) -- 11.3.4 Wavelet Transform (WT) -- 11.4 Multifractal Analysis -- 11.4.1 Multifractal DFA (MF-DFA) -- 11.4.2 Comparison with Related Multifractal Formalisms -- 11.5 Results of the Correlation Behaviour -- 11.5.1 River Runoff -- 11.5.2 Precipitation -- 11.6 Results of the Multifractal Behaviour -- 11.6.1 Fits by the Universal Multifractal Model -- 11.6.2 Fits by the Extended Multiplicative Cascade Model -- 11.6.3 Fits by the Bifractal Model -- 11.6.4 Results -- 11.7 Conclusion -- References -- 12 Long-Term Structures in Southern German Runoff Data -- Miguel D. Mahecha, Holger Lange, and Gunnar Lischeid -- 12.1 Introduction -- 12.2 Methods -- 12.2.1 Data Sets and Preparation -- 12.2.2 Dimensionality Reduction in Space -- 12.2.3 Dimensionality Reduction in Time -- 12.3 Results -- 12.3.1 Dimensionality Reduction -- 12.3.2 Dimensionality Reduction in the Time Domain -- 12.3.3 Regional Patterns and Hydrological Dependencies -- 12.4 Discussion -- 12.4.1 Methodological Outlook -- 12.5 Conclusion -- References -- 13 Seasonality Effects on Nonlinear Properties of Hydrometeorological Records.
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Valerie N. Livina, Yosef Ashkenazy, Armin Bunde, and Shlomo Havlin -- 13.1 Introduction -- 13.2 Methodology -- 13.2.1 Detrended Fluctuation Analysis and Multifractal Detrended Fluctuation Analysis -- 13.2.2 Volatility Correlations and Surrogate Test for Nonlinearity -- 13.3 The Way Periodicities Affect the Estimation of Nonlinearities: Conventional Seasonal Detrending -- 13.4 A New Method for Filtering Out Periodicities -- 13.5 Results -- 13.5.1 Tests of Artificial Data -- 13.5.2 Results of Volatility Analysis of Observed River Data -- 13.5.3 Results of Multifractal Analysis of Observed River Data -- 13.6 Conclusion -- References -- 14 Spatial Correlations of River Runoffs in a Catchment -- Reik Donner -- 14.1 Introduction -- 14.2 Description of the Data -- 14.3 Correlation Functions and Related Quantities -- 14.3.1 Pearson's Linear Correlation -- 14.3.2 Non-parametric (Rank-Order) Correlations -- 14.3.3 (Cross-) Mutual Information -- 14.3.4 Recurrence Quantification Analysis (RQA) -- 14.4 Mutual Correlations Between Different Stations -- 14.5 Ensemble Correlations -- 14.5.1 KLD-Based Dimension Estimates -- 14.5.2 Case Study I: The Christmas 1967 Flood -- 14.5.3 Non-parametric Ensemble Correlations -- 14.5.4 Case Study II: The January 1995 Flood -- 14.6 Conclusion -- References -- Subject Index.
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