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Floods in a Changing Climate: Extreme Precipitation [Hardback]

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(Florida Atlantic University)
  • Formāts: Hardback, 285 pages, height x width x depth: 281x222x17 mm, weight: 1020 g, Worked examples or Exercises; 51 Tables, black and white; 4 Plates, color; 20 Halftones, unspecified; 20 Halftones, black and white; 170 Line drawings, black and white
  • Sērija : International Hydrology Series
  • Izdošanas datums: 22-Nov-2012
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107018781
  • ISBN-13: 9781107018785
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  • Cena: 110,64 €
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Floods in a Changing Climate: Extreme PrecipitationPalielināt
  • Formāts: Hardback, 285 pages, height x width x depth: 281x222x17 mm, weight: 1020 g, Worked examples or Exercises; 51 Tables, black and white; 4 Plates, color; 20 Halftones, unspecified; 20 Halftones, black and white; 170 Line drawings, black and white
  • Sērija : International Hydrology Series
  • Izdošanas datums: 22-Nov-2012
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107018781
  • ISBN-13: 9781107018785
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781107018785.html
Keywords:
"Measurement, analysis and modeling of extreme precipitation events linked to floods is vital in understanding the world's changing climate. This book examines and documents the impacts of climate change and climate variability on extreme precipitation events, providing methods for assessment of the trends in these events, and their impacts. It also provides a basis to develop procedures and guidelines for climate-adaptive hydrologic engineering. Topics covered include approaches for assessment of hydrometerological floods, recent developments in hydrologic design for flood mitigation, and applications and limitations of improved precipitation forecasts, using information about internal modes of climate (teleconnections). State-of-the-art methodologies for precipitation analysis, estimation and interpolation are included, and exercises for each chapter, supported by modelling software and computational tools available online at www.cambridge.org/teegavarapu, enable the reader to apply and engage with theinnovative methods of assessment"--

Papildus informācija

Provides measurement, analysis and modeling methods for assessment of trends in extreme precipitation events, for academic researchers and professionals.
Foreword ix
Preface xi
List of abbreviations
xv
1 Precipitation and climate change
1(9)
1.1 Introduction
1(1)
1.2 Climate change and variability
1(1)
1.3 Precipitation processes and floods
1(4)
1.4 Impacts of climate change
5(1)
1.5 Internal modes of climate variability: teleconnections
6(1)
1.6 Extreme precipitation and floods in a changing climate: main issues
7(1)
1.7 Conclusions and summary
8(2)
Exercises
8(2)
2 Precipitation measurement
10(38)
2.1 Introduction
10(1)
2.2 Precipitation measurement in a historical context
10(1)
2.3 Ground-, radar-, and satellite-based measurements
10(1)
2.4 Measurement methods, errors, and accuracy
11(1)
2.5 Configurations of rain gages
11(2)
2.6 Radar measurement of precipitation
13(1)
2.7 Weather radar and the theory of reflectivity
14(7)
2.8 Evaluation of exponents and coefficient values in a Z-R power relationship
21(1)
2.9 Formulation for optimal coefficients and exponents
22(3)
2.10 Bias evaluation and corrections
25(3)
2.11 Evaluation of methods
28(1)
2.12 Weighting functions
29(1)
2.13 Performance evaluations with multiple stations
30(1)
2.14 Optimal parameters for weighting methods
30(1)
2.15 Bias corrections with limited rain gage data
31(1)
2.16 Satellite-based rainfall estimation
31(3)
2.17 Precipitation monitoring networks
34(1)
2.18 Clustering of rain gages
34(2)
2.19 Optimal density
36(1)
2.20 Optimal monitoring networks
36(1)
2.21 Methods for design
36(1)
2.22 Recommendations for rain gage placements
37(1)
2.23 Global precipitation data sets
38(3)
2.24 Global precipitation data sets: availability and formats
41(1)
2.25 Evaluation of observed gridded precipitation data sets
42(3)
2.26 Monitoring networks for extreme events
45(1)
2.27 Precipitation measurements in the future
45(1)
2.28 Summary and conclusions
46(2)
Exercises
46(1)
Websites for data acquisition and resources
47(1)
3 Spatial analysis of precipitation
48(67)
3.1 Spatial analysis of precipitation data
48(1)
3.2 Missing data estimation
49(1)
3.3 Spatial interpolation
50(1)
3.4 Deterministic and stochastic interpolation methods
51(6)
3.5 Revisions to the inverse distance weighting method
57(1)
3.6 Integration of the Thiessen polygon approach and inverse distance method
57(1)
3.7 Correlation coefficient weighting method
58(1)
3.8 Inverse exponential weighting method
58(1)
3.9 Regression models
58(1)
3.10 Trend surface models using local and global polynomial functions
59(1)
3.11 Example for trend surface models
60(2)
3.12 Thin-plate splines
62(1)
3.13 Natural neighbor interpolation
63(1)
3.14 Normal ratio method
63(1)
3.15 Nearest neighbor weighting method
63(2)
3.16 Variants of multiple linear regression methods
65(1)
3.17 Regression models using auxiliary information
65(1)
3.18 Geostatistical spatial interpolation
66(4)
3.19 Optimal functional forms
70(3)
3.20 Structure of optimization formulations
73(5)
3.21 Emerging interpolation techniques
78(3)
3.22 Artificial neural networks
81(1)
3.23 Universal function approximation-based kriging
81(2)
3.24 Classification methods
83(1)
3.25 Distance metrics as proximity measures
84(1)
3.26 Distance metrics for precipitation data
84(2)
3.27 Boolean distance measures for precipitation data
86(2)
3.28 Optimal exponent weighting of proximity measures
88(1)
3.29 Optimal K-nearest neighbor classification method
88(1)
3.30 Optimal K-means clustering method
89(1)
3.31 Proximity measures: limitations
90(1)
3.32 Use of radar data for infilling precipitation data
90(1)
3.33 Geographically weighted optimization
91(1)
3.34 Single and multiple imputations of missing data
92(2)
3.35 Temporal interpolation of missing data
94(1)
3.36 Data set selection for model development and validation
95(1)
3.37 Performance measures
96(2)
3.38 Qualitative evaluation
98(1)
3.39 Model selection and multi-model comparison
99(1)
3.40 Surface generation
100(1)
3.41 Geo-spatial grid-based transformations of precipitation data
101(5)
3.42 Statistics preserving spatial interpolation
106(1)
3.43 Data for model development
107(1)
3.44 Optimization issues: solvers and solution methods
107(1)
3.45 Spatial analysis environments and interpolation
108(1)
3.46 Data filler approaches: application in real time
108(1)
3.47 Local and global interpolation: issues
108(1)
3.48 Under- and overestimation
109(1)
3.49 Main issues and complexities of spatial analysis of precipitation data
109(1)
3.50 Spatial interpolation for global gridded precipitation data sets
109(1)
3.51 Spatial interpolation of extreme precipitation data
110(1)
3.52 Applicability of methods
110(1)
3.53 RAIN: Rainfall Analysis and Interpolation Software
110(1)
3.54 Use and application of RAIN software
111(1)
3.55 Conclusions and summary
111(4)
Exercises
112(3)
4 Extreme precipitation and floods
115(33)
4.1 Introduction
115(1)
4.2 Hydrometeorological aspects of precipitation
115(1)
4.3 Larger-scale precipitation systems
115(1)
4.4 Convective patterns
116(1)
4.5 Precipitation and river regimes
116(1)
4.6 Hydrometeorological aspects of floods: review of case studies
116(2)
4.7 Probable maximum precipitation
118(2)
4.8 Precipitation-based drivers and mechanisms influencing extreme floods
120(1)
4.9 Flooding mechanisms
120(1)
4.10 Flooding and shallow groundwater levels
120(1)
4.11 Soil moisture contributions to flooding
121(2)
4.12 Spatial and temporal occurrence of extreme events: dependence analysis
123(4)
4.13 Joint probability analysis
127(3)
4.14 Partial duration series analysis: peaks over thresholds
130(1)
4.15 Baseflow separation methods
131(2)
4.16 Extreme precipitation and flash floods
133(1)
4.17 Precipitation thresholds and floods
133(1)
4.18 Temporal difference in occurrence of peaks
134(1)
4.19 Cyclonic precipitation: episodic events
135(1)
4.20 Desk study approach
135(2)
4.21 Regression analysis
137(1)
4.22 Extreme precipitation events and peak flooding: example
138(2)
4.23 Assessment from dependence analysis
140(1)
4.24 Statistical analysis of peak discharge and precipitation data
141(3)
4.25 Floods in a changing climate: issues
144(1)
4.26 Conclusions and summary
145(3)
Exercises
145(3)
5 Climate change modeling and precipitation
148(21)
5.1 Downscaling precipitation
148(1)
5.2 Downscaling methods
148(1)
5.3 Downscaling at spatial level
148(1)
5.4 Downscaling at temporal level
149(1)
5.5 Statistical downscaling techniques
149(2)
5.6 Weather generators
151(1)
5.7 Regional climate model: dynamic downscaling
151(1)
5.8 Other approaches
152(1)
5.9 Statistically downscaled climate change projections: concept example
152(10)
5.10 Weather generator: concepts
162(5)
5.11 Downscaling precipitation: major issues
167(1)
5.12 Conclusions and summary
167(2)
Exercises
167(1)
Useful websites
168(1)
Resources for students
168(1)
6 Precipitation variability and teleconnections
169(24)
6.1 Introduction
169(1)
6.2 Southern Oscillation
170(1)
6.3 El Nino Southern Oscillation
170(5)
6.4 Decadal oscillations
175(2)
6.5 Teleconnections and extreme precipitation
177(8)
6.6 ENSO and precipitation
185(2)
6.7 Combined influence of AMO-ENSO phases
187(1)
6.8 Pacific Decadal Oscillation
187(1)
6.9 North Atlantic Oscillation
187(2)
6.10 Forecasts based on teleconnections
189(1)
6.11 Precipitation and teleconnections: global impacts
189(2)
6.12 Conclusions and summary
191(2)
Exercises
191(1)
Useful websites
192(1)
7 Precipitation trends and variability
193(32)
7.1 Historical and future trends
193(1)
7.2 Global precipitation trends
193(1)
7.3 USA precipitation changes
194(1)
7.4 Assessment of extreme precipitation trends: techniques
194(1)
7.5 Fitting probability distributions for extreme rainfall data
195(2)
7.6 Statistical distributions
197(1)
7.7 Parameter estimation
197(1)
7.8 Frequency factors
198(1)
7.9 Parametric and non-parametric tests
199(2)
7.10 Regional frequency analysis
201(1)
7.11 Illustrative examples
201(5)
7.12 Value of fitting a parametric frequency curve
206(1)
7.13 Extreme rainfall frequency analysis in the USA
207(1)
7.14 Uncertainty and variability in rainfall frequency analysis
208(3)
7.15 Assessment of sample variances
211(1)
7.16 Non-parametric methods
211(1)
7.17 Homogeneity
212(1)
7.18 Partial duration series
213(1)
7.19 Incorporating climate variability and climate change into rainfall frequency analysis
213(1)
7.20 Future data sources
213(1)
7.21 Statistical tests and trend analysis: example of extreme precipitation analysis in South Florida
214(1)
7.22 Different tests: moving window approaches
215(1)
7.23 Implications of infilled data
215(2)
7.24 Descriptive indices for precipitation extremes
217(4)
7.25 Rare extremes
221(1)
7.26 Trends based on GCM model simulations
222(1)
7.27 Software for evaluation of extreme precipitation data
222(1)
7.28 Conclusions and summary
222(3)
Exercises
222(2)
Useful website
224(1)
8 Hydrologic modeling and design
225(16)
8.1 Precipitation and climate change: implications on hydrologic modeling and design
225(1)
8.2 Emerging trends in hydrologic design for extreme precipitation
225(1)
8.3 Methodologies for hydrologic design
226(1)
8.4 Hydrologic design
227(1)
8.5 Adaptive hydrologic infrastructure design
228(3)
8.6 Hydrologic design example
231(2)
8.7 Example of water balance model
233(2)
8.8 Water budget model software
235(1)
8.9 Infrastructural modifications and adaptation to climate change
236(2)
8.10 Conclusions and summary
238(3)
Exercises
238(3)
9 Future perspectives
241(8)
9.1 Future hydrologic design and water resources management
241(1)
9.2 Uncertain climate change model simulations
241(1)
9.3 Future of hydrologic data for design
242(1)
9.4 Tools for climate-sensitive management of water resources systems
243(1)
9.5 Example: generation of compromise operating policies for flood protection
243(2)
9.6 Impacts of climate change on reservoir operations: example from Brazil
245(1)
9.7 Climate change and future hydrologic engineering practice
246(1)
9.8 Floods: stationarity and non-stationarity issues
247(1)
9.9 Extreme precipitation: issues for the future
247(1)
9.10 Institutional changes and adaptation challenges
247(1)
9.11 Conclusions and summary
248(1)
Exercises
248(1)
Glossary 249(4)
References 253(13)
Index 266
Ramesh S. V. Teegavarapu is an Assistant Professor in the Department of Civil, Environmental and Geomatics Engineering at Florida Atlantic University, and is leader of the Hydrosystems Research Laboratory in that department. His main area of specialization is water resources with focuses on climate change modeling, precipitation extremes and hydrological process. He is a member of the Water Resources Management Committee of the International Association for Hydro-Environment Engineering and Research (IAHR). Dr Teegavarapu currently serves on the editorial board of two international journals and has published over 100 articles in journals and conference proceedings. He has convened, chaired and moderated over fifty sessions at several international conferences and served as vice-chair for task committees related to radar rainfall and uncertainty analysis approaches under the Surface Water Hydrology Technical Committee (SWHTC) of the American Society of Civil Engineers (ASCE).