Floods in a Changing Climate: Extreme Precipitation

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Measurement, analysis and modeling of extreme precipitation events linked to floods is vital in understanding changing climate impacts and variability. This book provides 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. Academic researchers in the fields of hydrology, climate change, meteorology, environmental policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and climate adaptation will find this an invaluable resource. This volume is the first in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Hydrological Modeling by P. P. Mujumdar and D. Nagesh Kumar, Floods in a Changing Climate: Inundation Modeling by Giuliano Di Baldassarre and Floods in a Changing Climate: Risk Management by Slodoban Simonović.
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Table of contents :
Contents......Page 7

Foreword......Page 11
Preface......Page 13
Abbreviations......Page 17
1.3.1 Precipitation extremes......Page 21
1.3.2 Floods and flooding risks......Page 22
1.4.1 Climate change trends and projections......Page 25
1.5.1 Oscillatory behaviors......Page 26
1.6 Extreme precipitation and floods in a changing climate: main issues......Page 27
Exercises......Page 28
2.3 Ground-, radar-, and satellite-based measurements......Page 30
2.5.2 Tipping bucket rain gage......Page 31
2.5.6 Rain gage diversity and distribution......Page 32
2.6 Radar measurement of precipitation......Page 33
2.7 Weather radar and the theory of reflectivity......Page 34
2.7.2 Reflectivity-rainfall rate relationships......Page 35
2.7.3 Reflectivity measurements and extraction......Page 37
2.7.5 Reflectivity data processing: an example from the USA......Page 38
2.7.7 Rain Radar Retrieval System......Page 39
2.7.8 Z-R relationships......Page 40
2.8 Evaluation of exponents and coefficient values in a Z-R power relationship......Page 41
2.9.1 Multiple Z-R relationships: moving temporal window......Page 42
2.9.4 Optimization of Z-R relationships using probability matching......Page 43
2.9.8 Optimal Z-R relationships: key issues......Page 44
2.10 Bias evaluation and corrections......Page 45
2.10.2 Model II: local weighted bias based interpolation......Page 47
2.11 Evaluation of methods......Page 48
2.12.3 Difference in coefficient of variation......Page 49
2.14 Optimal parameters for weighting methods......Page 50
2.16 Satellite-based rainfall estimation......Page 51
2.16.2 Global precipitation measurement......Page 52
2.18 Clustering of rain gages......Page 54
2.21.2 Geostatistics-based method......Page 56
2.22 Recommendations for rain gage placements......Page 57
2.23.4 Global Historical Climatology Network......Page 58
2.23.5 Climatic Research Unit......Page 59
2.23.8 Global Climate Observing System......Page 60
2.24.1 Observed gridded data sets......Page 61
2.25 Evaluation of observed gridded precipitation data sets......Page 62
2.27 Precipitation measurements in the future......Page 65
Exercises......Page 66
Websites for data acquisition and resources......Page 67
3.1 Spatial analysis of precipitation data......Page 68
3.2.3 Interpolation in space and time......Page 69
3.3.2 Exact and inexact interpolation......Page 70
3.4.3 Gage mean estimator......Page 71
3.4.4 Climatological mean estimator......Page 72
3.4.5 Thiessen polygons......Page 73
3.4.6 Inverse distance weighting method......Page 74
3.4.7 Application of inverse distance weighting method: example......Page 75
3.6 Integration of the Thiessen polygon approach and inverse distance method......Page 77
3.9.1 Multiple linear regression......Page 78
3.10 Trend surface models using local and global polynomial functions......Page 79
3.11 Example for trend surface models......Page 80
3.12.1 Thin-plate splines with tension......Page 82
3.15 Nearest neighbor weighting method......Page 83
3.15.1 Revised nearest neighbor weighting method......Page 84
3.17 Regression models using auxiliary information......Page 85
3.18.2 Semi-variogram models......Page 86
3.18.4 Limitations of kriging......Page 88
3.18.8 Precipitation-specific kriging applications in hydrology......Page 89
3.19.1 Fixed function set genetic algorithm method......Page 90
3.19.2 FFSGAM for estimating missing precipitation data......Page 91
3.19.3 Mathematical programming formulation for optimal coefficients......Page 92
3.20.3 Model IA......Page 93
3.20.6 Model IIB: Stratification of data......Page 94
3.20.9 Model IV: Rain gage clusters......Page 95
3.20.11 Model VB......Page 96
3.20.14 Issues with objective functions......Page 97
3.21.5 Association rule mining-based spatial interpolation......Page 98
3.21.6 Data mining tool: WEKA......Page 100
3.23 Universal function approximation-based kriging......Page 101
3.24.2 Classification using single best classifier......Page 103
3.26.1 Euclidean......Page 104
3.26.11 Cosine and correlation similarity measures......Page 105
3.27.1 Simple matching......Page 106
3.27.6 Pearson......Page 107
3.29 Optimal K-nearest neighbor classification method......Page 108
3.30.1 Optimal weights for selected clusters......Page 109
Model I......Page 110
3.33 Geographically weighted optimization......Page 111
3.34 Single and multiple imputations of missing data......Page 112
3.34.2 Bootstrap re-sampling......Page 113
3.35 Temporal interpolation of missing data......Page 114
3.36 Data set selection for model development and validation......Page 115
3.37 Performance measures......Page 116
3.38 Qualitative evaluation......Page 118
3.39 Model selection and multi-model comparison......Page 119
3.40 Surface generation......Page 120
3.41 Geo-spatial grid-based transformations of precipitation data......Page 121
3.41.1 Area-weighting method......Page 124
3.41.4 Equal weights (average) method......Page 125
3.42.1 Optimization formulations......Page 126
3.44 Optimization issues: solvers and solution methods......Page 127
3.47 Local and global interpolation: issues......Page 128
3.50 Spatial interpolation for global gridded precipitation data sets......Page 129
3.53 RAIN: Rainfall Analysis and INterpolation Software......Page 130
3.55 Conclusions and summary......Page 135
Exercises......Page 136
4.3 Larger-scale precipitation systems......Page 139
4.6 Hydrometeorological aspects of floods: review of case studies......Page 140
4.7 Probable maximum precipitation......Page 142
4.7.6 Statistical estimation method......Page 143
4.10 Flooding and shallow groundwater levels......Page 144
4.11 Soil moisture contributions to flooding......Page 145
4.11.2 Adjusted curve numbers and antecedent moisture conditions......Page 146
4.12 Spatial and temporal occurrence of extreme events: dependence analysis......Page 147
4.12.1 Peak discharge and accumulated rainfall......Page 149
4.12.2 Adequacy of design: extreme precipitation and discharges......Page 150
4.13 Joint probability analysis......Page 151
4.13.4 Normality tests......Page 153
4.14 Partial duration series analysis: peaks over thresholds......Page 154
4.15 Baseflow separation methods......Page 155
4.17.1 Models with lead times......Page 157
4.18 Temporal difference in occurrence of peaks......Page 158
4.20 Desk study approach......Page 159
4.21 Regression analysis......Page 161
4.22 Extreme precipitation events and peak flooding: example......Page 162
4.22.1 Dependence analysis......Page 163
4.23 Assessment from dependence analysis......Page 164
4.24 Statistical analysis of peak discharge and precipitation data......Page 165
4.25 Floods in a changing climate: issues......Page 168
Exercises......Page 169
5.3 Downscaling at spatial level......Page 172
5.5.1 Approaches using transfer functions......Page 173
5.5.2 Weather typing......Page 174
5.7 Regional climate model: dynamic downscaling......Page 175
5.9.1 Special Report on Emissions Scenarios......Page 176
5.9.2 Bias-correction spatial disaggregation......Page 178
5.9.5 Selection of downscaled climate change model......Page 179
5.9.8 Results and analysis......Page 180
5.10 Weather generator: concepts......Page 186
Exercises......Page 191
Resources for students......Page 192
6.1.2 Oscillatory behavior......Page 193
6.3 El Niño Southern Oscillation......Page 194
6.3.3 ENSO influences in the USA......Page 195
6.4.1 Atlantic Multi-decadal Oscillation......Page 199
6.5.1 Assessment of AMO: example from southeastern USA......Page 201
6.5.2 Temporal windows......Page 202
6.5.5 Data windows: extreme precipitation events......Page 207
6.6 ENSO and precipitation......Page 209
6.9 North Atlantic Oscillation......Page 211
6.11 Precipitation and teleconnections: global impacts......Page 213
Exercises......Page 215
Useful websites......Page 216
7.2 Global precipitation trends......Page 217
7.4 Assessment of extreme precipitation trends: techniques......Page 218
7.5 Fitting probability distributions for extreme rainfall data......Page 219
7.5.1 Development of cumulative distribution functions......Page 220
7.7 Parameter estimation......Page 221
7.8 Frequency factors......Page 222
7.9.1 Goodness-of-fit tests for normal distributions......Page 223
7.9.2 Goodness-of-fit tests for other distributions......Page 224
7.11.1 Daily precipitation time series......Page 225
7.11.2 Annual extremes for different durations......Page 226
7.12 Value of fitting a parametric frequency curve......Page 230
7.13 Extreme rainfall frequency analysis in the USA......Page 231
7.14 Uncertainty and variability in rainfall frequency analysis......Page 232
7.14.3 Missing data......Page 233
7.14.5 Detection of change in moments......Page 234
7.16.1 Kernel density estimation......Page 235
7.17 Homogeneity......Page 236
7.20 Future data sources......Page 237
7.21.3 Mann-Kendall test with trend-free pre-whitening......Page 238
7.23 Implications of infilled data......Page 239
7.24 Descriptive indices for precipitation extremes......Page 241
7.24.1 Climate normals and indices......Page 243
7.25 Rare extremes......Page 245
Exercises......Page 246
Useful website......Page 248
8.2 Emerging trends in hydrologic design for extreme precipitation......Page 249
8.3.1 Data length and design storms......Page 250
8.4 Hydrologic design......Page 251
8.5 Adaptive hydrologic infrastructure design......Page 252
Step III: Final formulation......Page 253
8.5.3 Application to stormsewer design problem......Page 254
8.6 Hydrologic design example......Page 255
8.6.1 Solution......Page 256
8.7.1 Water balance model: Thomas model......Page 257
8.7.3 Parameter estimation......Page 258
8.8 Water budget model software......Page 259
8.9 Infrastructural modifications and adaptation to climate change......Page 260
Exercises......Page 262
9.2 Uncertain climate change model simulations......Page 265
9.3.1 Climate change projections and data sets......Page 266
9.5.1 Fuzzy set based reservoir operation model......Page 267
9.5.2 Fuzzy membership functions: preferences towards climate change......Page 268
9.6 Impacts of climate change on reservoir operations: example from Brazil......Page 269
9.7 Climate change and future hydrologic engineering practice......Page 270
9.10 Institutional changes and adaptation challenges......Page 271
Exercises......Page 272
Glossary......Page 273
References......Page 277
Index......Page 290
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Floods in a Changing Climate: Extreme Precipitation
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