Runoff prediction in ungauged basins synthesis across processes, places and scales
"Predicting water runoff in ungauged water catchment areas is vital to practical applications such as the design of drainage infrastructure and flooding defences, runoff forecasting, and for catchment management tasks such as water allocation and climate impact analysis. This important new book...
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| 245 | 1 | 0 | |a Runoff prediction in ungauged basins |b synthesis across processes, places and scales |c ed. by Günter Blöschl ... |
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| 520 | |a "Predicting water runoff in ungauged water catchment areas is vital to practical applications such as the design of drainage infrastructure and flooding defences, runoff forecasting, and for catchment management tasks such as water allocation and climate impact analysis. This important new book synthesises decades of international research, forming a holistic approach to catchment hydrology and providing a one-stop resource for hydrologists in both developed and developing countries. Topics include data for runoff regionalisation, the prediction of runoff hydrographs, flow duration curves, flow paths and residence times, annual and seasonal runoff, and floods. Illustrated with many case studies and including a final chapter on recommendations for researchers and practitioners, this book is written by expert authors involved in the prestigious IAHS PUB initiative. It is a key resource for academic researchers and professionals in the fields of hydrology, hydrogeology, ecology, geography, soil science, and environmental and civil engineering".. | ||
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Datensatz im Suchindex
| _version_ | 1819667651102769152 |
|---|---|
| adam_text | Titel: Runoff prediction in ungauged basins
Autor: Blöschl, Günter
Jahr: 2013
Contents
List of contributors page ix 3 A data acquisition framework for runoff
Foreword by Thomas Dunne xv prediction in ungauged basins 29
Preface xix 3.1 Why do we need data? 29
Abstract xxii 3.2 A hierarchy of data acquisition 30
3.2.1 Assessment based on global data sets 31
Introduction 1 3.2.2 Assessment based on national
1.1 Why we need runoff predictions 1 hydrological network and national surveys 31
1.2 Runoff predictions in ungauged basins 3.2.3 Assessment based on local field visits
are difficult 3 including reading the landscape 32
1.3 Fragmentation in hydrology 4 3.2.4 Assessment based on dedicated
1.4 The Prediction in Ungauged Basins initiative: a measurements 34
response to the challenge of fragmentation 5 3.3 Runoff data 34
1.5 What this book aims to achieve: synthesis across 3.3.1 What runoff data are needed for PUB? 34
processes, places and scales 6 3.3.2 What runoff data are there? 35
1.5.1 Synthesis across processes 7 3.3.3 How valuable are runoff data for PUB? 36
1.5.2 Synthesis across places 8 3.4 Meteorological data and water balance
1.5.3 Synthesis across scales 8 components 36
1.6 How to read the book and what to get out of it 9 3.4.1 What meteorological data and water
balance components are needed for
A synthesis framework for runoff PUB? 36
prediction in ungauged basins 11 3.4.2 Precipitation 36
2.1 Catchments are complex systems 11 3.4.3 Snow cover data 39
2.1.1 Co-evolution of catchment 3.4.4 Potential evaporation 39
characteristics 11 3.4.5 Remotely sensed data for calculating
2.1.2 Signatures: a manifestation of actual evaporation 40
co-evolution 13 3.4.6 Remote sensing of soil moisture and
2.2 Comparative hydrology and the Darwinian basin storage 40
approach 15 3.5 Catchment characterisation 41
2.2.1 Generalisation through comparative 3.5.1 Topography 41
hydrology 15 3.5.2 Land cover and land use 41
2.2.2 Hydrological similarity 18 3.5.3 Soils and geology 42
2.2.3 Catchment grouping: exploiting the 3.6 Data on anthropogenic effects 43
similarity concept for PUB 20 3.7 Illustrative examples of hierarchical data
2.3 From comparative hydrology to predictions in acquisition 44
ungauged basins 22 3.7.1 Understanding process controls on runoff
2.3.1 Statistical methods of predictions in (Tenderfoot Creek, Montana, USA) 44
ungauged basins 22 3.7.2 Runoff predictions using rainfall-runoff
2.3.2 Process-based methods of predictions models (Chicken Creek, Germany) 47
in ungauged basins 23 3.7.3 Forensic analysis of magnitude and
2.4 Assessment of predictions in ungauged basins 23 causes of a flood (Selska Sora, Slovenia) 49
2.4.1 Comparative assessment as a means of 3.8 Summary of key points 51
synthesis 23
2.4.2 Performance measures 25 4 Process realism: flow paths and storage 53
2.4.3 Level 1 and Level 2 assessments 26 4.1 Predictions: right for the right reasons 53
2.5 Summary of key points 26 4.2 Process controls on flow paths and storage 55
Contents
4.3 Inference of flow paths and storage from 6.3.4 Runoff estimation from short records 121
response characteristics 57 6.4 Process-based methods of predicting seasonal
4.3.1 Inference from runoff 57 runoff in ungauged basins 123
4.3.2 Inference from tracers 59 6.4.1 Derived distribution methods 123
4.4 Estimating flow paths and storage in ungauged 6.4.2 Continuous models 124
basins 64 6.5 Comparative assessment 126
4.4.1 Distributed process-based models 64 6.5.1 Level 1 assessment 127
4.4.2 Index methods 64 6.5.2 Level 2 assessment 129
4.4.3 Methods based on proxy data 65 6.6 Summary of key points 134
4.5 Informing predictions of runoff in ungauged
basins 66 7 Prediction of flow duration curves in
4.5.1 Process-based (rainfall-runoff) methods 67 ungauged basins 135
4.5.2 Statistical methods 67 7.1 For how long do we have water? 135
4.5.3 Role of field visits, reading the landscape, 7.2 Flow duration curves: processes and similarity 137
photos and other proxy data 68 7.2.1 Processes 138
4.5.4 Regional interpretation and similarity 68 7.2.2 Similarity measures 141
4.6 Summary of key points 69 7.2.3 Catchment grouping 145
7.3 Statistical methods of predicting flow duration
Prediction of annual runoff in ungauged curves in ungauged basins 147
basins 70 7.3.1 Regression methods 148
5.1 How much water do we have? 70 7.3.2 Index flow methods 148
5.2 Annual runoff: processes and similarity 71 7.3.3 Geostatistical methods 151
5.2.1 Processes 72 7.3.4 Estimation from short records 152
5.2.2 Similarity measures 78 7.4 Process-based methods of predicting flow
5.2.3 Catchment grouping 79 duration curves in ungauged basins 153
5.3 Statistical methods of predicting annual runoff 7.4.1 Derived distribution methods 153
in ungauged basins 83 7.4.2 Continuous models 154
5.3.1 Regression methods 83 7.5 Comparative assessment 156
5.3.2 Index methods 84 7.5.1 Level 1 assessment 156
5.3.3 Geostatistics and proximity methods 88 7.5.2 Level 2 assessment 158
5.3.4 Estimation from short records 88 7.6 Summary of key points 162
5.4 Process-based methods of predicting annual
runoff in ungauged basins 89 8 Prediction of low flows in ungauged basins 163
5.4.1 Derived distribution methods 89 8.1 How dry will it be? 163
5.4.2 Continuous models 90 8.2 Low flows: processes and similarity 164
5.4.3 Proxy data on annual runoff processes 91 8.2.1 Processes 164
5.5 Comparative assessment 92 8.2.2 Similarity measures 167
5.5.1 Level 1 assessment 92 8.2.3 Catchment grouping 170
5.5.2 Level 2 assessment 96 8.3 Statistical methods of predicting low flows in
5.6 Summary of key points 100 ungauged basins 172
8.3.1 Regression methods 172
Prediction of seasonal runoff in ungauged 8.3.2 Index low flow methods 175
basins 102 8.3.3 Geostatistical methods 176
6.1 When do we have water? 102 8.3.4 Estimation from short records 178
6.2 Seasonal runoff: processes and similarity 104 8.4 Process-based methods of predicting low
6.2.1 Processes 104 flows in ungauged basins 179
6.2.2 Similarity measures 111 8.4.1 Derived distribution methods 179
6.2.3 Catchment grouping 114 8.4.2 Continuous models 180
6.3 Statistical methods of predicting seasonal 8.4.3 Proxy data on low flow processes 180
runoff in ungauged basins 118 8.5 Comparative assessment 181
6.3.1 Regression methods 118 8.5.1 Level 1 assessment 182
6.3.2 Index methods 118 8.5.2 Level 2 assessment 184
6.3.3 Geostatistical and proximity methods 119 8.6 Summary of key points 188
Contents vii
9 Prediction of floods in ungauged basins 189 11 PUB in practice: case studies 270
9.1 How high will the flood be? 189 11.1 Predictions in Ungauged Basins in a societal
9.2 Floods: processes and similarity 190 context 270
9.2.1 Processes 191 11.2 Hydrological insights from long-term runoff
9.2.2 Similarity measures 196 patterns across Krishna Basin, India 272
9.2.3 Catchment grouping 200 11.3 Predicting mean annual runoff across
9.3 Statistical methods of predicting floods in Huangshui Basin, China 277
ungauged basins 203 11.4 An index approach to mapping annual
9.3.1 Regression methods 203 runoff in a Siberian catchment, Russia 280
9.3.2 Index flood methods 205 11.5 Predicting spatial patterns of inter-annual
9.3.3 Geostatistical methods 208 runoff variability in the Canadian Prairies 283
9.3.4 Estimation from short records 209 11.6 Seasonal flow prediction with uncertainty
9.4 Process-based methods of predicting floods in South Africa and Lesotho 289
in ungauged basins 211 11.7 Setting environmental flow targets in
9.4.1 Derived distribution methods 212 north-east USA 293
9.4.2 Continuous models 215 11.8 Continuous simulation of low flows for
9.4.3 Proxy data on flood processes 217 hydropower development in Ontario, Canada 297
9.5 Comparative assessment 219 11.9 Estimating flow duration curves for
9.5.1 Level 1 assessment 220 hydropower development in central Italy 300
9.5.2 Level 2 assessment 222 11.10 Implementing the EU flood directive in
9.6 Summary of key points 225 Austria 305
11.11 Revision of Australian Rainfall and Runoff
10 Prediction of runoff hydrographs in for improved flood predictions 309
ungauged basins 227 II.12 Understanding flow paths for hydrograph
10.1 What are the dynamics of runoff? 227 prediction in an Andean catchment, Chile 313
10.2 Runoff dynamics: processes and similarity 228 11.13 Frequency of runoff occurrence in ephemeral
10.2.1 Processes 229 catchments in France 317
10.2.2 Similarity measures 233 11.14 Overcoming data limitations for hydrograph
10.2.3 Catchment grouping 236 prediction, Luangwa Basin, Zambia 321
10.3 Statistical methods of predicting runoff 11.15 Remotely sensed lake levels to assist runoff
hydrographs in ungauged basins 238 modelling in Ghana 328
10.3.1 Regression methods 238 11.16 Model enhancements for urban runoff
10.3.2 Index methods 238 predictions in the south-west USA 332
10.3.3 Geostatistical methods 239 11.17 Runoff predictions to help meet Millennium
10.4 Process-based methods of predicting runoff Development Goals in Zimbabwe 337
hydrographs in ungauged basins 240 11.18 Runoff predictions in support of the National
10.4.1 Structure of rainfall-runoff models Water Audit, Australia 345
for ungauged basins 241 11.19 Distributed runoff predictions in the Mekong
10.4.2 Parameters of rainfall-runoff River basin 349
models in ungauged basins: 11.20 Implementing the EU Water Framework
overview 246 Directive in Sweden 353
10.4.3 A-priori estimation of model 11.21 Summary of key points 360
parameters 247
10.4.4 Transfer of calibrated model 12 Outcomes of synthesis 361
parameters from gauged 12.1 Learning from synthesis 361
catchments 251 12.2 Synthesis across processes, places and scales 363
10.4.5 Constraining model parameters 12.2.1 Synthesis across processes 363
by dynamic proxy data and 12.2.2 Synthesis across places 367
runoff 256 12.2.3 Synthesis across scales 369
10.5 Comparative assessment 262 12.2.4 Inter-comparison of methods 371
10.5.1 Level 1 assessment 263 12.3 Synthesis of Newtonian and Darwinian
10.5.2 Level 2 assessment 266 frameworks 374
10.6 Summary of key points 268 12.3.1 Evidence for co-evolution 374
viii Contents
12.3.2 Comparative hydrology and the 13.2.1 Viewing catchments as complex
Newtonian-Darwinian synthesis 376 systems 385
12.3.3 A new unified uncertainty framework 13.2.2 Comparative hydrology to detect
for PUB 379 co-evolution patterns 385
12.4 Synthesis and the science community 381 13.2.3 Newtonian-Darwinian synthesis 385
12.4.1 Accumulation of knowledge in the 13.2.4 The globe is our laboratory 385
hydrological sciences 381 13.3 Organising the hydrology community to
12.4.2 Role of the community 382 advance science and predictions 385
13.3.1 Capacity building 385
13 Recommendations 384 13.3.2 Collaborative endeavour 386
13.1 Advancing runoff predictions in ungauged 13.3.3 Knowledge accumulation 386
basins 384 13.3.4 Hydrology, a global science 386
13.1.1 Understanding as the key to better 13.4 Best practice recommendations for
predictions 384 predicting runoff in ungauged basins 386
13.1.2 Exploiting runoff signatures and
linking them 384 Appendix: Summary of studies used in the
13.1.3 Addressing uncertainty from a process comparative assessments 388
perspective 384 References 415
13.1.4 Data availability and predictions 385 Index 463
13.2 Advancing hydrological science globally
via PUB 385
|
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| id | DE-604.BV041385425 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T03:55:21Z |
| institution | BVB |
| isbn | 9781107028180 |
| language | English |
| lccn | 012036513 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026833329 |
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| physical | XXIII, 465 S. Ill., graph. Darst., Kt. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spellingShingle | Runoff prediction in ungauged basins synthesis across processes, places and scales SCIENCE / Earth Sciences / Hydrology bisacsh Geowissenschaften Mathematisches Modell Runoff Rain and rainfall Runoff Mathematical models Rain and rainfall Mathematical models Hydrology SCIENCE / Earth Sciences / Hydrology Niederschlag (DE-588)4132260-5 gnd Abfluss (DE-588)4000114-3 gnd Einzugsgebiet (DE-588)4151469-5 gnd Prognose (DE-588)4047390-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4132260-5 (DE-588)4000114-3 (DE-588)4151469-5 (DE-588)4047390-9 (DE-588)4114528-8 |
| title | Runoff prediction in ungauged basins synthesis across processes, places and scales |
| title_auth | Runoff prediction in ungauged basins synthesis across processes, places and scales |
| title_exact_search | Runoff prediction in ungauged basins synthesis across processes, places and scales |
| title_full | Runoff prediction in ungauged basins synthesis across processes, places and scales ed. by Günter Blöschl ... |
| title_fullStr | Runoff prediction in ungauged basins synthesis across processes, places and scales ed. by Günter Blöschl ... |
| title_full_unstemmed | Runoff prediction in ungauged basins synthesis across processes, places and scales ed. by Günter Blöschl ... |
| title_short | Runoff prediction in ungauged basins |
| title_sort | runoff prediction in ungauged basins synthesis across processes places and scales |
| title_sub | synthesis across processes, places and scales |
| topic | SCIENCE / Earth Sciences / Hydrology bisacsh Geowissenschaften Mathematisches Modell Runoff Rain and rainfall Runoff Mathematical models Rain and rainfall Mathematical models Hydrology SCIENCE / Earth Sciences / Hydrology Niederschlag (DE-588)4132260-5 gnd Abfluss (DE-588)4000114-3 gnd Einzugsgebiet (DE-588)4151469-5 gnd Prognose (DE-588)4047390-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | SCIENCE / Earth Sciences / Hydrology Geowissenschaften Mathematisches Modell Runoff Rain and rainfall Runoff Mathematical models Rain and rainfall Mathematical models Hydrology Niederschlag Abfluss Einzugsgebiet Prognose |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026833329&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bloschlgunter runoffpredictioninungaugedbasinssynthesisacrossprocessesplacesandscales |