Keywords:
Runoff.
;
Electronic books.
Description / Table of Contents:
Based on the prestigious IAHS PUB initiative, this full colour book synthesises world-wide research on catchment hydrology, providing a one-stop resource for hydrologists in developed and developing countries. It is a key resource for researchers and professionals in the fields of hydrology, geography, soil science, and environmental and civil engineering.
Type of Medium:
Online Resource
Pages:
1 online resource (492 pages)
Edition:
1st ed.
ISBN:
9781107057975
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=1182960
DDC:
551.488
Language:
English
Note:
Intro -- Contents -- Contributors -- Foreword -- Prediction in ungauged basins: context, challenges, opportunities -- Preface -- Abstract -- 1 Introduction -- 1.1 Why we need runoff predictions -- 1.2 Runoff predictions in ungauged basins are difficult -- 1.3 Fragmentation in hydrology -- 1.4 The Prediction in Ungauged Basins initiative: a response to the challenge of fragmentation -- 1.5 What this book aims to achieve: synthesis across processes, places and scales -- 1.5.1 Synthesis across processes -- 1.5.2 Synthesis across places -- 1.5.3 Synthesis across scales -- 1.6 How to read the book and what to get out of it -- 2 A synthesis framework for runoff prediction in ungauged basins -- 2.1 Catchments are complex systems -- 2.1.1 Co-evolution of catchment characteristics -- 2.1.2 Signatures: a manifestation of co-evolution -- 2.2 Comparative hydrology and the Darwinian approach -- 2.2.1 Generalisation through comparative hydrology -- 2.2.2 Hydrological similarity -- Climate similarity -- Catchment similarity -- Runoff similarity -- 2.2.3 Catchment grouping: exploiting the similarity concept for PUB -- Transferring information from gauged to ungauged locations -- 2.3 From comparative hydrology to predictions in ungauged basins -- 2.3.1 Statistical methods of predictions in ungauged basins -- 2.3.2 Process-based methods of predictions in ungauged basins -- 2.4 Assessment of predictions in ungauged basins -- 2.4.1 Comparative assessment as a means of synthesis -- 2.4.2 Performance measures -- 2.4.3 Level 1 and Level 2 assessments -- 2.5 Summary of key points -- 3 A data acquisition framework for runoff prediction in ungauged basins -- 3.1 Why do we need data? -- 3.2 A hierarchy of data acquisition -- 3.2.1 Assessment based on global data sets -- 3.2.2 Assessment based on national hydrological network and national surveys.
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3.2.3 Assessment based on local field visits including reading the landscape -- 3.2.4 Assessment based on dedicated measurements -- 3.3 Runoff data -- 3.3.1 What runoff data are needed for PUB? -- 3.3.2 What runoff data are there? -- 3.3.3 How valuable are runoff data for PUB? -- 3.4 Meteorological data and water balance components -- 3.4.1 What meteorological data and water balance components are needed for PUB? -- 3.4.2 Precipitation -- 3.4.3 Snow cover data -- 3.4.4 Potential evaporation -- 3.4.5 Remotely sensed data for calculating actual evaporation -- 3.4.6 Remote sensing of soil moisture and basin storage -- 3.5 Catchment characterisation -- 3.5.1 Topography -- 3.5.2 Land cover and land use -- 3.5.3 Soils and geology -- 3.6 Data on anthropogenic effects -- 3.7 Illustrative examples of hierarchical data acquisition -- 3.7.1 Understanding process controls on runoff (Tenderfoot Creek, Montana, USA) -- 3.7.2 Runoff predictions using rainfall-runoff models (Chicken Creek, Germany) -- 3.7.3 Forensic analysis of magnitude and causes of a flood (Selska Sora, Slovenia) -- 3.8 Summary of key points -- 4 Process realism: flow paths and storage -- 4.1 Predictions: right for the right reasons -- 4.2 Process controls on flow paths and storage -- 4.3 Inference of flow paths and storage from response characteristics -- 4.3.1 Inference from runoff -- Learning from temporal patterns of runoff in one catchment -- Learning from spatial patterns of runoff in many catchments -- 4.3.2 Inference from tracers -- Learning from temporal patterns of tracers in one catchment -- Learning from spatial patterns of tracers in many catchments -- 4.4 Estimating flow paths and storage in ungauged basins -- 4.4.1 Distributed process-based models -- 4.4.2 Index methods -- 4.4.3 Methods based on proxy data -- 4.5 Informing predictions of runoff in ungauged basins.
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4.5.1 Process-based (rainfall-runoff) methods -- 4.5.2 Statistical methods -- 4.5.3 Role of field visits, reading the landscape, photos and other proxy data -- 4.5.4 Regional interpretation and similarity -- 4.6 Summary of key points -- 5 Prediction of annual runoff in ungauged basins -- 5.1 How much water do we have? -- 5.2 Annual runoff: processes and similarity -- 5.2.1 Processes -- Climate forcing -- Catchment (physical) processes -- Catchment (biological) processes -- Effects of global change -- 5.2.2 Similarity measures -- 5.2.3 Catchment grouping -- 5.3 Statistical methods of predicting annual runoff in ungauged basins -- 5.3.1 Regression methods -- Mean annual runoff -- Inter-annual variability -- 5.3.2 Index methods -- Mean annual runoff -- Budyko-type models -- Inter-annual variability -- Budyko-type models -- Probability distribution of annual runoff -- 5.3.3 Geostatistics and proximity methods -- 5.3.4 Estimation from short records -- Correlation with longer runoff record -- Rainfall-runoff modelling -- 5.4 Process-based methods of predicting annual runoff in ungauged basins -- 5.4.1 Derived distribution methods -- 5.4.2 Continuous models -- Annual runoff and inter-annual variability -- 5.4.3 Proxy data on annual runoff processes -- Tree ring chronology and paleoclimatology -- Remote sensing -- 5.5 Comparative assessment -- 5.5.1 Level 1 assessment -- How good are the predictions in different climates? -- Which method performs best? -- How does data availability impact performance? -- Main findings of Level 1 assessment -- 5.5.2 Level 2 assessment -- To what extent does runoff prediction performance depend on climate and catchment characteristics? -- Which method performs best? -- Global scale results vs. local scale results -- Main findings of Level 2 assessment -- 5.6 Summary of key points.
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6 Prediction of seasonal runoff in ungauged basins -- 6.1 When do we have water? -- 6.2 Seasonal runoff: processes and similarity -- 6.2.1 Processes -- Climate forcing -- Catchment processes: storage in snow, ice and glaciers -- Catchment processes: storage in soil and groundwater -- Land surface processes and vegetation phenology -- Inter-annual variability in the flow regime -- Change (human impacts) -- 6.2.2 Similarity measures -- Runoff similarity indices -- Climate similarity indices -- Catchment similarity indices -- Visualisation of multidimensional indices -- 6.2.3 Catchment grouping -- Grouping based on runoff-regime types -- Grouping based on runoff: statistical approaches -- Grouping based on catchment characteristics and climate: contiguous region -- Grouping based on catchment characteristics and climate: non-contiguous regions -- 6.3 Statistical methods of predicting seasonal runoff in ungauged basins -- 6.3.1 Regression methods -- 6.3.2 Index methods -- 6.3.3 Geostatistical and proximity methods -- 6.3.4 Runoff estimation from short records -- 6.4 Process-based methods of predicting seasonal runoff in ungauged basins -- 6.4.1 Derived distribution methods -- 6.4.2 Continuous models -- 6.5 Comparative assessment -- 6.5.1 Level 1 assessment -- How good are the predictions in different climates? -- Which method performs best? -- How does data availability impact performance? -- Main findings of Level 1 assessment -- 6.5.2 Level 2 assessment -- To what extent does runoff prediction performance depend on climate and catchment characteristics? -- Which method performs best? -- Main findings of Level 2 assessment -- 6.6 Summary of key points -- 7 Prediction of flow duration curves in ungauged basins -- 7.1 For how long do we have water? -- 7.2 Flow duration curves: processes and similarity -- 7.2.1 Processes -- Climate forcing.
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Catchment characteristics -- Environmental change -- 7.2.2 Similarity measures -- Runoff similarity -- Climate similarity -- Catchment similarity -- 7.2.3 Catchment grouping -- 7.3 Statistical methods of predicting flow duration curves in ungauged basins -- 7.3.1 Regression methods -- 7.3.2 Index flow methods -- Parametric methods -- Rescaled flow duration curve -- 7.3.3 Geostatistical methods -- 7.3.4 Estimation from short records -- 7.4 Process-based methods of predicting flow duration curves in ungauged basins -- 7.4.1 Derived distribution methods -- 7.4.2 Continuous models -- 7.5 Comparative assessment -- 7.5.1 Level 1 assessment -- How good are the predictions in different climates? -- Which method performs best? -- How does data availability impact performance? -- Main findings of Level 1 assessment -- 7.5.2 Level 2 assessment -- To what extent does runoff prediction performance depend on climate and catchment characteristics? -- Which method performs best? -- Main findings of Level 2 assessment -- 7.6 Summary of key points -- 8 Prediction of low flows in ungauged basins -- 8.1 How dry will it be? -- 8.2 Low flows: processes and similarity -- 8.2.1 Processes -- Climate -- Catchment processes -- 8.2.2 Similarity measures -- Runoff similarity -- Climate similarity -- Catchment similarity -- 8.2.3 Catchment grouping -- Cluster analysis based on catchment/climate characteristics -- Residual pattern approach based on runoff and catchment/climate characteristics -- Regression trees -- Seasonality approach -- 8.3 Statistical methods of predicting low flows in ungauged basins -- 8.3.1 Regression methods -- 8.3.2 Index low flow methods -- 8.3.3 Geostatistical methods -- 8.3.4 Estimation from short records -- 8.4 Process-based methods of predicting low flows in ungauged basins -- 8.4.1 Derived distribution methods -- 8.4.2 Continuous models.
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8.4.3 Proxy data on low flow processes.
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