Floods in a Changing Climate: Hydrologic Modeling

Medientyp: E-Book

Titel: Floods in a Changing Climate : Hydrologic Modeling

Enthält: Contents; Foreword; Preface; Glossary; Abbreviations; 1 Introduction; 1.1 Hydrologic models; 1.2 Remote sensing for hydrologic modeling; 1.3 GIS and DEM for hydrologic modeling; 1.4 Assessment of climate change impacts; 1.5 Organization of the book; 2 Hydrologic modeling for floods; 2.1 Introduction; 2.1.1 Partitioning of rainfall; 2.1.2 Overland flow; 2.1.3 Excess rainfall and direct runoff; 2.2 Estimation of flood peak discharge; 2.2.1 Soil Conservation Service curve number method; 2.2.1.1 Flood hydrograph from the SCS method; 2.2.2 Rational method
2.3 Intensity-duration-frequency relationship2.4 Flood routing; 2.4.1 Hydraulic routing: the Saint-Venant equations; 2.4.2 Numerical solutions; 2.4.3 Hydrologic routing of floods: Muskingum method; 2.4.3.1 Basic equations; 2.5 A brief review of commonly used hydrologic models; 2.5.1 HEC-HMS; 2.5.1.1 Illustration of the model; 2.5.1.2 Model calibration and validation; 2.5.1.3 Performance evaluation of the model; 2.5.2 HEC-RAS; 2.5.2.1 Illustration; 2.5.3 Storm Water Management Model; 2.5.3.1 Illustration; 2.5.4 Requirements of data for hydrologic models; 2.6 Empirical models
2.6.1 Artificial neural network models2.6.1.1 Basic principles; 2.6.1.2 Back propagation; 2.6.1.3 Radial basis function; 2.6.1.4 Advantages and limitations of ANNs; 2.6.1.5 An illustration of use of ANNs for flow forecasting; 2.6.2 Fuzzy logic based models; 2.6.2.1 Fuzzy sets and fuzzy logic; 2.6.2.2 Membership function; 2.6.2.3 Fuzzy rules; 2.6.2.4 Fuzzy dynamic flood routing model for natural channels; 2.6.2.5 Model development; 2.6.2.6 Method of computation; 2.6.2.7 Illustration: flood routing in a natural channel; 2.7 Summary; Exercises; 3 Climate change impact assessment1
3.1 Introduction3.1.1 Climate change: emissions scenarios; 3.2 Projection of hydrologic impacts; 3.3 Dynamical downscaling approaches; 3.4 Statistical downscaling approaches; 3.4.1 Data needs and sources; 3.4.2 Choice of predictor variables; 3.4.3 Data preprocessing; 3.4.3.1 Interpolation; 3.4.3.2 Bias removal; 3.4.3.3 Dimensionality reduction; 3.4.4 Weather typing methods; 3.4.5 Weather generators; 3.4.6 Transfer functions; 3.5 Disaggregation models; 3.5.1 Deterministic disaggregation techniques; 3.5.2 Stochastic disaggregation techniques; 3.6 Macroscale hydrologic models
3.7 Hypothetical scenarios for hydrologic modeling3.8 Modeling of floods under climate change; 3.8.1 Flood regime description; 3.8.2 Underlying assumptions; 3.8.3 Flood frequency analysis; 3.8.4 Flood frequency analysis under climate change; 3.9 Uncertainty modeling; 3.9.1 Uncertainty modeling in regional impacts; 3.9.2 Uncertainty propagation; 3.10 Summary; Exercises; 4 Remote sensing for hydrologic modeling; 4.1 Introduction; 4.1.1 Spectral reflectance curves; 4.1.1.1 Spectral reflectance for vegetation; 4.1.1.2 Spectral reflectance for soil; 4.1.1.3 Spectral reflectance for water
4.1.2 Passive/active remote sensing

Beteiligte: Mujumdar, P. P. [VerfasserIn]; Nagesh Kumar, D. [Sonstige Person, Familie und Körperschaft]

Erschienen: Cambridge: Cambridge University Press, 2012

Ausgabe: Online-Ausg.

Umfang: Online-Ressource (1 online resource (210 p.))

Sprache: Englisch

ISBN: 9781139842563

Schlagwörter: Hochwasser > Hydrologie > Klimaänderung > Modellierung

Entstehung:

Anmerkungen: Description based upon print version of record

Beschreibung: 2.6.1 Artificial neural network models2.6.1.1 Basic principles; 2.6.1.2 Back propagation; 2.6.1.3 Radial basis function; 2.6.1.4 Advantages and limitations of ANNs; 2.6.1.5 An illustration of use of ANNs for flow forecasting; 2.6.2 Fuzzy logic based models; 2.6.2.1 Fuzzy sets and fuzzy logic; 2.6.2.2 Membership function; 2.6.2.3 Fuzzy rules; 2.6.2.4 Fuzzy dynamic flood routing model for natural channels; 2.6.2.5 Model development; 2.6.2.6 Method of computation; 2.6.2.7 Illustration: flood routing in a natural channel; 2.7 Summary; Exercises; 3 Climate change impact assessment1

3.1 Introduction3.1.1 Climate change: emissions scenarios; 3.2 Projection of hydrologic impacts; 3.3 Dynamical downscaling approaches; 3.4 Statistical downscaling approaches; 3.4.1 Data needs and sources; 3.4.2 Choice of predictor variables; 3.4.3 Data preprocessing; 3.4.3.1 Interpolation; 3.4.3.2 Bias removal; 3.4.3.3 Dimensionality reduction; 3.4.4 Weather typing methods; 3.4.5 Weather generators; 3.4.6 Transfer functions; 3.5 Disaggregation models; 3.5.1 Deterministic disaggregation techniques; 3.5.2 Stochastic disaggregation techniques; 3.6 Macroscale hydrologic models

3.7 Hypothetical scenarios for hydrologic modeling3.8 Modeling of floods under climate change; 3.8.1 Flood regime description; 3.8.2 Underlying assumptions; 3.8.3 Flood frequency analysis; 3.8.4 Flood frequency analysis under climate change; 3.9 Uncertainty modeling; 3.9.1 Uncertainty modeling in regional impacts; 3.9.2 Uncertainty propagation; 3.10 Summary; Exercises; 4 Remote sensing for hydrologic modeling; 4.1 Introduction; 4.1.1 Spectral reflectance curves; 4.1.1.1 Spectral reflectance for vegetation; 4.1.1.2 Spectral reflectance for soil; 4.1.1.3 Spectral reflectance for water

4.1.2 Passive/active remote sensing

Provides unique synthesis of various modeling methodologies used to aid planning and operational decision making, for academic researchers and professionals

Contents; Foreword; Preface; Glossary; Abbreviations; 1 Introduction; 1.1 Hydrologic models; 1.2 Remote sensing for hydrologic modeling; 1.3 GIS and DEM for hydrologic modeling; 1.4 Assessment of climate change impacts; 1.5 Organization of the book; 2 Hydrologic modeling for floods; 2.1 Introduction; 2.1.1 Partitioning of rainfall; 2.1.2 Overland flow; 2.1.3 Excess rainfall and direct runoff; 2.2 Estimation of flood peak discharge; 2.2.1 Soil Conservation Service curve number method; 2.2.1.1 Flood hydrograph from the SCS method; 2.2.2 Rational method. - 2.3 Intensity-duration-frequency relationship2.4 Flood routing; 2.4.1 Hydraulic routing: the Saint-Venant equations; 2.4.2 Numerical solutions; 2.4.3 Hydrologic routing of floods: Muskingum method; 2.4.3.1 Basic equations; 2.5 A brief review of commonly used hydrologic models; 2.5.1 HEC-HMS; 2.5.1.1 Illustration of the model; 2.5.1.2 Model calibration and validation; 2.5.1.3 Performance evaluation of the model; 2.5.2 HEC-RAS; 2.5.2.1 Illustration; 2.5.3 Storm Water Management Model; 2.5.3.1 Illustration; 2.5.4 Requirements of data for hydrologic models; 2.6 Empirical models. - 2.6.1 Artificial neural network models2.6.1.1 Basic principles; 2.6.1.2 Back propagation; 2.6.1.3 Radial basis function; 2.6.1.4 Advantages and limitations of ANNs; 2.6.1.5 An illustration of use of ANNs for flow forecasting; 2.6.2 Fuzzy logic based models; 2.6.2.1 Fuzzy sets and fuzzy logic; 2.6.2.2 Membership function; 2.6.2.3 Fuzzy rules; 2.6.2.4 Fuzzy dynamic flood routing model for natural channels; 2.6.2.5 Model development; 2.6.2.6 Method of computation; 2.6.2.7 Illustration: flood routing in a natural channel; 2.7 Summary; Exercises; 3 Climate change impact assessment1

2.3 Intensity-duration-frequency relationship2.4 Flood routing; 2.4.1 Hydraulic routing: the Saint-Venant equations; 2.4.2 Numerical solutions; 2.4.3 Hydrologic routing of floods: Muskingum method; 2.4.3.1 Basic equations; 2.5 A brief review of commonly used hydrologic models; 2.5.1 HEC-HMS; 2.5.1.1 Illustration of the model; 2.5.1.2 Model calibration and validation; 2.5.1.3 Performance evaluation of the model; 2.5.2 HEC-RAS; 2.5.2.1 Illustration; 2.5.3 Storm Water Management Model; 2.5.3.1 Illustration; 2.5.4 Requirements of data for hydrologic models; 2.6 Empirical models

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