Author: Juraj M. Cunderlik
Publisher:
ISBN: 9780771426247
Category :
Languages : en
Pages : 29
Book Description
Selection of Calibration and Verification Data for the HEC-HMS Hydrologic Model
Author: Juraj M. Cunderlik
Publisher:
ISBN: 9780771426247
Category :
Languages : en
Pages : 29
Book Description
Publisher:
ISBN: 9780771426247
Category :
Languages : en
Pages : 29
Book Description
Role of Calibration in the Application of HEC-6
Author: D. Michael Gee
Publisher:
ISBN:
Category : Calibration
Languages : en
Pages : 32
Book Description
Publisher:
ISBN:
Category : Calibration
Languages : en
Pages : 32
Book Description
The Hydrologic Modeling System (HEC-HMS)
Author: William J. Charley
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages : 16
Book Description
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages : 16
Book Description
Proceedings of the Seventh Federal Interagency Sedimentation Conference, March 25-29, 2001, Reno, Nevada, USA
Author:
Publisher:
ISBN:
Category : Sedimentation and deposition
Languages : en
Pages : 664
Book Description
Publisher:
ISBN:
Category : Sedimentation and deposition
Languages : en
Pages : 664
Book Description
The HEC Hydrologic Modeling System
Author: John Charles Peters
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages : 16
Book Description
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages : 16
Book Description
HEC River Analysis System (HEC-RAS)
Author: Gary W. Brunner
Publisher:
ISBN:
Category : HEC-RAS (Computer program)
Languages : en
Pages : 16
Book Description
Publisher:
ISBN:
Category : HEC-RAS (Computer program)
Languages : en
Pages : 16
Book Description
Developments in Informal Multi-Criteria Calibration and Uncertainty Estimation in Hydrological Modelling
Author: Mahyar Shafii Hassanabadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 129
Book Description
Hydrologic modelling has benefited from significant developments over the past two decades, which has led to the development of distributed hydrologic models. Parameter adjustment, or model calibration, is extremely important in the application of these hydrologic models. Multi-criteria calibration schemes and several formal and informal predictive uncertainty estimation methodologies are among the approaches to improve the results of model calibration. Moreover, literature indicates a general agreement between formal and informal approaches with respect to the predictive uncertainty estimation in single-criterion calibration cases. This research extends the comparison between these techniques to multi-criteria calibration cases, and furthermore, proposes new ideas to improve informal multi-criteria calibration and uncertainty estimation in hydrological modelling. GLUE is selected as a candidate informal methodology due to its extreme popularity among hydrological modellers, i.e., based on the number of applications in the past two decades. However, it is hypothesized that improvements can be applied to other certain types of informal uncertainty estimation as well. The first contribution of this research is an in-depth comparison between GLUE and Bayesian inference in the multi-criteria context. Such a comparison is novel because past literature has focused on comparisons for only single criterion calibration studies. Unlike the previous research, the results show that there can be considerable differences in hydrograph prediction intervals generated by traditional GLUE and Bayesian inference in multi-criteria cases. Bayesian inference performs more satisfactorily than GLUE along most of the comparative measures. However, results also reveal that a standard Bayesian formulation (i.e., aggregating all uncertainties into a single additive error term) may not demonstrate perfect reliability in the prediction mode. Furthermore, in cases with a limited computational budget, non-converged MCMC sampling proves to be an appropriate alternative to GLUE since it is reasonably consistent with a fully-converged Bayesian approach, even though the fully-converged MCMC requires a substantially larger number of model evaluations. Another contribution of this research is to improve the uncertainty bounds of the traditional GLUE approach by the exploration of alternative behavioural solution identification strategies. Multiple behavioural solution identification strategies from the literature are evaluated, new objective strategies are developed, and multi-criteria decision-making concepts are utilized to select the best strategy. The results indicate that the subjectivity involved in behavioural solution identification strategies impacts the uncertainty of model outcome. More importantly, a robust implementation of GLUE proves to require comparing multiple behavioural solution identification strategies and choosing the best one based on the modeller's priorities. Moreover, it appears that the proposed objective strategies are among the best options in most of the case studies investigated in this research. Thus, it is recommended that these new strategies be considered among the set of behavioural solution identification strategies in future GLUE applications. Lastly, this research also develops a full optimization-based calibration framework that is capable of utilizing both standard goodness-of-fit measures and many hydrological signatures simultaneously. These signatures can improve the calibration results by constraining the model outcome hydrologically. However, the literature shows that to simultaneously apply a large number of hydrological signatures in model calibration is challenging. Therefore, the proposed research adopts optimization concepts to accommodate many criteria (including 13 hydrologic signature-based objectives and two standard statistical goodness-of-fit measures). In the proposed framework, hydrological consistency is quantified (based on a set of signature-based measures and their desired level of acceptability) and utilized as a criterion in multiple calibration formulations. The results show that these formulations perform better than the traditional approaches to locate hydrologically consistent parameter sets in the search space. Different hydrologic models, most of which are conceptual rainfall-runoff models, are used throughout the thesis to evaluate the performance of the developed strategies. However, the developments explored in this research are typically simulation-model-independent and can be applied to calibration and uncertainty estimation of any environmental model. However, further testing of these methods is warranted for more computationally intensive simulation models, such as fully distributed hydrologic models.
Publisher:
ISBN:
Category :
Languages : en
Pages : 129
Book Description
Hydrologic modelling has benefited from significant developments over the past two decades, which has led to the development of distributed hydrologic models. Parameter adjustment, or model calibration, is extremely important in the application of these hydrologic models. Multi-criteria calibration schemes and several formal and informal predictive uncertainty estimation methodologies are among the approaches to improve the results of model calibration. Moreover, literature indicates a general agreement between formal and informal approaches with respect to the predictive uncertainty estimation in single-criterion calibration cases. This research extends the comparison between these techniques to multi-criteria calibration cases, and furthermore, proposes new ideas to improve informal multi-criteria calibration and uncertainty estimation in hydrological modelling. GLUE is selected as a candidate informal methodology due to its extreme popularity among hydrological modellers, i.e., based on the number of applications in the past two decades. However, it is hypothesized that improvements can be applied to other certain types of informal uncertainty estimation as well. The first contribution of this research is an in-depth comparison between GLUE and Bayesian inference in the multi-criteria context. Such a comparison is novel because past literature has focused on comparisons for only single criterion calibration studies. Unlike the previous research, the results show that there can be considerable differences in hydrograph prediction intervals generated by traditional GLUE and Bayesian inference in multi-criteria cases. Bayesian inference performs more satisfactorily than GLUE along most of the comparative measures. However, results also reveal that a standard Bayesian formulation (i.e., aggregating all uncertainties into a single additive error term) may not demonstrate perfect reliability in the prediction mode. Furthermore, in cases with a limited computational budget, non-converged MCMC sampling proves to be an appropriate alternative to GLUE since it is reasonably consistent with a fully-converged Bayesian approach, even though the fully-converged MCMC requires a substantially larger number of model evaluations. Another contribution of this research is to improve the uncertainty bounds of the traditional GLUE approach by the exploration of alternative behavioural solution identification strategies. Multiple behavioural solution identification strategies from the literature are evaluated, new objective strategies are developed, and multi-criteria decision-making concepts are utilized to select the best strategy. The results indicate that the subjectivity involved in behavioural solution identification strategies impacts the uncertainty of model outcome. More importantly, a robust implementation of GLUE proves to require comparing multiple behavioural solution identification strategies and choosing the best one based on the modeller's priorities. Moreover, it appears that the proposed objective strategies are among the best options in most of the case studies investigated in this research. Thus, it is recommended that these new strategies be considered among the set of behavioural solution identification strategies in future GLUE applications. Lastly, this research also develops a full optimization-based calibration framework that is capable of utilizing both standard goodness-of-fit measures and many hydrological signatures simultaneously. These signatures can improve the calibration results by constraining the model outcome hydrologically. However, the literature shows that to simultaneously apply a large number of hydrological signatures in model calibration is challenging. Therefore, the proposed research adopts optimization concepts to accommodate many criteria (including 13 hydrologic signature-based objectives and two standard statistical goodness-of-fit measures). In the proposed framework, hydrological consistency is quantified (based on a set of signature-based measures and their desired level of acceptability) and utilized as a criterion in multiple calibration formulations. The results show that these formulations perform better than the traditional approaches to locate hydrologically consistent parameter sets in the search space. Different hydrologic models, most of which are conceptual rainfall-runoff models, are used throughout the thesis to evaluate the performance of the developed strategies. However, the developments explored in this research are typically simulation-model-independent and can be applied to calibration and uncertainty estimation of any environmental model. However, further testing of these methods is warranted for more computationally intensive simulation models, such as fully distributed hydrologic models.
The Hydrologic Modeling System (HEC-HMS)
Author: Hydrologic Engineering Center (U.S.)
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages :
Book Description
Floods in a Changing Climate
Author: Slobodan P. Simonović
Publisher: Cambridge University Press
ISBN: 1139851624
Category : Science
Languages : en
Pages : 197
Book Description
Flood risk management is presented in this book as a framework for identifying, assessing and prioritizing climate-related risks and developing appropriate adaptation responses. Rigorous assessment is employed to determine the available probabilistic and fuzzy set-based analytic tools, when each is appropriate and how to apply them to practical problems. Academic researchers in the fields of hydrology, climate change, environmental science and policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and environmental economics, will find this an invaluable resource. This volume is the fourth 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: Extreme Precipitation by Ramesh Teegavarapu, Floods in a Changing Climate: Hydrologic Modeling by P. P. Mujumdar and D. Nagesh Kumar and Floods in a Changing Climate: Inundation Modelling by Giuliano Di Baldassarre.
Publisher: Cambridge University Press
ISBN: 1139851624
Category : Science
Languages : en
Pages : 197
Book Description
Flood risk management is presented in this book as a framework for identifying, assessing and prioritizing climate-related risks and developing appropriate adaptation responses. Rigorous assessment is employed to determine the available probabilistic and fuzzy set-based analytic tools, when each is appropriate and how to apply them to practical problems. Academic researchers in the fields of hydrology, climate change, environmental science and policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and environmental economics, will find this an invaluable resource. This volume is the fourth 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: Extreme Precipitation by Ramesh Teegavarapu, Floods in a Changing Climate: Hydrologic Modeling by P. P. Mujumdar and D. Nagesh Kumar and Floods in a Changing Climate: Inundation Modelling by Giuliano Di Baldassarre.
Urban Risk Assessments
Author: The World Bank
Publisher: World Bank Publications
ISBN: 0821389637
Category : Business & Economics
Languages : en
Pages : 276
Book Description
The Urban Risk Assessment (URA) is a framework for assessing disaster and climate risk in cities based on three pillars: a hazard impact assessment, an institutional assessment, and a socioeconomic assessment. The URA can be applied flexibly based on a city's available financial resources, available data, and institutional capacity.
Publisher: World Bank Publications
ISBN: 0821389637
Category : Business & Economics
Languages : en
Pages : 276
Book Description
The Urban Risk Assessment (URA) is a framework for assessing disaster and climate risk in cities based on three pillars: a hazard impact assessment, an institutional assessment, and a socioeconomic assessment. The URA can be applied flexibly based on a city's available financial resources, available data, and institutional capacity.