This book includes a collection of extended papers based on presentations given during the SimHydro 2023 conference, held in EDF Lab Chatou, France, with the support of Société Hydrotechnique de France (SHF), the Association Française de Mécanique (AFM), the Environmental and Water Resources Institute (EWRI), and the International Association for Hydro-Environment Engineering and Research (IAHR). SimHydro conferences, since 2010, have created a regular forum where major actors of the hydroinformatics domain and stakeholders meet, share, and debate about needs, innovations, and implementations of models and their inputs for decision making. For this new edition, the general theme of the conference is focused on “New modelling paradigms for water issues”. The papers address some of the key challenges faced by the water modelling community regarding processes to simulate such as water services, extreme events (floods, droughts, etc.), and hydrological cycle at catchment scale and to assess the added value of emerging concepts and methods such as Artificial Intelligence (AI) and Digital Twins that are gaining interests. It addresses the interests of practitioners, stakeholders, researchers, and engineers active in this field. This book represents Volume 1 of a two-volume book series.

lgrsnf/9819740711.pdf

FRANCE) SYMHYDRO (CONFERENCE) (2023: CHATOU

S.l, 2024

Preface
Volume 1. Flood Modelling and Mitigation Strategies
Contents
1 Development of Robust and Efficient Methods for Hydraulic/Hydrologic/Environmental Risk Numerical Simulation
1.1 Introduction
1.2 Unsteady Hydraulic Flows
1.2.1 River Flooding
1.2.2 Coupling Unsteady River Flow and Water Quality
1.2.3 Coupling Surface Flow and a Drainage Network Flow with Solute Transport
1.3 Unsteady Erosive Flows
1.3.1 Formulation of the Problem
1.3.2 Application to the Mine Tailings Dam Failure in Brumadinho (Brasil, 2019)
1.4 Conclusions
References
2 Coupling Mage with Melissa to Compute Ubiquitous Sobol Indices for River Hydraulics
2.1 Introduction
2.2 Mage and Melissa
2.2.1 Mage
2.2.2 Melissa
2.2.3 Coupling Mage with Melissa
2.3 The Lower Seine river model
2.3.1 Hydraulic Model
2.3.2 Ensemble Model
2.4 Results
2.4.1 Raw Data
2.4.2 Visualization and Interpretation
2.5 Conclusions
References
3 Comparison Between HEC-RAS and TELEMAC-2D Hydrodynamic Models of the Loire River, Integrating Levee Breaches
3.1 Introduction
3.2 Materials and Methods
3.2.1 Data
3.2.2 HEC-RAS: 2D Hydrodynamic Model
3.2.3 TELEMAC-2D: Hydrodynamic Model
3.3 Results
3.3.1 HEC-RAS
3.3.2 Comparison HEC-RAS Versus TELEMAC-2D
3.4 Conclusions
References
4 Digital Twin Technologies for Flood Management in Large Catchment: Challenges and Operational Solution
4.1 Introduction
4.2 “23·7” Catastrophic Flood Disaster
4.3 Modeling Strategy
4.4 Results and Discussion
4.5 Conclusion
References
5 Assessment of Flood Vulnerability Through a Multidimensional Index
5.1 Introduction
5.2 Methodology
5.2.1 Data Availability
5.2.2 Method for Constructing a Multidimensional Index
5.3 Preliminary Results
5.4 Conclusions
References
6 Hydrodynamic Modelling of Seine Bay and Estuary in Moderate and Extreme Conditions: With a Focus on Johanna Storm
6.1 Introduction
6.2 Model Configuration
6.2.1 Numerical Settings
6.2.2 Boundary Conditions
6.3 Measured Data
6.4 Results and Discussion
6.4.1 Reference Models
6.4.2 Enhancement of the Marine Boundary Conditions
6.4.3 Effects of the Johanna Storm Surge
6.5 Conclusions
References
7 Evaluation of Machine Learning Approaches for Flood Hazard Mapping Over the Argens Basin, France
7.1 Introduction, First Headings
7.2 Case Study, Reference Flood Hazard Maps, and Input Data
7.2.1 The Argens Basin
7.2.2 The 1000-Year Reference Flood Hazard Map
7.2.3 Other Input Data
7.3 Machine Learning Methods and Evaluation Metrics
7.3.1 Multicollinearity Analysis
7.3.2 Frequency Ratio (FR)
7.3.3 Random Forest (RF)
7.3.4 Extreme Gradient Boosting (XGB)
7.3.5 Artificial Neural Network-Back Propagation (ANN-BP)
7.3.6 Model Performance Evaluation and Comparison
7.4 Results
7.4.1 Multicollinearity Analysis and Relative Importance of GeFs
7.4.2 Flood Susceptibility Maps
7.4.3 Validation and Evaluation of Flood Hazard Maps
7.5 Discussion
7.6 Conclusions
References
8 Basement V4—A Multipurpose Modelling Environment for Simulation of Flood Hazards and River Morphodynamics Across Scales
8.1 Introduction
8.2 The Modelling Environment
8.2.1 Governing Equations
8.2.2 Numerical Solution Methods
8.3 Application Examples
8.3.1 Peak Discharge Capping
8.3.2 Lateral Flood Diversion Using Different Model Approaches
8.3.3 Progressive Dam Breaching Due to Overtopping
8.3.4 Tsunami Wave Propagation
8.4 Conclusions and Outlook
References
9 A Combined Pipe and Overland Flow Model to Support Urban Flood Risk Management: Case Study of the Espartes Watershed
9.1 Introduction
9.2 The Espartes Valley: A Highly Artificalized Watershed
9.3 Setting Up the Distributed Model
9.3.1 MIKE + modeling Tool
9.3.2 Espartes Watershed Model Setup
9.4 Model Calibration on Measured Data
9.4.1 Data
9.4.2 Calibration Results
9.5 Assessment of the Current Flood Hazard
9.5.1 Design Rainfall
9.5.2 Results
9.6 Conclusions
References
10 Fast Prediction of Flood Maps Based on Machine Learning Techniques: Application to Marine Flooding at Arcachon Lagoon (Gironde, France)
10.1 Introduction
10.2 Case Study
10.3 Statistical Methods
10.3.1 Overall Procedure
10.3.2 Dimension Reduction
10.3.3 Kriging Metamodelling
10.3.4 Protocol for Performance Analysis
10.4 Application
10.4.1 Analysis of the Error Related to DR
10.4.2 Analysis of the Error Related to the Combined DR-KM Approach
10.4.3 Analysis of the Error for an Historical Event
10.5 Conclusions
References
11 On the Application of Machine Learning into Flood Modeling: Data Consideration and Modeling Algorithm
11.1 Introduction
11.2 Scope
11.3 Study Area (Catchment) Considerations
11.4 Conditioning Factors
11.5 ML Models Employed for Flood Susceptibility Mapping
11.6 Summary and Conclusions
References
12 River Stage Prediction Using Hydrodynamic Modeling
12.1 Introduction
12.2 Study Area and Data Collection
12.3 Methodology
12.3.1 Overview of HEC-RAS Software
12.4 Results and Discussion
12.4.1 Unsteady Flow Analysis for 2010 Tidal Data
12.4.2 Unsteady Flow Analysis for 2011 Tidal Data
12.5 Conclusions
12.6 Data Availability Statement
12.7 Conflicts of Interest
References
13 Hydraulic Modelling for Flood Inundation Mapping to Assess the Impact of Check Dam in Araniyar River
13.1 Introduction
13.2 Study Area
13.2.1 Araniar Basin
13.2.2 Study Area Map
13.2.3 Lakshmipuram Anicut and A Reddipalayam Check Dam
13.3 Methodolody and Data Collection
13.3.1 Data Collection
13.4 Results and Discussion
13.4.1 Analysis from Google Earth Images
13.4.2 Simulation of 2D Flood Model Using HEC-RAS
13.4.3 Ground Water Quality Analysis
13.4.4 Analysing the Farmer’s Survey
13.5 Conclusion
References
14 Hydrodynamic Modeling Parameter Sensitivity Analysis Using UAV Based DEM and Satellite Image
14.1 Introduction
14.2 Study Area
14.2.1 Sabarmati River
14.2.2 Field Visit
14.2.3 Site Investigation
14.2.4 Data Collection
14.3 Methodology
14.3.1 Hydrodynamic Modelling
14.4 Result and Discussion
14.5 Conclusions
References
15 Numerical Modeling of Water Intrusion in Underground Spaces Throughout Stairs: Study Case of Paral·lel Metro Station in the City of Barcelona
15.1 Introduction
15.2 Materials and Methods
15.2.1 1:1 Scaled Physical Model and Discharge Definition
15.2.2 Rendering with CAD Tool and Numerical Modeling in the Software Flow-3D
15.3 Results and Discussion
15.4 Conclusion
References
16 Artificial Intelligence and GPU Card Calculations Applied to Flood Forecasting: Feedback from the Inundation Project
16.1 Presentation of the Inundation Project and Addressed Cientific Issues
16.2 Overview of Modeling Methods
16.3 Main Results on Artificial Intelligence
16.4 Main Results of Numerical Modeling Methods
16.5 Main Conclusions and Perspectives
References
17 1D Numerical Modelling of a Complex Tidal River: Case of the River Saigon, Vietnam
17.1 Introduction
17.2 Material and Methods
17.2.1 Study Site Location
17.2.2 Numerical Modelling
17.2.3 Hydraulics Experimental Data
17.3 Results
17.3.1 Experimental Data on the Tidal Wave Propagation
17.3.2 Impact of the Upstream Boundary Layer
17.3.3 Calibration of the Model Without Canals
17.3.4 Impact of Canals on the Saigon Hydrodynamics
17.4 Conclusions
References
18 Experimental and Numerical Investigation of Dam Break Flow Propagation Through Various Obstacle Configurations
18.1 Introduction
18.2 Laboratory Experiments
18.2.1 Experimental Setup
18.2.2 Performed Test and Configurations
18.2.3 Flow Measurement
18.3 Numerical model
18.4 Results
18.4.1 Water Height Time History
18.4.2 Free Surface Flow Description
18.4.3 Wave Impact Comparison
18.5 Conclusions
References
19 Flood Maps Definition for Off-Stream Reservoir Failure: Deterministic Versus Probabilistic Approach
19.1 Introduction
19.2 Materials and Methods
19.2.1 Numerical Modelling: Iber
19.2.2 Deterministic Approach
19.2.3 Probabilistic Approach
19.2.4 Study Site and Model Description
19.3 Results and Discussion
19.3.1 Deterministic Approach
19.3.2 Probabilistic Approach
19.3.3 Global Computing Time
19.3.4 Flood Mapping: Deterministic Versus Probabilistic
19.4 Conclusions
References
20 Flood Compound Modelling: Framework and Application to a Coastal Environment
20.1 Introduction
20.2 Methodology
20.2.1 Model Domain: Definition of the Area of Interest (AOI)
20.2.2 Type of Model and Boundary Conditions
20.3 Study Case: Florence Hurricane (2018)
20.3.1 Model Domain
20.3.2 Flood Drivers and Boundary Conditions
20.3.3 Data Catalog
20.4 Results
20.4.1 Precipitation Data Evaluation
20.4.2 Sensitivity Analysis
20.4.3 Flood Maps
20.5 Conclusions
References
21 Using Hydraulic Model for Riparian Zone Boundary Delineation and Analyzing Its Changes Under Different Flood Senarios
21.1 Introduction
21.2 Methods and Materials
21.2.1 Methodology
21.2.2 Materials
21.3 Results
21.3.1 Short-Term Simulation
21.3.2 Long-Term Simulation
21.4 Conclusion
References
22 Cellular Automata and Agent-Based Modelling for Inundation Simulation and Disaster Management
22.1 Introduction
22.2 Methodology
22.2.1 Agent-Based Model
22.2.2 Cellular Automata Inundation Model
22.3 Case Study
22.3.1 UK EA Hydraulic Benchmark Tests
22.3.2 Historical Events
22.4 Results and Discussion
22.4.1 UK EA Hydraulic Benchmark Tests
22.4.2 Historical Events
22.5 Conclusions
References
23 Calibrating 2D Flood Models in the Era of High Performance Computing
23.1 Introduction
23.2 Materials and Methods
23.2.1 Reconstruction of the Mandra Flood Event
23.2.2 Calibration Strategy
23.3 Results
23.3.1 Sensitivity Analysis
23.3.2 Grid-Search Calibration
23.3.3 Machine Learning-Based Calibration
23.4 Conclusions
References
24 What Is the Most Efficient Sampling-Based Uncertainty Propagation Method in Flood Modelling?
24.1 Introduction
24.2 UQ Analysis Framework
24.2.1 Generation of the Input Random Variables
24.2.2 Physical Solver and Quantities of Interest
24.2.3 Comparison Approach Using the Relative Histogram Difference
24.2.4 Definition and Characteristics of the Test Cases
24.3 Results and Discussion
24.3.1 Rapidly Propagating Flood over a Smooth Terrain
24.3.2 Carlisle 2005 Flooding
24.4 Conclusions
References
25 Enhancing Flood Analysis with a Lagrangian Transport Modeling and SERGHEI
25.1 Introduction
25.2 Lagrangian Particle-Tracking Model
25.2.1 Passive Particle-Tracking
25.2.2 Turbulence
25.3 Results
25.3.1 Test Case
25.3.2 Flood Event in July 2021
25.4 Conclusions
References
26 A Nonstationary Multivariate Framework for Modelling Compound Flooding
26.1 Introduction
26.2 Nonstationary Framework for Compound Flood Estimation
26.2.1 Step 1 Trend and Correlation Analysis
26.2.2 Step 2 Modelling Marginal Distributions of the Series of Data
26.2.3 Step 3 Building Copulas and Calculating the Joint Probability
26.2.4 Step 4 Generating the Quantiles
26.3 An Illustrative Case Study
26.3.1 Trend and Time-Varying Correlation of MMS and MMR
26.3.2 Stationary and Nonstationary Joint Probability Analysis
26.4 Conclusion
References
27 Counterfactual Analysis Applied to Flood Risk in Relation to Climate Change
27.1 Introduction
27.1.1 Counterfactual Analysis
27.1.2 Risk Assessment
27.2 Materials and Methods
27.2.1 Phase 1: Initialization
27.2.2 Phase 2: Downward Counterfactual
27.2.3 Phase 3: Risk Analysis
27.2.4 Future Projections
27.3 Case Study
27.3.1 Hydraulic Model
27.4 Methodology
27.4.1 Phase 1
27.4.2 Phase 2
27.4.3 Phase 3
27.5 Resultas and Discusion
27.6 Conclusions
27.7 Suggestions
References

2024-09-24