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Introduction to Inundation Modelling. Gareth Pender School of Built Environment. Heriot Watt University . Main river channel can’t contain the water. Spills onto the surrounding land. Flood. Fluvial Flooding. Models allow us to make predictions: - PowerPoint PPT Presentation
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Gareth PenderSchool of Built Environment
Heriot Watt University
Introduction to Inundation Modelling
Main river channel can’t contain the water.
Spills onto the surrounding land.
Flood
Fluvial Flooding
22 April 2023 3
Models allow us to make predictions:• Derive water levels/flood maps for events more extreme than
have been recorded so far• Derive results for specific return periods (eg 1 in 100)• Predict impacts of climate change• Assess ‘benefits’ of different flood defence interventions• Inform flood warning/response decisions• Predict what would happen if defences fail• And much more…
Models help us understand (and hopefully prepare for) what could happen
Models for Flood Risk Management
Some are simple
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Manning’s Equation
Some involve more complex hydraulics
One-dimensional computer modelling
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One-dimensional computer modelling
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CSM04
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SEC_PP_380U
HAMWU
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CREECH ToneFPS_9
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One-dimensional computer modelling
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In summary 1D models are:• good for design purposes• relatively easy to run• good with in-channel structures• lots of them already exist
Inundation extent2D phenomenon
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The most widely used k–ε turbulence closure model,
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Even more complicated techniques available
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2D depth averaged.
3D hydrostatic pressure
3D non-hydrostatic pressure
Plus sub-sets depending on how turbulence is modelled!• Mixing length model• k-ε model• Non-linear k-ε• Reynolds stress model• Algebraic stress model
u
v2D depth averaged.
Roger will discuss recent advances in this class of model.
So what’s new?Increasing availability of remotely sensed dataOpportunities for :• better model validation• new modelling methods
LiDAR (Light Detection And Ranging)• airborne mapping technique• uses a laser to measure the distance
between the aircraft and the ground• results in the production of cost-effective
terrain and vegetation height maps• 1 point per 4m2 density.
SAR (Synthetic Aperture Radar)• used for flood extent mapping • systems are mounted on both satellite and
airborne platforms• also being routinely used to provide
another source of topographic data to complement LiDAR.
CASI (Compact Airborne Spectrographic Imager)
• hyperspectral optical system measures light intensity.
• used to classify land use.• in a flood modelling used to identify
features on floodplains and provide additional data on vegetation properties
LIDAR Scanning ProcessLIDAR Scanning Process
IllustrativeLine of Flight
Scan Pattern
Flight PatternFlight Pattern
w-axisu-axis
Aircraft Attitude Control Aircraft Attitude Control -- IMUIMU
yaw
pitch
roll
v-axis
u-axis
w-axis
True Line of Flight
Gravitational Gravitational PotentialPotential
NorthNorth
Leicester
Combined LiDAR and CASI data
SAR image Inundation extent
Model validation
LiDARNew modelling methods
Rapid Flood Spreading MethodsSimple = very fast
Continuity + Friction Law
Pascal and I will discuss recent advances in this class of model.
Combining modelling methods inundation prediction?Recall 1D models are:• good for design purposes• relatively easy to run• good with in-channel structures• lots of them already exist• not so good for predicting inundation which is
essentially two-dimensional
• utilise 1D approach for the main river channel
• link to a 2D hydrodynamic solution for the flood plain
• the mesh for the 2D solution will be generated from topographic data collected remotely and include flow paths through urban areas.
Fluvial inundation
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Flood defence failure
Q
Flooding from storm water drains
Q
All three at onceplus coastal flooding
Q
Decision supportRisky!
Agency Databases MDSF Customised GIS & Database
Core Data: Background maps Existing flood maps DEM Property data Land use Environmental Rivers Etc Local Data: Local reports Etc
CFMP Outputs (electronic CFMPs)
General Features: Import Case/scenario management Metadata
Inception Phase: Collect and store View data
Flood Mapping: Import water levels Generate (or import) flood depth grids
Economic Analysis: Flood damages
Social Impacts: People affected Social flood vulnerability
Policy Evaluation: Compare baseline with scenarios Uncertainty Estimation: Acknowledge and estimate uncertainty
Case Definition: Climate Land use Flood Management
Further analysis, iteration, consultation and review leading to:
Catchment Flood
Management Plan
MDSF System Overview
External Tools: Hydrology Hydraulics
Before the Flood
Kislingbury
35
The End