Gareth Pender School of Built Environment

1

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

0

32

Sn

ARQ

Manning’s Equation

Some involve more complex hydraulics

One-dimensional computer modelling

fb SSgAx

hgA

A

Q

xt

Q

2

0

x

Q

t

A

Q

Q

T

h

Q

h

x

One-dimensional computer modelling

ToneFPS_5

ToneFPS_6

ToneFPS_8

CSM04

CREECH

SEC_PP_380U

HAMWU

CS1

CREECH ToneFPS_9

HAM

KNAPP

One-dimensional computer modelling

Q

Q

T

h

Q

x

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

Q

jiji

ij

jii uuxx

pg

x

UU

t

U

1

The most widely used k–ε turbulence closure model,

0

i

i

x

UContinuity

Momentum

Even more complicated techniques available

u

v

u

vw

u

vw

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

Q

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