hydraulic modelling for real time flood forecast applicationsduring the january 2005 flood event...

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Hydraulic Modelling for Real Time Flood Forecast Applications

Yiping Chen

20 June 2007

BHS/CIWEM SW Branch Meeting

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Introduction

Hydraulic River Modelling: Washland (Floodplain) Modelling Techniques

Integrated Flood Forecasting Approach

Flood Forecasting Applications

Forecast Uncertainties

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Hydraulic River Modelling - Channel

River = River cross-sections + distance between them

Δx

Q

Variation of discharge and water level at each cross-section.

h

Q

t

Q

t

Q

t

QAttenuation

Can predict impact on flood levels of loss or creation of floodplain volume

Conservation of mass and momentum (St Venant Eqns)

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Hydraulic River Modelling – Washland

Δx

Q

h

Q

t

Q

Washland

(2) Can be modelled by secondary flood channels

(3) Can be modelled by extended cross-sections

Linked by lateral spills

(1) Can be modelled as flood cells

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Channel – Floodplain interactions:

Floodplain areas obtained using LiDAR data

Lateral spill unit used to link channel and floodplains

Reservoir units used for over bank flows over floodplains

Ele

vatio

n m

AO

D

Washland Modelled as Flood Cells

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Channel – Floodplain interactions:

Floodplain XSs obtained using LiDAR data

Lateral spill unit used to link channel and floodplains

Secondary Channels used for over bank flows over floodplains

Washland Modelled as Secondary Channels

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Washland Modelled as Extended Cross-sections

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Integrated Flood Forecasting Approach

Rainfall

Rainfall Runoff model

Continuous runoff hydrograph

Hydrodynamic model

Continuous water levels

Evaporation

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Information Flow in Real Time FF System

FLUVIAL

TIDAL

REAL TIME DATA FORECASTS

WEATHER

Weather radar images / actuals

Meteosat satellite images

0-6 hour radar / NWP forecasts e.g. Nimrod/HYRAD

Tidal levels / wave buoys

Rain gauges / snow fall

River / reservoir levels / gate settings etc

5-day / heavy rainfall / severe weather forecasts

River flow forecasting models / catchment wetness estimation

5 day storm surge forecasts

Storm Tide Forecasting Service / Astronomical Tide PredictionsWind speed /

direction

Onshore wave estimates and overtopping models

DisseminationFloodcall

AVM / sirens etc

Emergency services

Flood wardens

Local authorities

Utilities

Flood defence staff

River / reservoir control structures

Triggers/alarms

Archiving / post event analysis

Figure 2. Possible information flow in a real time flood warning and forecasting system

Environment Agency

Other (Met Office etc)

Automatic weather stations

AlarmsMORECSMOSES

Estuaries

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Rainfall Runoff Processes

RAINFALLPOTENTIAL EVAPORATION

MODEL PARAMETERS

RUNOFF COMPONENTSEVAPORATIONRECHARGE

RAINFALLPOTENTIAL EVAPORATION

MODEL PARAMETERS

RUNOFF COMPONENTSEVAPORATIONRECHARGE

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PDM Schematic

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PDM Parameters

Parameter Description Function

rainfac Surface flow Controls runoff volume, by scaling the rainfall input

cmin Baseflow Minimum storage capacity cmax Baseflow Maximum storage capacity

b Surface flow Exponent of Pareto distribution controlling spatial variability of store capacity

be Baseflow Exponent in actual evaporation function k1 Surface flow Time constant for linear reservoir K2 Surface flow Time constant for linear reservoir kb Baseflow Baseflow time constant kg Baseflow Groundwater recharge time constant st Surface flow Soil tension storage capacity bg Baseflow Exponent of recharge capacity qconst Surface flow Flow constant to raise or lower flow levels tdly Baseflow Time delay applied to events

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PDM Calibration

PDM Calibration October 2000, December 2000 and December 2002 events

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200

Hours since event start

Flow

(m3/

s)

0

2

4

6

8

10

12

14

16

18

20

Observed Baseflow Component TSCAL Rainfall Intensity

Baseflow Calibration - Daily data

0

2

4

6

8

10

12

13/02/2002 24/05/2002 01/09/2002 10/12/2002 20/03/2003 28/06/2003 06/10/2003

Date

Flow

(m̂3/

s)

0

5

10

15

20

25

30

35

40

45

50

Daily Flows TSCAL Baseflow Rainfall

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Real Time Flood Forecast Modelling

Application

Case Study 1 – Eden

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Introduction

In January 2005, over 1800 properties flooded in CarlisleAn event more than 1 metre higher than any other in the previous 200 yearsExisting Eden FF model has been in use for several yearsFloodplains modelled by extended XS and significantly under-estimated flows and levels during the January 2005 flood eventAtkins built a separate Flood Defence strategy model in 2005 for the Carlisle area with more details, more up-to-date survey and more realistic representations of floodplainsAtkins were commissioned in 2006 to improve the Eden/Carlisle flood forecasting model based on the FD strategy model

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Eden Catchment (2335 km2)

Carlisle

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Model Configuration

Great Corby

Temple Sowerby

Udford

Greenholme

Armathwaite(no model node)

HarrabyGreen

Cummersdale

Sheepmount Linstock

Botcherby BridgeDenton Holme

Warwick Bridge

Low Crosby

Eden Hall(no model node)

Forecasting Point

River EdenRiver EamontRiver IrthingRiver PetterilRiver Caldew

Gauged Inflow

DS Boundary

Durranhill

Great Corby

Temple Sowerby

Udford

Greenholme

Armathwaite(no model node)

HarrabyGreen

Cummersdale

Sheepmount Linstock

Botcherby BridgeDenton Holme

Warwick Bridge

Low Crosby

Eden Hall(no model node)

Forecasting Point

River EdenRiver EamontRiver IrthingRiver PetterilRiver Caldew

Gauged Inflow

DS Boundary

Durranhill

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ISIS FF model Schematic

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FF Model Schematic (1140 Nodes)

Sheepmount

Caldew PetterilEden

Irthing

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Model Runtime Performance

8729415565881954100 year +30% (80 hr)

4925939614641791100 year +20% (80 hr)

33500455361443100 year design (80 hr)

3341791471319Jan 2005 (150 hr)

64594632686Jan 1999 (80 hr)

54594731915Feb 1995 (80 hr)

33495131687Feb 1990 (80 hr)

<37.5s75s150s300s

Number Used for Each Timestep

Total Model

Runtime(second)

Maximum Outflow(m3/s)

Flood Events (Simulation Duration)

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Model Convergence Performance

1% ProbabilityJan 2005 Flood Event

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Model ValidationJanuary 1999 Validation Event - Sheepmount

8.0

9.0

10.0

11.0

12.0

13.0

14.0

15.0

05/0

1/99

00

05/0

1/99

12

06/0

1/99

00

06/0

1/99

12

07/0

1/99

00

07/0

1/99

12

Stag

e (m

AO

D)

0

100

200

300

400

500

600

700

800

Flow (m

3sec-1)

1999 Gauged Stage FF Modelled Stage1999 Gauged Flow FF Modelled Flow

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Rating Comparison

Sheepmount Rating

7

8

9

10

11

12

13

14

15

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800Flow (m3/s)

Stag

e (m

AO

D)

EA Rating

Spot Gaugings

Jan-05 (Rating Extrapolate)

Flood Forecast Model

V41_MODEL (100yr)

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Real Time Flood Forecast Modelling

Application

Case Study 2 –Rother/Tillingham/Brede

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Rother/Tillingham/Brede Catchment

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Rother FF Model Schematic

Udiam

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Model CalibrationUdiam Level

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

21/10 26/10 31/10 05/11 10/11 15/11

Leve

l (m

AO

D)

Modelled Recorded

Oct 2000 Event

Udiam Level

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

14/12 19/12 24/12 29/12 03/01 08/01 13/01 18/01

Leve

l (m

AO

D)

Modelled Recorded

Dec 2000 Event

Udiam Level

2.0

2.5

3.0

3.5

4.0

4.5

5.0

04/01 14/01 24/01 03/02 13/02

Leve

l (m

AO

D)

Modelled Recorded

Jan 2002 Event

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Raingauge ProblemCrowhurst - February 2001

0

10

20

30

40

50

60

70

80

09/01/01 14/01/01 19/01/01 24/01/01 29/01/01 03/02/01 08/02/01 13/02/01 18/02/01 23/02/01

Date

Flow

(m^3

/s)

0

1

2

3

4

5

6

7

8

9

10

Rai

nfal

l (m

m)

Observed Q TBR Computed Q Revised Q Infilled Rainfall TBR Record

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Tillingham/Brede FF Model Schematic

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Modelling Problem Hundredhouse Bridge Level

4.5

5.0

5.5

6.0

6.5

7.0

7.5

06/12 11/12 16/12 21/12 26/12 31/12

Leve

l (m

AO

D)

Recorded Modelled

Hundredhouse Bridge Level

4.5

5.0

5.5

6.0

6.5

7.0

04/01 14/01 24/01 03/02 13/02Le

vel (

mA

OD

)

Recorded Modelled

Dec 1999

Jan 2002

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Longsection Profile

Node LabelTILL

1_09

277

TILL

1_09

239

TILL

1_09

202

TILL

1_09

165

TILL

1_09

146

TILL

1_09

136

TILL

1_09

126

TILL

1_09

074

TILL

1_09

022

TILL

1_08

971

TILL

1_08

919

TILL

1_08

818

Elev

atio

n (m

AD

)

5.4

5.3

5.2

5.1

5

4.9

4.8

4.7

4.6

4.5

4.4

4.3

4.2

4.1

4

3.9

3.8

3.7

3.6

3.5

3.4

3.3

3.2

3.1

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Site Investigation

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Additional Survey Locations

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Additional Survey Locations

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Revised Longsection Profile

Node LabelTILL

1_09

277

TILL

1_09

202

TILL

1_09

126

TI

LL1_

0911

4A

TILL

1_09

101

TILL

1_09

088

TILL

1_09

075

TILL

1_09

023

TILL

1_08

919

TILL

1_08

818

TILL

1_08

717

Elev

atio

n (m

A 6

5.8

5.6

5.4

5.2

5

4.8

4.6

4.4

4.2

4

3.8

3.6

3.4

3.2

Node LabelTILL

1_09

277

TILL

1_09

239

TILL

1_09

202

TILL

1_09

165

TILL

1_09

146

TILL

1_09

136

TILL

1_09

126

TILL

1_09

074

TILL

1_09

022

TILL

1_08

971

TILL

1_08

919

TILL

1_08

818

Elev

atio

n (m

AD

)

5.4

5.3

5.2

5.1

5

4.9

4.8

4.7

4.6

4.5

4.4

4.3

4.2

4.1

4

3.9

3.8

3.7

3.6

3.5

3.4

3.3

3.2

3.1

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Improved Modelling Results Hundredhouse Bridge Level

4.5

5.0

5.5

6.0

6.5

7.0

7.5

06/12 11/12 16/12 21/12 26/12 31/12

Leve

l (m

AO

D)

Recorded Modelled

Dec 1999

Hundredhouse Bridge Level

5.0

5.2

5.4

5.6

5.8

6.0

6.2

6.4

6.6

6.8

7.0

04/01 14/01 24/01 03/02 13/02Le

vel (

mA

OD

)

Recorded Modelled

Jan 2002

Hundredhouse Bridge Level

4.5

5.0

5.5

6.0

6.5

7.0

7.5

06/12 11/12 16/12 21/12 26/12 31/12

Leve

l (m

AO

D)

Recorded Modelled

Hundredhouse Bridge Level

4.5

5.0

5.5

6.0

6.5

7.0

04/01 14/01 24/01 03/02 13/02

Leve

l (m

AO

D)

Recorded Modelled

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Tillingham Tidal Sluice Control

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Tillingham Sluice Modelling ResultsRye Harbour Level

-1

0

1

2

3

4

5

22/10 27/10 01/11 06/11 11/11

Leve

l (mAOD)

Recorded

Tillingham Sluice Level

1.0

1.5

2.0

2.5

3.0

3.5

4.0

22/10 27/10 01/11 06/11 11/11

Leve

l (mAOD)

Modelled Recorded

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Key elements of modelling success

Good representation of the catchment hydrology

Good representation of the river hydraulics (cross-sections, roughness, etc) & hydraulic structures (operations)

Good representation of the interactions between channel and floodplains

Good stage – flow rating

Good model calibration / validation

Flood forecasting model must be robust and stable

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Sources of Uncertainties

Factor Typical sources of uncertainty Meteorological Weather radar • Meteorological conditions (e.g. bright band, orographic growth,

anomalous propagation-anaprop , attenuation etc) • Physical siting of the radar relative to the catchment (distance, local

topography, obstacles etc) Rain gauges • Exposure and altitude

• Sampling errors (interval, tipping bucket size etc) • Performance in snowfall, high winds, heavy rainfall etc

Quantitative Precipitation Forecasts (Nimrod/NWP)

• Parameters/spatial and temporal resolution/representation of atmospheric and land surface processes etc

• Representation of storm growth/decay and advection processes etc • Representation of local factors (e.g. orographic growth)

Fluvial River Flow Monitoring

• Rating curve accuracy, particularly at high flows • Influence of sedimentation, vegetation and debris

Coastal (including STFS and TRITON) Coastal Monitoring • Density of wave monitoring network (sampling error)

• Combination of wave and still water level • Shallow water effects • Instrument error • Errors in estimating mean sea level

Model Boundary Conditions

• Errors transferred from mesoscale models to boundaries of STFS offshore surge model

Choice of model type and structure

• Grid resolution – inadequate representation of local bathymetric and topographic features that cause changes in local water levels

• Coupling of offshore and nearshore models Calibration • Availability of sufficient extreme events for calibration

• Skill of person calibrating the model Operational • Changes in characteristics since model was calibrated

• Events outside the range of the model calibration • Instrument/telemetry downtime problems

Real Time Updating procedures

• Currently no formal updating used, however, potential to use upcoast error to correct for downcoast sites

Component Typical sources of uncertainty Catchment averaging procedures (raingauge inputs)

• Representation of physical processes (topography, elevation etc)

• Type of rainfall event (convective, frontal, orographic etc) • Rain gauge density and distribution • Instrumental problems at one or more of the rain gauges

used Choice of model type and structure

• Lumped, semi-distributed, distributed rainfall inputs • Representation of catchment runoff processes • River channel and floodplain representation • Under/over parameterisation (parsimony) • Flood defence loading/fragility (if represented) • Gate operations • Representation of ungauged inflows • Representation of abstractions/discharges

Model calibration • Effectiveness of optimisation routines • Choice of optimisation criteria • Availability of sufficient high flow events for calibration • Skill of person calibrating the model

Operational • Changes in catchment/channel characteristics since model was calibrated

• Events outside the range of the model calibration • Model stability problems • Representation of initial/antecedent conditions • Representation of snowmelt (if applicable) • Instrument/telemetry downtime problems (rainfall)

Real Time Updating procedures

• Appropriateness for the type of model used • Sophistication of calibration software • Quality of the high flow data used both for calibration and

in real time • Event specific problems (backwater, bypassing, debris etc) • Instrument/telemetry downtime problems (flows)

DetectionForecasting

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Ensemble Forecasting – Source KNMI

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Snowfall February 7th/8th 2007 - Source Met Office

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Probabilistic Forecasting- Source Met Office

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Thank you

Questions?