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?