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Advances in Flood Risk Management Science - Improved short term rainfall and urban flood prediction. Prof. Čedo Maksimović Nuno Simões, Li-Pen Wang, Susana Ochoa The Royal Society, London, 5 th September 2011. Contents:. Urban flood modelling Dual-drainage models - PowerPoint PPT Presentation
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www.floodrisk.org.uk Funder: EPSRC Grant: EP/FP202511/1
Advances in Flood Risk Management Science
- Improved short term rainfall and urban flood prediction
Prof. Čedo MaksimovićNuno Simões, Li-Pen Wang, Susana OchoaThe Royal Society, London, 5th September 2011
Contents:
• Urban flood modelling– Dual-drainage models
• Radar-based integrated rainfall forecasting– Methodology and key techniques– UK case study: Cranbrook catchment, Redbridge
• Rainbgauge-only-based spatial-temporal rainfall prediction– Methodology and key techniques– Portugal cast study: Coimbra
• Remarks
URBAN FLOOD MODELLING
Focus on estimating fast and reliable flood distributions over the target urban areas
1D/2D, 1D/1D and Hybrid models
1D Sewer Simulation
1D / 2D simulation
1D / 1D simulation
Hybrid1D/1D + 1D/2D
simulation
Interaction between 1D Overland Network and 2D Overland Network
1D-1D Hybrid 1D-2D
Simulation time
Event Model [hh:mm:ss] vs 1D1D vs hybrid
300 min 30 yr
1D1D 00:01:46Hybrid 00:04:31 156%1D2D 00:45:23 2469% 905%
300 min 100 yr
1D1D 00:02:11Hybrid 00:05:20 144%1D2D 01:11:10 3160% 1234%
300 min 200yr
1D1D 00:04:40Hybrid 00:05:49 25%1D2D 01:16:05 1530% 1208%
RADAR-BASED INTEGRATED RAINFALL FORECASTING
Integrate state-of-the-art rainfall forecasting and modelling techniques to produce reliable rainfall forecasts as inputs for urban pluvial flood modelling/forecasting
Radar-based integrated rainfall prediction
T = Future
T = Current
10 - 30 km
1 - 2 km
C-Band
X-Band1 km
100-500 m
Ground Raingauge Network
1 km
i
CALIBRATIONi
t
Numerical Weather Prediction: UM/MM5
Temporal
Spatial
Meteorological Radar
t
STATISTICALLYDOWNSCALING
Cranbrook catchment, London, UK
The drainage area of the Cranbrook catchment is approximately 910 hectares; the main water course is about 5.75 km long, of which 5.69 km are piped or culverted.
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0 30 60 90 120 150 180
1 -R
elati
ve E
rror
Lead Time (min)
Pipe 1455.1 (Upstream)
1 km - 5 min
1 km - 10 min
1 km - 15 min
2 km - 5 min
2 km - 10 min
2 km - 15 min
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1 -R
elati
ve E
rror
Lead Time (min)
Pipe 463.1 (Mid-Catchment)
1 km - 5 min
1 km - 10 min
1 km - 15 min
2 km - 5 min
2 km - 10 min
2 km - 15 min
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elati
ve E
rror
Lead Time (min)
Pipe 307.1 (Downstream)
1 km - 5 min
1 km - 10 min
1 km - 15 min
2 km - 5 min
2 km - 10 min
2 km - 15 min
Nimrod
NimrodNowcast
Y
YYError Relative
11
Uncertainties of using rainfall nowcasts over different spatial and temporal scales for event 2010/08/22-23.
Uncertainties of applying downscaled rainfall inputs to hydraulic modelling for event 2010/08/22-23.
0
0.04
0.08
0.12
0.16
0.2
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Flow
Dep
th (m
)
Time (sec)
pipe 1455.1 (upstream)
1kmMax (500m)min (500m)Max (250m)min (250m)
0.05
0.15
0.25
0.35
0.45
0 5000 10000 15000 20000 25000
Flow
Dep
th (m
)
Time (sec)
pipe 463.1 (mid-catchment)
1kmMax (500m)min (500m)Max (250m)min (250m)
0.1
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0 5000 10000 15000 20000 25000
Flow
Dep
th (m
)
Time (sec)
pipe 307.1 (downstream)
1kmMax (500m)min (500m)Max (250m)min (250m)
RAINGAUGE-ONLY-BASED SPATIAL-TEMPORAL RAINFALL PREDICTION
Combine local point rainfall information with interpolation techniques to provide reliable rainfall forecasts as inputs for urban pluvial flood modelling/forecasting
Raingauge-only-based rainfall prediction= Time series prediction + interpolation techniques
Example in Coimbra, Portugal
Raingauges
Levelgauges
Time series prediction (in 5 minutes): ability to generate extreme values
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int [
mm
/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
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-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
int [
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/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
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120
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
int [
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/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
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20
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60
80
100
120
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
int [
mm
/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
0
20
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60
80
100
120
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
int [
mm
/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
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120
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
int [
mm
/hh]
time [min]
obsfst: -5minfst: -10minfst: -15minfst: -20minfst: -25min
SVM
SVM of Smoothfrequency
SSA+SVM
StochasticSSA+SVM
StochasticSSA+SVM
StochasticSSA+SVM
SSA + SVM time series prediction plus IDW interpolation techniques
0.12
0.17
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0.27
0.32
0.37
0.42
0.47
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16:4
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18:1
0
Wat
er D
epth
[m
]
Time
Obs Rainfallfst: 17:20fst: 17:15fst: 17:10fst: 17:05fst: 17:00observed
0.15
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0.25
0.3
0.35
0.4
0.45
0.516
:15
16:4
3
17:1
2
17:4
1
18:1
0
Wat
er D
epth
[m
]
Time
Obs Rainfall
fst: 17:20
fst: 17:15
fst: 17:10
fst: 17:05
fst: 17:00
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
16:1
5
16:4
3
17:1
2
17:4
1
18:1
0
Wat
er D
epth
[m]
Time
Obs Rainfall
fst: 17:20
fst: 17:15
fst: 17:10
fst: 17:05
fst: 17:00
Prediction of water levels 30 minutes in advance
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16:1
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Rain
fall
Inte
nsity
[m
m/h
h]
Time
Obs Rainfall
fst: 17:20
fst: 17:15
fst: 17:10
fst: 17:05
fst: 17:00
17h25m 17h30m 17h35m
Remarks
• Radar-based integrated rainfall prediction can effectively reflect larger scale weather variation to local scales, but– Accuracy: Data combination techniques– Resolution: Super-resolution radar images / rainfall information
• Raingauge-only spatial-temporal rainfall prediction exhibits promising predictability, but– Lead time: Improved time series prediction models – Spatial variability: Interpolation techniques
• Hybrid dual-drainage modelling may be the solution to providing fast and reliable flood prediction, but– Flood prone areas: flood map generation– Calibration: Coupled with image processing techniques
Remaining issues Prospective work to address remaining issues
