Advances in Flood Risk Management Science - Improved short term rainfall and urban flood prediction

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Advances in Flood Risk Management Science-
Improved short term rainfall and urban flood
prediction

• Prof. Cedo Maksimovic
• Nuno Simões, Li-Pen Wang, Susana Ochoa
• The Royal Society, London, 5th September 2011

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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

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Urban flood modelling

• Focus on estimating fast and reliable flood
  distributions over the target urban areas

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1D/2D, 1D/1D and Hybrid models
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Interaction between 1D Overland Network and 2D
Overland Network



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Simulation time
Event Model hhmmss vs 1D1D vs hybrid
300 min 30 yr 1D1D 000146
300 min 30 yr Hybrid 000431 156
300 min 30 yr 1D2D 004523 2469 905

300 min 100 yr 1D1D 000211
300 min 100 yr Hybrid 000520 144
300 min 100 yr 1D2D 011110 3160 1234

300 min 200yr 1D1D 000440
300 min 200yr Hybrid 000549 25
300 min 200yr 1D2D 011605 1530 1208
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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

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Radar-based integrated rainfall prediction
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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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Uncertainties of using rainfall nowcasts over
different spatial and temporal scales for event
2010/08/22-23.


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Uncertainties of applying downscaled rainfall
inputs to hydraulic modelling for event
2010/08/22-23.
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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

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Raingauge-only-based rainfall prediction Time
series prediction interpolation techniques
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Example in Coimbra, Portugal
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Time series prediction (in 5 minutes) ability to
generate extreme values
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SSA SVM time series prediction plus IDW
interpolation techniques
17h25m
17h30m
17h35m
Prediction of water levels 30 minutes in advance
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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