Environmental and Water Resources Engineering Section (EWRE) and

Urban Water Research Group (UWRG)

Advanced modelling forecast and emergency

management of urban pluvial floods

February 8th, 2010

Prof. Čedo Maksimović

(c.maksimovic@imperial.ac.uk)

Acknowlwdgements

Dušan Prodanovićććć

João. Leitão

Jaena Ryu

S. Boonya-aroonet

Lipen Wang

Nuno Simões

Susana Ochoa-Rodriguez

Danielle Skilton

Types of Urban flooding

Catchment boundary

Receivingwater

Types of urban flooding :� Pluvial -> Originating from rivers� Coastal -> primarily tidal surges and waves � Ground water flooding, slow and lasting

and finally� Pluvial (Surface) Flooding

covered by this lecture

Urban flooding “happens” – September 2010 -Korea

2. Advances in surface runoff

modelling

Separate foul and

surface water drainage

Surfaceflow

Outfall

InletIndustrial Sanitary

Treatmentplant

Treated water

3. Pluvial flood modelling

MODELLING OF URBAN FLOODING;

BREAKTHROUGH

OR RECYCLING OF OUTDATED CONCEPTS?

State-of-the-art

Virtual reservoirin the city

centre

Piezometric line

∆Π

Virtual reality in modellingThis was “state of the art” for years

Subcatchment

Depression

Flooded area

Total rain

Flow in the sewerOverflow from depression

Surface runoff

Effective rain

Outflow to the surface

Legend:

0

0

12

3

4

5

6

7

8

1

2

3

Pond delineation and link with network

Developments for interactions

Interactions of the overland and sewer flow during flood period

Dual drainage model conceptsApproaches in surface flooding analysis:

•Distributed modelling (surface only)

•Interaction physically based surface runoff with flow in sewers

•Approximate surface delineation or

•Spatial approximation dependent on DTM/ land use

Effective rainfall

Sewer flow

Surface

component

Bi-directional

interaction

Dual drainage concept Dual drainage concept

Sub-surface

component

AOFD approach:

Automatic Overland Flow

Delineation

AOFD methodology

Pond 2

Pond 3

Pond 1

(iv)Pond 4

(iii)

(i)

Manholes

Manholes

(ii)

(vii)

(v)

(vi)

30 July 2002 1800 hours

(DEM), not filtered, 3D view

(DTM) 3D view

Tasks

• Ponds (storage nodes)

• Flow pathways (surface links)

• Pathways geometry (drainage capacity)

• Link surface system to subsurface system (interface)

• Contributing areas for sewer and surface ponds (catchment delineation)

• Generate input files for simulation model

Developments for the major system

Surface pathway network

Z [m]

X [m]

V=f(Z)

Z [m]

X [m]

V=f(Z)

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Catchment boundary

③③③③Out of catchment

Pond

Pond

Pond

Pond

Pond

①①①①Surface pathways

②②②②overspill

The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again.

3. Out of catchment

Pond

Pond

Pond

Pond

Pond

2. Surface pathways

2. overspill

Pathways drainage capacity

Estimate shape of open channels

Pond ornode

Pond ornode

DTM grid

4

3

2

1

Cowes, Isle of Wight

Catchment area = 2.8 sq.km

LiDAR DTM(+buildings) 1x1m (reduced 4x4m)

Drainage network: 570 manholes + 576 pipes

Analysis of ponds

4088 ponds found Removal of small ponds

Surface network

Rainfall and Flood Prediction

In flood forecast situations, one of the critical aspects is the period of time available between the acquisition of data, such as rainfall, and the results obtained by hydraulic simulations.

• AIM: predict in 15 minutes the flood magnitude and extension that might occur for the following 3 hours.

– Short term rainfall forecast

– Runoff surface flood (our focus)

Short term rainfall prediction

Two approaches:

• Use of raingauge network data for rainfallprediction

• Integrated techniques over multiple scales forshort-term rainfall prediction

25

Via interpolation techniques, it is possible to synthesise

“radar images” based upon a network of raingauges

IDW Kriging

A blend of interpolation techniques and time-series

forecasting

Spatial Rainfall prediction based upon raingauge only network

Pred

icted

Ob

served

42 Raingauge Stations in Great London => rainfall rates available every 30 min between 06/06/2006 and 19/10/2009 Interpolation with Kriging Method

SVM-based sub-hourly rainfall forecasting

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Expected vs 1st prediction

Series2

Series1

Series3

0

0.5

1

1.5

2

2.5

3

3.5

4

0 1 2 3 4 5 6 7

Expected vs prediction

0

0.5

1

1.5

2

2.5

3

3.5

0 1 2 3 4

Expected vs 1st prediction

Series2

Series1

Series3

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12

1st frequency (expected vs prediction)

Series2

Series3

Cascade-Based Rainfall Downscaling

Cascade-based downscaling techniques

2D + time rainfall downscaling (also known as “string

of beads” methodology)

10‐ 30

km1 km

0

5

10

15

20

25

30

35

40

0:00:00 12:00:00 0:00:00 12:00:00 0:00:00 12:00:00 0:00:00

Tota

l Rai

nfa

ll (m

m)

Time (minutes)Starting time corresponds to 00:00:00 hours of Friday 1st of October 2010

RAINFALL CUMMULATIVE

Beal HS Chadwell Heath Ursuline HS

Missing radar data

RAIN EVENT 1RAIN EVENT 1RAIN EVENT 1RAIN EVENT 1stststst ---- 3333rdrdrdrd OCTOBER 2010OCTOBER 2010OCTOBER 2010OCTOBER 2010

•Two consecutive events

•Complete data from 3 raingauges in Redbridge (+ level gauges)

•Radar data only for first/smaller event

RAIN EVENT 17RAIN EVENT 17RAIN EVENT 17RAIN EVENT 17thththth –––– 18181818thththth JANUARY 2011JANUARY 2011JANUARY 2011JANUARY 2011

0

5

10

15

20

25

30

35

12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00

Cu

mm

ula

tive

Rai

nfa

ll (m

m)

TimeData shown: 13:00:00 15/01/2011 to 14:00:00 20/01/2011

Beal_HS_Cummulative Ursuline HS

16/01/11 17/01/11 18/01/11 19/01/11

0

200

400

600

800

1000

1200

1400

1600

12:00:00 0:00:00 12:00:00 0:00:00 12:00:00 0:00:00 12:00:00 0:00:00 12:00:00 0:00:00

Wat

er D

epth

(m

m)

Time (minutes)

WATER DEPTH

Valentine Open Channel Valentine Sewer Cranbrook Sewer Roding River

16/01/2011 17/01/2011 18/01/2011 19/01/2011

•Two consecutive events

•Complete data from 2 raingauges in Redbridge (+ level gauges)

•Radar data only for first event

0

2

4

6

8

10

12

14

16

18

04:48:00 06:00:00 07:12:00 08:24:00 09:36:00 10:48:00 12:00:00

Cu

mm

ula

tive

Rai

nfa

ll (m

m)

Time17th January 2011

Comparison Nimrod vs. Raingauges

Ursuline_HS_Raingauge

Ursuline_HS_Nimrod

Beal_HS_Raingauge

Beal_HS_Nimrod

NOWCASTING

Integrated rainfall techniques over multiple scales for short-term rainfall prediction

• Both techniques allow to have very detailed networks in vulnerable areas and less detailed in other parts.

• The 1D/1D is much faster than 1D/2D models

• Physical based models have the advantage of being adjustable to future changes in the area

Short term flood prediction

Decrease of Hydraulic Simulation time

Model Assembly

Rain and level gauges

trigger alarms (and provide

boundary conditions)

Short term rainfall

forecast

Short term flood

forecast

Emergency management

(alarm-awareness raising

and event management)

Pre - processing:

Layers positively correlated with suitability

Fuzzy Sets membership function

(0 - 255 standardisation)

Parameters affecting infiltration

(suitability categories)

Primary Layers

(Field data)

4. Simulate Source Control application in accordance with the Characteristics of the Simulation Model

5. Simulation output to assess the effect of s.c. application POST PROCESSING

Reduction in max H for 10T

Blue – Green Corridors

and interaction with SUDS and

floods

Blue-Green Corridors

•Catchment wide development, creating connections between natural areas of habitat along river corridors and green spaces with urban flood management

•Integration of sustainable drainage systems into urban development

•This provides surface water flood risk reduction as well as enhanced aesthetics and amenity provision

Problem Areas

Potential Blue-Green Components

CONCLUSION

Modelling and forecasting of Urban Pluvial Flooding:

ART TO BE MASTERED

Thank you!

Prof. Čedo Maksimović

c.maksimovic@imperial.ac.uk