Sensor networks and urban pluvial flood modelling … · Sensor networks and urban pluvial flood...

Susana Ochoa-Rodríguez BHS Meeting – 28.02.2013

Sensor networks and urban pluvial flood modelling in an urban catchment

Susana Ochoa-Rodríguez

28th February 2013

Environmental virtual observatories: managing catchments with wellies, sensors and smartphones


Contents

1. Context

2. Pilot locations & sensor network + SOS

3. Rainfall processing techniques + WPS

4. Putting the jigsaw together

5. Conclusions


1. CONTEXT

• Urban pluvial flooding: challenges and needs

• Tackling the challenges


Extreme rainfall events exceed the capacity of the drainage system

URBAN PLUVIAL FLOODING

http://www.scientia.blog.br/wordpress/wp-content/uploads/São-Paulo.jpg


URBAN PLUVIAL FLOODING

• Insufficient capacity of sewer system

• Surface flow (overland system)

• Dynamic interactions between the two systems

• It’s localised and happens quickly – “flash floods”


Model Assembly for Pluvial Flood Modelling, Forecasting and Management

Rainfall Estimation / Forecasting

Flood Modelling / Forecasting

Management (urban planning, emergency)

Same “framework” as other types of flooding, but for urban pluvial flooding each step is a bit more complex

S u p p o r t e d b y d a t a (m o n i t o r i n g)




Management (urban planning,

emergency)

• The rainfall events which generate pluvial flooding are often associated with thunderstorms of small spatial scale (~ 10 km), whose magnitude and spatial distribution are difficult to monitor and predict (also: lead time vs. accuracy)

• Rainfall estimates/forecasts with fine spatial and temporal resolution required





emergency)

• Urban “jungle” is complex

• Interaction of sewer and overland systems

• Since flooding is localised, models must have fine spatio-temporal resolution

• Model detail vs. Runtime

Effective rainfall

Sewer flow

Surface component

Bi-directional interaction

Sub-surface component


• Urban catchments change constantly

• Complete flood records for calibration and verification are seldom available

• High uncertainty in boundary conditions

• High operational uncertainty (blockages, pipe burst, pump failure, change in geometry of roads and other channels, etc.)

• Individual sources of uncertainty are magnified by small scale




emergency)





emergency)

• Uncertainty in modelling and forecasting hinders decision making

• Low awareness

• Given rapid onset and short forecasting lead-times, the public become the principal responders, but they are not so willing to respond

• Lack of coordination between stakeholders involved

• Budgetary cuts

• …

Tackling the Challenges





• Improved rainfall monitoring (X-band radar, high quality radar protocols, rain gauges)

• Rainfall processing techniques to improve accuracy and resolution

• Improved nowcasting based on improved rainfall estimates

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








• Monitoring (Q, h)

• Development and testing of models of different levels of complexity

• Uncertainty analysis and uncertainty-based model calibration

• Definition of local pluvial flood triggers

• Pilot forecasting system





• Stakeholder engagement in local flood risk management

• Decision making for emergency management and spatial planning supported by improved monitoring, modelling and forecasting





• Improved rainfall monitoring (X-band radar, high quality radar protocols, rain gauges)

• Rainfall processing techniques to improve accuracy and resolution

• Improved nowcasting based on improved rainfall estimates

• Monitoring (Q, h)

• Development and testing of models of different levels of complexity

• Uncertainty analysis and uncertainty-based model calibration

• Definition of local pluvial flood triggers

• Pilot forecasting system

WEB SERVICES are being used to enable easy access to data in a standardise way and implementation of processing modules which can be used interoperably.

• Stakeholder engagement in local flood risk management

• Decision making for emergency management and spatial planning supported by improved monitoring, modelling and forecasting


2. PILOT LOCATIONS AND SENSOR NETWORKS

• 3 urban catchments

• Rainfall and flow/depth monitoring


PILOT LOCATIONS

• Cranbrook (London Borough of Redbridge)

• Purley (London Borough of Croydon)

• Torquay City Centre (Torbay, Devon)


Cranbrook Catchment (LB Redbridge)

• Area: aprox. 900 ha

• Cran Brook: 5.75 km (5.69 km culverted)

• Predominantly urban catchment

• Sub-catchment of Roding River catchment

• Subject to fluvial & surface flooding (Several flood events reported since 1926, most recent events in October 2000 and February 2009)

(b) Valentines Park

(a) Cranbrook Road (c) Sewer Outfall

(c) Roding River

(a)

(b)

(c)

(d)


Flooding Mechanisms

• Overland flow routes associated with surface water flooding generally follow natural drainage pathways


Sewer outfall

Roding River

• Overland flow routes associated with surface water flooding generally follow natural drainage pathways

• Interaction of pluvial and fluvial flooding

• Tidal influence

• Highly urbanised area = rapid response to rainfall

Flooding Mechanisms


Local Monitoring System

22

All sensors are equipped with data acquisition and real-time wireless communication units (which use sim-cards)

• 3 tipping bucket rain gauges

With 1 min data “sampling”

• 2 pressure sensor for monitoring water levels in the Roding River

Real time frequency: 5/10 min

• 1 sensor for water depth measurement in sewers

Real time frequency: 5/10 min.

• 1 sensor for water flow (v + d) measurement in sewers

Real time frequency: 5/10 min.

• 1 sensors for water depth measurement in open channels

Sampling frequency: 5/10 min


Local monitoring system


Chenies Thurnham

Radar type C-band C-band

Polarisation Single-polarisation* Dual-polarisation

Doppler (yes/no) No* Yes

Antenna Parabolic 3.6 m diameter, 43 dB gain

Beamwidth 1°

Frequency range 5.4 – 5.8 GHz

Wave length 4 – 8 cm

Range resolution 1 km up to 50 km range / 2 km up to 75 km range

Temporal resolution 5 min scan repeat cycle**

Elevations (°) 0.5, 1.5, 2.5, 4.0, 5.0 0.5, 1.0, 1.5, 2.5, 4.0

*Currently being upgraded to dual-pol and doppler **Within the RainGain project the potential benefits of reducing the repetition cycle to 2-3 min will be tested.

UK Met Office C-Band Radars


Selex RainScanner

Radar type X-band

Polarisation Single-polarisation

Doppler (yes/no) No

Antenna Parabolic, pencil beam antenna

Beamwidth 2°

Frequency range 8 to 12 GHz

Wave length 2.5 – 4 cm

Range resolution 30 m

Temporal resolution 1 min

Elevations (°) 0.5, 1.5, 2.5, 4.0, 5.0

ICL’s X-Band Radar (will be installed on 9th March 2013 on the rooftop of the Royal Free

Hospital in Hampstead)

• Smaller wavelength makes X band radar more sensitive and able of detecting smaller particles (e.g. drizzle, light snow, cloud formation).

• X band radars attenuate very easily, so they are used for short range weather observation.

We are using a Sensor Observation Service (SOS) server to store rainfall data from multiple sources in a standardised way

DVD RW

SOS server

SOS Client

Rain gauge networks

Level sensors

A user can query data from the SOS server using simple

scripts written in any computing language making use of existing SOS libraries

(which contain standard SOS functions)

• SOS qualified servers must have a standard interface

• This standard interface comprises a number of standard (and flexible enough) functions which allow querying data from the database

Exchange takes place using standard protocols


Standard SOS functions (Server) • Querying observations and sensor metadata

• Getting representations of observed features

• Adding new sensors and removing existing ones

• Inserting/deleting sensor observations

SOS User

• A number of libraries freely available, which allow:

• Querying and downloading data from the SOS server

• Performing standard functions

• Outputting data in standard GML format and other formats (which depend on the library you use – e.g. sos4R can output CSV, arrays which can be used internally for analysis and visualisation, formats compatible with Web Processing Services)

Example of a query from a user

using an SOS library

for ‘R’

Example of output in GML (Geography Markup Language) standard format

Plotting of data downloaded from SOS server (plotting done using functions of programming language)


3. RAINFALL PROCESSING TECHNIQUES

• Aimed at improving accuracy and resolution of rainfall estimates

• Two techniques have been developed, tested and implemented as Web Processing Services (WPS):

Merging of radar and raingauge rainfall estimates

Rainfall downscaling


“… Rainfall is the main input for urban pluvial flood models and the

uncertainty associated to it dominates the overall uncertainty in the

modelling and forecasting of these type of flooding… ’’

(Golding, 2009)

We really need to get the rainfall right!


Two essential characteristics of rainfall estimates

• Accuracy: critical! Especially for urban hydrological applications, where errors in rainfall estimates are magnified by the small scale

• Resolution: for urban hydrological applications spatial and temporal resolution must be high

Sensors commonly used for estimation of rainfall at catchment scales

RAINGAUGE RADAR

Accuracy

Coverage, spatial characterisation of rainfall field

Resolution

Raingauge Weather Radar

Further processing of radar rainfall estimates can improve its applicability (in terms of accuracy and resolution) to urban hydrological applications


Two techniques have been studied

• Gauge-based radar rainfall adjustment: aims at combining the advantages of radar and raingauge sensors to have a better spatial description and local accuracy of urban rainfall

• Cascade-based spatial downscaling: aims at producing rainfall estimates with finer spatial resolution (< 1 km)


RADAR-RAINGAUGE MERGING OR ADJUSMENT Aim: To combine the advantages of radar and raingauge sensors to have a better spatial description and local accuracy of urban rainfall

Interpolated rainfall field

Radar image

Output rainfall field

0

5

10

15

20

25

00:05 01:05 02:05 03:05 04:05 05:05 06:05 07:05

Rai

n D

ep

th (

mm

)

Time (5 min)

Cumulative Rain Depth (23/08/2010 event)@Beal RG

Beal_RG Radar 1km

Beal HS raingauge rainfall depth accumulations: 23/08/2010 event

Why we need to adjust radar rainfall data?

Raingauge

Collocated radar pixel

Urban drainage models are normally calibrated using raingauge data


Based on their assumption, gauge-based radar rainfall adjustment techniques can be

classified into two types

Mean Bias Correction

Error Variance Minimisation

a) b)

c) d)

e) f)

g)

Block-Kriging interpolation

comparison

error field fitted by an exponential variogram

combination

output

RG data Radar data

Principle of Bayesian Data Combination

[Image : Ehret et al., 2008] [Source: Todini, 2001]

In this process the variance of the error is

minimised

Four recorded events in the period of 2010 – 2012 over the Cranbrook catchment were studied

Event Duration RG total(1)

(mm) Radar@RG

total(2) (mm) Radar total

(mm) Sample bias Bi = (1)/(2)

23/08/2010 8 hr 23.53 7.29 6.80 3.23

26/05/2011 9 hr 15.53 5.10 4.88 3.04

05/06/2011 24 hr 20.87 9.43 9.48 2.21

03/01/2011 13 hr 8.93 7.72 7.55 1.16

Bias is event-varying, separate adjustment for each event is required

Stratiform

Convective

0

5

10

15

20

25

0:05 1:05 2:05 3:05 4:05 5:05 6:05 7:05

Rai

n d

ep

th (

mm

)

Time (5 min)

Mean Rainfall Accummulation: 23/08/2010 event

Mean RGs

Radar 1km

Corrected Radar 1km

Bayesian Radar 1km

Both rainfall profiles and accumulations can be significantly improved through adjustment

0

2

4

6

8

10

12

14

16

0:05 1:05 2:05 3:05 4:05 5:05 6:05 7:05

Rai

nra

te (

mm

/hr)

Time (5min)

Mean Rainrate: 23/08/2010 event

Mean RGs

Radar 1km

Corrected Radar 1km

Bayesian Radar 1kmMean bias adjusted

Bayesian adjusted

Mean bias adjusted

Bayesian adjusted

Raingauge

Radar

Radar

Raingauge

Simulation of flow depths is substantially improved by using merged rainfall data as input (23/08/2010 event)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.60

5

10

15

20

25

30

0000 0100 0200 0300 0400 0500 0600 0700 0800 0900 1000

23 August 2010 (Time, GMT)

Flo

w D

ep

th (

m)

Rai

n (

mm

/hr)

Pipe 463.1 (Mid-stream)

RGs

Radar 1km

Corrected Radar 1km

Bayesian Radar 1km

Obs. 463.1(Mid-Stream)Observations

Simulation of flow depths is substantially improved by using merged rainfall data as input (26/05/2011 event)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.60

10

20

30

40

50

60

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100

26 May 2011 (Time, GMT)

Flo

w D

ep

th (

m)

Rai

n (

mm

/hr)

Pipe 463.1 (Mid-stream)

RGs

Radar 1km

Corrected Radar 1km

Bayesian Radar 1km

Obs. 463.1 (Mid-stream)


Rainfall Cascade Generator

q

τ

τ(q)

q

τ

τ*(q)

τ(q)

Log (spatial scale)

Log (q-th moment)

q = q5q = q4

q = q3q = q2

q = q1

V14km

V8kmLevel n = 8 km

Level n = 4 km

Level n = 2 km

Level n = 1 km

V24km

V34km V4

4km

V112km V12

2km

V132km

V142km

V1211km

V1221km

V1231km

V1241km

wi1 wi2

wi4wi3

wi1

wi2 wi4

wi3

wi1 wi2

wi4wi3

Principle of cascade based spatial downscaling

Scaling analysis

fitting

Feature curve

downscaling

Adjustment of 1 km data -> Downscaling to 500 m 20100823 event 20110526 event

Combined Application – Merging & Downscaling

Measurement of the impact of bias and resolution

Us: range of water depth estimates resulting from downscaling

Uadjusted: difference in water depth estimates resulting from bias correction

Us

Uadjusted

t

Flow

level

Water Depth Us

Mean Max Mean Max Mean Max

1 km - 500 m 37.74 59.68 13.42 46.10 10.81 31.52

1 km - 250 m 43.89 103.03 16.23 62.80 12.50 31.34

1 km - 125 m 37.59 62.32 17.34 52.21 11.63 20.00

1 km - 500 m 47.66 67.35 24.04 56.59 21.61 32.89

1 km - 250 m 53.14 76.62 30.79 73.13 28.45 36.78

1 km - 125 m 64.14 72.34 28.28 60.35 24.53 39.98

1 km - 500 m 105.47 113.33 49.09 57.43 33.28 39.78

1 km - 250 m 135.40 190.98 60.53 70.27 41.57 44.85

1 km - 125 m 170.46 219.50 72.26 98.61 46.49 62.45

1 km - 500 m 100.15 93.73 31.18 18.87 19.76 16.75

1 km - 250 m 118.29 105.62 37.31 25.74 24.72 19.17

1 km - 125 m 124.20 102.58 32.54 17.72 19.95 13.35

scale

23/08/2010 event

26/05/2011 event

05-06/06/2011 event

03/01/2012 event

Upstream

1455.1

Midestream

463.1

Downstream

307.1

Uadjusted

Us X 100


• In most cases, gauge-based radar rainfall adjustment shows larger impact on the subsequent hydraulic outputs:

In general Accuracy is more important than Resolution!

• However, it may be possible that in small areas the impact of fine-scale spatial variability of rainfall is relatively more important than the bias observed in radar estimates.

• We will continue to study the impact of bias and resolution based on more rainfall events

These two rainfall processing techniques have been implemented as Web

Processing Services (WPS)

Client

WPS server

Hydrological Modelling

Downscaling

Data Merging

• WPS Interface Standard provides rules for standardising inputs and outputs for invoking geospatial processing services as web services

• Allow taking advantage of distributed computing – more efficient than doing this in your own computer

• Since it follows a standard interface, it can be easily used by clients

• Facilitates knowledge sharing – testing of algorithms without you having to implement them


4. PUTTING THE JIGSAW TOGETHER

We now have a series of building blocks which use standard interfaces and can be easily linked

DVD RW

SOS server

Client

Rain gauge networks

WPS server

Level sensors

Hydrological Modelling

Downscaling

Data Merging


5. CONCLUSIONS


Sensor Observation Services (SOS)

• Enable storing and accessing data from multiple sensors in a standardised way

• Data are centralised but can be easily accessed by a number of ‘clients’ (who can use existing libraries and simple scripts for querying the SOS database)

• Allow interoperational use of data

• Facilitate access to data, data exchange and display

• Useful for participatory modelling / management of hydrological systems


Web Processing Services (WPS)

• Allow taking advantage of distributed computing

• Facilitate knowledge transfer and sharing

• Facilitate quick testing and comparison of different models / algorithms


Web Services as a whole

• Make hydrological monitoring and modelling resource-effective

• Highly flexible – you jigsaw can be as simple or complex as you wish

• Facilitate knowledge transfer and sharing

• Standard formats can facilitate and speed up implementation of models / tools

• Plenty of tools freely available online


Thank you for your attention

Susana Ochoa-Rodríguez

[email protected]

Sensor networks and urban pluvial flood modelling … · Sensor networks and urban pluvial flood...

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