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Susana Ochoa-Rodríguez SPN7 Conference – 30.08.2013

On the possibility of calibrating urban storm-water drainage models using gauge-based adjusted radar rainfall

estimates

Presenter: Susana Ochoa-Rodríguez

Authors: S. Ochoa-Rodriguez, L. Wang, N. Simões, C. Onof and C. Maksimovic

7th International Conference on Sewer Processes & Networks

Sheffield, UK, 30th August 2013


1. CONTEXT


Urban storm water drainage models

• These models are essential tools for:

• Urban planning

• Real time operation

• Urban flood forecasting and warning


“… 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!

Sensors commonly used for estimation of rainfall at catchment scales

RAINGAUGE RADAR

Accuracy

Coverage, spatial characterisation of rainfall field

Raingauge Weather Radar


Traditionally: calibration of models using only rain gauge data

May lead to overly conservative models due assumption of uniformly

distributed rain fields

In recent years: radar data increasingly available, have started to be used operationally (for calibration and RT applications)

Insufficient accuracy of radar rainfall estimates has proved problematic (e.g. it may result in physically

unfeasible parameters)


Dynamic local radar-raingauge merging or adjustment

Aim: To combine the advantages of radar and raingauge sensors to have a better spatial description and local accuracy of urban rainfall

Gauge-based adjusted radar rainfall estimates have not (are not normally) used for calibration of urban drainage

models


OBJECTIVE OF THIS WORK

To explore the possibility of improving the applicability of radar rainfall estimates to the calibration of urban storm-water drainage models by employing gauge-based radar

rainfall adjustment techniques


2. EXPERIMENTAL SITE AND DATASET


BEDDINGTON CATCHMENT, LONDON BOROUGH OF CROYDON

• Whole drainage area: 64 km2

• Highly urbanised

• Highly susceptible to surface water flooding

Sewer Network

Model of the sewer system of the Purley Area

• Number of nodes: 10,205

• Number of links: 10,500

• Pipe length: 708 km

• Semi-distributed model

• Number of subcatchments: 5,185

• Mean subcatchment area: 1.2 ha

• Verified in 2011 using predominantly radar data (but this was difficult!)

Rainfall and flow monitoring data for model calibration

• Medium term flow survey data:

– Carried out by TW between 28/01/11 and 13/07/11

– 79 flow gauges

– 18 rain gauges

– 2 min resolution

• Radar (Nimrod) data available at 1 km – 5 min


3. METHODOLOGY


Applied different rainfall inputs to the recently verified model of the catchment:

1. Original raingauge data, applied using Thiessen Polygons

2. Original radar (Nimrod) data

3. Merged rainfall data (using Bayesian methodology)

(3 storms were analysed – the same used for calibration)

Given the problems reported by the modeller when verifying the model using predominantly radar rainfall estimates, it was expected that a better goodness of fit could be achieved by simply inputting merged rainfall inputs

into the recently verified model of Beddington

Characteristics of rainfall events under consideration, as measured by raingauges and radar

MAX/MIN RAINFALL DEPTH AND INTENSITY AT INDIVIDUAL RG / RD GRID

Event Period

(length in hr)

RG Total

(mm)

RG Peak

(mm/hr)

RD Total

(mm)

RD Peak

(mm/hr)

Max/min Max/min Max/min Max/min

E1 05-07/05/11 (72 hrs) 43.8/23.8 10.8/3.6 87.5/7.2 40.2/1.2

E2 17-18/06/11 (48 hrs) 21.0/7.8 26.4/4.2 47.0/2.8 69.4/1.6

E3 05-07/07/11 (57 hrs) 10.8/4.8 16.8/2.9 18.7/1.5 31.2/0.7

AREAL AVERAGE RAINFALL DEPTH AND INTENSITY

Event Period

(length in hr)

RG Total

(mm)

RG Peak

(mm/hr)

RD Total

(mm)

RD Peak

(mm/hr)

Areal Areal Areal Areal

E1 05-07/05/11 (72 hrs) 34.5 8.0 36.4 7.1

E2 17-18/06/11 (48 hrs) 14.9 9.2 18.7 6.6

E3 05-07/07/11 (57 hrs) 7.4 4.4 7.9 2.5


4. RESULTS


Relative Error (RE) and R2 obtained for the different rainfall inputs (estimated based on

flow and depth measurements at 79 locations)

E1 E2 E3 E1 E2 E3

MEAN RE† – PEAK FLOW RATE MEAN RE† – PEAK FLOW DEPTH

RG 30.57% 40.56% 40.40% 90.96% 76.29% 20.79%

RD 28.27% 36.96% 53.59% 46.21% 24.76% 26.34%

MERGED 24.91% 29.75% 37.51% 32.34% 21.12% 18.93%

MEAN R2 – FLOW RATE MEAN R2 – FLOW DEPTH

RG 0.694 0.697 0.641 0.705 0.702 0.691

RD 0.666 0.667 0.575 0.703 0.656 0.664

MERGED 0.701 0.703 0.633 0.748 0.713 0.718 † MEAN RE%=OPeak-SpeakOPeak,

where Opeak and Speak represent the maximum observed and simulated flows


5. CONCLUSIONS AND FUTURE WORK


Conclusions & Future Work

• It is possible to calibrate storm-water drainage models using gauge-based adjusted rainfall inputs

• In fact, better results may be achieved when using gauge-based adjusted rainfall inputs, as these can better capture the spatial structure and accuracy of rainfall fields

• Future work: Uncertainty-based calibration of storm water drainage models considering rainfall uncertainty explicitly


THANK YOU

Susana Ochoa-Rodríguez

[email protected]

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