Improving rainfall nowcasting and urban runoff forecasting .../file/Ochoa... · Susana Ochoa-Rodríguez SPN7 Conference – 29.08.2013 • Forecasting urban runoff / urban pluvial

Susana Ochoa-Rodríguez SPN7 Conference – 29.08.2013

Improving rainfall nowcasting and urban runoff forecasting through dynamic radar-raingauge rainfall adjustment

Presenter: Susana Ochoa-Rodríguez

Authors: S. Ochoa-Rodriguez, M. A. Rico-Ramirez, S.A. Jewell, A. N. A. Schellart, L. Wang, C. Onof and C. Maksimovic

7th International Conference on Sewer Processes & Networks

Sheffield, UK, 29th August 2013


1. CONTEXT AND OBJECTIVES

In the context of urban storm water management, good quality runoff (urban pluvial flood) forecasts play an ever increasing role!

These would enable successful implementation of measures to reduce risk of flooding, pollution, etc.

RTC of sewer systems

Deployment of demountable flood defences & property level protection

measures

Evacuation /closure of critical areas


• Forecasting urban runoff / urban pluvial flooding is hard, due to small spatial and temporal scales that characterise it

• A lot of work has been done in recent years, but quality of forecasts is still insufficient: uncertainties are still too high and limit operational use

Not so easy!


i. Uncertainties in input measurements; i.e. Quantitative Precipitation Estimates (QPEs);

ii. Uncertainties in meteorological models, namely radar nowcasting or Numerical Weather Prediction (NWP) models, used to generate Quantitative Precipitation Forecasts (QPFs);

iii. Uncertainties in hydrological models (parametric uncertainty, uncertainty in model structure and solution, and uncertainty in the measurement of responses used for calibration).

Sources of uncertainty in flood forecasting (Todini, 2004):

Todini, E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743-6






Dominant sources of

uncertainty in urban runoff / urban pluvial

flood forecasting (Golding, 2009)

Golding, B. W. 2009. Uncertainty propagation in a London flood simulation. Journal of Flood Risk Management 2(1), 2-15






Little attention has been given

to this source of uncertainty at urban scales, especially for forecasting purposes






For urban pluvial flooding:

Nowcasting forecasts are

generally more suitable than

NWP forecasts (Liguori et al., 2012)

Liguori S. et al. 2012. Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmospheric Research 103, 80-95.






Nowcasting: extrapolation of radar images →

Quality of forecast highly dependent on

quality of radar QPEs (i)!


Radar rainfall estimates are subject to significant uncertainties

The accuracy of radar rainfall estimates is usually insufficient, particularly in the case of extreme rainfall magnitudes

(Einfalt et al., 2005)

Possibility to overcome this problem: dynamically adjusting radar estimates based on raingauge measurements (e.g. Wang et al., 2013)

Benefits of radar-raingauge rainfall adjustment in terms of Quantitative Precipitation Forecasts (QPFs) not yet explored


Explore the possibility of improving rainfall nowcasts and associated urban runoff forecasts

through dynamic radar-raingauge rainfall adjustment

Objective of this work:


2. METHODOLOGY AND DATASETS

Radar and raingauge measurements

(domain: 500 km x 500 km)

• Radar data: quality controlled multi-radar composite product generated with UKMO Nimrod system; 1 km and 5 min res.

• Raingauge data: from 1064 raingauges, quality controlled by the UK Met Office; 15 min resolution



Gauge-based adjustment: Mean field bias & KED

Generation of QPFs with STEPS Nowcasting model

Urban runoff forecasting: inputting QPFs to

InfoWorks model of urban catchment in

London

Assessment of results of each stage at small (urban) scale, using data from a local

monitoring system of an urban catchment in London

• 2 water depth sensors in sewers

• 3 water depth sensor in open channels/river

• Area: 9 km2

• 3 tipping bucket raingauges

Local monitoring system of Cranbrook catchment (London)


Two event periods analysed:

• 15-18 Jul 2011

• 3-8 Aug 2011


MODELS

Radar-raingauge dynamic adjustment was done using 2 commonly used techniques:

• Mean field bias adjustment:

𝐵𝑖𝑎𝑠𝑙𝑎𝑠𝑡 3ℎ = 𝐴𝑙𝑙 𝑟𝑎𝑖𝑛𝑔𝑎𝑢𝑔𝑒𝑠 𝑖𝑛 𝑑𝑜𝑚𝑎𝑖𝑛𝑙𝑎𝑠𝑡 3ℎ 𝐴𝑙𝑙 𝑟𝑎𝑑𝑎𝑟 𝑔𝑟𝑖𝑑𝑠 𝑖𝑛 𝑑𝑜𝑚𝑎𝑖𝑛𝑙𝑎𝑠𝑡 3ℎ

𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅𝑎𝑑𝑎𝑟𝐹𝑖𝑒𝑙𝑑𝑡 = 𝐵𝑖𝑎𝑠𝑙𝑎𝑠𝑡 3ℎ ∙ 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑅𝑎𝑑𝑎𝑟 𝐹𝑖𝑒𝑙𝑑𝑡

• Kriging with external drift (KED):

• Simple method to include radar rainfall estimates in the raingauge interpolation process

• Rainfall estimate at a given point is the linear combination of known raingauge values:

𝑍𝐾𝐸𝐷∗ 𝑥0 = 𝜆𝑖

𝐾𝐸𝐷 ∙ 𝑍𝐺 𝑥𝑖𝑛

𝑖=1

• The weighting factor 𝜆𝑖𝐾𝐸𝐷 is constrained by the spatial

association between radar values







London

Generation of Quantitative Precipitation Forecasts (QPFs) with the UK Met Office STEPS

nowcasting model

(Using as input original and adjusted radar rainfall estimates)

• The STEPS model blends radar-based nowcasts and NWP forecasts in order to produce better forecast

• In this work only the radar-based deterministic nowcasts produced by STEPS were used

• This nowcasting model is based on spectral decomposition with incorporation of optical flow equation

• The model was setup to run at 1 km spatial resolution and 15 min temporal resolution

• The model was setup to produce 6 h forecast initialised every 60 min.







London

Runoff forecasting by inputting Quantitative Precipitation Forecasts (QPFs) into the storm

water drainage model of the Cranbrook catchment, Greater London

• Model setup in InfoWorks CS and verified in 2011

• Only sewer system was modelled (not the surface)

• 9 km2 catchment area, 1763 nodes and 1816 pipes

• Rainfall applied through subcatchments

• New UK model is used for estimating runoff

• Flow in sewers simulated based on full St Venant equations







London


3. RESULTS

• The quality of the results of each stage was tested at small scale (against monitoring data from the Cranbrook catchment)

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SUB-EVENTS 2.2 and 2.3: Rainfall Intensity

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- Radar largely underestimates rainfall over the Cranbrook area (this seems to be due to radar beam blockage)

- Adjustments were done at too large scales and no improvements were achieved at the local scale of urban catchments

- Need to apply adjustment (both mean bias and KED) at smaller domains – but this is not straight forward!

Large scale dataset: Radar and raingauge measurements



Assessment of QPEs at small scale using local raingauges from

urban catchment in London


Assessment of QPFs at small scale using local raingauges from


Runoff forecasts – inputting QPFs to InfoWorks model of urban

catchment in London

Assessment of runoff forecasts using local water depth gauges

from urban catchment in London

- Quantitatively (relative error): all QPFs perform badly – mainly due to underestimation of QPEs

- In terms of correlation (pattern) and consistency:

- Nimrod and bias adjusted QPFs show consistent behaviour

- KED QPFs show inconsistent behaviour










catchment in London












catchment in London



Original radar (Nimrod ) Forecasts

KED Adjusted Forecasts

KED-Adjusted QPFs present

inconsistent behaviour, the

storm even changes direction

Reason: KED adjustment does not take into account the temporal correlation of the radar rainfall field; therefore, the adjustment affects the rain field in the time domain .

Consequently, the nowcasting model is not able to properly capture the movement the storm

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Water depth forecast – Nimrod QPFs

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E 2.1 - Flow depth forecasts: Bias-adjusted input

Water depth forecast – Bias-adj QPFs

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Water depth forecast – KED QPFs

- Quantitatively (relative error): better results (than QPFs alone)

- In terms of correlation (pattern) and consistency:

- Nimrod and bias adjusted QPFs present very similar and consistent behaviour

- KED QPFs present inconsistent behaviour










catchment in London




4. SUMMARY OF CONCLUSIONS AND OUTLOOK


• Radar-raingauge adjustments done at too large scales cannot improve Quantitative Precipitation Estimates (QPEs) at the small scales characteristic of urban catchments

→ the domain should be split into sub-domains

• KED adjusted radar rainfall fields may not be appropriate for generating Quantitative Precipitation Forecasts (QPFs), as this method alters the temporal structure of rainfall fields, so the nowcasting model cannot capture and reproduce the movement of the storm

Conclusions


Outlook

• Work is required to confirm these initial findings

• Work is underway to determine the appropriate ‘size’ of sub-domains

• Work is required to determine how to deal with the edges of the sub-domains

• Other adjustment techniques which can preserve the spatial and temporal structure of rainfall fields are being tested


THANK YOU

Susana Ochoa-Rodríguez

[email protected]

Documents

Improving rainfall nowcasting and urban runoff forecasting .../file/Ochoa... · Susana Ochoa-Rodríguez SPN7 Conference – 29.08.2013 • Forecasting urban runoff / urban pluvial