Sensor networks and urban pluvial flood modelling in an urban catchment

Transcription

1 Environmental virtual observatories: managing catchments with wellies, sensors and smartphones Sensor networks and urban pluvial flood modelling in an urban catchment 28 th February 2013

2 Contents 1. Context 2. Pilot locations & sensor network + SOS 3. Rainfall processing techniques + WPS 4. Putting the jigsaw together 5. Conclusions

3 1. CONTEXT Urban pluvial flooding: challenges and needs Tackling the challenges

4 URBAN PLUVIAL FLOODING Extreme rainfall events exceed the capacity of the drainage system

5 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

6 Model Assembly for Pluvial Flood Modelling, Forecasting and Management Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) 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) Same framework as other types of flooding, but for urban pluvial flooding each step is a bit more complex

7 Rainfall Estimation / Forecasting Flood Modelling / Forecasting 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

8 Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, 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 Bi-directional interaction Effective rainfall Surface component Sub-surface component Sewer flow

9 Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) 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

10 Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, 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

11 Tackling the Challenges Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency)

12 Tackling the Challenges Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) 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 Numerical Weather Prediction: UM/MM5 T = Current STATISTICALLY DOWNSCALING i i Temporal km 1-2 km 1 km m 1 km t Spatial Meteorological Radar C-Band X-Band CALIBRATION T = Future t Ground Raingauge Network

13 Tackling the Challenges Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) 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

14 Tackling the Challenges Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) Stakeholder engagement in local flood risk management Decision making for emergency management and spatial planning supported by improved monitoring, modelling and forecasting

15 Tackling the Challenges Rainfall Estimation / Forecasting Flood Modelling / Forecasting Management (urban planning, emergency) 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 Stakeholder engagement in local flood risk management Decision making for emergency management and spatial planning supported by improved monitoring, modelling and forecasting 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.

16 2. PILOT LOCATIONS AND SENSOR NETWORKS 3 urban catchments Rainfall and flow/depth monitoring

17 PILOT LOCATIONS Cranbrook (London Borough of Redbridge) Purley (London Borough of Croydon) Torquay City Centre (Torbay, Devon)

18 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)

19 (a) (c) Roding River (b) (b) Valentines Park (c) (c) Sewer Outfall (a) Cranbrook Road (d)

20 Overland flow routes associated with surface water flooding generally follow natural drainage pathways Flooding Mechanisms

21 Flooding Mechanisms Sewer outfall 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 Roding River

22 Local Monitoring System 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 All sensors are equipped with data acquisition and real-time wireless communication units (which use sim-cards) 22

23 Local monitoring system

24 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 GHz Wave length UK Met Office C-Band Radars 4 8 cm Range resolution Temporal resolution 1 km up to 50 km range / 2 km up to 75 km range 5 min scan repeat cycle** Elevations ( ) 0.5, 1.5, 2.5, 4.0, , 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.

25 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 ICL s X-Band Radar (will be installed on 9 th March 2013 on the rooftop of the Royal Free Hospital in Hampstead) cm Range resolution Temporal resolution 30 m 1 min Elevations ( ) 0.5, 1.5, 2.5, 4.0, 5.0 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.

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27 Level sensors Rain gauge networks DVD RW We are using a Sensor Observation Service (SOS) server to store rainfall data from multiple sources in a standardised way SOS server 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 SOS Client 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)

28 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)

29 Example of a query from a user using an SOS library for R

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

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

32 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

33 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!

34 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

35 Sensors commonly used for estimation of rainfall at catchment scales Raingauge Weather Radar RAINGAUGE RADAR Accuracy Coverage, spatial characterisation of rainfall field Resolution Further processing of radar rainfall estimates can improve its applicability (in terms of accuracy and resolution) to urban hydrological applications

36 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)

37 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

38 Rain Depth (mm) Why we need to adjust radar rainfall data? 25 Beal HS raingauge Cumulative rainfall Rain Depth depth (23/08/2010 accumulations: 23/08/2010 RG event Beal_RG Radar 1km Raingauge 10 Collocated radar pixel :05 01:05 02:05 03:05 04:05 05:05 06:05 07:05 Time (5 min) Urban drainage models are normally calibrated using raingauge data

39 Based on their assumption, gauge-based radar rainfall adjustment techniques can be classified into two types Mean Bias Correction Error Variance Minimisation

40 Principle of Bayesian Data Combination RG data Radar data a) b) Block-Kriging interpolation comparison c) d) combination f) e) error field fitted by an exponential variogram In this process the variance of the error is minimised output g) [Image : Ehret et al., 2008] [Source: Todini, 2001]

41 Four recorded events in the period of over the Cranbrook catchment were studied Convective Event Duration RG total (1) (mm) Stratiform total (2) (mm) Radar total (mm) Sample bias B i = (1)/(2) 23/08/ hr /05/ hr /06/ hr /01/ hr Bias is event-varying, separate adjustment for each event is required

42 Both rainfall profiles and accumulations can be significantly improved through adjustment Rain depth (mm) Rainrate (mm/hr) Mean Rainrate: 23/08/2010 event Bayesian adjusted Mean bias adjusted Raingauge Mean RGs Radar 1km Corrected Radar 1km Bayesian Radar 1km Radar 2 0 0:05 1:05 2:05 3:05 4:05 5:05 6:05 7:05 Time (5min) Mean RGs Radar 1km Corrected Radar 1km Bayesian Radar 1km Mean Rainfall Accummulation: 23/08/2010 event Raingauge Bayesian adjusted Mean bias adjusted Radar 0 0:05 1:05 2:05 3:05 4:05 5:05 6:05 7:05 Time (5 min)

43 Rain (mm/hr) Flow Depth (m) Simulation of flow depths is substantially improved by using merged rainfall data as input (23/08/2010 event) 0 Pipe (Mid-stream) RGs Radar 1km Observations Corrected Radar 1km Bayesian Radar 1km Obs (Mid-Stream) August 2010 (Time, GMT)

44 Rain (mm/hr) Flow Depth (m) Simulation of flow depths is substantially improved by using merged rainfall data as input (26/05/2011 event) 0 Pipe (Mid-stream) RGs Radar 1km Corrected Radar 1km Bayesian Radar 1km Obs (Mid-stream) May 2011 (Time, GMT)

45 Principle of cascade based spatial downscaling Level n = 8 km V 8km Scaling analysis Feature curve Log (q-th moment) τ Level n = 4 km w i1 w i3 V 3 4km 4km 4km V 1 V 2 w i2 w i4 V 4 4km q = q1 q = q2 q = q3 q = q4 q = q5 τ(q) q Log (spatial scale) Level n = 2 km Level n = 1 km w i3 w i1 V 13 2km wi2 w i4 2km 2km V 11 V 12 V 14 2km w i1 1km V 123 1km V 124 w i2 w i4 w i3 Rainfall Cascade Generator τ τ*(q) τ(q) q V 121 1km V 122 1km downscaling fitting

46 Combined Application Merging & Downscaling Adjustment of 1 km data -> Downscaling to 500 m event event

47 Measurement of the impact of bias and resolution Water Flow Depth level Us Uadjusted t Uadjusted: Us: difference in water depth estimates resulting from bias correction range of water depth estimates resulting from downscaling

48 Us Uadjusted X 100 scale Upstream Midestream Downstream Mean Max Mean Max Mean Max 1 km m /08/2010 event km m km m km m /05/2011 event km m km m km m /06/2011 event km m km m /01/2012 event 1 km m km m km m

49 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

50 These two rainfall processing techniques have been implemented as Web Processing Services (WPS) WPS Interface Standard provides rules for standardising inputs and outputs for invoking geospatial processing services as web services Data Merging WPS server Hydrological Modelling Downscaling 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 Client

51 4. PUTTING THE JIGSAW TOGETHER We now have a series of building blocks which use standard interfaces and can be easily linked

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53 DVD RW Rain gauge networks Data Merging Hydrological Modelling Level sensors Downscaling SOS server WPS server Client

54 5. CONCLUSIONS

55 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

56 Web Processing Services (WPS) Allow taking advantage of distributed computing Facilitate knowledge transfer and sharing Facilitate quick testing and comparison of different models / algorithms

57 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

58 Thank you for your attention