Bailey, A

Ochoa-Rodriguez, S

Onof, C

Schellart, ANA

Wang, L

Willems, P

English

Spiral - Imperial College Digital Repository

Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 Verifying Urban Drainage Models with Variable Rainfall Input Presenter: Andrew Bailey  Authors: Andrew Bailey, Susana Ochoa-Rodriguez, Li-Pen Wang, Alma Scherllart, Christian J. Onof, Patrick Willems  UDG Autumn Conference & Exhibition 2014 Blackpool, 11th – 13th Nov 2014 Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 1. CONTEXT Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 “… 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 In order to improve the applicability of radar rainfall estimates, these could be adjusted based on raingauge estimates Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 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 This is not a new concept, but adjustments have been normally applied at large scales and the resulting estimates are still of insufficient accuracy for urban hydrological applications AND Suitability of methods for urban scales has not been explored Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 OBJECTIVE OF THIS WORK  Explore the possibility of improving the applicability of radar rainfall estimates through dynamic gauge-based adjustment, focusing on two aspects:  (1) Improving the verification (calibration) process  (2) Impact of gauge density on radar-rain gauge adjustment results  Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 GENERAL METHODOLOGY  Original raingauge (RG) Interpolated raingauge (RG) Original Radar (RD) 3 Merged rainfall products Different  Rainfall  Inputs NSE, Correlation, Relative  Error in peaks Comparison of hydraulic outputs Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 2. RAINFALL PROCESSING TECHNIQUES APPLIED IN THIS STUDY 1. Block-kriging (BK) interpolation of raingauge values:   – Values at unknown locations (i.e. radar grids) are estimated based on the linear combination of known neighbouring values 𝑍∗ 𝑥0 = 𝜆𝑖 ∙ 𝑍 𝑥𝑖     – The interpolated field serves as baseline for comparison with other spatial products as well as starting point for some of the adjustment techniques   Rainfall estimate at a given radar grid Weighting factor estimated based on spatial association of observations Known raingauge estimate 2. Mean field bias adjustment: – Mean raingauge rainfall records over a specific area are assumed to be truth, able to represent the areal rainfall vol.  𝐵𝑖𝑎𝑠𝑙𝑎𝑠𝑡 1ℎ = 𝐴𝑙𝑙 𝑟𝑎𝑖𝑛𝑔𝑎𝑢𝑔𝑒𝑠 𝑖𝑛 𝑑𝑜𝑚𝑎𝑖𝑛𝑙𝑎𝑠𝑡 1ℎ 𝐴𝑙𝑙 𝑟𝑎𝑑𝑎𝑟 𝑔𝑟𝑖𝑑𝑠 𝑖𝑛 𝑑𝑜𝑚𝑎𝑖𝑛𝑙𝑎𝑠𝑡 1ℎ    𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅𝑎𝑑𝑎𝑟𝐹𝑖𝑒𝑙𝑑𝑡  = 𝐵𝑖𝑎𝑠𝑙𝑎𝑠𝑡 1ℎ ∙ 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑅𝑎𝑑𝑎𝑟 𝐹𝑖𝑒𝑙𝑑𝑡  3. 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 = 𝜆𝑖𝐾𝐸𝐷 ∙ 𝑍𝐺 𝑥𝑖𝑛𝑖=1  • The weighting factor 𝜆𝑖𝐾𝐸𝐷 is constrained by the spatial association between radar values   3. Bayesian (BAY) gauge-based radar rainfall merging:   – Main idea: analyse the uncertainty of rainfall estimates from different sources (in this case radar and raingauge sensors) and combine them such that the overall uncertainty is minimised  a) b) c) d) e) f) g) Block-Kriging interpolation comparison error field fitting (by an exponential variogram Combination (Kalman filter) 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 4. Singularity-Sensitive Bayesian (SIN) gauge-based radar rainfall merging:   – Recently developed to overcome a shortcoming of the original Bayesian (BAY) and other merging methods, which tend to smooth storm extremes initially observed in radar images – This method identifies local extremes (i.e. singular points) and extracts them from the radar image before the merging takes place. After the merging is finished, the singularities are applied back and proportionally to the rainfall field.      Original Radar Non –Singular Radar Merged Singularity Back Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 3. EXPERIMENTAL CATCHMENTS AND DATASETS • Cranbrook catchment, London • Portobello catchment, Edinburgh SW BIRMINGHAM • Drainage area: 67 km2 • Urbanised  • Sewer model in InfoWorks CS: • 2,916 nodes and 2,906  conduits, subcatchments • Verified in 2011 using same flow survey used in this study • Local monitoring data from medium term flow survey carried out between Apr-Jun’11 • 12 raingauges • 32 flow gauges PORTOBELLO CATCHMENT, EDINBURGH Storm events used in this study  These events were the very same events used in the verification of the model (which was done using raingauge (RG) data as input) Event Date (duration) RG Total (mm) RG Peak Intensity (mm/h) Storm 1 06-07/05/2011 (7h) 9.25 11.21 Storm 2 23/05/2011 (24h) 7.70 5.03 Storm 3 21-22/06/2011 (24 h) 32.96 8.46 RADAR DATA AVAILABLE FOR BOTH CATCHMENTS For both catchments Nimrod (multi-radar composite) data with 1 km and 5 min resolution are available Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 3. RESULTS - RAINFALL  • Rainfall depth accumulations • Spatial structure of rainfall fields • Ability of different rainfall estimates to reproduce rainfall rates in comparison to raingauges CRANBROOK CATCHMENT PORTOBELLO CATCHMENT Rainfall Estimates  Storm 1 Storm 2 Storm 1 Storm 2 Storm 3 RG 23.53 15.53 9.25 7.70 32.96 RD 6.80 4.77 9.67 10.80 25.85 Areal average total rainfall accumulations CRANBROOK CATCHMENT PORTOBELLO CATCHMENT Rainfall Estimates  Storm 1 Storm 2 Storm 1 Storm 2 Storm 3 RG 23.53 15.53 9.25 7.70 32.96 RD 6.80 4.77 9.67 10.80 25.85 RG/RD BIAS 3.46 3.26 0.96 0.71 1.28 The RG/RD bias is event varying   Need for dynamic and local adjustment Areal average total rainfall accumulations CRANBROOK CATCHMENT PORTOBELLO CATCHMENT Rainfall Estimates  Storm 1 Storm 2 Storm 1 Storm 2 Storm 3 RG 23.53 15.53 9.25 7.70 32.96 RD 6.80 4.77 9.67 10.80 25.85 BK 22.23 12.75 9.02 7.50 30.69 MFB 18.06 11.11 8.47 7.13 31.94 BAY 18.8 12.31 8.80 7.51 26.94 SIN 19.47 14.07 9.66 7.56 33.73 All adjustment methods can, in general, reduce RG/RD cumulative bias, leading to areal total accumulations similar to those recorded by raingauges Areal average total rainfall accumulations Cranbrook catchment – peak intensity image (Storm 2) Portobello catchment – peak intensity image (Storm 1) MFB and BAY methods can better preserve the spatial variability of the rainfall field, as originally captured by the radar 0102030400 10 20 30 40BK/RD/Adjusted Rain Rate (mm/hr)RG Rain Rate (mm/hr)Rainfall estimates comparison: Cranbrook Storm 2RD BK MFB BAY SIN024681012140 2 4 6 8 10 12 14BK/RD/Adjusted Rain Rate (mm/hr)RG Rain Rate (mm/hr)Rainfall estimates comparison: Portobello Storm 1RD BK MFB BAY SINComparison of areal average RG rain rates VS. areal average rain rates of radar and merged estimates • Radar (RD) accuracy in terms of rainfall rates is poor • MFB does not provide significant improvement in this regard • Bayesian techniques (especially SIN) can properly reproduce low as well as high intensities Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 4. RESULTS – HYDRAULIC OUTPUTS CRANBROOK CATCHMENT: Observed vs. Simulated flow depth at mid-stream gauging station (Storms 1 and 2) • RD largely underestimates • MFB not enough • BAY and SIN perform very well, even better than original RG Performance Measures/ Rainfall estimates RG RD BK MFB BAY SIN   Storm 1 RE in peak depth 0.111 -0.481 0.125 -0.176 0.073 0.004 R – depth 0.874 0.618 0.881 0.814 0.913 0.902 NSE – depth  0.283 0.315 0.452 0.696 0.772 0.800   Storm 2 RE in peak depth 0.126 -0.538 0.061 -0.130 0.072 0.098 R – depth 0.838 0.751 0.808 0.813 0.834 0.857 NSE – depth 0.522 0.492 0.680 0.658 0.711 0.676 CRANBROOK CATCHMENT: Performance Measures PORTOBELLO CATCHMENT : Observed vs. Simulated flow depth and rate at up-stream gauging station • In spite of small RG/RD bias, RD underestimates peaks  • MFB not enough  • BAY ok  • SIN better at capturing peak PORTOBELLO CATCHMENT : Observed vs. Simulated flow depth and rate at mid-stream gauging station • In spite of small RG/RD bias, RD underestimates peaks  • MFB not enough  • BAY and SIN perform well PORTOBELLO CATCHMENT : Observed vs. Simulated flow depth and rate at down-stream gauging station • RD underestimates even more (cumulative effect?) • MFB not enough • RG overestimates peak • Even BK performs better than RG • BAY and SIN perform well PORTOBELLO CATCHMENT: Performance Measures (Storm 1) Errors in peak depths Depth (m) RG RD BK MFB BAY SIN Errors in peak flow rates Flow rate (m3/s) RG RD BK MFB BAY SIN NSEs for simulated flow depths NSE coefficient RG RD BK MFB BAY SIN NSEs for simulated flow rates NSE coefficient RG RD BK MFB BAY SIN Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 5. CONCLUSIONS AND FUTURE WORK Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 Conclusions • In general, all adjustment methods improve the applicability of the original RD rainfall estimates to urban hydrological applications, although the degree of improvement provided by each adjustment method is different.  • MFB is insufficient for satisfactorily correcting the errors in RD estimates and this is evident in the associated hydraulic outputs -> more dynamic and spatially varying adjustment methods are required for urban hydrological applications. • Overall, the BAY and SIN rainfall estimates lead to significantly better simulation results than the MFB adjusted estimates and the original RD estimates, with the SIN estimates performing particularly well at reproducing peak depths and flows. Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 Conclusions • The benefits of merging are clearly evident in an operational context, such as the one analysed in the Cranbrook catchment. In this case, the BAY and SIN merged estimates led to simulation results even better than those obtained when using point RG estimates as input. • In a verification context (i.e. Portobello catchment), the merged estimates also performed in general better than original RD estimates, but the real benefit of the merged products is likely to become more evident when the models are re-verified.  Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 Future Work • A lot more testing required! • Re-verify models using merged estimates as input (or as an alternative data source) • Analyse impact of raingauge density. The benefits of the merging are likely to become more evident when fewer gauges are available! • Analyse scale at which adjustments should be applied • Analyse sensitivity of the Bayesian singularity-sensitive to the degree of “singularity” that is removed    Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 Main ‘Message’ • Weather radar is a valuable source of data, but must be used carefully • Merging has shown potential to improve radar estimates thus leading to improved model outputs (and very likely improved model calibration/verification), but more testing is needed • I want you to know that there are techniques available for improving the applicability of radar estimates • Radar has happened since 2002! maybe worth mentioning in the new code of practice?  Verifying Urban Drainage Models with Variable Rainfall Input  - Andrew Bailey et al. UDG Autumn Conference & Exhibition 2014 THANK YOU Susana Ochoa-Rodríguez sochoaro@imperial.ac.uk 051015202500:05 01:05 02:05 03:05 04:05 05:05 06:05 07:05Rain Depth (mm)Time (5 min)Cumulative Rain Depth (23/08/2010 event)@Beal RGBeal_RG Radar 1kmBeal 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 d) Block-Kriging interpolation Singularity extraction BK rain gauge field Non-Singular (NS) radar field Local singularity (α) field Error field fitting Comparison  (error field construction) e) f) g) Combination (Kalman filter) Singularity recovery Reconstructed field h) Integration of local singularity analysis Images at each step of the Bayesian data merging with/without local singularity analysis Non-singular Radar  Non-singular Merged Nimrod (Original) Block-Kriged RGs Bayesian Merged  Singularity-sensitive Merged FINAL PRODUCTS Nimrod (Original) Block-Kriged RGs Bayesian Merged  Singularity-sensitive Merged 

Verifying Urban Drainage Models with Variable Rainfall Input

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Verifying Urban Drainage Models with Variable Rainfall Input

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