Progress in flood forecasting across Britain from advances in hydrological modelling and numerical weather prediction
Download as PPTX, PDF0 likes181 views
The document discusses advancements in flood forecasting across Britain, emphasizing the benefits of high-resolution numerical weather prediction (NWP) and distributed hydrological modeling. It highlights operational improvements in real-time flood risk assessments using ensemble forecasting and grid-to-grid models, particularly for ungauged catchments. Additionally, challenges related to predicting convective rainfall events and the development of a surface water flooding hazard impact model are addressed.
Progress in flood forecasting across Britain from advances in hydrological modelling and numerical weather prediction
- 1. Dr Steven Cole, Robert Moore, Dr Seonaid Anderson Progress in Weather Forecasting, RMetS National Meeting ECMWF, Reading, 19 December 2018 Progress in flood forecasting across Britain from advances in hydrological modelling and numerical weather prediction
- 2. 1 km (2011) 1 km (2011) 4 km (2008) 12 km (2005)4 km (2008) 12 km (2005) Caldew (246km2) Time (hours) Flowm3s-1 High-resolution NWP during orographically enhanced rainfall events provides benefits for flood forecasting and warning Raingauge Flood Warning Level Roberts, Cole et al., 2009, Met. Apps Carlisle 2005 floods – high-res NWP Observation
- 3. Distributed Model (G2G) Recent development Lumped Model Traditional approach • One model for each gauging station • Many calibrated parameters • Flow estimates for one location only • Uses catchment average rainfall Gauging station • One model for large regions (UK) • Few parameters, use spatial datasets • Flow estimates in each grid (1km2) • Uses gridded rainfall estimates Distributed hydrological modelling Cole and Moore (2009), JoH Potential for ungauged sites!
- 4. • Uses spatial datasets on terrain, soil/geology, land-cover • Responds to spatial variation of rainfall input • Used operationally across Britain at a 1km, 15 min resolution Grid-to-Grid (G2G) Distributed Model Soil moisture River flow Moore et al., IAHS Publ. 305 (2006) Price et al.; Cranston & Tavendale, Water Management (2012)
- 5. Catchment-wide storm: similar response Lower catchment storm: earlier, larger peak Upper catchment storm: later, larger peak 355000 360000 365000 370000 375000 415000420000425000430000435000 Storm rainfall total 355000 360000 365000 370000 375000 415000420000425000430000435000 355000 360000 365000 370000 375000 415000420000425000430000435000 mmhr-1mmFlowm3s-1 Time (days) Flowm3s-1 Catchment Hyetograph Lumped Model Distributed Model Catchment-wide storm Lower catchment storm Upper catchment storm Impact of spatial extent and location of storm on flood response? Extreme Flood Response Moore et al. (2006), IAHS Pub. 305 Same catchment rainfall total Same catchment rainfall total Varied hydrological response important for ensembles 355000 360000 365000 370000 375000 415000420000425000430000435000 355000 360000 365000 370000 375000 415000420000425000430000435000
- 6. 4 km NWP1 km NWP Boscastle 2004 12 km NWP UK radar 20 km radius from BoscastleCourtesy Nigel Roberts, JCMM (Met Office) • Up to 200m fell in 4 hours from sequence of convective cells • 1 or 4km NWP major improvement over 12km product • Still uncertainty in NWP rainfall intensities and location Forecasts from 03 UTC
- 7. Boscastle 2014 – Ensemble forecasting 1km NWP pseudo-ensemble G2G Model 1km river flow ensemble Comparison with river flow observations Acknowledgements: Collaboration with JCMM (Met Office) • Simple pseudo-ensemble method to capture NWP uncertainties • Generate gridded ensemble flood forecasts using G2G • Operational system now uses MOGREPS-UK 2.2km ensemble
- 8. Risk Maps of Flood Exceedance Acknowledgements: Collaboration with JCMM (Met Office) 920 km2 This example employs: • 1km rainfall pseudo-ensemble • 10 year return period flow thresholds • 24 hour forecast horizon Potential to forecast flood risk hotspots Probability of exceeding a given flow threshold, for a given forecast horizon
- 9. • Rapid Response Catchments are typically small & ungauged • Challenge to develop forecast/warning capability • Use rainfall forecast ensembles (~2km, 24h, 12 members) • Case study experience (6-7 July 2012) Forecast time origin Q(T) return period threshold used Circles denote gauging stations Solid outline: area <50km2 Observed flow exceeds threshold during forecast Percentage of ensembles that exceeded the Q(T) threshold at some point during forecast 0 0-33% 34-66% >67% Rapid Response Catchments
- 10. Threshold Forecast Origin Qmed/2 Qmed Q(5) Q(50) Case study: 6-7 July 2012 06-07-2012 07:15
- 11. Threshold Forecast Origin Qmed/2 Qmed Q(5) Q(50) Case study: 6-7 July 2012 06-07-2012 07:15 06-07-2012 19:15
- 12. Threshold Forecast Origin Qmed/2 Qmed Q(5) Q(50) Case study: 6-7 July 2012 06-07-2012 07:15 06-07-2012 19:15 07-07-2012 00:15
- 13. Threshold Forecast Origin Qmed/2 Qmed Q(5) Q(50) Case study: 6-7 July 2012 06-07-2012 07:15 06-07-2012 19:15 07-07-2012 00:15 07-07-2012 07:15 Early signal of flood risk “hotspots”
- 14. G2G for Scotland – Snowmelt modelling Model Performance (R2) No Snowmelt
- 15. G2G for Scotland – Snowmelt modelling No Snowmelt With Snowmelt Model Performance (R2) Major improvement in model performance
- 16. Real-world example from SEPA
- 17. National-scale Surface Water Flooding tool ● Challenge to provide real-time national SWF guidance Dominance of convective rainfall events that are hard to predict SWF Hazard Impact Model developed by Natural Hazards Partnership Real-time trial at FFC ongoing, planned to be operational in 2019 SWF HIM Grid-to-Grid Hydrology SWF Hazard Footprint Impact Library Updated Flood Map for Surface Water National Population Database (NPD) National Receptor Database (NRD) Real-time SWF risk outputs Visual Weather Dissemination Flood Guidance Statement MOGREPS-UK Ensembles
- 18. Dr Steven Cole: scole@ceh.ac.uk Robert Moore: rm@ceh.ac.uk Dr Seonaid Anderson: seodey@ceh.ac.uk