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Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr University Bochum. Deutscher Wetterdienst (DWD, German National Weather Service), Offenbach. Funding: German Ministry of Education and Research (BMBF), Coordination: PTJ.

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Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

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  1. Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr University Bochum Deutscher Wetterdienst (DWD, German National Weather Service), Offenbach Funding: German Ministry of Education and Research (BMBF), Coordination: PTJ Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings? Jörg Dietrich, Yan Wang, Michael Denhard & Andreas Schumann

  2. Outline of the presentation • Introduction • Case study: hindcasts for the Mulde river basin • Development of an ensemble based flood forecast scheme • Conclusions and future work J. Dietrich et al., ISFD Toronto, May 2008

  3. Uncertainties in Flood Forecasting • Future development of the atmosphere cannot be perfectly forecasted • Initial states and boundary conditions of models may be uncertain in time and space • Model structure may be insufficient (model and parameter uncertainty) • Inadequate human interaction • Technical problems • Solution for some of the data and model uncertainties: • computation of several simulations which frame uncertainty -> ensemble techniques • probabilistic instead of deterministic forecast J. Dietrich et al., ISFD Toronto, May 2008

  4. Types of Ensembles • Single System Ensembles • Perturbation of initial and boundary conditions, different convection schemes (physically based ensembles) • Perturbation of model parameters • Multiple Systems Ensembles • Combination of forecasts from different models • Lagged Average Ensembles • Combination of actual forecasts with forecasts from earlier model runs • … • Ensembles aim at characterizing forecast uncertainty, but there will remain uncertainty about uncertainty. J. Dietrich et al., ISFD Toronto, May 2008

  5. Ensembles in Operational Flood Management • Reliabilityis the ability of a system to perform and maintain its functions in routine circumstances, as well as hostile or unexpected circumstances. • Assessment of extreme event predictions? • Model extrapolation (unobserved situation) • Decision rules • Can ensembles improve decisions (economy: ratio between true and false alarms, flood defence: longer lead time)? • Challenges in developing an ICT system • Tremendous amount of data • Computational efficiency of the models J. Dietrich et al., ISFD Toronto, May 2008

  6. Mulde Case Study • Characteristics of the river basin: • Low mountains, fast reaction to rainfall events, flash floods • Several vulnerable cities • 2002: return periods up to > 500 a • Study area: 6200 km² • Operational flood forecast system - requirements: • Meso-scale resolution (headwaters with approx. 100 – 500 km² area) • Short to very short lead times • Support decisions about flood alerts/pre-alerts Grimma, 2002-08-13. Source: dpa J. Dietrich et al., ISFD Toronto, May 2008

  7. Study Area – Chemnitz Sub-catchment J. Dietrich et al., ISFD Toronto, May 2008

  8. Operational Ensemble Systems Used • COSMO-LEPS • Single system physically based ensemble, 16 members • Medium range (132 h lead time) • Meso-scale (10 km horizontal resolution) • SRNWP-PEPS • Multiple systems ensemble, 23 members (17 cover Mulde area) • Short range (48 h lead time) • Meso-scale (7 km horizontal resolution) • COSMO-DE • Deterministic model, lagged average ensemble: 7 members • Very short range (21 h lead time) • Local scale (2.8 km horizontal resolution, resolving convection) Molteni et al., 2001 Denhard and Trepte, 2006 Steppeler et al., 2003 J. Dietrich et al., ISFD Toronto, May 2008

  9. Hindcasts with Raw Ensembles (2002-2006) • Comparison of different ensemble prediction systems • Aim of study: development of a scheme for adaptive combination of ensembles from different sources and with different lead times • Hydrological model: calibrated, assumed as perfect • True alerts: • 2002-08: extreme flood, underestimated • 2006-02/03 flood caused by rainfall/snowmelt, overestimated • False alerts: • 2005-07, 2005-08: meteorology (no flood alert issued) • 2006-08: rainfall true but overestimated, low soil moisture • Missings: • rainfall: not investigated, flood (T > 2 y, meso-scale): none J. Dietrich et al., ISFD Toronto, May 2008

  10. 2002 Flood: COSMO-LEPS Hindcast +5 d Aug 08th Aug 09th Aug 10th Aug 11th J. Dietrich et al., ISFD Toronto, May 2008

  11. 2002 Flood: COSMO-DE Hindcast +21 h coloured: early good performers J. Dietrich et al., ISFD Toronto, May 2008

  12. 2006 False Alert: COSMO-LEPS • Synoptic forecast: up to 290 mm rainfall within 3 days • Water release from reservoir initiated • 80 mm within 36 hrs, low soil moisture, peak discharge T < 2 y J. Dietrich et al., ISFD Toronto, May 2008

  13. Hindcasts: Alarm Level Exceedance discharge, m³/s J. Dietrich et al., ISFD Toronto, May 2008

  14. Lessons learnt from Hindcasts • COSMO-LEPS shows best performance at +2 to +3 days lead time, but often a large spread -> meteorological uncertainty high compared to hydrological uncertainty • COSMO-DE tends to under predict rainfall at certain model runs -> solution: lagged average ensemble, physical ensemble is scheduled for 2010 • SRNWP-PEPS performs well, but has outliers -> solution: plausibility check, calibration • We need more hindcasts to improve probabilistic assessment and to develop decision rules! J. Dietrich et al., ISFD Toronto, May 2008

  15. global prediction systems deterministic local model COSMO-DE observations radar, rain gauges meso-scale ensembles COSMO-LEPS SRNWP-PEPS assimilation Lagged Average-Ensemble (LAF) 2002 2006 2005 calibration, Bayesian Model Average (BMA) probabilistic weather scenario: multi-model ensemble from PEPS, COSMO-LEPS, COSMO-DE model average, m approx. 10 Ensemble Combination - Meteorology observations radar, rain gauges assimilation 2007 J. Dietrich et al., ISFD Toronto, May 2008

  16. Ensemble Calibration with BMA • Bayesian Model Averaging assigns weights to ensemble members based on training period • Daily recalibration: 12 of 19 members have significant weights, 3 best members > 50%, overfitting possible Nov 1st – 14th 2006 Mulde catchment COSMO-LEPS median (F19) SRNWP-PEPS (F2-F18) COSMO-DE (F1) BMA further reading: J. McLean Sloughter, Adrian E. Raftery and TilmannGneiting: Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging. Technical Report, Department ofStatistics, University of Washington accumulated relative weight day J. Dietrich et al., ISFD Toronto, May 2008

  17. Hydrological Modelling System ArcEGMO • (Semi-)Distributed, GIS-based rainfall-runoff model • Modular system combinig several conceptual sub-models 1. Runoff generation HSC: total input HMX: input dynamic GNX: hydraulic conductivity 1 3 2 2. Runoff concentration C1, CC1, C2, CC2: storage coefficients S1, S2: storage capacity 3. Channel routing Edited from Becker et al., 2002 -> 5 sensitive parameters for flood modelling J. Dietrich et al., ISFD Toronto, May 2008

  18. Calibration and Testing – Würschnitz/Chemnitz • 30 flood events from 1954 – 2006, 2 y < T < 250 y • 6 – 24 1h-stations, disaggregation of approx. 60 1d-stations (nearest neighbour) C 1996/07 1998/11 2002/08 V 1997/07 1978/05 1994/03 J. Dietrich et al., ISFD Toronto, May 2008

  19. Ensemble Generation - Hydrology 5d(1d) 2d(12h) 21h(3h) Probabilistic weather scenario COSMO-LEPS SRNWP-PEPS COSMO-DE LAF training period historic flood events deterministic hydrological modelling ArcEGMO parameter ensemble ArcEGMO inference preconditions event type 12-24 hrly comp. 3 hrly comp. assimilation sequential ensemble update observations probabilistic runoff scenario for the headwaters flood routing/inundation models J. Dietrich et al., ISFD Toronto, May 2008

  20. Hydrological Parameter Ensembles • Analysis of historic flood events • Stable parameters for slow reacting runoff components • Parameters for fast reacting runoff components (mainly infiltration rate resp. generation of surface runoff) are subject of uncertainty • Problem: overlay of data uncertainty and parameter uncertainty in calibration (sp./temp. resolution of high rainfall intensities!) • A priori generation of sets of efficient parameters • Monte-Carlo simulation with restricted parameter ranges • Classification of flood events (rainfall intensity, antecedent precipitation) • Simulation with a small subset of efficient parameters • -> physically based hydrological ensemble (single model) J. Dietrich et al., ISFD Toronto, May 2008

  21. Update of Ensemble Weighting (Hydrology) • Bayesian update of parameter ensembles based on data assimilation • Update of weights, not re-calibration of parameters! yellowline: observed discharge blueline: model average lightblue: uncertainty band (Q95-Q5) forecast forecast discharge relative weight J. Dietrich et al., ISFD Toronto, May 2008

  22. Conclusions and Outlook • Ensemble forecasts can be an integral part of an operational flood forecast system. • Ensembles can, but not necessarily must improve flood forecasts. • Limited resources require adaptive strategies for the operational application of a probabilistic flood prediction chain. • Further work: • Ensemble calibration using empirical orthogonal functions (Denhard et al. in prep.) • Near real-time updating of the hydrological ensembles using assimilated observed data • Analysis of 2007 – 2008 forecasts: improve basis for decision rules J. Dietrich et al., ISFD Toronto, May 2008

  23. Thank you very much for your attention! • Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings? • Contact: Joerg.Dietrich@rub.de • Acknowledgements: Flood Management and Reservoir Authorities of Saxonia, BAH Berlin, DHI-WASY Dresden J. Dietrich et al., ISFD Toronto, May 2008

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