Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?Jrg Dietrich, Yan Wang, Michael Denhard & Andreas Schumann

J. Dietrich et al., ISFD Toronto, May 2008

J. Dietrich et al., ISFD Toronto, May 2008Outline of the presentationIntroductionCase study: hindcasts for the Mulde river basinDevelopment of an ensemble based flood forecast schemeConclusions and future work*


Uncertainties in Flood ForecastingFuture development of the atmosphere cannot be perfectly forecastedInitial states and boundary conditions of models may be uncertain in time and spaceModel structure may be insufficient (model and parameter uncertainty)Inadequate human interaction Technical problemsSolution for some of the data and model uncertainties: computation of several simulations which frame uncertainty -> ensemble techniques probabilistic instead of deterministic forecastJ. Dietrich et al., ISFD Toronto, May 2008*


J. Dietrich et al., ISFD Toronto, May 2008Types of EnsemblesSingle System EnsemblesPerturbation of initial and boundary conditions, different convection schemes (physically based ensembles)Perturbation of model parametersMultiple Systems EnsemblesCombination of forecasts from different modelsLagged Average EnsemblesCombination of actual forecasts with forecasts from earlier model runsEnsembles aim at characterizing forecast uncertainty, but there will remain uncertainty about uncertainty.*


J. Dietrich et al., ISFD Toronto, May 2008Ensembles in Operational Flood ManagementReliability is 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 rulesCan ensembles improve decisions (economy: ratio between true and false alarms, flood defence: longer lead time)?Challenges in developing an ICT systemTremendous amount of dataComputational efficiency of the models

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J. Dietrich et al., ISFD Toronto, May 2008Mulde Case StudyCharacteristics of the river basin: Low mountains, fast reaction to rainfall events, flash floodsSeveral vulnerable cities2002: return periods up to > 500 aStudy area: 6200 kmOperational flood forecast system - requirements:Meso-scale resolution (headwaters with approx. 100 500 km area)Short to very short lead timesSupport decisions about flood alerts/pre-alertsGrimma, 2002-08-13. Source: dpa*


Study Area Chemnitz Sub-catchmentJ. Dietrich et al., ISFD Toronto, May 2008*


Operational Ensemble Systems UsedCOSMO-LEPSSingle system physically based ensemble, 16 membersMedium range (132 h lead time)Meso-scale (10 km horizontal resolution)SRNWP-PEPSMultiple systems ensemble, 23 members (17 cover Mulde area)Short range (48 h lead time)Meso-scale (7 km horizontal resolution)COSMO-DEDeterministic model, lagged average ensemble: 7 membersVery short range (21 h lead time)Local scale (2.8 km horizontal resolution, resolving convection)J. Dietrich et al., ISFD Toronto, May 2008*Molteni et al., 2001Denhard and Trepte, 2006Steppeler et al., 2003


Hindcasts with Raw Ensembles (2002-2006)Comparison of different ensemble prediction systemsAim of study: development of a scheme for adaptive combination of ensembles from different sources and with different lead timesHydrological model: calibrated, assumed as perfectTrue alerts:2002-08: extreme flood, underestimated2006-02/03 flood caused by rainfall/snowmelt, overestimatedFalse alerts:2005-07, 2005-08: meteorology (no flood alert issued)2006-08: rainfall true but overestimated, low soil moistureMissings:rainfall: not investigated, flood (T > 2 y, meso-scale): none

J. Dietrich et al., ISFD Toronto, May 2008*


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


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


2006 False Alert: COSMO-LEPSJ. Dietrich et al., ISFD Toronto, May 2008*Synoptic forecast: up to 290 mm rainfall within 3 daysWater release from reservoir initiated80 mm within 36 hrs, low soil moisture, peak discharge T < 2 y


Hindcasts: Alarm Level ExceedanceJ. Dietrich et al., ISFD Toronto, May 2008discharge, m/s*

EPSCOSMO-DE + 21h (Dt=1h)SRNWP-PEPS + 48 h (Dt=1h)COSMO-LEPS + 132 h (Dt=3h)InitializationmaxObs+21maxDetmaxMedLAFmaxObs+48maxMedmaxQ75maxObs+132maxMedmaxQ7507.08.200273.514.547.308.08.200287.111.418.209.08.200287.127.559.310.08.20027.310.58.287.1124.9156.711.08.200226.841.820.887.1108.5195.112.08.200289.984.856.387.1112.1115.805.07.20053.33.74.93.18.223.506.07.20051.32.12.10.710.930.007.07.20050.71.61.60.711.126.408.07.20050.71.54.00.64.58.409.07.20050.42.63.30.410.018.029.07.20053.40.41.24.43.918.930.07.20053.44.85.34.47.615.731.07.20050.52.82.84.42.713.201.08.20050.41.31.34.811.917.002.08.20054.88.512.45.413.134.903.08.20054.813.415.25.416.216.204.08.20052.711.911.95.49.612.705.08.20058.28.19.65.413.846.806.08.20058.211.314.15.416.419.907.08.20056.08.59.75.412.016.003.08.20060.10.90.10.10.20.38.126.649.204.08.20060.11.70.20.212.0137.08.116.230.905.08.20069.615.52.69.613.081.58.17.012.806.08.20069.321.86.29.616.8136.28.120.728.1

Alarm 1Alarm 2Alarm 3Alarm 413.325.643.270.8


Lessons learnt from HindcastsCOSMO-LEPS shows best performance at +2 to +3 days lead time, but often a large spread -> meteorological uncertainty high compared to hydrological uncertaintyCOSMO-DE tends to under predict rainfall at certain model runs -> solution: lagged average ensemble, physical ensemble is scheduled for 2010SRNWP-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*


J. Dietrich et al., ISFD Toronto, May 2008observationsradar,rain gaugesEnsemble Combination - Meteorology2007assimilation*


Ensemble Calibration with BMABayesian Model Averaging assigns weights to ensemble members based on training periodDaily recalibration: 12 of 19 members have significant weights, 3 best members > 50%, overfitting possibleJ. Dietrich et al., ISFD Toronto, May 2008Nov 1st 14th 2006Mulde catchmentCOSMO-LEPS median (F19)SRNWP-PEPS (F2-F18)COSMO-DE (F1)accumulated relative weightday*BMA further reading: J. McLean Sloughter, Adrian E. Raftery and Tilmann Gneiting: Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging. Technical Report, Department of Statistics, University of Washington


Hydrological Modelling System ArcEGMO(Semi-)Distributed, GIS-based rainfall-runoff modelModular system combinig several conceptual sub-modelsJ. Dietrich et al., ISFD Toronto, May 2008*2. Runoff concentrationC1, CC1, C2, CC2:storage coefficientsS1, S2:storage capacity1. Runoff generation3. Channel routing123HSC:total inputHMX:input dynamicGNX:hydraulic conductivityEdited from Becker et al., 2002-> 5 sensitive parameters for flood modelling


Calibration and Testing Wrschnitz/Chemnitz30 flood events from 1954 2006, 2 y < T < 250 y6 24 1h-stations, disaggregation of approx. 60 1d-stations (nearest neighbour)J. Dietrich et al., ISFD Toronto, May 20081978/051994/031997/07VC2002/081998/111996/07*


J. Dietrich et al., ISFD Toronto, May 2008Ensemble Generation - Hydrologyobservationsflood routing/inundation models probabilistic runoff scenario for the headwatersassimilationparameter ensembleArcEGMOtraining periodhistoric flood eventspreconditionsevent typeinferencesequential ensemble update12-24 hrly comp.3 hrly comp.5d(1d)2d(12h)21h(3h)COSMO-LEPSSRNWP-PEPSCOSMO-DE LAFdeterministic hydrological modelling ArcEGMO*Probabilistic weather scenario


Hydrological Parameter EnsemblesAnalysis of historic flood eventsStable parameters for slow reacting runoff componentsParameters for fast reacting runoff components (mainly infiltration rate resp. generation of surface runoff) are subject of uncertaintyProblem: overlay of data uncertainty and parameter uncertainty in calibration (sp./temp. resolution of high rainfall intensities!)A priori generation of sets of efficient parametersMonte-Carlo simulation with restricted parameter rangesClassification 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*


Update of Ensemble Weighting (Hydrology)Bayesian update of parameter ensembles based on data assimilationUpdate of weights, not re-calibration of parameters!J. Dietrich et al., ISFD Toronto, May 2008*yellow line:observed discharge

blue line:model average

light blue:uncertainty band (Q95-Q5)forecastforecastrelative weightdischarge


J. Dietrich et al., ISFD Toronto, May 2008Conclusions and OutlookEnsemble 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 dataAnalysis of 2007 2008 forecasts: improve basis for decision rules*


J. Dietrich et al., ISFD Toronto, May 2008Thank you very much for your attention!Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?Contact: [email protected]: Flood Management and Reservoir Authorities of Saxonia, BAH Berlin, DHI-WASY Dresden*


ensemble mean[mm][%]P >50mmSRNWP-PEPS Verification24h precipitation, 1.5 d lead timerun: 22.08.2005 0 UTCavailable: 22.08.2005 6:05 UTCvalid: 22.08.- 23.08.2005 6 UTC*J. Dietrich et al., ISFD Toronto, May 2008


Ensemble Calibration with BMABayesian Model Averaging assigns weights to ensemble members based on training period

J. Dietrich et al., ISFD Toronto, May 2008*J. McLean Sloughter, Adrian E. Raftery and Tilmann Gneiting: Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging. Technical Report, Department of Statistics, University of Washington


J. Dietrich et al., ISFD Toronto, May 2008Probabilistic Forecast of 2002 Flood (Reanalysis)*


Forecast ReliabilityReliability is the ability of a person or system to perform and maintain its functions in routine circumstances, as well as hostile or unexpected circumstances.The IEEE defines it as ". . . the ability of a system or component to perform its required functions under stated conditions for a specified period of time.When reliability is considered from the perspective of the consumer of a technology or service, actual reliability measures may differ dramatically from perceived reliability. One bad experience can be magnified in the mind of the customer, inflating the perceived unreliability of the product. (Wikipedia)J. Dietrich et al., ISFD Toronto, May 2008*


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Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

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