can ensemble forecasts improve the reliability of extreme flood warnings? jörg dietrich, yan wang,...

Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

Jörg Dietrich, Yan Wang, Michael Denhard & Andreas Schumann

Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr University Bochum

Funding: German Ministry of Education and Research (BMBF), Coordination: PTJ

Deutscher Wetterdienst (DWD, German National Weather Service), Offenbach

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

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

2

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 3


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.

4


Ensembles in Operational Flood Management

▫ Reliability 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 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

5


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

6

Study Area – Chemnitz Sub-catchment


XY

XY

XY

XY

XY

XYXY

XY

XY

XY

XY

XY

XY

4412

4422

Harthau

Tannenberg

Chemnitz 1

Jahnsdorf 1

Niederschlema

Altchemnitz 1

Niederzwönitz

TS Stollberg ZP

Burkhardtsdorf 2

Legend

XY discharge gauges

rain gauges (10 min)

rain gauges (1d)

main rivers

hydrotope classification

Slow, non-forest

Slow, forest

Fast, non-forest

Fast, forest

Groundwater Interaction

Settlement

Water0 2 41 Kilometers ¯

7

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)J. Dietrich et al., ISFD Toronto, May 2008 8

Molteni et al., 2001

Denhard and Trepte, 2006

Steppeler et al., 2003

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


2002 Flood: COSMO-LEPS Hindcast +5 d


Würschnitz, gauge Jahnsdorf 1, COSMO-LEPS/ArcEGMOinitialization: 08/08/2002 12:00 UTC

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observed dischargedischarge forecast for ensemble membersinterquartile range of ensemble discharge forecastmedian of ensemble discharge forecastflood alarm level 4


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Schwarze Pockau, gauge Zöblitz, COSMO-DE forecasts 2002

observed discharge

simulated discharge driven by 3 hrly. COSMO-DE forecast runs (black/coloured lines)

HQ 100

simulated discharge using observed rainfall

2002 Flood: COSMO-DE Hindcast +21 h


coloured: early good performers

11

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

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Würschnitz, gauge Jahnsdorf 1, false alarm August 2006COSMO-LEPS/ArcEGMO forecast

observed dischargesimulated discharge for ensemble members75% - 25% quantiles of simulated dischargemedian of simulated dischargesimulated discharge for median of met. ensemble

Hindcasts: Alarm Level ExceedanceEPS COSMO-DE + 21h (Dt=1h) SRNWP-PEPS + 48 h (Dt=1h) COSMO-LEPS + 132 h (Dt=3h)

Initialization maxObs+21 maxDet maxMedLAF maxObs+48 maxMed maxQ75 maxObs+132 maxMed maxQ7507.08.2002 73.5 14.5 47.308.08.2002 87.1 11.4 18.209.08.2002 87.1 27.5 59.310.08.2002 7.3 10.5 8.2 87.1 124.9 156.711.08.2002 26.8 41.8 20.8 87.1 108.5 195.112.08.2002 89.9 84.8 56.3 87.1 112.1 115.805.07.2005 3.3 3.7 4.9 3.1 8.2 23.506.07.2005 1.3 2.1 2.1 0.7 10.9 30.007.07.2005 0.7 1.6 1.6 0.7 11.1 26.408.07.2005 0.7 1.5 4.0 0.6 4.5 8.409.07.2005 0.4 2.6 3.3 0.4 10.0 18.029.07.2005 3.4 0.4 1.2 4.4 3.9 18.930.07.2005 3.4 4.8 5.3 4.4 7.6 15.731.07.2005 0.5 2.8 2.8 4.4 2.7 13.201.08.2005 0.4 1.3 1.3 4.8 11.9 17.002.08.2005 4.8 8.5 12.4 5.4 13.1 34.903.08.2005 4.8 13.4 15.2 5.4 16.2 16.204.08.2005 2.7 11.9 11.9 5.4 9.6 12.705.08.2005 8.2 8.1 9.6 5.4 13.8 46.806.08.2005 8.2 11.3 14.1 5.4 16.4 19.907.08.2005 6.0 8.5 9.7 5.4 12.0 16.003.08.2006 0.1 0.9 0.1 0.1 0.2 0.3 8.1 26.6 49.204.08.2006 0.1 1.7 0.2 0.2 12.0 137.0 8.1 16.2 30.905.08.2006 9.6 15.5 2.6 9.6 13.0 81.5 8.1 7.0 12.806.08.2006 9.3 21.8 6.2 9.6 16.8 136.2 8.1 20.7 28.1

Alarm 1 Alarm 2 Alarm 3 Alarm 413.3 25.6 43.2 70.8


discharge, m³/s

13

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!



observationsradar,rain gauges

Ensemble Combination - Meteorology

2007

globalprediction systems

meso-scale ensemblesCOSMO-LEPSSRNWP-PEPS

deterministic local model COSMO-DE

Lagged Average-

Ensemble (LAF)

assimilationobservationsradar,rain gauges

200620022005

probabilistic weather scenario: multi-model ensemble from PEPS, COSMO-LEPS, COSMO-DE

model average, m approx. 10

calibration, Bayesian Model Average

(BMA)

assimilation

15

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 2006Mulde catchmentCOSMO-LEPS median (F19)SRNWP-PEPS (F2-F18)COSMO-DE (F1)

accu

mul

ated

rel

ativ

e w

eigh

t

day

16

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 model

▫ Modular system combinig several conceptual sub-models


2. Runoff concentrationC1, CC1, C2, CC2:storage coefficients

S1, S2:storage capacity

1. Runoff generation

3. Channel routing

1

2

3

HSC:total input

HMX:input dynamic

GNX:hydraulic conductivity

Edited from Becker et al., 2002

-> 5 sensitive parameters for flood modelling

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)


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observed precipitation

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ArcEGMO simulation

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1978/05 1994/03

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observed precipitationobserved dischargeArcEGMO simulationAlarm4

Alarm3

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observed discharge

ArcEGMO simulation

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1996/07

18


Ensemble Generation - Hydrology

observations

flood routing/inundation models

probabilistic runoff scenario for the headwaters

assimilation

parameter ensembleArcEGMO

training periodhistoric flood events

preconditionsevent type

inference

sequential ensemble

update

12-24 hrly comp. 3 hrly comp.

5d(1d)2d(12h)

21h(3h)

COSMO-LEPS SRNWP-PEPS COSMO-DE LAF

deterministic hydrological modelling ArcEGMO

19

Probabilistic weather scenario

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)


Update of Ensemble Weighting (Hydrology)

▫ Bayesian update of parameter ensembles based on data assimilation

▫ Update of weights, not re-calibration of parameters!


yellow line:observed discharge

blue line:model average

light blue:uncertainty band (Q95-Q5)

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1 18 35 52 69 86 103 120 137 154 171 188

T=36

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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

22


Thank you very much for your attention!

▫ Can Ensemble Forecasts Improve the Reliability of Extreme Flood Warnings?

▫ Contact: [email protected]▫ Acknowledgements: Flood Management and

Reservoir Authorities of Saxonia, BAH Berlin, DHI-WASY Dresden

23

can ensemble forecasts improve the reliability of extreme flood warnings? jörg dietrich, yan wang,...

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