Flood forecasting Fredrik Wetterhall European Centre for Medium-Range Weather Forecasts

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Outline Introduction Operational forecasting systems (EFAS, GloFAS) S2S hydrological forecasting

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Flooding – a global challenge

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Flooding – a global challenge

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS October 29, 2014 Causes of flooding snowmelt runoff rainfall ice jams and other obstructions coastal storms (tsunamis, cyclones, hurricanes) urban stormwater runoff; dam failure (or the failure of some other hydraulic structure). Etc …

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Forecasting chain EPS Hydrology Warning Preprocessing/ calibration Postprocessing Verification Feedback to the model

Forecasts can fail because: The initial conditions are not accurate enough, e.g. due to poor coverage and/or observation errors, or errors in the assimilation (initial uncertainties). The model used to assimilate the data and to make the forecast describe only in an approximate way the true atmospheric phenomena (model uncertainties). A combination of the two phenomena As a further complication, the atmosphere is a chaotic system! t=0 t=T1 t=T2 Why do forecasts fail?

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS October 29, 2014 Early probabilistic flood warnings across Europe Transboundary 50 partners Partners provide: Observations Feedback on warning performance Development of decisions EFAS has the largest collection of hydro-meteorological observations in Europe! European Flood awareness system EFAS

What are the Benefits ? National Hydrological ServicesEuropean Commission Novel information Added value - Catchment based information - River basins larger than 4000 sq.km and regional cross-border dimension - Longer lead-times up to 15 days through probabilistic information - Network of operational services - Promotion of novel tools, techniques and data sets (e.g. satellite data) - Comparable information across Europe - Tool for anticipation of crisis management: - Civil Protection aid assistance during crisis - COPERNICUS Mapping Service

Expert Knowledge of Member States Real-time data (EU-FLOOD-GIS/ETN-R) Europ. Data Layers Meteo – Data / forecasts Historical Data Static Data EFAS partner network Alert EFAS user interface DATA Hydrological modeling Linz, AT – 31/05/ UTC How do we actually do flooding prediction? Schematic view

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS EFAS - Time series

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS EFAS - Time series simplified Single deterministic forecasts EPS forecasts

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS EFAS - Condensing information Nr of EPS exceeding thresholds

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Previous forecasts Today’s forecast Event forecast Evaluation of persistence in time and consistence between forecasts are important EFAS - Looking back in time

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 15 How reliable and accurate is our flooding prediction?

Flood warning for Passau, Germany 30 May 2013 Donau at Passau (DE), 30/5/2013 Although the flood was predicted it was not high enough. Top discharge should have been 10000

to Going higher resolution: T L 3999 (5 km) TP fc (+72h) 32 km ENS 16 km HRES 65 km 5 km Observations

to Increased resolution + modified cloud physics HRES (16 km) Observations Higher resolution and physics: T L 3999 (5 km) TP fc (+72h)

UK floods, December 2012 – Trent River Trent at Dunham Bridge near Gainsborough, 27/12/2012

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 20 EFAS flash floods Medium High Severe Alert Extremity

A global challenge? The Global Flood Awareness Systems

hydrological model Decision support information Global probabilistic weather forecast (ECMWF) GloFAS – A global challenge

Streamflow simulations with ERA-Interim global atmospheric reanalysis as meteo input Comparison with observed discharge data (~1400 world stations)

Ensemble streamflow predictions Forecast peak flow detected ~10-15 days in advance SE Asia floods Sept/Oct 2011 (Chao Praya and Mekong)

Username: training Password: tra1000

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Forecasting chain in future EFAS EPS Hydrology Warning Preprocessing/ calibration Postprocessing Verification Feedback to the model S2S Multimodel

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS S2S hydrological forecasting EFAS (European Flood Awareness System): operational system for early flood and flash flood warnings over Europe (up to 15 days lead time) Growing incentive for hydrological forecasts at longer lead times: –Applications: hydropower management, spring flood prediction, low flows prediction for navigation, agricultural water needs... –Increase in NWP skill Aims: –Produce seasonal streamflow predictions for Europe using ECMWF dynamical seasonal forecasts –Provide probabilistic outlooks against model reforecasts for seasonal predictions beyond 15 days 27

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Data 28

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Evaluation strategy 29 Scores computed: –On weekly catchment discharge averages – –For each season (DJF, MAM, JJA, SON) –Lead time: weeks –Against EFAS-WB Two main studies European catchments map used for the analysis (74 catchments)

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Evaluation strategy 30 Meteorological forcings (MF) versus initial conditions (IC)

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Evaluation strategy 31 Meteorological forcings (MF) versus initial conditions (IC) Reverse-ESP: 15 resampled years of initial conditions and ‘perfect’ meteorological forcing data (Wood and Lettenmaier, 2008) MF lead the uncertainty over the IC  variance ESP > variance rESP

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results 32 1) Seasonal predictability over Europe Decreasing accuracy with lead time On average still some accuracy until 8 weeks Increasing geographical disparities with lead time Seasonal more accurate than ESP on average until 4 weeks Increasing gap during 2 nd week between seasonal and ESP KGE for all seasons combined

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS KGE over all catchments and seasons

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results 34 1) Seasonal predictability over Europe Higher predictability in summer Gain of using seasonal forecast increases in winter for lead times 1 to 4 weeks

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results 35 1) Seasonal predictability over Europe Seasonal shows highest gain in predictability in winter: –Iberian Peninsula –Scandinavia (Baltic Sea) In summer predictability largest for: –Scandinavia (Baltic Sea) –Around Mediterranean Sea –South of North Sea Lead time at which CRPSS ≤ 0

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results CRPSS SYS4 vs ESP 36

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results 37 1) Seasonal predictability over Europe Decreasing skill with lead time, but still skilful until about 6 weeks Seasonal and ESP show similar ROC score for week 1, then seasonal’s ROC scores higher Large decrease in skill for ESP between 1 and 2 weeks Both systems more skilful to resolve low flows than high flows

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results Meteorological forcings (MF) versus initial conditions (IC) Critical Lead Time (CLT) (Yossef et al., 2013): lead time at which var ESP > var rESP 1.High CLTs, leeward: groundwater fed rivers during winter 2.Low CLTs, windward: moist westerly winds 3.Low CLTs: precipitation driven flows in winter 4.High CLTs: drier antecedent moisture conditions 5.High CLTs: snowmelt driven discharges

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Take-home messages Overall gain of using seasonal forecasts from 1 – 4 weeks lead time Especially in winter: Iberian Peninsula and Scandinavia (Baltic Sea) 39 MF leads uncertainty over IC from 2 weeks of lead time on (average for Europe) Seasonal transitions between hydrological states (wet, dry) crucial in this process Seasonal more skilful to resolve low and high flows from the 2 nd - 8 th week lead time Lower flows more skilfully resolved than upper flows

Multimodel system: test of three models Hydrological models LISFLOOD HTESSEL/ CAMA EHYPE Characteristic Hydrological model with channel routing HTESSEL coupled with CamaFlood Semi- distributed conceptual model Resolution5 km gridded 80 km land surface model 25 km routing Catchment based (varying resolution) Driving data 5 km gridded observed data ERA-Interim corrected with GPCP/ 5 km gridded observed data ERA-Interim corrected with GPCC/ 5 km gridded observed data Test period: Observational discharge:212 stations from GRDC

LISFLOOD

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS HTESSEL and CaMa-Flood

E-HYPE – pan European HYPE application

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results: Nash-Sutcliffe and mean relative error

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Multimodel – how useful is it for decision making?

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Create one supermodel to rule them all?

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Results: Bayesian model averaging BMA improves NSE in 76% of the cases

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Conclusions The different models perform differently depending on basin characteristics, such as size and elevation BMA improves the performance of the models in most cases, and gives reasonable results even if the individual models are not doing well in a particular catchment Without proper calibration it is difficult to see a great benefit from the 5km gridded dataset Combining models will be a challenge