flood forecasting fredrik wetterhall european centre for medium-range weather forecasts

Flood forecastingFredrik 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


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 …


Forecasting chain

EPSHydrology

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 Services European 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

0

500

1000

1500

2000

2500

8/23/02 0:00 8/24/02 0:00 8/25/02 0:00 8/26/02 0:00 8/27/02 0:00 8/28/02 0:00 8/29/02 0:00 8/30/02 0:00 8/31/02 0:00

Dessau/Rosslau Wittenberg Torgau Riesa Dresden Labe Decin Labe/Usti N.L. Vltava/Prague

EFAS partner network

Alert email

EFAS user interface

DA

TA

Hydrological modeling

Linz, AT – 31/05/2013 00 UTC

How do we actually do flooding prediction? Schematic view


EFAS - Time series


EFAS - Time series simplified

Single deterministic forecasts

EPS forecasts


EFAS - Condensing information

Nr of EPS exceeding thresholds


Pre

viou

s fo

reca

sts

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

2013-05-31 to 2013-06-03

Going higher resolution: TL3999 (5 km) TP fc (+72h)

32 km ENS

16 km HRES

65 km

5 km

Observations

2013-05-31 to 2013-06-03

Increased resolution + modified cloud physics

HRES (16 km)

Observations

Higher resolution and physics: TL3999 (5 km) TP fc (+72h)

UK floods, December 2012 – Trent River

Trent at Dunham Bridge near Gainsborough, 27/12/2012


EFAS flash floods Medium

High

Severe

Alert Extremity

A global challenge? The Global Flood Awareness Systems

hydrological model

Decision support informationGlobal 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)

www.globalfloods.eu

Username: trainingPassword: tra1000


Forecasting chain in future EFAS

EPSHydrology

Warning

Preprocessing/calibration

Postprocessing

Verification

Feedback to the model

S2SMultimodel


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


Data


Evaluation strategy

•Scores computed:

– On weekly catchment discharge averages

– 1990 - 2013

– For each season (DJF, MAM, JJA, SON)

– Lead time: 1 - 8 weeks

– Against EFAS-WB

•Two main studiesEuropean catchments map used for the analysis (74 catchments)


Evaluation strategyMeteorological forcings (MF) versus initial

conditions (IC)


Evaluation strategyMeteorological 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


Results1) 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 2nd week between seasonal and ESP

KGE for all seasons combined


KGE over all catchments and seasons



•Higher predictability in summer

•Gain of using seasonal forecast increases in winter for lead times 1 to 4 weeks



•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


Results CRPSS SYS4 vs ESP



•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


Results2. 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


Take-home messages

Overall gain of using seasonal forecasts from 1 – 4 weeks lead time

Especially in winter: Iberian Peninsula and Scandinavia (Baltic Sea)

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 2nd - 8th week lead time

Lower flows more skilfully resolved than upper flows

Multimodel system: test of three models

Hydrological models

LISFLOODHTESSEL/ CAMA

EHYPE

CharacteristicHydrological model with channel routing

HTESSEL coupled with CamaFlood

Semi-distributed conceptual model

Resolution 5 km gridded80 km land surface model 25 km routing

Catchment based (varying resolution)

Driving data5 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: 1990-2010Observational discharge: 212 stations from GRDC

LISFLOOD


HTESSEL and CaMa-Flood

E-HYPE – pan European HYPE application


Results: Nash-Sutcliffe and mean relative error


Multimodel – how useful is it for decision making?


Create one supermodel to rule them all?

•Bayesian Model Averaging (BMA) accounts for the model uncertainty by averaging over the best models according to posterior model probability

•The BMA methodology applied for this study performs analysis assuming a uniform distribution on model priors and using a simple BIC (Bayesian Information Criterion) approximation to construct the prior probabilities on the regressions coefficients (Raftery, Hoeting, Volinsky, Painter & Yeung 2010).

•

•If M = {,…, } denotes the set of models and if is the quantity of interest (e.g. stream-flow), then the posterior distribution of given the data D is:

This is an average of the posterior distributions under each model weighted by the corresponding posterior model probabilities.

𝑝𝑟 (∆|𝐷 ¿=∑𝑘=1

𝐾

𝑝𝑟 (∆|𝑀𝑘 ,𝐷 )𝑝𝑟 (𝑀𝑘∨𝐷)


Results: Bayesian model averaging

BMA improves NSE in 76% of the cases


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

flood forecasting fredrik wetterhall european centre for medium-range weather forecasts

Documents

time european centre

thresholds european

modeleuropean centre

data sets

flooding prediction

europe transboundary

europe tool

partners partners