Ensemble Hydrological Ensemble Hydrological ForecastingForecasting

Bob Moore and David Jones, CEH WallingfordClive Pierce, JCHMR (Met Office), Wallingford

OverviewOverview

Drought ManagementDrought Management

Flood ForecastingFlood ForecastingPresentPresentFuture?Future?

Thames surface water resource system

Drought management in the Thames Basin

•Historical rainfall ensembles

•Categorical probability rainfall forecast weighting

Risk assessment procedure for drought management

Risk assessment of reservoir storage shortfall and demand restrictions

Flood Forecasting

Rainfall sources:Radar: single site Radar: Gandolf Raingauge

HYRADRainfall displaysCatchment rainfall FFMS

Flood forecastsDecision support

Flood Warning

Dissemination Response

Weather radar, first alert and flood forecasting systems

First Alert:

Improved display of radar rainfall across scales; 0 to 6 hours, 1, 2, 5 km

HYRAD has been adopted as national standard by

Environment Agency across England & Wales

First alert warning products

12 hour ahead rainfall forecast from Mesoscale Model, Nimrod radar actual

and catchment hyetographUnified NWP Model grids:

global & mesoscale

12 km, 0 to 2 days

Flow Forecasting & Modelling Flow Forecasting & Modelling System (FFMS) DesignSystem (FFMS) Design

Telemetry interface

DATABASE Graphical user interface

Report generatorSHELL

KERNEL

MODELSCatchment rainfallRainfall-runoffChannel flowLakeUpdatingControl RulesWater Quality..

INFORMATION CONTROL

ALGORITHM

MODEL CALIBRATION

FACILITY

Telemetry DataRadar / Satellite / MesoscaleNWP model data

REAL-TIME OFF-LINE

PrinterFax

User

1-REGION-ALL

1-NONTIDAL-T

1-NONTIDAL-N

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1-REGION-N

1-REGION-T

1-REGION

1-NONTID-ALL

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QL-KIRKNW1

QX-WOOLER1

QL-MORWCK1

QX-STAMFM1

QL-MITFRD1

QL-CHESLS1

QX-NABURN1

QL-DONCST1

QL-BEALWR1

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LU-TADCST1

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RADAR-HAMEL7067

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RADAR-HAMEL7023

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RF-CANNON1

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LU-WAKEFD1QX-DEWSBY1

RADAR-HAMEL 46

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QX-MALTON1

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QX-POOLBR1

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RF-BURNHP1

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LU-ALDWRK1

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QL-BATBRG1

QL-CRAKEH1

LU-SKIPBR1

QX-HUNSNG1

RADAR-HAMEL7052

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QX-DRONFD1

LU-HADYHL1

QX-CHESTF1

LU-CHESTF1

QX-HADYHL2

LU-COTTNL1

QX-STOCKB1

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QL-NORTHP1

LU-MALTON1

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QL-ADDGHM1

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RADAR-HAMEL7065

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RF-LUDHLL1

RADAR-HAMEL 36

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SX-MALHMT1

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SD-MALHMT1

COMAT-NWPENUSERN-NWPEN

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USEAT-NWPEN

SYNAT-NWPEN

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SW-MRCOTT1

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RADAR-HAMEL7068

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RF-SKPBTH1

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ST-REDMRF1

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SF-REDMRF1

SP-REDMRF1

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SF-REDMRF1

SP-REDMRF1

EV-WINGWR1

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BCKRN-NPENN

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BCKRN-NPENN

RF-GOLDSC1

BCKPE-YSINE

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RF-DARRAS1

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BCKRN-NPENN

BCKRN-WOLDS

XR-HGHMOW1

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ST-LANGST3

SF-LANGST3

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FLOW3-STAMFM

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FIXF-REDEBR

FLOW3-STANHP

FIXF-BEDBRN

FLOW3-ALSTON

FIXF-BATBRG

SNOW2-GORPLE

EVAP-MALHMT

FIXF-WESTAY

EVAP-LWRDUN

COM-NPENN

SNOW2-OSMOTH

FIXF-GOUTHW

COM-WPENN

FLOW3-BROADW

SNOW2-ARKENG

FLOW2-QUEENS

COM-NWPEN

SNOW2-MRCOTT

FLOW3-RIPPND

FLOW3-WALSDN

FLOW3-TODMDN

CALD3-HEBDBR

SNOW2-MALHMT

FLOW3-CLDENE

CALD3-SOWRBY

FLOW2-LOWHSS

FLOW2-KETTLW

COLN-ICI0003

CALD3-ELLAND

FLOW2-GRINTN

SNOW2-SKPBTH

URE0-BAINBR

WHAR-GRASTN3

FLOW3-CHERRY

COLN-COLNEB3

CALD3-BRIGHS

FLOW2-KILDWK

FLOW2-BURNBR

NIDD-BIRSTW

FLOW2XKIRBYW

FLOW2-KIRBYWSWAL-RICHLW2

FLOW2-RIPON0

URE0-KILGRM

WHAR-ADDGHM3

FLOW3-NORTHP

CALD3-HEATON

FLOW2-KEIGHL

AIRE-STOCKB3

NIDD-HUNSNG

URE0-WESTWK3

WHAR-OTLEY02

CALD-DEWSBY3

AIRE-COTTNL3

NIDD-SKIPBR

TYNE3-FEATHS

WHAR-ARTHNG2

CALD-WAKEFD3

AIRE-ARMLEY3

SNOW2-REDMRF

FLOW3-STAVLY

WEAR3-WITTNP

TYNE3-HAYDNB

TYNE3-REAVHL

SNOW2-KELDHD

SNOW2-HGHMOW

SNOW2-LANGST

WHAR-FLINTM

FLOW2-FARRER

CALD3-METHLY

AIRE-FLEETW

WEAR3-SUNDBR

COQU3-ROTHBY

TYNE3-CRAGHL

TYNE3-BYWELL

FLOW3-SCARBH

FLOW2-BOYNTN

FLOW2-WALDEN

WHAR-TADCST

WEAR3-CHESLS

COQU3-MORWCK

TYNE3-RIDNGM

NONTID-N-DUM

FLOW3-INGSBR

FLOW3-GATEHS

FLOW3-NORMAN

FLOW3-KIRKBY

FLOW3-CROOKH

SWAL-LEEMNG2

FLOW3-MARISH

FLOW3-HADYUP

FLOW3-DRONFD

SWAL-CRAKEH4

FLOW3-HADYHL

FLOW3-SHEEPB

URE0-ALDWRK3

FLOW2-BARNSL

FLOW2-HIGHFD

ROTH3-WHITTN

OUSE-SKELTN

AIRE-CASTLF3

DEAR-ADWICK

FLOW2-HADFDS

ROTH3-WOODHS

OUSE-VIKING

AIRE-BEALWR

DON0-DONCST

OUSE-NABURN

TIDAL-N-DUM

RYE03-NESS00

RYE03-HOWEBR

DERW3-MALTON

DERW3-KIRKHM

DERW3-BUTTCR

DERW3-ELVING

NONTIDAL-DUM

TIDAL-HYD2

NONT-ALL-DUM

TIDAL-DUM2

TID-ALL-DUM

3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500

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Not toa standard scale Yorkshire

Northeast RFFS Model Network

Yorkshire RFFS Forecast Requirement

Propagation of uncertainty ?

Model Network configuration

Flood forecasting and warning

0.0

0.4

0.8

1.2

02 - JAN 03 - JAN01 - JAN

Rai

nfal

l ( m

m )

0

2468

1012

02 - JAN 03 - JAN01 - JAN

Flow

( m

3s-1

)

b) Flow

a) Rainfall

Observed

Forecast (known rainfall)

Forecast (unknown rainfall)

What ifs? Informal ensembles

River Seine at Paris

Land path

River path

Surface flow

Sub-surface flow

Return flow

AreaArea--wide flow wide flow modellingmodelling

AreaArea--wide Hydrological Grid Modelwide Hydrological Grid Modelwith rainfall ensembleswith rainfall ensembles

Probability products?

STSTochastic ochastic EEnsemble nsemble PPrediction rediction SSystemystemModel designModel design

Cascade framework to model dynamic scaling behaviourCascade framework to model dynamic scaling behaviour

merging extrapolation nowcasts with NWP forecastmerging extrapolation nowcasts with NWP forecast

Sources of uncertainty / errorSources of uncertainty / errordiagnosed velocity fields diagnosed velocity fields Lagrangian temporal evolution Lagrangian temporal evolution NWP forecastNWP forecast

initial state initial state -- radar inferred rain rateradar inferred rain rate

Forecast evolutionForecast evolutionblends extrapolation, NWP and noise cascades blends extrapolation, NWP and noise cascades stochastic noisestochastic noise

•• replaces extrapolated features beyond their life timesreplaces extrapolated features beyond their life times•• introduces features unresolved by NWPintroduces features unresolved by NWP

100 member ensemble100 member ensemble

Stochastic noise progressively dominates the forecast from Stochastic noise progressively dominates the forecast from the smallest scales upwards.the smallest scales upwards.

This process is arrested by the NWP forecast.This process is arrested by the NWP forecast.

4 km resolution4 km resolution6 hour forecast6 hour forecast100 members100 members

Towards probabilistic hydrological Towards probabilistic hydrological forecastingforecasting

Uncertainty in rainfall input dominates (Uncertainty in rainfall input dominates (Moore, 2002Moore, 2002))

Ignore other error sources Ignore other error sources egeg. rainfall. rainfall--runoff modelrunoff model

Ensemble of river flow from ensemble of rain accumulationEnsemble of river flow from ensemble of rain accumulation

Underestimates total uncertainty (Underestimates total uncertainty (Krzyztofowicz, 2001Krzyztofowicz, 2001))

Case study: 21 December 2002Case study: 21 December 2002CatchmentCatchment

River Mole, 142 kmRiver Mole, 142 km22, S.E. England, S.E. England

Rainfall forecastRainfall forecastSTEPS 100 member ensemble (no NWP)STEPS 100 member ensemble (no NWP)15 minute accumulation15 minute accumulationrange 6 hoursrange 6 hours

River flow forecastRiver flow forecastProbability Distributed Model (PDM)Probability Distributed Model (PDM)Calibrated using radar dataCalibrated using radar dataForecasts from 3 time originsForecasts from 3 time originsForecast range extended using zero rainfallForecast range extended using zero rainfall

Probability Distributed ModelProbability Distributed Model

Moisture storage

Runoff

SUBSURFACE STORAGE

Surfacerunoff

River Flow

SURFACE STORAGE

Potential EvaporationE

Rainfallf P

INPUT

k1 S1

k2 S2

Baseflow: kb Sb3

cmax

cminGroundwater

recharge:kg (S - St)bg

5%, median, 95% points of flow forecast

Forecast flow ensemble

Forecast rmse and mae

Probability Flood Forecasts from radar

rainfall nowcastensembles

CEH/Met Office/Australian Bureau of Meteorology collaboration

August 2002 August 2002 European floodsEuropean floods