ELDAS ELDAS Case Study 5100:Case Study 5100:

UK FloodingUK Flooding

Eleanor BlythEleanor BlythVicky BellVicky Bell

Bob MooreBob Moore(CEH Wallingford, UK)(CEH Wallingford, UK)

Case Study ObjectiveCase Study Objective

Case Study ObjectiveCase Study Objective::To quantify the added value to Flood To quantify the added value to Flood Forecasting that ELDAS soil moisture Forecasting that ELDAS soil moisture could makecould make

The questionThe question::How can we use ELDAS soil moisture to How can we use ELDAS soil moisture to

improve flood forecasting ?improve flood forecasting ?

Operational flood forecastingOperational flood forecasting The most common methods of data assimilation in flood The most common methods of data assimilation in flood

forecasting models involve the use of observed flow data forecasting models involve the use of observed flow data to adjust the hydrological model states in real-time (to adjust the hydrological model states in real-time (state state correctioncorrection), or to make predictions of future errors and ), or to make predictions of future errors and account for them (account for them (error predictionerror prediction). ).

The former is heavily dependent on the structure of the The former is heavily dependent on the structure of the simulation model, whilst the latter is essentially simulation model, whilst the latter is essentially independent of it. independent of it.

A model incorporating observed flows through state-A model incorporating observed flows through state-correction, error-prediction or some other scheme is said correction, error-prediction or some other scheme is said to be operating in to be operating in updating modeupdating mode. .

Use of river flow to update (or ‘nudge’) Use of river flow to update (or ‘nudge’) the surface and sub-surface storesthe surface and sub-surface stores

When an error, When an error, =Q-q =Q-q, occurs between model flow, , occurs between model flow, qq, , and observed flow, and observed flow, QQ, one can “attribute the blame” to , one can “attribute the blame” to mis-specification of the state variables (water stores)mis-specification of the state variables (water stores)

We can then “correct” the stores to achieve agreement We can then “correct” the stores to achieve agreement between observed and modelled flow. between observed and modelled flow.

Mis-specification may, for example, have arisen through Mis-specification may, for example, have arisen through errors in rainfall measurement which propagate through errors in rainfall measurement which propagate through the values of the store water contents, or the flow rates the values of the store water contents, or the flow rates out of the stores.out of the stores.

The PDM Probability Distributed The PDM Probability Distributed ModelModel

The standard PDM uses a Pareto distribution of moisture stores..

Moisture storage

Runoff

SUBSURFACE STORAGE

Surfacerunoff

River Flow

SURFACE STORAGE

Potential EvaporationRainfallINPUT

OUTPUT

Baseflow

Groundwaterrecharge

Detail of the PDMDetail of the PDM

SUBSURFACE STORAGE

Surfacerunoff

SURFACE STORAGE

k1 S1

k2 S2

Baseflow: kb Sb3

cmax

cmin

Groundwaterrecharge:kg (S - St)bg

Moisture Storage

Evaporation Rainfall

Runoff

River Flow

State correctionState correction

Soil Moisture

Sub-surfacestore

Surface store

RAIN

Surface runoff

Sub-surface runoff

Error in flowRouting

Adjust the stores in real-time to improve flow estimates

Can we adjust PDM soil moisture using ELDAS soil-moisture estimates ??

Hydrograph

State correctionState correction State correction is essentially a form of State correction is essentially a form of

negative feedbacknegative feedback

This feedback can sometimes give rise to an This feedback can sometimes give rise to an over- or under-shooting behaviour particularly over- or under-shooting behaviour particularly on the rising limb and peak of the flood on the rising limb and peak of the flood hydrograph.hydrograph.

Time lags can occur if soil moisture is adjusted Time lags can occur if soil moisture is adjusted in this way, as the correction may not affect in this way, as the correction may not affect runoff until the next wet period.runoff until the next wet period.

Use of ELDAS soil moistureUse of ELDAS soil moisture In current operational practice observed river flow is In current operational practice observed river flow is

used to update the surface and sub-surface water stores used to update the surface and sub-surface water stores of the flood model. of the flood model.

The moisture held in the soil store is generally left The moisture held in the soil store is generally left untouched because of the problems associated with time untouched because of the problems associated with time lags between observed flow and soil moisture. lags between observed flow and soil moisture.

However, it is possible that adjustment of the modelled However, it is possible that adjustment of the modelled soil moisture using information derived from ELDAS soil moisture using information derived from ELDAS could prove to be more robust, as the adjustment will be could prove to be more robust, as the adjustment will be to a model state (store) prior to runoff-production and to a model state (store) prior to runoff-production and flow-routing.flow-routing.

Programme of WorkProgramme of WorkThe approachThe approach will be trialled on a rainfall-runoff model used will be trialled on a rainfall-runoff model used

worldwide for operational flood forecasting (PDM):worldwide for operational flood forecasting (PDM):

1.1. The PDM will be calibrated with reference to flow observations The PDM will be calibrated with reference to flow observations from the Thames basin and run in simulation mode. Data from the from the Thames basin and run in simulation mode. Data from the Autumn 2000 floods will be used.Autumn 2000 floods will be used.

2.2. The time-series of modelled soil moisture from the PDM will be The time-series of modelled soil moisture from the PDM will be compared to soil moisture information from ELDAS models. compared to soil moisture information from ELDAS models.

3.3. Several methods to make this comparison, e.g comparing soil Several methods to make this comparison, e.g comparing soil moisture deficits after defining and calculating the field capacity of moisture deficits after defining and calculating the field capacity of a land-surface scheme, or comparing the volume of hydrologically a land-surface scheme, or comparing the volume of hydrologically active soil moisture in the landscape, will be tested. active soil moisture in the landscape, will be tested.

Programme of WorkProgramme of Work

5.5. Depending on the results of the comparison, soil moisture Depending on the results of the comparison, soil moisture information from ELDAS models will be incorporated in information from ELDAS models will be incorporated in the PDM rainfall-runoff model, and simulated river flow the PDM rainfall-runoff model, and simulated river flow will be compared to observations.will be compared to observations.

5.5. If time permits, the approach will also be trialled within a If time permits, the approach will also be trialled within a distributed rainfall-runoff model. distributed rainfall-runoff model. At present, most operational flow-forecasting systems employ At present, most operational flow-forecasting systems employ

“lumped” rainfall-runoff models. However, schemes such as the “lumped” rainfall-runoff models. However, schemes such as the EFFS are using a distributed approach to flood forecasting, so EFFS are using a distributed approach to flood forecasting, so the use of ELDAS soil moisture within a distributed modelling the use of ELDAS soil moisture within a distributed modelling framework is an important consideration.framework is an important consideration.

Case study area: Case study area: The Thames BasinThe Thames Basin

Area 12,917 km Area 12,917 km 22

Population ~12 millionPopulation ~12 million Landuse - a mixture of Landuse - a mixture of

rural areas and heavily rural areas and heavily urbanised areas such as urbanised areas such as London and ReadingLondon and Reading

Case study area: Case study area: The Thames BasinThe Thames Basin

180 km

140 km

London

Oxford

Reading

Case study detailsCase study detailsTarget Area and resolution:Target Area and resolution: Catchments in the Thames region - area of Catchments in the Thames region - area of

the order of 25000 kmthe order of 25000 km2 2

(180 km by 140 km)(180 km by 140 km)

Target Period:Target Period: August – November 2000August – November 2000

Required post-processing output:Required post-processing output: Spatial patterns of Spatial patterns of runoff runoff and and soil moisturesoil moisture

ELDAS Case Study 5100:ELDAS Case Study 5100:UK FloodingUK Flooding

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