1. ELDAS Case Study 5100: UK Flooding
Eleanor Blyth
Vicky Bell
Bob Moore
(CEH Wallingford, UK)

2. Case Study Objective
Case Study Objective: To quantify the added value to Flood Forecasting that ELDAS soil moisture could make
The question:
How can we use ELDAS soil moisture to improve flood forecasting ?

3. Operational flood forecasting
The most common methods of data assimilation in flood forecasting models involve the use of observed flow data to adjust the hydrological model states in real-time (state correction), or to make predictions of future errors and account for them (error prediction).
The former is heavily dependent on the structure of the simulation model, whilst the latter is essentially independent of it.
A model incorporating observed flows through state-correction, error-prediction or some other scheme is said to be operating in updating mode.

4. Use of river flow to update (or ‘nudge’) the surface and sub-surface stores
When an error,  =Q-q, occurs between model flow, q, and observed flow, Q, one can “attribute the blame” to mis-specification of the state variables (water stores)
We can then “correct” the stores to achieve agreement between observed and modelled flow.
Mis-specification may, for example, have arisen through errors in rainfall measurement which propagate through the values of the store water contents, or the flow rates out of the stores.

5. The PDM Probability Distributed Model
INPUT
Rainfall
Potential Evaporation
OUTPUT
Runoff
SURFACE STORAGE
Surface
runoff
Moisture storage
Groundwater
recharge
SUBSURFACE
STORAGE
River
Flow
Baseflow
The standard PDM uses
a Pareto distribution
of moisture stores..

6. Detail of the PDM cmax cmin Evaporation Rainfall Runoff
SUBSURFACE
STORAGE
Surface
runoff
SURFACE STORAGE
k1 S1
k2 S2
Baseflow: kb Sb3
cmax
cmin
Groundwater
recharge:
kg (S - St)bg
Moisture Storage
Evaporation
Rainfall
Runoff
River
Flow

7. State correction Surface store Soil Moisture Sub-surface store
RAIN
Surface store
Hydrograph
Soil Moisture
Surface runoff
Error in flow
Routing
Sub-surface store
Adjust the stores in real-time to improve flow estimates
Sub-surface runoff
Can we adjust PDM soil moisture using ELDAS soil-moisture estimates ??

8. State correction
State correction is essentially a form of negative feedback
This feedback can sometimes give rise to an over- or under-shooting behaviour particularly on the rising limb and peak of the flood hydrograph.
Time lags can occur if soil moisture is adjusted in this way, as the correction may not affect runoff until the next wet period.

9. Use of ELDAS soil moisture
In current operational practice observed river flow is used to update the surface and sub-surface water stores of the flood model.
The moisture held in the soil store is generally left untouched because of the problems associated with time lags between observed flow and soil moisture.
However, it is possible that adjustment of the modelled soil moisture using information derived from ELDAS could prove to be more robust, as the adjustment will be to a model state (store) prior to runoff-production and flow-routing.

10. Programme of Work
The approach will be trialled on a rainfall-runoff model used worldwide for operational flood forecasting (PDM):
The PDM will be calibrated with reference to flow observations from the Thames basin and run in simulation mode. Data from the Autumn 2000 floods will be used.
The time-series of modelled soil moisture from the PDM will be compared to soil moisture information from ELDAS models.
Several methods to make this comparison, e.g comparing soil moisture deficits after defining and calculating the field capacity of a land-surface scheme, or comparing the volume of hydrologically active soil moisture in the landscape, will be tested.

11. Programme of Work
Depending on the results of the comparison, soil moisture information from ELDAS models will be incorporated in the PDM rainfall-runoff model, and simulated river flow will be compared to observations.
If time permits, the approach will also be trialled within a distributed rainfall-runoff model.
At present, most operational flow-forecasting systems employ “lumped” rainfall-runoff models. However, schemes such as the EFFS are using a distributed approach to flood forecasting, so the use of ELDAS soil moisture within a distributed modelling framework is an important consideration.

12. Case study area: The Thames Basin
Area 12,917 km 2
Population ~12 million
Landuse - a mixture of rural areas and heavily urbanised areas such as London and Reading

13. Case study area: The Thames Basin
London
Oxford
Reading
140 km
180 km

14. Case study details Target Area and resolution: Target Period:
Catchments in the Thames region - area of the order of km2 (180 km by 140 km)
Target Period:
August – November 2000
Required post-processing output:
Spatial patterns of runoff and soil moisture

15. ELDAS Case Study 5100: UK Flooding
The END