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

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 ?

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.

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.

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..

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

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 ??

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.

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.

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.

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.

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

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

Case study details Target Area and resolution: Target Period: Catchments in the Thames region - area of the order of 25000 km2 (180 km by 140 km) Target Period: August – November 2000 Required post-processing output: Spatial patterns of runoff and soil moisture

ELDAS Case Study 5100: UK Flooding The END