A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies

A simple distributed rainfall-runoff model, configured on a square grid to make best use of weather radar data, was developed in Part 1 (Bell and Moore, 1998). The simple form of the basic model, referred to as the Simple Grid Model or SGM, allows a number of model variants to be introduced, including probability-distributed storage and topographic index representations of runoff production and formulations which use soil survey and land use data. These models are evaluated here on three catchments in the UK: the Rhondda in south Wales, the Wyre in north-west England and the Mole in the Thames Basin near London. Assessment is initially carried out in simulation mode to focus on the conversion of rainfall to runoff as influenced by (i) use of radar or raingauge input, (ii) choice of model variant, and (iii) use of a lumped or distributed model formulation. Weather radar data, in grid square and catchment average form, and raingauge data are used as alternative estimates of rainfall input to the model. Results show that when radar data are of good quality, significant model improvement may be obtained by replacing data from a single raingauge by 2 km grid square radar data. The performance of the Simple Grid Model with optimised isochrones is only marginally improved through the use of different model variants and is generally preferred on account of its simplicity. A more traditional lumped rainfall-runoff model, the Probability-Distributed Moisture model or PDM, is used as a benchmark against which to assess the performance of the distributed models. This proves hard to better, although the distributed formulation of the Grid model proves more reliable for some storm and catchment combinations where spatial effects on runoff response are evident. Assessment is then carried out in updating mode to emulate a real-time forecasting environment. First, a state updating form of the Grid Model is developed and then assessed against an ARMA error-prediction technique. Both state updating and error prediction give much improved model performance when compared with simulation mode results. No one updating technique is superior, with the simulation model formulation having greatest impact on forecast accuracy. However, when the results from the different catchments are considered together i is apparent that in the rapidly responding Rhondda catchment state updating gives slightly better results, while in the slower responding Wyre and Mole catchments, error prediction is slightly superior. This is attributed to the greater difficulty of reliably adjusting states when there are significant time delays associated with the catchment response. In general, the influence of rainfall input type, model variant and distributed versus lumped model reflect the results obtained in simulation mode. Updating doesn't fully compensate for a poor rainfall input or a deficient rainfall-runoff model formulation, especially for longer forecast lead times.