Va. Bell et Rj. Moore, A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies, HYDROL E S, 2(2-3), 1998, pp. 283-298
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 Moor
e, 1998). The simple form of the basic model, referred to as the Simple Gri
d Model or SGM, allows a number of model variants to be introduced, includi
ng 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 Rhondd
a 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 o
f radar or raingauge input, (ii) choice of model variant, and (iii) use of
a lumped or distributed model formulation. Weather radar data, in grid squa
re and catchment average form, and raingauge data are used as alternative e
stimates of rainfall input to the model. Results show that when radar data
are of good quality, significant model improvement may be obtained by repla
cing data from a single raingauge by 2 km grid square radar data. The perfo
rmance of the Simple Grid Model with optimised isochrones is only marginall
y improved through the use of different model variants and is generally pre
ferred on account of its simplicity. A more traditional lumped rainfall-run
off 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 G
rid model proves more reliable for some storm and catchment combinations wh
ere spatial effects on runoff response are evident. Assessment is then carr
ied out in updating mode to emulate a real-time forecasting environment. Fi
rst, 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 simulati
on mode results. No one updating technique is superior, with the simulation
model formulation having greatest impact on forecast accuracy. However, wh
en the results from the different catchments are considered together i is a
pparent that in the rapidly responding Rhondda catchment state updating giv
es slightly better results, while in the slower responding Wyre and Mole ca
tchments, 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 influen
ce of rainfall input type, model variant and distributed versus lumped mode
l reflect the results obtained in simulation mode. Updating doesn't fully c
ompensate for a poor rainfall input or a deficient rainfall-runoff model fo
rmulation, especially for longer forecast lead times.