An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment

This study evaluates two (of the many) modelling approaches to flood forecasting for an upland catchment (the River South Tyne at Haydon Bridge, England). The first modelling approach utilizes 'traditional' hydrological models. It consists of a rainfall-runoff model (the probability distributed model, or PDM) for flow simulation in the upper catchment. Those flows are then routed to the lower catchment using two kinematic wave (KW) routing models. When run in forecast-mode, the PDM and KW models utilize model updating procedures. The second modelling approach uses neural network models, which use a 'pattern-matching' process to produce model forecasts. Following calibration, the models are evaluated in terms of their fit to continuous stage data and flood event magnitudes and timings within a validation period. Forecast times of I h, 2 It and 4 h are selected (the catchment has a response time of approximately 4 h). The 'traditional' models generally perform adequately at all three forecast times. The neural networks produce reasonable forecasts of small- to medium-sized flood events but have difficulty in forecasting the magnitude of the larger flood events in the validation period. Possible modifications to the latter approach are discussed. (C) Crown copyright 2002. Reproduced with the permission of Her Majesty's stationery office. Published by John Wiley Sons, Ltd.