The rainfall input is one of the main factors influencing the magnitude of the runoff response during a flood event. Its temporal and spatial distribution significantly contributes to the formation of hydrograph shape, peak discharge and flood volume. a novel approach to the evaluation of the role of the temporal rainfall pattern of hydrograph is presented in this contribution. The methodology ...

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-param...

The paper presents a nonlinear heteroscedastic model for flow forecasting. The model is composed of two submodels: the former provides the expected value of the flow, conditional on available information, e.g. past flow and precipitation records; the latter provides the variance of the prediction error as a function of past values of the prediction error itself and precipitation measures. The p...

This paper presents a distributed model that is in operational use for forecasting flash floods in northern Austria. The main challenge in developing the model was parameter identification which was addressed by a modelling strategy that involved a model structure defined at the model element scale and multi-source model identification. The model represents runoff generation on a grid basis and...

This paper reviews non-probabilistic approaches of modelling uncertainty, particularly in flood forecasting and introduces a fuzzy set theory-based method for treating precipitation uncertainty in rainfall-runoff modelling, which allows the temporal and/or spatial disaggregation of precipitation. The results of the fuzzy set theory-based method are compared with the probabilistic approach using...

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. Th...

Tri-yearly recurrence of flooding in Malaysia has made floods the most important significant natural disaster in the country in terms of cost and damage to property. Many hydrological and hydraulic flood models have been implemented but are yet to meet the requirement of a near real-time flood forecasting. This study envisage the “Nowcasting System” through the operational coupling of quantitat...

It will be useful to attain a quick and accurate flood forecasting, particularly in a flood-prone region. The accomplishment of this objective can have far reaching significance by extending the lead time for issuing disaster warnings and furnishing ample time for citizens in vulnerable areas to take appropriate action, such as evacuation. In this paper, a novel hybrid model based on recent art...

Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caus...