In many regions where rainfall runoff models are required, there is a lack of streamflowdata available to calibrate the model parameters. Along with streamflow data simply notbeing recorded, there are many reasons for a lack of a suitable data set for modelcalibration, such as significant modifications to catchment characteristics, or longperiods of unseasonable rainfall producing unrepresentative relationships. Generally, foran ungauged catchment, it is desirable to implement a model with as few free parametersas possible, provided the conceptualization of the model is suitable for the catchmentunder consideration. The Australian Water Balance Model (AWBM) is a rainfall runoffmodel that is commonly used in Australia. Typically it has 7 parameters, howevermethods are available to determine the storage size and capacity parameters for theAWBM based on an estimate of the average annual runoff. This is a great advantagewhen applying the model to ungauged catchments. However, there are two AWBMparameters which cannot be determined by this approach, namely the baseflow indexand baseflow recession constant. The aim of this paper is to develop regionalizationrelationships to allow these two parameters to be estimated for ungauged catchments,based on the characteristics of the catchment that can be easily identified. GeneralRegression Neural Networks are used to identify the relationships between modelparameters and catchment characteristics. The results indicate that by using only easilyidentifiable characteristics of an ungauged catchment, suitable estimates of the unknownAWBM parameter values can be obtained, thereby allowing reasonable rainfall-runoffmodels to be developed. While the relationships developed in this work are specific toAustralian catchments, the methodology used can be easily adapted to developrelationships for other regions.
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