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Effects of conditional parameterization on performance of rainfall-runoff model regarding hydrologic non-stationarity


Enli Wang, CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, GPO Box 1666, Canberra ACT 2601, Australia.



Better parameterization of a hydrological model can lead to improved streamflow prediction. This is particularly important for seasonal streamflow forecasting with the use of hydrological modelling. Considering the possible effects of hydrologic non-stationarity, this paper examined ten parameterization schemes at 12 catchments located in three different climatic zones in east Australia. These schemes are grouped into four categories according to the period when the data are used for model calibration, i.e. calibration using data: (1) from a fixed period in the historical records; (2) from different lengths of historical records prior to prediction year; (3) from different climatic analogue years in the past; and (4) data from the individual months. Parameterization schemes were evaluated according to model efficiency in both the calibration and verification period. The results show that the calibration skill changes with the different historic periods when data are used at all catchments. Comparison of model performance between the calibration schemes indicates that it is worth calibrating the model with the use of data from each individual month for the purpose of seasonal streamflow forecasting. For the catchments in the winter-dominant rainfall region of south-east Australia, a more significant shift in rainfall-runoff relationships at different periods was found. For those catchments, model calibration with the use of 20 years of data prior to the prediction year leads to a more consistent performance. Copyright © 2011 John Wiley & Sons, Ltd.