Relating BTOPMC model parameters to physical features of MOPEX basins

This paper focuses on parameter regionalization for the block-wise use of the TOPMODEL with the Muskingum–Cunge routing method (BTOPMC). Pre-determined quantitative relationships with physical basin characteristics are used to reduce uncertainty in parameter estimation and enable prediction in ungauged basins. BTOPMC parameter values, which have previously resulted in efficient model performance, are applied to ten Model Parameter Estimation Experiment (MOPEX) basins without any parameter adjustment to test model performance using ‘blind’ a priori parameter estimates. It is quantitatively shown that parameters can not be satisfactorily estimated a priori without parameter adjustment, either through calibration or transfer functions linking parameters to physical basin features, referred to as ‘improved’ a priori parameter estimation. To compare model performance using different parameter estimation techniques, BTOPMC parameters are re-estimated by automatic optimization. As anticipated, model performance improves significantly when these optimized parameters are used. Good correlation of model performance is also observed between the 19-year validation and 20-year calibration results when optimized parameters are used. This indicates that BTOPMCʹs predictive accuracy depends strongly on calibration accuracy, implying that well-optimized parameter values can ensure the accuracy of model predictions. It appears reasonable to use optimized parameter values, selected from successful modelling applications, to make transfer functions which ‘improve’ a priori parameter estimates. The ‘improved’ a priori parameter estimation is achieved through multiple linear regression analysis relating BTOPMC parameters to soil and vegetation types within the basin being modelled. The results suggest that the preliminary parameter transfer functions are encouraging and that ‘improved’ a priori parameter estimation techniques have the potential to reduce parameter uncertainty and enable prediction in ungauged basins. To improve parameter estimation and predictive accuracy of the model, further research to determine more accurate values, and their relationship with physical basin characteristics, is needed.