ASSESSMENT OF HYDROLOGIC PARAMETER UNCERTAINTY AND THE WORTH OF MULTIRESPONSE DATA

Conceptual catchment models with more than four or five parameters cal ibrated to streamflow data often have poorly identified parameters. Th is study reassesses the role of the computationally efficient multinor mal approximation to parameter uncertainty and considers the worth of multiresponse data to improve identifiability. A case study involving the nine-parameter CATPRO model presents three findings. First, when a n overparameterized model is calibrated to streamflow data, the parame ter covariance matrix can help identify the poorly defined parameters and provide insight about the structural reasons for poor identifiabil ity. Second, when multiresponse data are available to calibrate the ca tchment model, the multinormal approximation may provide an adequate d escription of parameter uncertainty. Third, augmenting streamflow data with other response time series data may not reduce parameter uncerta inty. Augmenting streamflow data with groundwater level data did littl e to reduce the uncertainty in the poorly defined CATPRO parameters, w hereas augmenting with stream salinity data substantially reduced para meter uncertainty. This suggests the worth of multiresponse data shoul d, where possible, be assessed a priori.