A dynamic approach to the identification of conceptual hydrological models

Conceptual hydrological modeling requires the identification of a suitable model structure and the estimation of parameter values through calibration against a record of historical data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modeling procedures. This presentation introduces an innovative approach to analyze the performance of a model in a dynamic fashion resulting in an improved use of available information. This approach is an attempt to avoid the loss of information through aggregation of the model residuals in time and over a range of hydrological behavior. Model structures can be evaluated with respect to the failure of individual components in a hypothesis-testing framework, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied in a case study to a model structure consisting of typical conceptual components.