Rainfall characteristics define the value of streamflow observations for distributed watershed model identification

We present evidence that the characteristics of rainfall events strongly control the value of streamflow observations for the identification of distributed watershed models. A series of synthetic rainfall events with different spatio-temporal extents and dynamics are used to investigate spatially-distributed global parameter sensitivities for a typical watershed model. The model's parametric sensitivities vary greatly with rainfall distribution characteristics, location of the model cell in relation to the watershed's gauged outlet, and, to a lesser degree, the initial model states. This study demonstrates that the information content of streamflow is a dynamic property and that distributed model identification methodologies should consider the impact of spatio-temporal rainfall dynamics.