  Understanding dynamics of hydrological responses is essential in producing
skillful runoff forecast. This can be quantitatively done by tracking changes
in hydrology model parameters that represent physical characteristics. In this
study, we implement a Bayesian estimation method in continuously estimating
hydrology model parameters given observations of rainfall and runoff for small
watersheds. The method is coupled with a conceptual hydrology model using a
Gamma distribution-based Instantaneous Unit Hydrograph. The whole analytical
framework is tested using synthetic data as well as observational data from the
Fall Creek watershed. The results show that the Bayesian method can well track
the hidden parameters that change inter-annually. Then the model is applied to
examine temporal and spatial variability of the rainfall-runoff responses and
we find 1) a systematic shift in the rainfall-runoff response for the Fall
Creek watershed around 1943 and 2) a statistically significant relationship
between rainfall-runoff responses and watershed sizes for selected NY
watersheds. Our results demonstrate potential of the Bayesian estimation method
as a rapid surveillance tool in monitoring and tracking changes of hydrological
responses for small watersheds.
