A bootstrap approach to assess parameter uncertainty in simple catchment models

Catchment models simulate water and solute dynamics at catchment scales and are invaluable tools for natural resource management. Parameters for catchment models can provide useful information about the importance of the hydrological processes involved. We propose and demonstrate a bootstrap approach to assess parameter uncertainty in dynamic catchment models. This approach, which is non-Bayesian and essentially non-parametric, requires no distributional assumptions about parameters and only weak assumptions about the distributional form of the model residuals. It is able to handle autocorrelated model errors which are very common in the application of dynamic hydrological models at catchment scales. The ability of our bootstrap approach to assess parameter uncertainty is demonstrated using numerical experiments with the abc hydrological model and an application of a conceptual model of salt load from an irrigated catchment in southeastern Australia.