The parsimonious model structures of semi
-empirical conceptual
watershed models (e.g., SPARROW,
GREEN) offer
considerable advantages over physically
-based watershed models to incorporate stream
water monitoring data and
effectively accommodate rigorous error analysis. Nonetheless, even these model
structures demonstr
ate intrinsic
equifinality problems due to multicollinearity of model parameters. Bayesian
inference techniques offer a robust and
formal statistical calibration methodology to address model
equifinality issues with watershed inverse analysis. In our
presentation, we first summarise known case-
studies of Bayesian inference implementation for semi
-empirical
watershed models (USA, Canada, China).
We then provide an overview of relevant Bayesian statistical formulations to
explicitly consider watershed
spatial heterogeneity, serial correlation, and inter
- and intra-
annual dynamics. Finally,
we outline the
strengths and weaknesses of inverse watershed models within a Bayesian inference context for
recursive
calibration and data assimilation, including hotspot identification, loading source apportionment,
representation of legacy nutrients, and quantification of all major sources of uncertaint