From HUP to MCP: Analogies and extended performances

This paper discusses the analogies and the performances of two uncertainty post-processors, the Hydrologic Uncertainty Processor (HUP) introduced by and the Model Conditional Processor (MCP), which was proposed by for the assessment of predictive uncertainty, as an alternative to HUP. The paper shows analytically and through a numerical example that the two uncertainty processors are strongly related and explains why MPC results into improved performances with respect to the HUP, when used with the same level of information. The simplicity of its derivation, the extended capabilities of MCP at tackling multi-predictand, multi-site, multi-model, multi-time problems together with the possible use of Truncated Normal Distributions in order to overcome heteroscedasticity in the residuals make MCP more easily applicable than HUP to describe predictive uncertainty in real time flood forecasting applications.