Stewart W. Franks

TL;DR: The IAHS Decade on Predictions in Ungauged Basins (PUB) as discussed by the authors is a new initiative launched by the International Association of Hydrological Sciences (IAHS) aimed at formulating and implementing appropriate science programmes to engage and energize the scientific community, in a coordinated manner, towards achieving major advances in the capacity to make predictions in ungauged basins.

TL;DR: In this article, the authors focus on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios, and highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall runoff data with large unknown errors.

TL;DR: An overview of the MOPEX experiment is presented and a key finding is that existing a priori parameter estimation procedures are problematic and need improvement.

TL;DR: In this article, a Bayesian total error analysis methodology for rainfall-runoff models is proposed, which allows the modeler to directly and transparently incorporate, test, and refine existing understanding of all sources of data.

TL;DR: The Bayesian total error analysis (BATEA) methodology directly addresses both input and output errors in hydrological modeling, requiring the modeler to make explicit, rather than implicit, assumptions about the likely extent of data uncertainty.