Battling hydrological monsters: Distinguishing between data uncertainty, 
structural errors and numerical artifacts in rainfall-runoff modelling

Dmitri Kavetski, Benjamin Renard, Martyn P. Clark, Fabrizio Fenicia, 
Mark Thyer and George Kuczera

Confronted with frequently poor model performance, rainfall-runoff 
modellers have in the past blamed a  plethora of sources of uncertainty, 
including rainfall and runoff errors, non-Gaussianities, model 
nonlinearities, parameter uncertainty, and just about everything else 
from Pandorra's box. Moreover, recent work has suggested astonishing 
numerical artifacts may arise from poor model numerics and confound the 
Hydrologist. In our opinion, progress in terms of understanding of 
catchment dynamics and, when possible, reducing predictive errors, 
requires disentangling individual sources of error. Here, we present an 
overview of how might this be accomplished. First, we outline robust and 
efficient numerical error control methods to avoid unnecessary numerical 
artifacts. We then demonstrate that the formidable interaction between 
data and structural errors, irresolvable in the absence of independent 
knowledge of data accuracy, can be approached using statistical analysis 
of rainfall gauge networks and rating curve records. Structural model 
deficiencies, which we consider the key unresolved challenge, can then 
be explored using flexible model configurations, paving the way for more 
meaningful model comparison and improvement. Importantly, informative 
diagnostic measures are available for each component of the analysis. 
This paper surveys several recent developments along these research 
directions, summarized in a series of real-data case studies, and 
indicates areas of ongoing and future interest.