Benchmarking observational uncertainties for hydrology: Rainfall, river discharge and water quality

Hilary Mcmillan*, Tobias Krueger, Jim Freer

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

254 Citations (Scopus)


This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved 'culture of engagement' with observational uncertainties.

Original languageEnglish
Pages (from-to)4078-4111
Number of pages34
JournalHydrological Processes
Issue number26
Publication statusPublished - 30 Dec 2012


  • Data uncertainty
  • Error distributions
  • Hydrology
  • Hydrometric data
  • Observational uncertainty
  • Water quality data


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