Volume 4, Issue 3 e1209
Overview

The ‘dirty dozen’ of freshwater science: detecting then reconciling hydrological data biases and errors

Robert L. Wilby

Corresponding Author

Robert L. Wilby

Department of Geography, Loughborough University, Loughborough, UK

Correspondence to: [email protected]Search for more papers by this author
Nicholas J. Clifford

Nicholas J. Clifford

School of Social, Political and Geographical Sciences, Loughborough University, Loughborough, UK

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Paolo De Luca

Paolo De Luca

Department of Geography, Loughborough University, Loughborough, UK

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Shaun Harrigan

Shaun Harrigan

Centre for Ecology & Hydrology, Wallingford, UK

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John K. Hillier

John K. Hillier

Department of Geography, Loughborough University, Loughborough, UK

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Richard Hodgkins

Richard Hodgkins

Department of Geography, Loughborough University, Loughborough, UK

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Matthew F. Johnson

Matthew F. Johnson

School of Geography, University of Nottingham, Nottingham, UK

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Tom K.R. Matthews

Tom K.R. Matthews

Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK

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Conor Murphy

Conor Murphy

Department of Geography, Maynooth University, County Kildare, Ireland

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Simon J. Noone

Simon J. Noone

Department of Geography, Maynooth University, County Kildare, Ireland

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Simon Parry

Simon Parry

Centre for Ecology & Hydrology, Wallingford, UK

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Christel Prudhomme

Christel Prudhomme

Centre for Ecology & Hydrology, Wallingford, UK

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Steve P. Rice

Steve P. Rice

Department of Geography, Loughborough University, Loughborough, UK

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Louise J. Slater

Louise J. Slater

Department of Geography, Loughborough University, Loughborough, UK

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Katie A. Smith

Katie A. Smith

Centre for Ecology & Hydrology, Wallingford, UK

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Paul J. Wood

Paul J. Wood

Department of Geography, Loughborough University, Loughborough, UK

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First published: 24 March 2017
Citations: 45
Conflict of interest: The authors have declared no conflicts of interest for this article.

Abstract

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected, or erroneously archived data introduce uncertainty regarding the magnitude, rate, and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/measurement procedures, postprocessing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation, and comparison between sites, outlier, and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation, or urbanization); modifications to instrumentation or inconsistent observer behavior; mismatched or misrepresentative sampling in space and time; treatment of missing values, postprocessing and data storage errors. Also for raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data. WIREs Water 2017, 4:e1209. doi: 10.1002/wat2.1209

This article is categorized under:

  • Science of Water > Methods
  • Science of Water > Water and Environmental Change

Graphical Abstract

Metadata for site changes is one way of explaining anomalous behavior within the information flows needed for high-quality water management.