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

Wilby, Robert L.; Clifford, Nicholas J.; De Luca, Paolo; Harrigan, Shaun; Hillier, John K.; Hodgkins, Richard; Johnson, Matthew F.; Matthews, Tom K.R.; Murphy, Conor; Noone, Simon J.; Parry, Simon; Prudhomme, Christel; Rice, Steve P.; Slater, Louise J.; Smith, Katie A. ORCID: https://orcid.org/0000-0003-1060-9103; Wood, Paul J.. 2017 The 'dirty dozen' of freshwater science: detecting then reconciling hydrological data biases and errors. Wiley Interdisciplinary Reviews: Water, 4 (3), e1209. 19, pp. https://doi.org/10.1002/wat2.1209

Before downloading, please read NORA policies.

Preview

Text N516014JA.pdf - Published Version Available under License Creative Commons Attribution Non-commercial 4.0. Download (2MB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/wat2.1209
UKCEH and CEH Sections/Science Areas:	Rees (from October 2014)
ISSN:	2049-1948
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Hydrology
Date made live:	27 Mar 2017 09:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/516014

Actions (login required)

View Item

Document Downloads

More statistics for this item...