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The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Wilby, RLW and Clifford, NJ and De Luca, P and Harrigan, S and Hillier, J and Hodgkins, R and Matthews, TR and Johnson, MF and Murphy, C and Noone, S and Parry, S and Prudhomme, C and Rice, S and Slater, L and Smith, KA and Wood, P The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors. Wiley Interdisciplinary Reviews. ISSN 1939-5078 (Accepted)

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Abstract

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces 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, post-processing 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 urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as 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: Article
Uncontrolled Keywords: 1702 Cognitive Science
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions: Natural Sciences and Psychology
Publisher: Wiley
Date Deposited: 21 Feb 2017 09:41
Last Modified: 12 Sep 2017 04:09
URI: http://researchonline.ljmu.ac.uk/id/eprint/5641

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