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

    Wilby, Robert L. and Clifford, Nicolas J. and De Luca, Paola and Harrigan, Shaun and Hillier, John K. and Hodgkins, Richard and Johnson, Matthew F. and Matthews, Tom K.R. and Murphy, Conor and Noone, Simon and Parry, Simon and Prudhomme, Christel and Rice, Steve P. and Slater, Louise J. and Smith, Karen A. and Wood, Paul J. (2017) The ‘dirty dozen’ of freshwater science: detecting then reconciling hydrological data biases and errors. WIREs Water.

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    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: Article
    Keywords: ‘dirty dozen’; freshwater science; detecting; reconciling; hydrological data biases; errors;
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 8883
    Identification Number:
    Depositing User: Conor Murphy
    Date Deposited: 11 Oct 2017 13:34
    Journal or Publication Title: WIREs Water
    Publisher: Wiley
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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