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

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 d...

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Bibliographic Details

Main Authors:	Robert Wilby, Nicholas Clifford, Paolo De Luca, Shaun Harrigan, John Hillier, Richard Hodgkins, Matthew F. Johnson, Tom K.R. Matthews, Conor Murphy, Simon Noone, Simon Parry, Christel Prudhomme, Stephen Rice, Louise Slater, Katie Smith, Paul Wood
Format:	Default Article
Published:	2017
Subjects:	Other earth sciences not elsewhere classified Data biases Exploratory data analysis Detection Attribution Hydrological change Homogeneity Earth Sciences not elsewhere classified
Online Access:	https://hdl.handle.net/2134/24454
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