Why the Provenance of Data Matters: Assessing Fitness for Purpose for Environmental Data

While fitness for purpose is the principle universally accepted among scientists as the correct approach to obtaining data of appropriate quality, many scientists or end-users of data are not in a position to specify exactly what quality of data are required for a specific analysis. Agencies that collect environmental observations provide data as is offering no guarantee or warranty concerning the accuracy of information contained in the data, in particular, no warranty either expressed or implied is made regarding the condition of the product or its fitness for any particular purpose. While the increasing implementation of ISO 9002 will benefit users in the future, the reality is that many of the existing databases generally contain data that were not gathered with present standards and protocols, or the same methods over the period of record. Usually, long-term records will contain observations that have been made with several different observation techniques, sometimes several locations, and frequently a progression of quality assurance and workup techniques, and these changes may not be well documented. While it is important that hydrometric and climate services focus on capturing data that are fit for their intended purpose, the burden for assessing the actual suitability for use lies entirely with the user. Some general principles for assessing fitness for purpose are proposed.

[1]  C. Chatfield Model uncertainty, data mining and statistical inference , 1995 .

[2]  Paul H. Whitfield,et al.  Designing monitoring programs for water quality based on experience in Canada I. Theory and framework , 2009 .

[3]  Rui Zou,et al.  Robust Water Quality Model Calibration Using an Alternating Fitness Genetic Algorithm , 2004 .

[4]  C. Shu,et al.  Regional low‐flow frequency analysis using single and ensemble artificial neural networks , 2009 .

[5]  Patrice M. Pelletier,et al.  Uncertainties in the single determination of river discharge: a literature review , 1988 .

[6]  Taha B. M. J. Ouarda,et al.  Intercomparison of homogenization techniques for precipitation data , 2008 .

[7]  John Ewen,et al.  Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses , 2004 .

[8]  Wolfgang Ludwig,et al.  Impact of recent climate change on the hydrology of coastal Mediterranean rivers in Southern France , 2010 .

[9]  T. Marsh,et al.  Capitalising on river flow data to meet changing national needs — a UK perspective , 2002 .

[10]  Robert Jeansoulin,et al.  ONTOLOGICAL APPROACH OF THE FITNESS OF USE OF GEOSPATIAL DATASETS , 2004 .

[11]  Sinan Sahin,et al.  Homogeneity analysis of Turkish meteorological data set , 2010 .

[12]  Roger Wood,et al.  Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling. , 2009, The Analyst.

[13]  John R. Dymond,et al.  Accuracy of discharge determined from a rating curve , 1982 .

[14]  Paul H. Whitfield,et al.  A Practical Model Integrating Quality Assurance Into Environmental Monitoring , 1993 .

[15]  Paul H. Whitfield,et al.  Estimates of Canadian Pacific Coast runoff from observed streamflow data , 2011 .

[16]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[17]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[18]  J. Wolfowitz,et al.  An Exact Test for Randomness in the Non-Parametric Case Based on Serial Correlation , 1943 .

[19]  Jane E. Klobas,et al.  Beyond information quality: fitness for purpose and electronic information resource use , 1995, J. Inf. Sci..

[20]  Tom Fearn,et al.  What exactly is fitness for purpose in analytical measurement , 1996 .

[21]  Anthony J. Jakeman,et al.  Good Modelling Practice , 2008 .

[22]  John Ewen,et al.  Validation of catchment models for predicting land-use and climate change impacts. 1. Method , 1996 .

[23]  David R. Easterling,et al.  Critical issues for long-term climate monitoring , 1995 .

[24]  R. Carney,et al.  Data management and accountability in behavioral and biomedical research. , 1992, The American psychologist.

[25]  W. D. Hogg,et al.  Continuity of climatological observations with automation ‐ temperature and precipitation amounts from AWOS (Automated Weather Observing System) , 2002 .

[26]  Blair Trewin,et al.  Exposure, instrumentation, and observing practice effects on land temperature measurements , 2010 .

[27]  David R. Easterling,et al.  Effects of Recent Thermometer Changes in the Cooperative Station Network , 1991 .

[28]  Ian Strangeways Using Google Earth to evaluate GCOS weather station sites , 2009 .

[29]  Paul H. Whitfield,et al.  CONFLICTING PERSPECTWES ABOUT DETECTION LIMITS AND ABOUT THE CENSORING OF ENVIRONMENTAL DATA1 , 1994 .

[30]  J. S. G. McCulloch,et al.  All our yesterdays: a hydrological retrospective , 2007 .

[31]  R. Forthofer,et al.  Rank Correlation Methods , 1981 .

[32]  B. Hawkins,et al.  A framework: , 2020, Harmful Interaction between the Living and the Dead in Greek Tragedy.

[33]  Michael Thompson,et al.  A decision theory approach to fitness for purpose in analytical measurement. , 2002, The Analyst.

[34]  Robin T. Clarke,et al.  On the (mis)use of statistical methods in hydro-climatological research , 2010 .

[35]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[36]  J. Metcalfe,et al.  Rainfall Measurement in Canada: Changing Observational Methodsand Archive Adjustment Procedures , 1997 .

[37]  Michael H. Ramsey,et al.  Modifying uncertainty from sampling to achieve fitness for purpose: a case study on nitrate in lettuce , 2007 .

[38]  Michael H. Ramsey,et al.  Uncertainty from sampling, in the context of fitness for purpose , 2007 .

[39]  Andrew S. Pullin,et al.  Data credibility: A perspective from systematic reviews in environmental management , 2009 .

[40]  Richard B. Lammers,et al.  Record Russian river discharge in 2007 and the limits of analysis , 2009 .

[41]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[42]  Kevin E Lansey,et al.  Filtering bad measurement data for water distribution system demand estimation. , 2010 .

[43]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[44]  Rj Howarth,et al.  A simple fitness-for-purpose control chart based on duplicate results obtained from routine test materials , 2002 .

[45]  Carole A Foy,et al.  Standardisation of data from real-time quantitative PCR methods – evaluation of outliers and comparison of calibration curves , 2005, BMC biotechnology.

[46]  Paul H. Whitfield,et al.  Assessing Detectability of Change in Low Flows in Future Climates from Stage Discharge Measurements , 2006 .

[47]  Steven C. Sherwood,et al.  Simultaneous Detection of Climate Change and Observing Biases in a Network with Incomplete Sampling , 2007 .

[48]  Michael Thompson,et al.  Recent trends in inter-laboratory precision at ppb and sub-ppb concentrations in relation to fitness for purpose criteria in proficiency testing , 2000 .

[49]  Ali Tokay,et al.  Comparison of Rain Gauge Measurements in the Mid-Atlantic Region , 2010 .

[50]  B. Endres-Niggemeyer,et al.  Beyond information quality , 1994 .

[51]  Francis W. Zwiers,et al.  Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes , 2005 .