Methodologies for uncertainty assessment

Data Uncertainty

Description

Uncertainty in data may be described in 13 uncertainty categories depending on how data varies in time and space (Brown, 2004; Brown et al., 2005).

Each data category is associated with a range of uncertainty models, for which more specific probability density functions (pdfs) may be developed with different simplifying assumptions (e.g. Gaussian; second-order stationarity; degree of temporal and spatial autocorrelation). Furthermore, correlation in time and space is characterised by correlogram/variogram functions. Categorical data (3) differ from numerical data (1, 2), because the categories are not measured on a numerical scale.

A software tool for supporting the assessment of data uncertainty within the above framework is being developed within the EU FP5 project HarmoniRiB. This tool will become publicly available by 2006.

Resources required

Assessment of uncertainty requires a basic understanding of uncertainty concepts such as probability distribution and correlogram functions and their relation to the scale of measurement.

Uncertainty assessment deduced from specific information on the individual sources of uncertainty or from data analysis is a laborious and difficult task requiring substantial resources. This approach requires a specific knowledge on the various sources of uncertainty such as instrument accuracy, transformation functions from variable actually measured (e.g. water table) to variable of interest (e.g. discharge), aggregation in time and/or space, representativeness of sampling, etc.

Assessments based on expert judgements and literature values from similar settings are often the only feasible way in practice. Such assessments may be supported by guidelines for assessing uncertainty in various types of data originating from meteorology, soil physics and geochemistry, hydrogeology, land cover, topography, discharge, surface water quality, ecology and socio-economics (Van Loon and Refsgaard, 2005). This report has been prepared on the basis of literature reviews.

Strengths and limitations

+         Important input when assessing uncertainty of model output

+         Useful feedback information to design of monitoring programmes

-         May require a lot of work

-         Complex issue with many possibilities to make theoretically inconsistent assessments. Especially the correlation structure and its link with the scale of support may be difficult to understand

References

http://www.harmonirib.com

http://www.swift-wfd.com

Brown JD (2004) Knowledge, uncertainty and physical geography: towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers, 29(3), 367-381.

Brown JD, Heuvelink GBM and Refsgaard JC (2005) An integrated framework for assessing and recording uncertainties about environmental data. Accepted for publication in Water Science and Technology.

Refsgaard JC, Nilsson B, Brown J, Klauer B, Moore R, Bech T, Vurro M, Blind M, Castilla G, Tsanis I and Biza P (2005) Harmonised Techniques and Representative River Basin Data for Assessment and Use of Uncertainty Information in Integrated Water Management (HarmoniRiB). Accepted for publication in Environmental Science and Policy.

Van Loon E and Refsgaard JC (eds.) (2005) Guidelines for assessing data uncertainty in hydrological studies. First draft version prepared September 2004. Final version to be published beginning of 2005 on http://www.harmonirib.com.