Uncertainty analysis at large scales: limitations and subjectivity of current practices - a water quality case study

Uncertainty analysis for large-scale model studies is a challenging activity that requires a different approach to uncertainty analysis at a smaller scale. However, in river basin studies, the practice of uncertainty analysis at a large scale is mostly derived from practice at a small scale. The lim...

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Published inWater science and technology Vol. 56; no. 6; pp. 1 - 9
Main Authors Bijlsma, R.M, Groenendijk, P, Blind, M.W, Hoekstra, A.Y
Format Journal Article
LanguageEnglish
Published England IWA Publishing 01.01.2007
Subjects
Alterra - Centre for Water and Climate
Alterra - Centrum Water en Klimaat
ALTERRA Wageningen UR
basins
Bias
Case studies
Decision making
estimation
Fertilizers
Fresh Water - analysis
Freshwater
hydrologic models
Livestock
model validation
Models, Theoretical
Nitrogen
Nutrient concentrations
nutrients
Phosphorus
Risk Assessment - methods
Risk Assessment - statistics & numerical data
River basins
river water
Rivers
Sensitivity analysis
simulation models
Subjectivity
Surface water
Uncertainty
Uncertainty analysis
Validity
Wageningen Environmental Research
water pollution
Water Pollution - analysis
Water quality
Netherlands
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Summary:Uncertainty analysis for large-scale model studies is a challenging activity that requires a different approach to uncertainty analysis at a smaller scale. However, in river basin studies, the practice of uncertainty analysis at a large scale is mostly derived from practice at a small scale. The limitations and inherent subjectivity of some current practices and assumptions are identified, based on the results of a quantitative uncertainty analysis exploring the effects of input data and parameter uncertainty on surface water nutrient concentration. We show that: (i) although the results from small- scale sensitivity analysis are often applied at larger scales, this is not always valid; (ii) the current restriction of the uncertainty assessment to uncertainty types with a strong evidence base gives structurally conservative estimates; (iii) uncertainty due to bias is usually not assessed, but it may easily outweigh the effects of variability; (iv) the uncertainty bandwidth may increase for higher aggregation levels, although the opposite is the standard assumption.
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ISSN:0273-1223
1996-9732
DOI:10.2166/wst.2007.580