Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)

Refsgaard, Jens Christian; Nilsson, Bertel; Brown, James Dean; Klauer, Bernd; Moore, Roger; Bech, Thomas; Vurro, Michele; Blind, M.; Castilla, Guillermo; Tsanis, Ioannis K.; Biza, Pavel

doi:10.1016/j.envsci.2005.02.001

Cited by 29 publications

(20 citation statements)

References 11 publications

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“…Uncertainty analysis in environmental modelling can provide information on the 'goodness' of a result and enables us to pin down the sources of error in the modelling process, their relative importance when propagated to the model outputs, and consequently the priorities for model improvement (Willems, 2008). Furthermore, without uncertainty quantification, complementary to a measure of the goodness of a result, it is difficult to judge the reliability of the results regarding the value of the impact for water engineering decision support (Reichert, 1997;Refsgaard et al, 2005;Schaarup-Jensen et al, 2005;Krysanova et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Freni

Mannina

2010

Journal of Hydrology

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Keywords:Bayesian approach Prior knowledge Uncertainty assessment Urban stormwater quality modelling s u m m a r yMathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the ''a priori" hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in ''a priori" distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases.

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Section: Introductionmentioning

confidence: 99%

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Freni

Mannina

2010

Journal of Hydrology

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“…The HarmoniRiB framework application is briefly outlined in Sect. 3.3, while Refsgaard et al (2005) present further details about the HarmoniRiB project.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in geological and hydrogeological data

Nilsson

Højberg

Refsgaard

et al. 2007

Hydrol. Earth Syst. Sci.

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Abstract. Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification.

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“…For example, uncertainty guidance documents have resulted from research programmes developed for addressing uncertainty in the Water Framework Directive (WFD) implementation process (Refsgaard et al 2005a. Various sources of uncertainty in this process can be characterised for IWRM in general (van der Keur et al 2008) and for WFD Water Framework Directive (WFD, Directive 2000/60/EC) implementation process in particular, e.g.…”

Section: Uncertainty Guidelinesmentioning

confidence: 99%

“…Various sources of uncertainty in this process can be characterised for IWRM in general (van der Keur et al 2008) and for WFD Water Framework Directive (WFD, Directive 2000/60/EC) implementation process in particular, e.g. with respect to modelling (Refsgaard et al 2005a and the role of public participation (Newig et al 2005;Henriksen et al 2009). A Common Implementation Strategy (CIS) has been agreed on for implementation of the WFD in member states.…”

Section: Uncertainty Guidelinesmentioning

confidence: 99%

“…Refsgaard et al (2005a) and van Loon and Refsgaard (2005) developed methodologies for quantifying uncertainty for management information (http:// harmonirib.geus.info/). They established a practical methodology and a set of tools for assessing and describing uncertainty originating from data and models used in decision making processes for the production of integrated water management plans.…”

Section: Domain Specific Uncertainty Guidelinesmentioning

confidence: 99%

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Identifying Uncertainty Guidelines for Supporting Policy Making in Water Management Illustrated for Upper Guadiana and Rhine Basins

Keur

Brugnach

Dewulf

et al. 2010

Water Resour Manage

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In recent years, guidelines have been developed for supporting water managers in dealing with uncertainty in integrated water resources management (IWRM). Usually such guidelines have concentrated on certain aspects of processes in IWRM, notably on uncertainty associated with the modelling process and monitoring data. While this is of undisputed importance for supporting water managers in making well balanced and informed decisions, less attention has been paid to guiding policy makers in where uncertainty may emerge when considering the whole

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Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)

Cited by 29 publications

References 11 publications

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Uncertainty in geological and hydrogeological data

Identifying Uncertainty Guidelines for Supporting Policy Making in Water Management Illustrated for Upper Guadiana and Rhine Basins

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