Identification and classification of uncertainties in the application of environmental models

Warmink, Jord Jurriaan; Janssen, Judith; Booij, Martijn J.; Krol, Martinus S.

doi:10.1016/j.envsoft.2010.04.011

Cited by 138 publications

(93 citation statements)

References 35 publications

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“…However, runoff beyond the forecast horizon T hor is also uncertain (Figure 2b), and in addition it will influence the optimal control actions because the emptying times of the detention basins will most often be longer than the available runoff forecast horizon. At the present stage, uncertainty in runoff beyond T hor is not quantified (according to the uncertainty level classification presented by Warmink et al (2010), T hor represents the limits for qualitative uncertainty). DORA accounts for this qualitative uncertainty by using the discount term C hor,i in Eq.…”

Section: Discount Functionmentioning

confidence: 99%

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Vezzaro

Grum

2014

Journal of Hydrology

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An innovative and generalised approach to the integrated real time control of urban drainage systems is presented. The Dynamic Overflow Risk Assessment (DORA) strategy aims to minimise the expected Combined Sewer Overflow (CSO) risk by considering (i) the water volume presently stored in the drainage network, (ii) the expected runoff volume (calculated by radar-based nowcast models) and -most important -(iii) the estimated uncertainty of the runoff forecasts. The inclusion of uncertainty allows for a more confident use of Real Time Control (RTC). Overflow risk is calculated by a flexible function which allows for the prioritisation of the discharge points according to their sensitivity and intended use. DORA was tested on a hypothetical example inspired by the main catchment in the city of Aarhus (Denmark). An analysis of DORA's performance over a range of events with different return periods, using a simple conceptual model, is presented. Compared to a traditional local control approach, DORA contributed to reduce CSO volumes from the most sensitive points while reducing total CSO volumes discharged from the catchment. Additionally, the results show that the inclusion of forecasts and their uncertainty contributed to further improving the performance of drainage systems. The results of this paper will contribute to the wider usage of global RTC methods in the management of urban drainage networks.

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Section: Discount Functionmentioning

confidence: 99%

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Vezzaro

Grum

2014

Journal of Hydrology

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“…Understanding sensitivity and uncertainty is central to environmental modelling (Nossent et al, 2011;Refsgaard et al, 2007;Smith and Smith, 2007;Warmink et al, 2010), and the use of meteorological and soil data is common to a range of subject areas (Moussiopolous et al, 2004;Post et al, 2006;Sharples et al, 2009). The effect of different meteorological and soil inputs on model results is therefore of significant interest beyond crop modelling.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Pogson

Hastings

Smith

2012

Environmental Modelling & Software

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Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of separate input parameters, we assess results obtained from a crop growth model using a selection of entire meteorological and soil input datasets, since these define modelled conditions. Yields are found to vary significantly only where the combination of inputs makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results highlight the significance of soil water parameters, which are likely to become increasingly critical in areas affected by climate change. Differences between datasets demonstrate the need to consider the dataset-dependence of parameterised model terms, both for model validation and predictions based on alternative datasets.

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“…[12] There is also an extensive literature on the classification of different types of uncertainty [Hoffman and Hammonds, 1994;Helton and Burmaster, 1996;Walker et al, 2003;Van der Sluijs et al, 2005;Refsgaard et al, 2006Refsgaard et al, , 2007Refsgaard et al, , 2013Warmink et al, 2010;Spiegelhalter and Riesch, 2011]. There are multiple classifications of varying degrees of complexity.…”

Section: Describing Uncertaintiesmentioning

confidence: 99%

A guide to good practice in modeling semantics for authors and referees

Beven

Young

2013

Water Resources Research

106

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[1] This opinion piece makes some suggestions about guidelines for modeling semantics that can be referred to by authors and referees. We discuss descriptions of model structures, different forms of simulation and prediction, descriptions of different sources of uncertainty in modeling practice, the language of model validation, and concepts of predictability and fitness-for-purpose. While not expecting universal agreement on these suggestions, given the loose usage of words in the literature, we hope that the discussion of the issues involved will at least give pause for thought and encourage good practice in model development and applications.Citation: Beven, K., and P. Young (2013), A guide to good practice in modeling semantics for authors and referees, Water Resour.

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Identification and classification of uncertainties in the application of environmental models

Cited by 138 publications

References 35 publications

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

A guide to good practice in modeling semantics for authors and referees

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