Towards a limits of acceptability approach to the calibration of hydrological models: Extending observation error

Liu, Yanli; Freer, J. E.; Beven, Keith; Matgen, Patrick

doi:10.1016/j.jhydrol.2009.01.016

Cited by 154 publications

(167 citation statements)

References 33 publications

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“…an assumption that both hazard and consequences can be treated as aleatory variables, even if the estimates of the probabilities might be conditional and derived solely from expert elicitation or estimates of odds. The difficulty of 25 specifying odds or probabilities for epistemic uncertainties means that any resulting decisions will necessarily be conditional on the assumptions (as discussed, for example, by Pappenberger and Beven, 2006;Beven, 2009;Sutherland et al, 2013;Rougier and Beven, 2013;and Juston et al 2013). …”

Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

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Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

Beven¹,

Aspinall²,

Bates³

et al. 2017

Preprint

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Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, 20 especially in terms of event or scenario probabilities, even as elicited probabilities rationalised on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic 25 uncertainties is outlined and implications of applying the principles to natural hazard science are discussed.

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Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

“…Liu et al, 2009;Blazkova and Beven, 2009). Such limits can be normalised across different types and magnitudes of evaluation variables.…”

mentioning

confidence: 99%

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

Beven¹,

Aspinall²,

Bates³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, there may be scope for using other forms of likelihood or belief measures in hydrological modelling. Such informal likelihood measures have been defined based on limits of acceptability defined from evaluation-data uncertainty (Blazkova and Beven, 2009;Krueger et al, 2010;Liu et al, 2009) but also based on traditional performance measures (Freer et al, 2003). One of the most widely used performance measures in hydrology is the Nash-Sutcliffe model efficiency (R eff ).…”

Section: Introductionmentioning

confidence: 99%

“…Uncertainty in discharge data, which has been shown to be sometimes substantial (Di Baldassarre and Montanari, 2009;Pelletier, 1988;Krueger et al, 2010;PetersenOverleir et al, 2009) and influence the calibration of hydrological models (McMillan et al, 2010;Aronica et al, 2006), is usually not accounted for in model evaluation with traditional performance measures. Novel approaches in environmental modelling that include evaluation-data uncertainty in model calibration include Bayesian calibration to an estimated probability-density function of discharge (McMillan et al, 2010), Bayesian calibration with a simplified error model (Huard and Mailhot, 2008;Thyer et al, 2009), fuzzy rule based performance measures (Freer et al, 2004) and limits-of-acceptability calibration in GLUE for rainfallrunoff modelling (Liu et al, 2009), flood mapping (Pappenberger et al, 2007), environmental tracer modelling (Page et al, 2007) and flood-frequency estimation (Blazkova and Beven, 2009). Here we explore the limits-of-acceptability GLUE approach applied to flow-duration curves, which could be a way of dealing with some of the effects of nonstationary epistemic errors on the identification of feasible model parameters in real applications (Beven, 2006(Beven, , 2010Beven and Westerberg, 2011;Beven et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Calibration of hydrological models using flow-duration curves

Westerberg

Guerrero

Younger

et al. 2011

Hydrol. Earth Syst. Sci.

239

243

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Abstract. The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested -based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WAS-MOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time Correspondence to: I. K. Westerberg (ida.westerberg@hyd.uu.se) series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

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“…Nevertheless, the estimation of the uncertainty in observations with which the model is compared should be the starting point in model evaluation. For instance, the methodology recently proposed by Liu et al (2009) to assess model performance by using limits of acceptability (Beven, 2006) is based on the assessment of observation uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in river discharge observations: a quantitative analysis

Baldassarre

Montanari

2009

Hydrol. Earth Syst. Sci.

541

271

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Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible.

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Towards a limits of acceptability approach to the calibration of hydrological models: Extending observation error

Cited by 154 publications

References 33 publications

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

Calibration of hydrological models using flow-duration curves

Uncertainty in river discharge observations: a quantitative analysis

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