Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework

Choi, Hyung Tae; Beven, Keith

doi:10.1016/j.jhydrol.2006.07.012

Cited by 163 publications

(145 citation statements)

References 46 publications

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“…A number of such measures have been used within GLUE over the years (see, e.g., Choi and Beven, 2007). Romanowicz and Beven (2006) found that the choice of different measures influences the shape of the resulting distribution, but to a much lesser extent the uncertainty bands for the predictions.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Using UKCP09 probabilistic climate information for UK water resource planning

Christierson

Vidal²,

Wade

2012

Journal of Hydrology

103

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Water companies in the United Kingdom have considered climate change in their water resources plans for more than a decade through studies funded by UK Water Industry Research (UKWIR). This paper presents an initial assessment of the impact of the UK Climate Projections 2009 (UKCP09) on river flows at a national scale for the 2020s under the A1B scenario and the implications for water resource planning. A daily hydrological modelling framework based on two conceptual model structures and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology has been applied to 70 catchments across the UK. A Latin Hypercube Sampling approach was adopted to handle the probabilistic nature of UKCP09. Results show a decrease in mean annual flow over most of the UK, with negative median values of all monthly changes except in winter over the western and northern mountainous areas. Furthermore the results indicate a high likelihood of a significant decline in summer flows. An analysis of variance shows that the main uncertainty in river flow changes comes from the spread in climate projections. Finally results are found to be consistent with a previous UKWIR assessment based on individual projections from 6 Global Climate Models (GCMs) under the A2 scenario. The reduction in summer low-flows, critical for water resources, appears however more limited with UKCP09. Although most expected changes are within natural variability, the drier conditions overall and the greater spread of results with respect to previous assessments indicates a need for testing the robustness of water resource plans.

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Section: Hydrological Modellingmentioning

confidence: 99%

Using UKCP09 probabilistic climate information for UK water resource planning

Christierson

Vidal²,

Wade

2012

Journal of Hydrology

103

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“…In the past, simulation models have optimized their parameters as if they were steady over time. But in fact representing the time varying nature of hydrological responses related to seasonality and the changing antecedent conditions in the system is an interesting aspect of the problem of finding acceptable models (Choi and Beven, 2007). Two studies addressing this problem (Wagener et al, 2003;Freer et al, 2003) confirm that hydrological processes switch their dynamic behaviour between different seasons or periods and this is not expressed properly in most models.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…Two studies addressing this problem (Wagener et al, 2003;Freer et al, 2003) confirm that hydrological processes switch their dynamic behaviour between different seasons or periods and this is not expressed properly in most models. Choi and Beven (2007) formulate and evaluate a GLUE-based approach for multi-period and multi-criteria model conditioning of a physically-based distributed model (TOPMODEL) with time-varying hydrological data. In this approach the model calibration is based on identifying periods of different hydrological characteristics.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Rossa¹,

Liechti

Zappa

et al. 2011

Atmospheric Research

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Publication informationAtmospheric Research, 100 (2-3): 150-167Publisher Elsevier This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT AbstractQuantifying uncertainty in flood forecasting is a difficult task, given the multiple and strongly nonlinear model components involved in such a system. Much effort has been and is being invested in the quest of dealing with uncertain precipitation observations and forecasts and the propagation of such uncertainties through hydrological and hydraulic models predicting river discharges and risk for inundation. The COST 731 Action is one of these and constitutes a European initiative which deals with the quantification of forecast uncertainty in hydro-meteorological forecast systems.COST 731 addresses three major lines of development: (1) combining meteorological and hydrological models to form a forecast chain, (2) propagating uncertainty information through this chain and make it available to end users in a suitable form, (3)

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“…H is a shaping factor that in this application is fixed to 1. A combined likelihood measure inspired by Choi and Beven (2007) was calculated by multiplication of the dry and wet weather likelihoods:…”

Section: Implementation Of Gluementioning

confidence: 99%

“…GLUE has become an increasingly popular tool for model evaluation and uncertainty estimation of environmental models (Mitchell et al, 2009;Piñol et al, 2009;Juston et al, 2010;Staudt et al, 2010) and particularly within hydrological modelling from where the methodology originated (see e.g. Choi and Beven, 2007;Xiong and O'Connor, 2008;Blazkova and Beven, 2009a, b;Jin et al, 2010). Several GLUE applications have also been seen within urban drainage water quantity and quality modelling, (Aronica et al, 2005;Lindblom et al, 2007;Freni et al, 2008Freni et al, , 2009bMannina and Viviani, 2010;Lindblom et al, 2011), but GLUE, as well as Bayesian inverse methods (e.g.…”

mentioning

confidence: 99%

Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation?

Breinholt

Grum

Madsen

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Monitoring of flows in sewer systems is increasingly applied to calibrate urban drainage models used for long-term simulation. However, most often models are calibrated without considering the uncertainties. The generalized likelihood uncertainty estimation (GLUE) methodology is here applied to assess parameter and flow simulation uncertainty using a simplified lumped sewer model that accounts for three separate flow contributions: wastewater, fast runoff from paved areas, and slow infiltrating water from permeable areas. Recently GLUE methodology has been critisised for generating prediction limits without statistical coherence and consistency and for the subjectivity in the choice of a threshold value to distinguish "behavioural" from "nonbehavioural" parameter sets. In this paper we examine how well the GLUE methodology performs when the behavioural parameter sets deduced from a calibration period are applied to generate prediction bounds in validation periods. By retaining an increasing number of parameter sets we aim at obtaining consistency between the GLUE generated 90 % prediction limits and the actual containment ratio (CR) in calibration. Due to the large uncertainties related to spatiotemporal rain variability during heavy convective rain events, flow measurement errors, possible model deficiencies as well as epistemic uncertainties, it was not possible to obtain an overall CR of more than 80 %. However, the GLUE generated prediction limits still proved rather consistent, since the overall CRs obtained in calibration corresponded well with the overall CRs obtained in validation periods for all proportions of retained parameter sets evaluated. When focusing on wet and dry weather periods separately, some inconsistencies were however found between calibration and validation and we address here some of the reasons why we should not expect the coverage of the prediction limits to be identical in calibration and validation periods in real-world applications. The large uncertainties result in wide posterior parameter limits, that cannot be used for interpretation of, for example, the relative size of paved area vs. the size of infiltrating area. We should therefore try to learn from the significant discrepancies between model and observations from this study, possibly by using some form of non-stationary error correction procedure, but it seems crucial to obtain more representative rain inputs and more accurate flow observations to reduce parameter and model simulation uncertainty.

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Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework

Cited by 163 publications

References 46 publications

Using UKCP09 probabilistic climate information for UK water resource planning

Using UKCP09 probabilistic climate information for UK water resource planning

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation?

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