Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling

Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan

doi:10.1016/j.jhydrol.2007.12.026

Cited by 173 publications

(126 citation statements)

References 32 publications

(81 reference statements)

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“…For each catchment, 5000 parameter sets for each model structure were randomly sampled within the defined parameter ranges, thus providing 10,000 different catchment models. For simplicity uniform sampling was chosen over more complex and efficient sampling strategies (e.g., Markov Chain Monte-Carlo, Blasone et al, 2008), which is in line with other applications of the GLUE methodology (Beven, 2000;Cloke et al, 2010). Constant for non-linear reservoir (m 3 .days) 0 500 500 10 000 500 10 000…”

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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“…The other method commonly used in statistics is inflation of the modelling error variance to account for its unknown structure and being non-additive (Romanowicz and Beven, 2006). A similar approach was applied by Werner (2004) and Blasone et al (2008).…”

Section: Calibration and Validation Of The Mss Modelmentioning

confidence: 99%

Maximising the usefulness of flood risk assessment for the River Vistula in Warsaw

Kiczko

Romanowicz

Osuch

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Abstract. The derivation of the flood risk maps requires an estimation of maximum inundation extent for a flood with a given return period, e.g. 100 or 500 yr. The results of numerical simulations of flood wave propagation are used to overcome the lack of relevant observations. In practice, deterministic 1-D models are used for that purpose. The solution of a 1-D model depends on the initial and boundary conditions and estimates of model parameters based on the available noisy observations. Therefore, there is a large uncertainty involved in the derivation of flood risk maps using a single realisation of a flow model. Bayesian conditioning based on multiple model simulations can be used to quantify this uncertainty; however, it is too computer-time demanding to be applied in flood risk assessment in practice, without further flow routing model simplifications. We propose robust and feasible methodology for estimating flood risk. In order to decrease the computation times the assumption of a gradually varied flow and the application of a steady state flow routing model is introduced. The aim of this work is an analysis of the influence of those simplifying assumptions and uncertainty of observations and modelling errors on flood inundation mapping and a quantitative comparison with deterministic flood extent maps. Apart from the uncertainty related to the model structure and its parameters, the uncertainty of the estimated flood wave with a specified probability of return period (so-called 1-in-10 yr, or 1-in-100 yr flood) is also taken into account. In order to derive the uncertainty of inundation extent conditioned on the design flood, the probabilities related to the design wave and flow model uncertainties are integrated. In the present paper that integration is done whilst taking into account the dependence of roughness coefficients on discharge. The roughness is parameterised based on maximum annual discharges. This approach allows for the relationship between flood extent and flow values to be derived, thus giving a cumulative assessment of flood risk. The methods are illustrated using the Warsaw reach of the River Vistula as a case study. The results indicate that deterministic and stochastic flood inundation maps cannot be quantitatively compared. We show that the proposed simplified approach to flood risk assessment can be applied even when breaching of the embankment occurs, with the condition that the flooded area is small enough to be filled rapidly.

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“…These two quantiles for all simulation steps constitute the simulation limits, which characterise the uncertainty associated with the parameterization of the model conditioned on the model structure, input and calibration data, the parameter sets being used, and the subjective choices made in GLUE (e.g., the selection of likelihood measure and rejection threshold value). If the 90% simulation intervals are large enough to cover most of the observations, it means the parameter variability alone can account for the total output uncertainty (Blasone et al, 2008). However, many GLUE applications show that the prediction limits can not encompass the observations at the percentage equalling to the specified certainty level (e.g., the above defined 90% prediction limits) (Beven, 2006;Montanari, 2005) due to the uncertainties in the modelling process.…”

Section: The Glue Frameworkmentioning

confidence: 99%

Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet

Sun

Ishidaira

Bastola

2010

Hydrol. Earth Syst. Sci.

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Abstract. Rainfall-runoff models are common tools for river discharge estimation in the field of hydrology. In ungauged basins, the dependence on observed river discharge data for calibration restricts applications of rainfall-runoff models. The strong correlation between quantities of river cross-sectional water surface width obtained from remote sensing and corresponding in situ gauged river discharge has been verified by many researchers. In this study, a calibration scheme of rainfall-runoff models based on satellite observations of river width at basin outlet is illustrated. One distinct advantage is that this calibration is independent of river discharge information. The at-a-station hydraulic geometry is implemented to facilitate shifting the calibration objective from river discharge to river width. The generalized likelihood uncertainty estimation (GLUE) is applied to model calibration and uncertainty analysis. The calibration scheme is demonstrated through a case study for simulating river discharge at Pakse in the Mekong Basin. The effectiveness of the calibration scheme and uncertainties associated with utilization of river width observations from space are examined from model input-state-output behaviour, capability of reproducing river discharge and posterior parameter distribution. The results indicate that the satellite observation of the river width is a competent surrogate of observed discharge for the calibration of rainfall-runoff model at Pakse and the proposed method has the potential for improving reliability of river discharge estimation in basins without any discharge gauging.

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Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling

Cited by 173 publications

References 32 publications

Using UKCP09 probabilistic climate information for UK water resource planning

Using UKCP09 probabilistic climate information for UK water resource planning

Maximising the usefulness of flood risk assessment for the River Vistula in Warsaw

Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet

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