The future of distributed models: Model calibration and uncertainty prediction

Beven, Keith; Binley, Andrew

doi:10.1002/hyp.3360060305

Cited by 3,668 publications

(3,018 citation statements)

References 27 publications

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“…The 'Generalized Likelihood Uncertainty Estimation' (GLUE) (Beven and Binley, 1992;Beven and Freer, 2001) was also coupled with MACRO . The motivating theory behind GLUE assumes that all models and measurements are to some extent wrong, so that many parameter sets may represent the observations equally well.…”

Section: Intact Soil Columns and Lysimeters Transient Flowmentioning

confidence: 99%

A review of model applications for structured soils: a) Water flow and tracer transport

Köhne

Šimůnek

2009

Journal of Contaminant Hydrology

282

224

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Section: Intact Soil Columns and Lysimeters Transient Flowmentioning

confidence: 99%

A review of model applications for structured soils: a) Water flow and tracer transport

Köhne

Šimůnek

2009

Journal of Contaminant Hydrology

282

224

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“…Many advances have been made in terms of using model calibration methods to reduce the uncertainties inherent in the specification of model parameters (Duan et al, 1992;Beven and Binley, 1992;Wang et al, 2014Wang et al, , 2016. As hydrological models are highly nonlinear, treating uncertainties in different phases of hydrological modeling independently may lead to biased model parameter estimates.…”

Section: Uncertainties In Hydrological Forecastingmentioning

confidence: 99%

Hydrological monitoring and seasonal forecasting: Progress and perspectives

et al. 2016

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Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment, preparedness and mitigation. In recent decades, developments in ground and satellite measurements have made the hydrometeorological information readily available, and advances in information technology have facilitated the data analysis in a real-time manner. New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution. These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status, and earlier warning of extreme hydrological events such as droughts and floods. This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting, reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting, and briefly introduces the current monitoring and forecasting practices in China. The grand challenges and perspectives for the near future are also discussed, including acquiring and extracting reliable information for monitoring and forecasting, predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity, and filling the gap between numerical models and the end user. We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.

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“…Once the STUMP model is capable of simulating the TSS behaviour in the system to a satisfactory degree, the uncertainty in the estimated MP fluxes is thus reduced. This was tested by optimising the STUMP settling/resuspension parameters by applying the pseudo-Bayesian GLUE methodology (Beven and Binley, 1992), which is suitable for stormwater quality models (Freni et al, 2009a), i.e. for models where the level of available information justifies a limited number of prior assumptions.…”

Section: Reduction Of Output Uncertainty For the Dynamic Modelmentioning

confidence: 99%

“…Lindblom et al (2007a), Mannina and Viviani (2010), Rodriguez et al (2010); while Lindblom et al (2007b) presented a comparison of the latter with grey-box modelling. Freni et al (2009a) suggested that pseudo-Bayesian methods (namely the Generalized Likelihood Uncertainty Estimation (GLUE) methodology (Beven and Binley, 1992)) can be appropriate for stormwater quality models, i.e. in condition where only a limited number of prior assumptions can be proved.…”

Section: Introductionmentioning

confidence: 99%

Modelling the fate of organic micropollutants in stormwater ponds

Vezzaro

Eriksson

Ledin

et al. 2011

Science of The Total Environment

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Urban water managers need to estimate the potential removal of organic micropollutants (MP) in stormwater treatment systems to support MP pollution control strategies. This study documents how the potential removal of organic MP in stormwater treatment systems can be quantified by using multimedia models. The fate of four different MP in a stormwater retention pond was simulated by applying two steady-state multimedia fate models (EPI Suite and SimpleBox) commonly applied in chemical risk assessment and a dynamic multimedia fate model (Stormwater Treatment Unit Model for Micro Pollutants -STUMP). The four simulated organic stormwater MP (iodopropynyl butylcarbamate -IPBC, benzene, glyphosate and pyrene) were selected according to their different urban sources and environmental fate. This ensures that the results can be extended to other relevant stormwater pollutants. All three models use substance inherent properties to calculate MP fate but differ in their ability to represent the small physical scale and high temporal variability of stormwater treatment systems. Therefore the three models generate different results. A Global Sensitivity Analysis (GSA) highlighted that settling/resuspension of particulate matter was the most sensitive process for the dynamic model. The uncertainty of the estimated MP fluxes can be reduced by calibrating the dynamic model against total suspended solids data. This reduction in uncertainty was more significant for the substances with strong tendency to sorb, i.e. glyphosate and pyrene and less significant for substances with a smaller tendency to sorb, i.e. IPBC and benzene. The results provide support to the elaboration of MP pollution control strategies by limiting the need for extensive and complex monitoring campaigns targeting the wide range of specific organic MP found in stormwater runoff.Please cite the published version of this article as: . Modelling the fate of organic micropollutants in stormwater ponds.

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The future of distributed models: Model calibration and uncertainty prediction

Cited by 3,668 publications

References 27 publications

A review of model applications for structured soils: a) Water flow and tracer transport

A review of model applications for structured soils: a) Water flow and tracer transport

Hydrological monitoring and seasonal forecasting: Progress and perspectives

Modelling the fate of organic micropollutants in stormwater ponds

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