Model bias and complexity – Understanding the effects of structural deficits and input errors on runoff predictions

Giudice, Dario Del; Reichert, Peter; Bareš, Vojtěch; Albert, Carlo; Rieckermann, Jörg

doi:10.1016/j.envsoft.2014.11.006

Cited by 37 publications

(26 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the normality and independence of residuals assumption is usually unverified, rigorous implementation of the Bayesian framework generally requires data transformation or precise bias description which inevitably dictates the nature of the likelihood function (Del Giudice et al, 2013;Del Guidice et al, 2014). This likelihood function may however not systematically represent the modeller's perception of model performance, which is generally assessed from much simpler criterion (Dotto et al, 2011;McMillan and Clark, 2009).…”

Section: Discussion and Perspectives For Future Researchmentioning

confidence: 99%

Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements

et al. 2015

View full text Add to dashboard Cite

ABSTRACT:The suitability of a commonly used accumulation and wash-off model for continuous modelling of urban runoff contamination was evaluated based on 11-month turbidity and flow-rate records from an urban street. Calibration and uncertainty analysis were performed using a Markov Chain Monte-Carlo sampling method for both suspended solids loads (discharge rates) and concentration modelling. Selected models failed at replicating suspended solids concentration over the complete monitoring period. The studied dataset indeed suggests that the accumulation process is rather unpredictable and cannot be satisfactorily represented with usual accumulation models unless short periods are considered. Regarding suspended solid loads modelling, noticeably better performance was achieved, but similar results could as well be obtained with much simpler constant concentration models. Unless providing very accurate estimates of concentrations in runoff, accounting for their temporal variability during rain events may therefore not always be necessary for pollutant loads modelling, as loads are in fact mostly explained by runoff volumes.

show abstract

Section: Discussion and Perspectives For Future Researchmentioning

confidence: 99%

Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Bayarri et al, 2007), the identification of systematic model bias (e.g. Williams et al, 2006;Dietzel and Reichert, 2012;Del Giudice et al, 2015) and objective selection of the "best" model (e.g. Dettmer et al, 2010;Del Giudice et al, 2015) can be added to this list.…”

Section: Introductionmentioning

confidence: 99%

Employing statistical model emulation as a surrogate for CFD

Moonen

Allegrini

2015

Environmental Modelling & Software

View full text Add to dashboard Cite

“…Nevertheless, the deficiencies in model structure and model parameters also substantially contribute to the uncertainties of results in some cases, as shown in Figure 10c. The proposed bootstrap method would be promising to further quantify the fraction of rainfall input-caused uncertainty from the whole simulation error, after integrating some existing approaches (e.g., [34]). …”

Section: Effect Of Rainfall Station Density On Hydrological Simulationsmentioning

confidence: 99%

“…The foundation of this method is the assumption that the model structure error represents model limitations and input uncertainty, then the effect of rainfall uncertainty can be explained with the changes in the model bias. For example, Del Giudice et al [34] used a Bayesian framework to analyze five configurations of the EPA-SWMM (Environmental Protection Agency-Storm Water Management Model) with different distribution of reservoirs and conduits, as well as different numbers of parameters for sewer flow simulations and the results showed a progressive decrease of bias with increasing model parameterization, but with an upper bound of model performance caused by rainfall input.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

View full text Add to dashboard Cite

Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

show abstract

Model bias and complexity – Understanding the effects of structural deficits and input errors on runoff predictions

Cited by 37 publications

References 50 publications

Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements

Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements

Employing statistical model emulation as a surrogate for CFD

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Contact Info

Product

Resources

About