Flood modelling: parameterisation and inflow uncertainty

Mukolwe, Micah; Baldassarre, Giuliano Di; Werner, Micha; Solomatine, Dimitri

doi:10.1680/wama.12.00087

Cited by 14 publications

(13 citation statements)

References 40 publications

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“…Therefore, the effects of rating curve uncertainty (Di Baldassarre and Montanari, 2009;Sikorska et al, 2013;Coxon et al, 2014;Mukolwe et al, 2014) and precipitation forecast uncertainty (Kobold and Sušelj, 2005;Shrestha et al, 2013) are accommodated as well.…”

Section: Upper Severn Catchments: Yeaton Llanyblodwel and Llanerfylmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

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Section: Upper Severn Catchments: Yeaton Llanyblodwel and Llanerfylmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…These models have even been applied in large‐scale flood‐extent mapping case studies (e.g. Mukolwe, Dibaldassarre, Werner, & Solomatine, 2014), also in basin‐scale flood‐risk management studies (Amarnath, Umer, Alahacoon, & Inada, ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating flood extent mapping of two hydraulic models, 1D HEC‐RAS and 2D LISFLOOD‐FP in comparison with aerial imagery observations in Gorgan flood plain, Iran

Rahimzadeh

Bahremand

Noura

et al. 2019

Natural Resource Modeling

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We compared flood mapping techniques using a one‐dimensional (1D) hydraulic model HEC‐RAS and two‐dimensional (2D) LISFLOOD‐FP for a 10‐km reach of Gorgan River in Iran. Both models were run using the same hydrologic input data. The input into the models was a steady discharge of 90 cm, corresponds to a flood peak occurred on March 25, 2012. Flood maps generated using these two models were compared with an observed flood inundation map, using F‐statistic. The roughness coefficients of the models were calibrated by maximizing the value of the F‐statistic. Based on the F‐statistic, LISFLOOD‐FP gives a slightly better result (F = 0.69) than HEC‐RAS (F = 0.67). Visual comparison of the flood extents generated by the two models showed reasonably good agreement. Validation was done using a flood event occurred on May 31, 2014. The LISFLOOD‐FP model gave a better result for validation as well. The 2D model showed more consistency in comparison with the 1D model.

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“…To date, different literature have assessed uncertainties caused by different hydraulic models to either the input data and model parameters/conditions (see e.g. Aronica et al 2002;Pappenberger et al 2005;Di Baldassarre and Montanari 2009;Mason et al 2009;Mukolwe et al 2014). Even so, there remain some issues that are not addressed or not explicitly discussed in the earlier studies, which can be essential, to understand the causes and effects of sensitivity of flood modelling results.…”

Section: Research Issues and Contributionsmentioning

confidence: 99%

Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance

Lim

Brandt

2019

Geomatics, Natural Hazards and Risk

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In comprehending flood model results, we performed sensitivity analyses and evaluated how different combinations of digital elevation model (DEM) resolution and Manning's roughness affect flood maps produced from a 2D hydraulic model. Moreover, we analysed how the estimation of accuracy can further be influenced by the performance measure and the area's topography. Various combinations of DEM and Manning's n produced different results, in terms of quantified performance in relation to actual flood extent and the generated flood boundaries. High-resolution DEMs performed better with higher Manning's n; while lower n values were better for lower resolution DEMs. Furthermore, although lower resolution DEMs (25 and 50 m) received higher quantified performances, there are more discrepancies in the flood maps and water surface elevations (WSE) produced by them. The current statistical estimators of model performance do not necessarily provide an accurate estimate of which combination of DEM resolution and roughness are more suitable for application to modelling. Different statistical estimates have different assumptions, which can affect the model selection. Therefore, a more holistic approach towards model selection should be adopted that gives equal importance to statistical estimators, as well as the quality of flood inundation extents.

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Flood modelling: parameterisation and inflow uncertainty

Cited by 14 publications

References 40 publications

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Evaluating flood extent mapping of two hydraulic models, 1D HEC‐RAS and 2D LISFLOOD‐FP in comparison with aerial imagery observations in Gorgan flood plain, Iran

Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance

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