Modelling a River Channel With Distant Floodbanks.

Ervine, Alan; Macleod, Andrew

doi:10.1680/iwtme.1999.31265

Cited by 28 publications

(15 citation statements)

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“…One-dimensional models (see e.g. Fread, 1984Fread, , 1993Samuels, 1990;Ervine and MacLeod, 1999) describe the river channel and floodplain as a series of cross-sections perpendicular to the flow direction. Numerical solution of the controlling equations for prescribed inflow and outflow boundary conditions then enables the cross-section averaged velocity and water depth at each location to be calculated.…”

Section: Model Selectionmentioning

confidence: 99%

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Aronica¹,

Bates²,

Horritt³

2002

Hydrological Processes

369

406

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Abstract:In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain-averaged (zerodimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here 'raw' binary pattern data to define a zero-dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n-dimensional parameter spaces.

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Section: Model Selectionmentioning

confidence: 99%

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Aronica¹,

Bates²,

Horritt³

2002

Hydrological Processes

369

406

View full text Add to dashboard Cite

show abstract

“…However, these models have been criticised not only because of the expectation that representation of floodplain flow as a two-dimensional (2D) flow interacting with the channel flow will give more accurate predictions of flood wave propagation Aronica et al, 1998;Bates et al, 1992;Bates et al, 1998;Cunge, 1975;Dutta et al, 2000;Ervine and MacLeod, 1999;Gee et al, 1990;Hromadka et al, 1985), but also for the usage of the Manning equation (which can be also a criticism for higher dimensional models). This flow equation is computed:…”

Section: Introductionmentioning

confidence: 99%

“…Some studies (Trieste and Jarrett, 1987) have demonstrated discrepancies between calibrated effective model values and roughnesses which have been estimated based only on the nature of the channel and flood plain surfaces, despite many sources of guidance about how to choose a value, such as photographs (Arcement and Schneider, 1989;Chow et al, 1988), tables (Chadwick and Morfett, 1999;Chow, 1959;Chow et al, 1988;King, 1918), composite formulae (Barkau, 1997;Bathurst, 1994;Dingman and Sharma, 1997;Knight et al, 1989;Li and Zhang, 2001; Rice et al, 1998;Riggs, 1976) or measurement programs (Ackers, 1991;Dingman and Sharma, 1997;Ervine and MacLeod, 1999;Harunurrashid, 1990). These estimates have usually been based on velocities measured for local velocity profiles or across a single cross-section.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

Pappenberger

Beven

Horritt

et al. 2005

Journal of Hydrology

400

328

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“…Finally, these St. Venant equations can be simplified even further to the one dimensional form (Fread, 1984;Ervine and MacLeod, 1999), where flow momentum is conserved between two cross sections. These 1D equations are expressed as:…”

Section: Numerical Modelling Methodology For Downscalingmentioning

confidence: 99%

Rural land management impacts on catchment scale flood risk

Pattison¹

2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source• a link is made to the metadata record in Durham E-Theses• the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full Durham E-Theses policy for further details. Most approaches to this problem aim to upscale from individual grid cells to whole catchments, something that restricts the complexity of possible process representation, produces models that may not be parsimonious with the data needed to calibrate them and, faced with data uncertainties, provides computational limitations on the extent to which model uncertainty can be fully explored. Rather than upscaling to problems of concern, this thesis seeks to downscale from locations of known flood risk, as a means of identifying where land use management changes might be beneficial and then uses numerical modelling to identify the kinds of management changes required in those downscaled locations. Thus, the aim of this thesis is to test an approach to understanding the impacts of rural land management upon flood risk based upon catchment-to-source downscaling.This thesis uses the case study of the River Eden catchment (2400 km 2 ) as a test (1) it was shown to have a significant impact on downstream flood risk; and (2) it had range of data and information needed for modelling land use changes.The second part of this thesis explored the land management scenarios that could be used to reduce flood risk at the catchment scale. The scenarios to be tested were determined through a stakeholder participation approach, whereby workshops were held to brainstorm and prioritise land management options, and then to identify specific locations within the Eamont sub-catchment where they could tested. There were two main types of land management scenarios chosen: (1) landscape-scale changes, including afforestation and compaction; and (2) corresponding to a 5.8% decrease in peak discharge. A key conclusion is that land management practices have been shown to have an effect on catchment scale flooding, even for extreme flood events. However, the effect of land management scenarios are both spatially and temporally dependent i.e. the same land management practice has different effects depending on where it is implemented, and when implemented in the same location has different effects on different flood events.

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Modelling a River Channel With Distant Floodbanks.

Cited by 28 publications

References 0 publications

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

Rural land management impacts on catchment scale flood risk

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