Numerical Modelling of Floodplain Flow

Bates, Paul; Horritt,; Hunter, Neil; Mason, David C.; Cobby, David M.

doi:10.1002/0470015195.ch11

Cited by 19 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with previous research (Bates et al, 1998;Bates et al, 2005;Baugh et al, 2013;Callow et al, 2007;Jarihani et al, 2015;Sanders, 2007), our study found that the quality of DEMs govern the success of hydrodynamic models of low-gradient river systems, especially for small flood (< 2000 GL) events. As found by Jarihani et al, (2015), Manning's n roughness is typically low and dominated by the H-DEM surface accuracy and roughness that also impact the physical realism of values in such large systems due to the DEMs hydraulically-rough floodplain surfaces (Jarihani et al, 2015).…”

Section: Initial Model Calibrationsupporting

confidence: 91%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

View full text Add to dashboard Cite

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

show abstract

Section: Initial Model Calibrationsupporting

confidence: 91%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

View full text Add to dashboard Cite

show abstract

“…The assumption of flow condition that the main river and the floodplains can be simulated as a single channel leads to no storage effects in flood inundation modeling. Although the assumption is applied to the flood inundation modeling using hydraulic models [73][74][75], it still has a weakness that the simulated water levels using 1D hydraulic model can be overestimated or underestimated because the stored water in floodplain is not considered. In addition, the roughness is a key uncertainty source in flood inundation modeling.…”

Section: Approximation Of Flood Discharge Using Hec-ras and The Glue mentioning

confidence: 99%

An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge

Jung

Merwade

Yeo

et al. 2013

Water

View full text Add to dashboard Cite

Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE) methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in OPEN ACCESSWater 2013, 5 1599 ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure.

show abstract

“…The use of simulation in geosciences has increased in recent years as users realized its potential to provide solutions and detect opportunities, mainly in the case of disasters (Bates et al, 2005;Parrot, 2009;Parrot and Collet, 2009;Delahaye and Douvinet, 2013). This increasing field of research is more used not only as high resolution data of the earth surface are available for public use, but also as open source algorithms are free downloaded in the net.…”

Section: Introductionmentioning

confidence: 99%

“…The wave may surpass bankfull height, and adjacent plains will act as storage or as a route to continue the movement of the water (Bates et al, 2005). The maximum discharge level is recorded upstream; meanwhile, the effect of translation downstream flattens out the wave.…”

Section: Introductionmentioning

confidence: 99%

Lateral Flooding Associated to Wave Flood Generation on River Surface

Ramírez-Núñez¹,

Parrot²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This research provides a wave flood simulation using a high resolution LiDAR Digital Terrain Model. The simulation is based on the generation of waves of different amplitudes that modify the river level in such a way that water invades the adjacent areas. The proposed algorithm firstly reconstitutes the original river surface of the studied river section and then defines the percentage of water loss when the wave floods move downstream. This procedure was applied to a gently slope area in the lower basin of Coatzacoalcos river, Veracruz (Mexico) defining the successive areas where lateral flooding occurs on its downstream movement.

show abstract

Numerical Modelling of Floodplain Flow

Cited by 19 publications

References 0 publications

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge

Lateral Flooding Associated to Wave Flood Generation on River Surface

Contact Info

Product

Resources

About