Modelling floodplain flows using a two‐dimensional finite element model

Bates, Paul; Anderson, M. G.; Baird, Laura; Walling, D. E.; Simm, David

doi:10.1002/esp.3290170604

Cited by 106 publications

(92 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These mathematical models, hereafter referred to as flood inundation models, traditionally require the channel and floodplain Manning coefficients to be calibrated with in situ measurements (e.g. water height, flow) 5 from gauging stations at the downstream boundary of the model (Bates et al, 1992). For example, assuming a constant Manning coefficient over the channel and floodplain, one can calibrate both parameters by minimizing e.g.…”

Section: Introductionmentioning

confidence: 99%

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Gobeyn

Wesemael

Neal

et al. 2017

Advances in Water Resources

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Gobeyn

Wesemael

Neal

et al. 2017

Advances in Water Resources

Self Cite

View full text Add to dashboard Cite

show abstract

“…The relative roughness was used to aid the spatial parameterization of the Manning's roughness within the vegetation classes. Numerous researchers give guidance on selection of Manning's values for various vegetation classes [6,8,12,15,65]. After a careful review and investigation of Manning's values by comparing the study site vegetation characteristics and those given in the literature, the value ranges shown in Table 3 were adopted for the study area.…”

Section: Methodsmentioning

confidence: 99%

“…The floodplain model domain was discretized into 100 m by 100 m grid cells. The upstream model boundary was represented as a daily time-flow boundary condition at Mara Mine (5H2) station (3) and at Tigithe Bridge (2) while the downstream boundary condition as the levels of Lake Victoria (6). An internal river gauge at point 5 was used for model performance assessment (Figure 1).…”

Section: Hydraulic Modelingmentioning

confidence: 99%

“…To predict the impacts of flood wave on the floodplain, the hydraulic processes of the river have to be assessed at optimal temporal and spatial scales using hydraulic models. Several efforts have been made to develop hydraulic models that can simulate flow patterns and predict extreme flood levels in rivers and floodplains [5][6][7][8][9][10]. The development of an accurate and reliable hydraulic model that well describes surface water flow across a large wetland floodplain depends on topographic data and hydraulic roughness, viz.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

et al. 2015

View full text Add to dashboard Cite

Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning's roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning's coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water OPEN ACCESSRemote Sens. 2015, 7 837 levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m −1/3 /s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m −1/3 /s and channel roughness nc = 0.021 m −1/3 /s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning's coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R 2 ), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R 2 = 0.95, which was improved in SWL2 to E = 0.95 and R 2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R 2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models.

show abstract

“…Then, hydraulic geometry equations such as [Bates et al, 1992] .Without direct velocity mea surements, the calibration of surface area to discharge will be less accurate than that obtained for stage at gaging stations. However, the surface water time series itself can be used in many types of analyses, and it be comes increasingly valuable as the period of record lengthens.…”

Section: Discharge Estimationmentioning

confidence: 99%

Space‐based measurement of river runoff

Brakenridge¹,

Nghiem²,

Anderson³

et al. 2005

EoS Transactions

118

View full text Add to dashboard Cite

Observations of river inundation areas, water levels, and flow variability from orbital sensors have the potential to directly measure the runoff component of the Earth's hydrologic cycle [Birkett et al., 2002; Brakenridge et al., 1998; Sippel et al., 1994, 1998; Townsend, 2001]. A remote‐sensing‐based measurement strategy for rivers and streams is emerging: Surface water data can be collected, their accuracy evaluated, and the results disseminated without regard to political boundaries. The results can be used to address a wide variety of applications. In this article, the needs for such measurements, a river reach‐based methodology for their collection, and some sample results are presented. Because the international observational capability is increasing, some future opportunities for improving this strategy are also described.

show abstract

Modelling floodplain flows using a two‐dimensional finite element model

Cited by 106 publications

References 9 publications

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

Space‐based measurement of river runoff

Contact Info

Product

Resources

About