Simple spatially-distributed models for predicting flood inundation: A review

Hunter, Neil; Bates, Paul; Horritt, M. S.; Wilson, Matthew

doi:10.1016/j.geomorph.2006.10.021

Cited by 385 publications

(298 citation statements)

References 158 publications

(194 reference statements)

Supporting

Mentioning

292

Contrasting

Unclassified

Order By: Relevance

“…As these events constitute a mechanism of aquifer recharge, especially in flat regions, their extent and duration are important for GSI predictions. The challenge for modelers is to control the seemingly minor topographic inaccuracies that may dramatically impact the simulation of overbank floods (Marks and Bates 2000;Hunter et al 2007;Merwade et al 2008). Inaccuracies are commonly caused by: (1) elevation discrepancies resulting from a mismatch of channel-riparian boundaries; (2) DEM errors in the channel and the floodplain, for example, owing to the presence of vegetation; and (3) a DEM that is too coarse to represent tributaries and lateral breaches in the embankments.…”

Section: Channel-representation Issues the Digital Elevation Modelmentioning

confidence: 99%

Channel Representation in Physically Based Models Coupling Groundwater and Surface Water: Pitfalls and How to Avoid Them

et al. 2014

View full text Add to dashboard Cite

Recent models that couple three-dimensional subsurface flow with two-dimensional overland flow are valuable tools for quantifying complex groundwater/stream interactions and for evaluating their influence on watershed processes. For the modeler who is used to defining streams as a boundary condition, the representation of channels in integrated models raises a number of conceptual and technical issues. These models are far more sensitive to channel topography than conventional groundwater models. On all spatial scales, both the topography of a channel and its connection with the floodplain are important. For example, the geometry of river banks influences bank storage and overbank flooding; the slope of the river is a primary control on the behavior of a catchment; and at the finer scale bedform characteristics affect hyporheic exchange. Accurate data on streambed topography, however, are seldom available, and the spatial resolution of digital elevation models is typically too coarse in river environments, resulting in unrealistic or undulating streambeds. Modelers therefore perform some kind of manual yet often cumbersome correction to the available topography. In this context, the paper identifies some common pitfalls, and provides guidance to overcome these. Both aspects of topographic representation and mesh discretization are addressed. Additionally, two tutorials are provided to illustrate: (1) the interpolation of channel cross-sectional data and (2) the refinement of a mesh along a stream in areas of high topographic variability.

show abstract

Section: Channel-representation Issues the Digital Elevation Modelmentioning

confidence: 99%

Channel Representation in Physically Based Models Coupling Groundwater and Surface Water: Pitfalls and How to Avoid Them

et al. 2014

View full text Add to dashboard Cite

show abstract

“…However, the variable of interest for most end-users is the water level at different places in the floodplain, or a product derived from this, e.g. the flood extent (Hunter et al, 2007). In order to evaluate the inundation predicted by a model, the simulations should 15 be validated with spatially-distributed water level data or flood extent data.…”

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

“…In addition, topography interacts with spatially-distributed friction in a complex manner in the timings of the flood wave. In some studies, adaptive time-step of storage cell models have been shown to increase sensitivity to floodplain friction [72]. For the above results the model performance was good when calibrated and spatial roughness were implemented in the model.…”

Section: Resultsmentioning

confidence: 67%

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

Simple spatially-distributed models for predicting flood inundation: A review

Cited by 385 publications

References 158 publications

Channel Representation in Physically Based Models Coupling Groundwater and Surface Water: Pitfalls and How to Avoid Them

Channel Representation in Physically Based Models Coupling Groundwater and Surface Water: Pitfalls and How to Avoid Them

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

Contact Info

Product

Resources

About