Evaluation of 1D and 2D numerical models for predicting river flood inundation

Horritt, M. S.; Bates, Paul

doi:10.1016/s0022-1694(02)00121-x

Cited by 925 publications

(642 citation statements)

References 14 publications

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“…The spatial detail of the simulated inundation depths depends on the discretization level of the model setup which is usually below 100 m horizontal resolution (Horritt and Bates, 2002;Falter et al, 2014). The near-real-time application of hydrodynamic-numeric models is hampered by the need to provide appropriate estimates of initial and boundary conditions, to assimilate model simulations and observations (Matgen et al, 2007) and by considerable computational costs .…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

“…The near-real-time application of hydrodynamic-numeric models is hampered by the need to provide appropriate estimates of initial and boundary conditions, to assimilate model simulations and observations (Matgen et al, 2007) and by considerable computational costs . Computation time depends particularly on the size of the computational domain and its spatial resolution (Falter et al, 2013) and the complexity level of model equations (Horritt and Bates, 2002). Alternatively, the inundated areas and inundation depths can be calculated in advance for a set of flood scenarios.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

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Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

197

129

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Abstract. During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. Information provided by eyewitnesses via social media is a valuable information source, which should be explored in a more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eyewitnesses extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information, as well as to interpret and integrate the posts into mapping procedures in a timely manner. To support rapid inundation mapping we propose a methodology and develop "PostDistiller", a tool to filter geolocated posts from social media services which include links to photos. This spatial distributed contextualized in situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Section: State Of the Art And Related Workmentioning

confidence: 99%

Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

197

129

View full text Add to dashboard Cite

show abstract

“…However, such model requires relatively more detail topographic data compared to 1D model. Horritt and Bates (2002) point out that, without detail topographic data, the two models (1D and 2D) show relatively similar performances. Since this research was using topographic data with 30 m horizontal resolution, the 1D approach has been selected to complete the flow dynamic and maps the inundated areas of the Upper Citarum Watershed.…”

Section: Introductionmentioning

confidence: 87%

Effects of Forest Cover Change on Flood Characteristics in the Upper Citarum Watershed

Dasanto

Pramudya

Boer

et al. 2014

jtfm

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“…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%

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

et al. 2015

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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.

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Evaluation of 1D and 2D numerical models for predicting river flood inundation

Cited by 925 publications

References 14 publications

Social media as an information source for rapid flood inundation mapping

Social media as an information source for rapid flood inundation mapping

Effects of Forest Cover Change on Flood Characteristics in the Upper Citarum Watershed

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

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