A comparison of simplified conceptual models for rapid web-based flood inundation mapping

McGrath, Heather; Bourgon, Jean-François; Proulx-Bourque, Jean-Samuel; Nastev, Miroslav; Ezz, Ahmad Abo El

doi:10.1007/s11069-018-3331-y

Cited by 61 publications

(51 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hazard assessment consists instead in the determination of the degree of inundation, described not only by the spatial extent of the flooded area but also, and more importantly, by the water depth (and velocity) distribution within it, as key variable(s) to assess flood damage and risk. Despite continuous improvements in hydraulic modelling, which have led to the availability of several hydrodynamic models with different complexity [5][6][7][8][9][10][11], high resolution flood hazard maps, including information of interest, are generally available only for small portions of flood prone areas [12][13][14]. As a matter of fact, running complex hydrodynamic models is largely demanding in terms of high requirements for input data quality, computational time and long-lasting calibration processes.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, a new group of flood hazard estimating procedures has emerged in recent years as a viable substitute for more complex hydraulic simulations, when a rapid determination of flood depths in large-scale and/or data-sparse areas is needed. These methods, referred as "zero-dimensional (0D) models" or "simplified conceptual models" [8,10,14], do not involve the solution of physically based equations and rely on simplified hydraulic assumptions and/or considerations on the characteristics of the terrain. Some of these models are suitable for scenario modelling in ex-ante quantitative flood risk assessments within FRMPs: the Planar method (or TVD model) [17], Rapid Flood Spreading Method (RFSM) [18] and Height Above Nearest Drainage network method (HAND) [19,20] are examples of 0D models that supply a rapid prediction of the extent of inundation and the flood depth distribution within the flooded area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Scorzini

Radice

Molinari

2018

Water

View full text Add to dashboard Cite

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Scorzini

Radice

Molinari

2018

Water

View full text Add to dashboard Cite

show abstract

“…However, depending on the topography of the region of interest, existence of natural or artificial infrastructures that affect the water discharge like waterfalls or dams, the HAND model can produce different accuracies. In the studies reported by Momo [28] and McGrath et al [29], the HAND model was able to obtain flood extents that compared closely to actual flooded areas; however, in Reference [30], the generated HAND model considerably overestimated the real flooded extent. Despite the capability of the HAND model to predict the flood extent and its straightforward approach, the model is highly susceptible to the accuracy of hydrologically conditioned Digital Terrain Models (DTM).…”

Section: Introductionmentioning

confidence: 92%

Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest

et al. 2020

Self Cite

View full text Add to dashboard Cite

Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen’s kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data.

show abstract

“…Moussa and Cheviron, 2015). Some methods choose to couple 1-D and 2-D models, the former for streamflow routing and the latter for overbank flow (Morales-Hernández et al, 2016). Despite the accuracy of such models, studies often try to further simplify them because of the large computing time to simulate small areas and the lack of precise data required to run these models.…”

Section: Introductionmentioning

confidence: 99%

“…Other methods derive inundation maps from topographic information only: one can cite EXZECO (Pons et al, 2010), which introduces elevation noise in the DEM in order to create a single map of "maximum flow accumulation" that can be seen as a potential inundation area, and HAND ("height above nearest drainage"), a descriptor originally used for terrain classification (Rennó et al, 2008;Nobre et al, 2011), which has recently been adapted to static flood inundation mapping (Nobre et al, 2016) and is increasingly used to produce flood maps (e.g. Afshari et al, 2018;Speckhann et al, 2018;McGrath et al, 2018). HAND calculates the difference between river cells' elevation and that of the connected floodplain cells, thus giving relative height information which can be compared to observed flood depths and the corresponding inundation extent.…”

Section: Introductionmentioning

confidence: 99%

Inundation mapping based on reach-scale effective geometry

Rebolho¹,

Andréassian²,

Moine

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The production of spatially accurate representations of potential inundation is often limited by the lack of available data as well as model complexity. We present in this paper a new approach for rapid inundation mapping, MHYST, which is well adapted for data-scarce areas; it combines hydraulic geometry concepts for channels and DEM data for floodplains. Its originality lies in the fact that it does not work at the cross section scale but computes effective geometrical properties to describe the reach scale. Combining reach-scale geometrical properties with 1-D steady-state flow equations, MHYST computes a topographically coherent relation between the “height above nearest drainage” and streamflow. This relation can then be used on a past or future event to produce inundation maps. The MHYST approach is tested here on an extreme flood event that occurred in France in May–June 2016. The results indicate that it has a tendency to slightly underestimate inundation extents, although efficiency criteria values are clearly encouraging. The spatial distribution of model performance is discussed and it shows that the model can perform very well on most reaches, but has difficulties modelling the more complex, urbanised reaches. MHYST should not be seen as a rival to detailed inundation studies, but as a first approximation able to rapidly provide inundation maps in data-scarce areas.

show abstract

A comparison of simplified conceptual models for rapid web-based flood inundation mapping

Cited by 61 publications

References 11 publications

A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest

Inundation mapping based on reach-scale effective geometry

Contact Info

Product

Resources

About