Inundation mapping based on reach-scale effective geometry

Abstract

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.

Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods

“…All of these methods are based on a local dischargewater height relationship determined from (i) the cross section and longitudinal profile geometries and (ii) a local hydraulic formula, i.e., Manning-Strickler (Zheng et al, 2018a, b;Johnson et al, 2019;Garousi-Nejad et al, 2019) or Debord (Rebolho et al, 2018). The cross-sectional geometry is either extracted locally from the DTM for the Au-toRoute method (Follum et al, 2017(Follum et al, , 2020 or averaged at the river reach scale based on a height above nearest drainage (HAND) raster (Nobre et al, 2011) for the following methods: f2HAND (Speckhann et al, 2017), GeoFlood (Zheng et al, 2018a), MHYST (Rebolho et al, 2018), and hydrogeomorphic FHM (Tavares da Costa et al, 2019). These approaches are very efficient in terms of computation times and can therefore be suitable either for real-time inundation forecasting at continental scales (Liu et al, 2018) or for probabilistic or multi-scenario modeling (Teng et al, 2017).…”

Real-Time Flood Mapping on Client-Side Web Systems Using HAND Model

“…Flow Directions Drainage Area and Zones HAND Model Figure 2. Steps of HAND algorithm procedure to generate flood maps (adapted from [42]).…”

“…We used the D8 algorithm to compute the flow directions. The D8 algorithm Steps of HAND algorithm procedure to generate flood maps (adapted from [42]).…”

Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model

“…Among the flooding information, the potential inundation region and associated area are supposed to be known in advance. In spite of the difficulty in obtaining real-time measured observations (e.g., water level), they could be established through the hydraulic numerical models under consideration of the design rainfall events regarding the various return periods [4][5][6]. For example, Chen et al [4] established a potential inundation-map database by means of the hydraulic numerical model (HEC_RAS) with the design rainfall events of the various return periods.…”