Flood Hazard Mapping with Distributed Hydrological Simulations and Remote-Sensed Slackwater Sediments in Ungauged Basins

Abstract

We present a basin-scale method to assimilate hydrological data from remote-sensed flood evidence and map civil infrastructures with risk of flooding. As in many rural areas with a semi-arid climate, the studied catchments do not contain stream gauge, and precipitation data does not capture the spatial variability of extreme hydrological events. Remote-sensed flood evidence as slackwater sediments were available at the whole basin, allowing the paleohydrological reconstruction at many sites across the catchment. The agreement between the predicted and observed inundation area was excellent, with an error lower than 15% on average. In addition, the simulated elevations overlapped the observed values in the flooded areas, showing the accuracy of the method. The peak discharges that provoked floods recorded the spatial variability of the precipitation. The variation coefficients of the rainfall intensity were 30% and 40% in the two studied basins with a mean precipitation rate of 3.1 and 4.6 mm/h, respectively. The assumption of spatially uniform precipitation leads to a mean error of 20% in evaluating the local water discharges. Satellite-based rainfall underpredicted the accumulated precipitation by 30–85.5%. Elaborating an inventory of the civil infrastructures at risk was straightforward by comparing the water surface elevation and transport network. The reconstructed maps of rainfall rate were used in the distributed hydrological model IBERPLUS to this end. Recent flood events that overtopped the infrastructures at risk verified our predictions. The proposed research methods can be easily applied and tested in basins with similar physical characteristics around the Mediterranean region.

Technological Advances to Rescue Temporary and Ephemeral Wetlands: Reducing Their Vulnerability, Making Them Visible

“…For the sake of conciseness, we provide a summary of the main characteristics and refer the reader to [32] for extra details.…”

“…This module makes it possible to calculate the rainfall-runoff transformation in an entire basin, which accumulates the surface waters in the depressions on the landscape. We neglected other physical processes, such as water infiltration into the soil, underground flow, evapotranspiration, and percolation [23], because of the short duration of the simulated storms and the hydrophobic soil conditions developing under the current soil uses and management [32]. Furthermore, we activated the IBER+ plugin to take advantage of the parallelisation functionalities in the GPU, ensuring a much higher calculation acceleration than the standard version.…”

“…For the first time, we show that distributed hydrological-hydraulic model driven by maximum precipitation is a novel tool to detect potential wetland locations based on the simulated water depth and flow velocity fields. Only presently can we exploit the advantages of the proposed approach because of: (i) the cheap cost of workstations with powerful graphics processing units (GPU), (ii) the release of free or open-source software for distributed hydrological modelling based on the two-dimensional Saint-Venant equations and GPU (e.g., IBER+ [23][24][25], TRITON [26], SERGHEI-SWE [27], LISFLOOD-FP [28]), (iii) the availability of open topographic [29], meteorological [30] and hydrological data in some countries as Spain (see references in [31] and [32]). After identifying a possible wetland, we can verify its existence by cross-referencing historical orthophotos, cartography, and maps with place names, conducting inventories, and making on-site visits.…”

“…We employed the SIOSE v2018 database of the National Geographic Institute of Spain to determine them. For the olive grove surface lacking vegetation around the crops (>80% of the drainage area), we set the value of Manning roughness to n = 0.025 s•m −1/3 since it adapts to soils with mostly silt and clay contents [32].…”