A detailed delineation of flood-prone areas over large regions represents a challenge that cannot be easily solved with today's resources. The main limitations lie in algorithms and hardware, but also costs, scarcity and sparsity of data and our incomplete knowledge of how inundation events occur in different river floodplains. We showcase the implementation of a data-driven web application for regional analyses and detailed (i.e., tens of meters) mapping of floodplains, based on (a) the synthesis of hydrogeomorphic features into a morphological descriptor and (b) its classification to delineate flood-prone areas. We analysed the skill of the descriptor and the performance of the mapping method for European rivers. The web application can be effectively used for delineating flood-prone areas, reproducing the reference flood maps with a classification skill of 88.59% for the 270 major river basins analysed across Europe and 84.23% for the 64 sub-catchments of the Po River.
The design, implementation and demonstration of a novel and generic computational forecast framework for multi-scale prediction of extreme sea levels and associated flooding is presented. Denoted Water Information Forecast Framework (WIFF), it integrates process-based models for waves, tides and surges from regional to local scales, predicting the flooding of coastal areas, and supporting the routine and emergency management of coastal resources. WIFF manages the simulations and the real-time monitoring data, archives the data and makes the information available through a WebGIS that targets users with distinct access privileges. Additionally, the web component of WIFF adapts automatically and transparently to any device. WIFF also provides ways to assess the model accuracy and generates tailored products based on model results and observations. WIFF is demonstrated in the prediction of extreme water levels in the Portuguese coast, simulating processes at different scales: at basin scales, waves are simulated in the North Atlantic and in the Portuguese shelf, and sea levels due to tides and atmospheric forcings are simulated in the Northeast Atlantic; at estuarine scales, high-resolution, fully coupled wave/circulation predictions are performed in the Tagus estuary to account for wave-current interactions. User-oriented georeferenced products are generated, including automatic model/ data comparisons, targeting the needs of civil protection agents and combining for the first time an agile, service-oriented platform with high-resolution, process-rich predictions of the Tagus dynamics.
Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of digital elevation models (DEMs) has advanced the modeling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMs is dissected in the context of hydrogeomorphology. Ten sets of terrain data are analyzed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems.
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