Study of wave runup using numerical models and low-altitude aerial photogrammetry: A tool for coastal management
Introduction
The administrative Region of Liguria (Italy, NW Mediterranean) is often hit by severe storms (Parodi et al., 2012, Rebora et al., 2013, Fiori et al., 2014), that are usually associated with high waves, or sea storms, hitting the coastline (Orlandi et al., 2008, Pasi et al., 2011). As most of the coastal urban development in the Region happens near the shoreline with few physical barriers to stop wave runup, the main damages caused by extreme wave events are due to sea waves hitting infrastructures such as roads and railroads or commercial properties, such as beach resorts that are kept on the beach also during the winter season. This scenario is common to the majority of Italian and Northern Mediterranean coastal areas (Jiménez et al., 2011). Here, one need of coastal managers is to assess the impact of extreme wave events in the immediate aftermath of a storm or, more adequately, to be able to predict to some extent the areas more vulnerable to runup of extreme swells (Ruggiero et al., 2001). To do this, it is necessary to have on one side accurate and timely data on coastal topography, geomorphology and impact of the swell. On the other side, models calculating the maximum runup of swell waves can be implemented if the coastal topography and wave parameters are known with sufficient accuracy.
The main tools that can be employed to obtain reliable topographic and geomorphological data in coastal areas are LIDAR or aerial surveys (e.g. White and Wang, 2003). In a large effort to provide reliable topographic data for the Italian coastlines, the Italian ministry of Environment is performing coastal LIDAR surveys along the national coasts, and recently made available coastal orthophotos from its web portal (www.pcn.minambiente.it). Airborne techniques have the advantage of providing wide coverage and accurate topography (point cloud and orthophotos), but their high cost and the necessity to deploy an aircraft to obtain such data makes virtually (and economically) impossible to perform surveys repeated regularly in time or each time that a sea storm hits the shoreline of interest. The solution is therefore to adopt surveys on the ground (Morton et al., 1993, Cariolet and Suanez, 2013), that are more repeatable in time, renouncing to the detail that a point cloud and an orthophoto can give.
In an ideal situation, if the topography of a beach is known with sufficient detail and wave data are available from an offshore buoy, it is possible to implement a model that simulates the runup along transects on the shore. Varying the intensity and direction of the swell, one could perform pre-event assessments of the runup, which are the information ultimately necessary to coastal managers to assess the expected damages due to incoming waves. The weak point of such workflow is that before being used in this way, a validation process that uses observations to tune model parameters for the specific area is necessary.
In order to validate a runup model for an area, one needs accurate topographic data of the beach and the evaluation of the runup elevation repeated for a different set of sea storms with different wave height, period and direction. The ideal setting would be to repeat LIDAR and orthophoto surveys after each event to detect the maximum wave runup and compare it with the modeled one to perform a best-fitting analysis and ultimately tune the model parameters in the area of interest. To do this, either costs are too high or one needs to abandon the synoptic view and rely on ground surveys, which can cover smaller areas.
In this paper we show a workflow that, starting from rapid surveys performed with Unmanned Aerial Vehicles (UAVs), allowed us to obtain accurate beach topography and information on observed wave runup for two sea storms in the Ligurian Sea, NW Mediterranean. We then set up a modeling chain, which includes concatenated wave and runup models, and compare modeled and observed runup values. We conclude that this workflow is rapid, low-cost and effective, and can be exported in other Mediterranean areas.
Section snippets
Methods
As outlined in the introduction, in this paper we propose a workflow to obtain runup observations and compare them with modeled runup values. The workflow is summarized in different steps hereafter, while in the next sections we describe more in detail the study area and the different aspects of the methodology employed.
- 1)
We collected aerial photos of the study area immediately after two swell events.
- 2)
From the first set of aerial pictures and a set of ground control points surveyed with
Observed and modeled runup comparison
The DEM obtained from the procedure of merging the multibeam and the point cloud from UAV photos is represented in Fig.5a. In Fig. 5b and c are shown the orthophotos with calculated and observed runup. The DEM varies from 6 m (elevation of railroad located landward) and −10 m (which is below the closure depth for Ligurian beaches, that was estimated by Vacchi et al., 2012 at 5–9 m). CSHORE model has been implemented on 5 different transects on the study area for the two different wave events.
Final remarks and future research directions
In this study, we presented a workflow that uses field data obtained from a UAV as both source of input and setting of a modeling chain that calculates wave runup on the shore. The UAV approach allowed gathering observations rapidly and accurately (on-land DEM with accuracy of ±12 cm) and low-cost. This accuracy is in line with that obtained by other studies employing this technique, that lies in the range of 11–31 cm (Niethammer et al., 2012, Hugenholtz et al., 2013, Grenzdoerffer et al., 2008
Acknowledgments
This work is part of the project MIRAMAR, funded by the PO CRO European Social Fund, Regione Liguria 2007–2013 Asse IV “Capitale Umano/Human Capital”. A. Rovere's acknowledges the Institutional Strategy of the University of Bremen, funded by the German Excellence Initiative, and ZMT, the Leibniz Center for Tropical Marine Ecology. The authors wish to acknowledge C. Cavallo and Regione Liguria for providing multibeam data; R. Ivaldi (Istituto Idrografico della Marina, Genoa) for assistance in
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