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CHAPTER 3 Tsunami Maximum Flooding Assessment in GIS CHAPTER 3 Tsunami maximum flooding assessment in GIS environment G. Mastronuzzi1,2, S. Ferilli3, A. Marsico1, M. Milella4, C. Pignatelli5, A. Piscitelli4, P. Sansò6 & D. Capolongo1 1Dip. di Scienze della Terra e Geoambientali, Università degli Studi “Aldo Moro”, Via Orabona 4 – 70125 Bari, Italy 2LAGAT-TA Laboratorio Gis Geo-Ambientale e di Telerilevamento di Taranto, Università degli Studi “Aldo Moro”, Polo Jonico, II Facoltà di Scienze MM.FF.NN, Via A. De Gasperi (Paolo VI) – 74100 Taranto, Italy 3Dip. di Informatica, Università degli Studi “Aldo Moro”, Via Orabona 4 – 70125 Bari, Italy 4Environmental Surveys s.r.l, Via della Croce 156 – 74123 Taranto, Italy 5Geo Data Service s.r.l, Via della Croce 156 – 74123 Taranto, Italy 6Dip. di Scienze e Tecnologie Biologiche e Ambientali, Università del Salento, Via per Arnesano, 73100 Lecce, Italy Abstract The presence of mega-boulders scattered landward along gently sloping rocky coasts is attributed to the impact of tsunami or of exceptional storms. Con- sidering the original position and the size of the largest boulder is possible to estimate the characteristics of the wave that moved them in the past, and then estimate the maximum inundation. The present roughness of the coastal area conditions the capacity of the tsunami inland penetration in case of a future event. The knowledge of the parameters of the possible tsunami together with the coastal topography and roughness make possible to estimate automatically scenarios of probable fl ooding. 1 Introduction The recent events of Chile (February 27, 2010) and Tohoku Oji tsunamis (March 11, 2011) highlighted the extensive fl ooding of the coastal area due to their impact. WIT Transactions on State of the Art in Science and Engineering, Vol 65 , © 2013 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) doi:10.2495/978-1-84564-770-4/003 62 Tsunami The implementation of models that simulate the complex processes occurring during inundation allows run-up and fl ooded area to be assessed and civil protec- tion plans to be realized. The growing concentration of population and human activities in coastal areas, in fact, determines the increase of exposed values and, consequently, rises up hazard, vulnerability and risk. The implementation of the knowledge concerning every signifi cant change of the coastal environment in relation to the increase of human activities and natural evolution allows the construction of predictive models and/or risk assessment. Deposits consisting of large boulders, isolated or sparse or accumulated in fi elds and berms, can be recognised frequently along the rocky coast. This geomorpho- logical evidence testifi es the impact of extreme waves – tsunamis and/or excep- tional storms – occurred in the recent past [1–10]. Recent papers indicated that detailed survey of boulder fi elds and accumulations (shape, size(s), rock density, etc.) is useful to evaluate the minimum wave height able to detach/move boulders from the circalittoral/adlittoral environments and accumulate them inland. Starting from these features, some formulas have been developed to describe the tsunamis/ storms features and in particular their height, HT or Hs, respectively [11–16]. Furthe rmore, the estimation of the minimum wave height allows us to calculate the fl ooding limit along coasts characterised by fl at profi le by applying the empirical formula of Hills and Mader [17]: 1.33 −2 Xmax = (HT) n k (1) where HT is the height of the tsunami to the coastline, n is the Manning coeffi - cient, k = 0.06 is a constant that is used for many tsunamis [18]. In most cases, the coastal area is characterized by the sloping profi le; hence, it is possible to consider (2) [13]: 1.33 −2 α Xmax = D + (HFL) n k cos (2) In this equation, D represents the inland position (distance from the coastline) of the largest boulder displaced by tsunami. HFL is the height of the fl ood running inland corresponding to HT – HC where the latter is the cliff height. Evidently, the values obtained by (1) and (2) are strongly infl uenced by the Manning number (n); it takes into account the hydraulic roughness that is a quan- tifi cation of the resistance that an irregular surface opposes to the water fl ow in a channel [19, 20]. There are, therefore, different Manning coeffi cients, depending on the surface features that water fl ow encounters during its propagation inland (sand, rock, vegetation, buildings, etc.). This parameter is diffi cult to be quantifi ed if it is not directly measured, but photographic evidence and/or post-event surveys allowed us to correlate classes of surfaces to n values that express their roughness [21]. In every coastal area, the Manning number is not constant over time due to growth of vegetation, processes of coastal weathering, karstifi cation, abrasion and urban development [22]. Numerous studies carried out on open channels in case of fl ows without lateral boundaries suggest that the roughness of a given surface WIT Transactions on State of the Art in Science and Engineering, Vol 65 , © 2013 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) Tsunami Maximum Flooding Assessment in GIS Environment 63 crossed by a fl ow can be obtained considering the standard deviation of the bottom and the fl ow depth [23–25]: ⎛⎞Z 1-⎜⎟0 kR6 ⎝⎠R n = (3) g ⎡⎤⎛⎞ZZ ⎛⎞ ⎢⎥⎜⎟00--ln ⎜⎟ 1 ⎣⎦⎝⎠RR ⎝⎠ where k is the constant of Von Karman, g is the gravitational constant, R is the depth of the fl ow and Z0 is the standard deviation of the surface bottom. This equa- tion may correspond to the case of fl ood produced by tsunami(s). The terrestrial laser scanner (TLS) survey permits to obtain in situ measurement of the micro-topography of a surface; this technique supplies a point cloud from which it is possible to reconstruct a digital terrain model (DTM) and the relative standard deviation with a defi nition of the order of millimetres [26]. The objective of our current researches is to improve a digital tool we recently developed [27]; it, operating in a Geographic Information System (GIS) environment, is able to calculate automatically the limit of inundation due to a tsunami of known height (HT). Five coastal areas of southern Apulia marked by large limestone and/or cal- carenite boulders deposited by tsunami(s) in historical time have been chosen as case study [5, 9, 28, 29] (Fig. 1). Their TLS surveys allow the Nott’s equation [11] for the joint bounded scenario to be improved and a new equation aimed at assess- ing the maximum fl ooding due to tsunami to be proposed [13]. This work is an attempt to extend to the entire perimeter of Southern Apulia the conclusions obtained in the case study of Torre Castiglione [27]. With this aim, the following steps have been carried out: (i) TLS survey to obtain different roughness coeffi cients as a function of the prevailing surfaces features (coastal karstifi ed Figure 1: Geographical position of studied areas. WIT Transactions on State of the Art in Science and Engineering, Vol 65 , © 2013 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) 64 Tsunami calcarenite, covered karstifi ed calcarenite, sand, vegetation, etc.); (ii) assessment of the minimum wave necessary to transport the largest boulder; (iii) implementation of an informatic tool that allows the automatic identifi cation and extent of the different types of surface by orthophotos analysis and the defi nition of fl ooding area using (2). 2 Coastal geomorphology The fi ve study areas considered in this work are placed both along the Adriatic side (Torre Santa Sabina, Brindisi) and the Ionian one (Torre Sant’Emiliano and Torre Squillace – Province of Lecce; Torre Castiglione and Punta Saguerra – Province of Taranto) of southern Apulia (Fig. 1). These coastal areas show seaward sloping surfaces, staircase arranged and placed at different elevation above the mean sea level; they are shaped on bioclastic/algal calcarenite except at Torre Sant’Emiliano, where the coast is shaped directly in the Mesozoic limestone that represents the local basement. These surfaces correspond to Middle–Late Pleistocene marine terraces produced by the superimposition of regional uplift and sea level changes [30]. In particular, the local landscape is characterised by sub-horizontal surfaces generally placed between the sea level and 5 m above mean sea level with the exception of Torre Sant’Emiliano, where the lowest marine terrace is placed at about 10 m above mean sea level. Seaward, surfaces are limited by a cliff 4/6 m high, only partly emerged except in Torre Sant’Emiliano locality, where sea bottom is very deep, about 30 m below mean sea level. The adlittoral areas are characterised inland by a standard sequence of (i) karstifi ed rock; (ii) soil cover; (iii) vegetation. The fi rst one is characterised by pinnacles, solution pools and erosive channels due to the combined effect of wave erosion and spray induced karst. The result is a very irregular surface (Fig. 2). Landward a rapid change in the microtopography can be surveyed. This area is the result of soil-stripping Figure 2: Pinnacles, solution pools and erosive channels defi ne a very irregular surface due to the combined effect of wave erosion and spray induced karst that characterize the adlitoral area near the coastline: (A) algal cal- carenite of Punta Saguerra, Taranto (fractures due to the marine under- cutting are also readable); (B) algal calcarenite of Torre Castiglione, Taranto; (C) biocalcarenite of Torre Santa Sabina, Brindisi. WIT Transactions on State of the Art in Science and Engineering, Vol 65 , © 2013 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) Tsunami Maximum Flooding Assessment in GIS Environment 65 phenomena most likely related to the action of exceptional waves (Fig.
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