A storm surge model implementation and identification of coastal areas in risk of inundation, in the Mediterranean Sea.

Transcription

1 International Congress on Environmental Modelling and Software Brigham Young University BYU ScholarsArchive 4th International Congress on Environmental Modelling and Software - Barcelona, Catalonia, Spain - July 28 Jul 1st, 12: AM A storm surge model implementation and identification of coastal areas in risk of inundation, in the Mediterranean Sea. Y. Androulidakis Yannis N. Krestenitis Y. Kontos G. Georgakopoulos Follow this and additional works at: Androulidakis, Y.; Krestenitis, Yannis N.; Kontos, Y.; and Georgakopoulos, G., "A storm surge model implementation and identification of coastal areas in risk of inundation, in the Mediterranean Sea." (28). International Congress on Environmental Modelling and Software This Event is brought to you for free and open access by the Civil and Environmental Engineering at BYU ScholarsArchive. It has been accepted for inclusion in International Congress on Environmental Modelling and Software by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu, ellen_amatangelo@byu.edu.

2 iemss 28: International Congress on Environmental Modelling and Software Integrating Sciences and Information Technology for Environmental Assessment and Decision Making 4 th Biennial Meeting of iemss, M. Sànchez-Marrè, J. Béjar, J. Comas, A. Rizzoli and G. Guariso (Eds.) International Environmental Modelling and Software Society (iemss), 28 A storm surge model implementation and identification of coastal areas in risk of inundation, in the Mediterranean Sea. Y. Androulidakis, Y. N. Krestenitis, Y. Kontos and G. Georgakopoulos Laboratory of Maritime Engineering & Maritime Works, Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece (iandroul@civil.auth.gr) Abstract: Low-elevation coastal areas and their populations are at risk during and after the appearance of a storm surge event. Coastal flooding as a result of storm surge events is examined in this paper for a number of areas around the Mediterranean Sea. A 2- dimensional hydrodynamic model has been implemented in the region, calculating the sea level altimetry in a 1/1 o x1/1 o grid. The sea level rise due to storm surge events is examined for the period In situ measurements were collected from stations around the Mediterranean coasts, which were compared with the simulation results and several parameters of the model have been tested and calibrated. Potential inundation zones were then identified using a 9-m resolution digital elevation model (DEM). At these zones, the sea-level alterations were calculated for the study period, where the area affected by sea level rise of 1 m for various regions was estimated. Moreover, the implication of storm tracks in major sea level rise incidents in the Mediterranean was investigated for the studied period. The computation and plotting of storm tracks, was accomplished using an algorithm that tracked down sea level pressure (SLP) minima, recording their development. Keywords: storm surge, modelling, coastal inundation, Mediterranean Sea 1. INTRODUCTION Low-elevation land areas and their populations are at risk during and after the appearance of a storm surge event above or near coastal areas. High sea levels and the strong forces exerted by accompanying waves, impact directly or by over-topping of sea defenses on humans, property and habitats. They may even cause loss of life, damage (through inundation and waves), loss of habitat and useful land, property, infrastructure, services and so forth. Traveling mid-latitude low pressure systems act to raise the sea level directly below them, but this effect alone is quite weak in semi enclosed basins such as the Mediterranean Sea [Pirazzoli, 2]. The most important meteorological factors are the associated winds, turning anticlockwise in the northern hemisphere. These winds tend to drag the water in the same direction, with a deflection to the right due to the Coriolis force. Although, the Mediterranean Sea is not on the main storm track of the European and North Atlantic area [Rogers, 1997], storm track events originated mainly from Africa, with a direction from south to north, affect significantly local sea-level alteration. This paper describes the major storm surge events that appeared in the Mediterranean region in additions with the inundation areas in danger for the period Numerical modeling, storm tracks computation and plotting, in situ sea level measurements, satellite images and a digital elevation model were used for this purpose. 1187

3 2. STORM SURGE MODEL AND SIMULATION OF SEA LEVEL ALTERATION 2. 1 Introduction A 2-dimensional hydrodynamic model has been implemented in the region, calculating the sea level altimetry in a 1/1 o x1/1 o grid (Figure 1). The sea level rise due to storm surge events is examined for the period Wind data, atmospheric pressure (SLP) and wave data [Soukissian et al., 27] are the model s forcing. Several parameters of the model have been tested and calibrated, such as the surface friction coefficient and sea bed roughness coefficient. 2.2 Model Description Figure 1. Storm surge model domain and bathymetry The basic equations of the model are the depth-averaged shallow water equations: 2 2 u u u z 1 P 1 τχ u u + v + u + v fu+ g = + k t x y x ρ x ρ (h+ z) ρ ο(h+ z) 2 2 v v v z 1 P 1 τy u u + v + u + v + fu+ g = + k t x y y ρ y ρ (h+ z) ρ ο(h+ z) ο ο ο ο z (h+ z)u (h+ z)v + + = (3) t x y where t is time, (x,y) are spatial coordinates, z is the water level elevation, u, v are the x and y components of the depth-mean current, h is the undisturbed water depth, g is the acceleration of gravity, f is the Coriolis parameter, k is the bottom friction coefficient, τ x and τ y are the x and y components of the wind stress, ρ o is the density of the water and p is the atmospheric pressure at sea level. The calculation of the climatological wind stress fields is based on the transformation wind velocity data at 1m, to the zonal/meridional components of the wind stress exerted on the sea surface, according to the formula (1) (2) τ = ρ Α C D W W (4) where ρα is the air density, W is the wind velocity and CD is the surface friction coefficient. Experiments with various values of the surface friction coefficient were executed, including the calculation, according the formula [Smith & Banke, 1975]: 1 3 CD= W (5) Tidal boundary conditions where imposed in the Gibraltar open boundary by using specific tidal components measured in area s stations and the tidal harmonic analysis: Η=.1 Ho j cos (F j dt 2π-B j π / 18 (6) where F j, Ho j, B j are area s tidal measured components and dt is the model s time-step. The contribution of the wind factor, the wave factor, the atmospheric pressure factor and the tidal factor at the sea elevation field was examined both separately and simultaneously. Additional characteristics of the model are given in Table 1 [De Vries et al., 1995]. 1188

4 Table 1. Storm surge model characteristics Grid C Depths h at ζ Bottom Friction Quadratic Manning N=E b 2 g/h 1/3 Coastal Boundary Condition No normal flow Advection Smagorinsky Integration Scheme Explicit leap frog Time Step (sec) Additional data A thorough search of sea level data from gauge stations around the Mediterranean was done for the period Data were collected from the websites of the Med-GLOSS program and the European Sea-level Service. Moreover, sea level measurements have been gathered for 5 stations in the Greek region, regarding the years 1992 until 24 showing the mean sea level alteration and the extreme values and dates that these values have been appeared. Additional sea-level data were collected by using satellite data concerned the study period and area. These altimetry data were collected from the French space agency (Aviso/Altimetry project) and regard the composition of various scans of different satellite projects (Topex/Poseidon, Jason-1, ERS-1 and ERS-2, EnviSat, and Doris) offering continuous recording of the study area 3. STORM SURGE DETECTION AND STORM-TRACK CALCULATION Literally, storm tracks can be identified by following low pressure centers on synoptic charts and plotting their trajectories on maps, thereby producing cyclone tracks in the pure sense. A method for automated detection and tracking of storms or cyclones is also presented by the National Aeronautics and Space Administration - Goddard Institute for Space Studies (NASA-GISS) (see reference) and a similar simplified methodology is used in this study. 3.1 Basic characteristics of the method used The computation and plotting of storm tracks, is accomplished by using an algorithm that identifies and tracks sea level pressure (SLP) minima. The algorithm searches for and identifies absolute minima from the gridded field of every 12-hour period of the desired year. The minimum for the ensuing 12-hour SLP grid is searched, and its position is again located. Any SLP minima within a critical radius of 144 km are joined by a segment, representing the path of the low pressure center during that 12-hour period (a cyclone's center can travel at a mean speed of no more than 12 km/hr or 144 km / 12 hr). Any two associated minima identify one storm track segment, as long as the storm lasts at least 36 hours. If at any time two segments on the same track are found to define an acute angle of less than 85, the low pressure centers are considered to represent separate storms (extra tropical cyclones are not found to "double back" on themselves over 12 hours). Finally, throughout the full duration of a storm track, SLP in its center is required to be less than 115 hpa (low pressure means SLP<115 mbars). In this way, storm tracks are computed and plotted on a Mediterranean map. The use of absolute minimum SLPs instead of local minima (like NASA s method) produces the risk of not identifying some storm tracks generated by the passage of secondary low pressure centers. For that, but also for presentation reasons and visual confirmation, a contour map for every 12-hour period is plotted, creating a slideshow of 73 maps (732 for leap years) for every year, presenting low pressure regions (SLP<=115mBars) and their development. 3.2 Correlation procedure The full understanding of the magnitude of the impact a storm track may have to the sea level height (SLH) of adjacent areas, also requires studying the relationship between SLP and SLH in the Mediterranean basin in general and in specific areas of interest in particular. The Pearson product-moment correlation coefficient (r) [Pearson, 1929 and Pearson, 1931], first introduced by Francis Galton [1988], is used to document the relationship 1189

5 between 12hourly mean SLP and 12hourly max SLH measured in several gauge stations around the Mediterranean basin. The correlation coefficient indicates the strength and direction of a linear relationship between two random variables and usually refers to the departure of two variables from independence. The correlation coefficient between two properties x and y (r x,y ), which is commonly used in similar studies [Moron, Ullmann, 25], is given below: rx, y = Cov( X, Y ) σ x σ y (7) where 1 r, 1 x y n 1 and Cov( X, Y ) = ( xi µ x ) ( yi µ y ) (8) n i= 1 where µ x, µ y are the averages of x, y respectively. The correlation is 1 in the case of an increasing linear relationship, 1 in the case of a decreasing linear relationship. The closer the coefficient is to either 1 or 1, the stronger the correlation between the variables. For every gauge station in the eastern Mediterranean, for each year between 2 and 24, the correlation coefficient between the mean 12-hourly SLP and the max 12-hourly of observed SLH is computed. On Table 2 the SLP- SLH correlations are presented for every gauge stations, for the years that SLH timeseries exist. Then, the local SLH maxima are searched and every one of them is considered as day. A six-day window is considered preceding the day of local max SLH appearance (time lags 5, 4, 3, 2, 1 and ). For each 6-day window the SLP- SLH correlation coefficient is again computed and it is compared to the annual correlation. Table 2. Mean 12hr SLP max 12hr SLH corr. coeff. at Mediterranean gauge stations (x(-1)) Station Ancona(IT) Antalya(TR) Alexandroupoli (GR) Barcelona(ES) Catania(IT) Chios(GR) Dubrovnik(CR) Genova(IT) Lefkas(GR) Naples(IT) Otranto(IT) Rovinj(HR) Split(HR) Trieste(IT) Zadar(HR) RESULTS Sea surface elevation timeseries data from several stations around the Mediterranean are compared with model output showing good performance of the model in a qualitative level. Especially in areas characterized as in danger and in areas that in situ measurements exist, model output of SLH was produced, in order to check the possibility of flooding and to test the performance of the model, respectively. Two characteristic cases are presented below. A storm track (15 th of 21) appeared on 1 st of March, 21 (Figure 2) affecting the sea surface elevation in western Mediterranean. Though, the storm s center does not move directly above the affected coastal areas (523 km NW of Barcelona gauge station at the time of max SLH, afternoon of 2 nd of March, 21), the low pressure system s extend (Figure 3) and its strength (SLP=981 hpa in the center and 994 hpa above Barcelona station, lowest 12hr mean value for 21), affect the SLH of the adjacent areas. 119

6 Figure 2. Storm tracks 15, 63 and 64 related to SLH- rise in west and central Mediterranean respectively Figure 3. Contour map presenting an atmospheric low pressure system above western Mediterranean on the afternoon of the 2 nd of March, 21. In Figure 4, the modeling and in-situ 21 sea elevation timeseries are presented for Barcelona. High SLH values appeared (>m) at the beginning of March (15 th storm track of 21) and in mid November (highlighted in red circles). S e a s u rfa c e a lte ra tio n In Situ Measurements Model Results Time-Days -.6 Figure 4. Barcelona model and in-situ surface elevation during year 21 Table 3 represents Barcelona year 21 max SLH analysis, from which useful conclusions can be made about the storm tracks relationship with big sea level heights. The SLH-SLP Barcelona correlation for the year 21 is r=-.55 regarding the in situ measurements (Figure 5a) and r=-.51 regarding the SLH model results (Figure 5b). As expected, the correlation for the in situ values is higher, but the small variance between the two values confirms the good model performance. Likewise, the correlation for the 6-day-window of the SLH-rise incident described above (relevant to the 15 th storm track 21) r=-.84, significantly greater than the annual one. The case studied here is 2 nd on the list of the highest observed SLH values in Barcelona station, with a sea level height of 4.7cm, displaying the lowest SLP of all. Most of the incidents are related with the appearance of a storm track nearby, while one could argue that the more the implication of a storm track in the rise of SLH (smaller distance, lower sea level pressures, greater extend of low pressure system), the closer the correlation to

7 Table 3. Barcelona 21 max SLH and storm track implication analysis. Gauge station year r SLP SLH SLH annual average BARCELONA hr mean 12hr max (observed) Incident SLH (cm) SLP (hpa).6 r day 12hr Storm track storm distance (km) cm storm direction ( ) 78 S-N (12-123) 523 S-N W-E ( ) 776 SW-NE ( ) W-E (1-4) 82 S-N ( ) 82-12hr max SLH (m) hr mean SLP (hpa) Figure 5a. SLH SLP correlation for Barcelona gauge station, year hr max SLH (m) hr mean SLP (hpa) Figure 5b. SLH SLP correlation for Barcelona model output, year 21 Another interesting case ascertained at the beginning of December 22, where the successive storm tracks 63 and 64 (Figure 2) affected the sea level elevation in several areas of central and eastern Mediterranean (Figure 6). One could support that it is, in fact, a single, integral storm track that projects an anti-clockwise rotation of the low pressure center around Italy. The existence of two separate successive storm tracks is suggested by the method because during the studied 12-hour period the storm seems to double-back on itself. This criterium applies mostly to extratropical cyclones, while this one is obviously restrained within the Mediterranean basin, hence, it is safe to assume that storm tracks 63 and 64 are, in fact, two phases of the same integral storm surge. The events presented in Figure 6 represent the transition from the first phase (storm track 63) to the second (64) of the aforementioned unified storm surge event. 1192

8 Figure 6. Satellite sea-level altimetry of the Mediterranean on the 4th of December 22 and 2 specific affected areas (left). An SLP contour map showing the spatial extension of the low pressure system (right). In Figure 7, the alteration of sea level is presented for 2 areas in Adratiatic Sea, as calculated for the period of the 63 storm track. The highest SLH values were detected in the Adriatic Sea, where the low pressure centre (min SLP=998 hpa) clouds over for days. Sea surface elevation (m) Split Sea surface elevation (m) Venice Figure 7. Model s SLH Time output for Split and Venice on 4th of December, 22 Potential inundation zones were calculated with the 9-m resolution shuttle radar topography mission (SRTM) digital elevation model (DEM). SRTMDEM has spatial resolution of 9 m, with horizontal and vertical accuracies of 45 and 15 m, respectively [Sun et al. 23]. The absolute vertical accuracy of the SRTM DEM is about 15 m, and the relative accuracy to the coastline is less than 1 m. The resolution of the SRTM DEM is, therefore, satisfactory to determine vulnerable coastal areas. Areas like the Venice Lagoon and its city Venice, are very sensitive to water level variations, given that the city of Venice is only about 1 meter above mean sea level and the medium depth of the lagoon is about 1 meter. The area where the land elevation is lower than 1m, produced by the 9-m elevation model is presented in red in Figure 8. The city of Venice often experiences flooding events induced by abnormally high water levels at the three entrances of the lagoon. Water levels significantly higher than the expected astronomical tide level are reported several times a year, especially between October and January [Bargagli et al, 21]. So, events like the one of the 4 th of December could affecte significantly all the coastal zone around the lagoon. Figure 8. Map of Venice Lagoon of coastal vulnerability to sea-level rise up to 1m. 1193

9 CONCLUSIONS The atmospheric pressure influence is an important factor of simulation performance and hence at modelling results. Additionally, the influence of the wind stress is also significant. As far as the storm track identification method is concerned, the fact that the algorithm tracks absolute SLP minima (instead of local minima NASA proposes) produces the risk of not identifying some storm tracks generated by the passage of secondary low pressure centres. On the other hand, the visualization of low pressure systems and the correlation coefficient calculation strengthen the efficiency of the above method. Visualization of secondary lows, covers the gaps that the storm track identification method produced, while the correlation coefficient calculation and the relation between the 6-day-window and the annual SLP-SLH correlation coefficient indicates possible errors in relating high sea level alterations to storm surge events. Modelling the sea-level alteration due to storm surge events can be employed as a common prediction method of SLH-rise incidents all over the Mediterranean. So, the combination of atmospheric forecasting modelling with a storm surge hydrodynamic model can estimate with good accuracy the near future storm surge events and their accompanied sea level alterations. Combined with a method of storm tracks identification and observation of their dominant characteristics together with the detection of coastal areas in risk of inundation, a useful tool, in terms of generating reasonably based scenarios of fore coming flooding events in the coastal areas of the Mediterranean, can arise. Warning systems of coastal flooding improve authorities preparedness and help coastal human society to obtain proper measurements in cases of near future-coming extreme meteorological incidents. In sustainable and integrated development of the coastal zone, a storm surge event is a crucial factor, affecting significantly the quality of life. Additionally, hind casting storm surge events and detecting previous inundation cases can offer to stakeholders a better view of possible changes and corrections that need to be done in already developed risky coastal areas. REFERENCES Archiving, Validation and Interpretation of Satellite Oceanographic data - Aviso/Altimetry project, Bargagli, A., A. Carillo, G. Pisacane, P.M. Ruti, M.V. Struglia, and N. Tartaglione, An Integrated Forecast System Over The Mediterranean Basin: Extreme Surge Prediction In The Northern Adriatic Sea, Volume Monthly Weather Review 13,22. De Vries, H., M. Breton, T. Mulder, Y. Krestenitis, J. Ozer, R. Proctor, K. Ruddick, J.C. Salomon and A. Voorrips, A Comparison Of 2D Storm Surge Models Applied To Three Shallow European Seas, Environmental Software, Vol. 1, No. 1, Pp 23-42, Moron, V., A. Ullmann, Relation between sea-level pressure and sea-level height in the Camargue (French Mediterranean coast), International Journal of Climatology 25, , 25. National Aeronautics and Space Administration - Goddard Institute for Space Studies (GISS), Smith, S.D. and E.G. Banke, Variation Of The Sea Surface Drag Coefficient With Wind Speed. Quart. J. Roy. Meteor. Soc., 11, pp , Soukissian, T., M. Hatzinaki, G. Korres, A. Papadopoulos, G. Kallos and E. Anadranistakis, Wind and Wave Atlas of the Hellenic Seas, Hellenic Centre for Marine Research Publ., 3pp, 27. Sun, G., Ranson, K.J., Kharuk, V.I., and K. Kovacs, Validation Of Surface Height From Shuttle Radar Topography Mission Using Shuttle Laser Altimeter, Remote Sensing Of Environment, 88, , 23. Rogers, J.C., North Atlantic Storm Track Variability And Its Association To Both North Atlantic Oscillation And Climate Variability Of Northern Europe, J. Climate, 1, ,1997. Pearson, E.S., Some Notes On Sampling Tests With Two Variables, Biometrika, 21, ,1929. Pearson, E.S., The Test Of Significance For The Correlation Coefficient. J. Amer. Statist. Ass., , Pirazzoli, P.A., Surges, Atmospheric Pressure And Wind Change And Flooding Probability On The Atlantic Coast Of France. Oceanologica Acta, 23, ,