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Journal of Geodynamics 51 (2011) 233–244

Contents lists available at ScienceDirect

Journal of Geodynamics

journa l homepage: ht tp : / /www.e lsev ier .com/ locate / jog

ertical crustal motions from differential tide gauge observations and satelliteltimetry in southern Italy

arla Braitenberg ∗, Patrizia Mariani, Lavinia Tunini, Barbara Grillo, Ildikò Nagyepartment of Geosciences, University Trieste, Via Weiss 1, 34100 Trieste, Italy

r t i c l e i n f o

rticle history:eceived 26 July 2010eceived in revised form0 September 2010ccepted 30 September 2010vailable online 8 October 2010

eywords:ertical crustal movementsatellite altimetryide gaugesea levelalabria and Sicily

a b s t r a c t

Our goal is to determine vertical crustal movement rates from tide gauge and satellite altimetry measure-ments. Tide gauges measure sea level, but as they are fixed to the crust, they sense both sea surface heightvariations and vertical crustal movements. The differential sea level rates of sufficiently nearby stationsare a good means to determine differential crustal movement rates, when sea level height variationscan be assumed to be homogeneous. Satellite altimetric measurements determine sea surface heightvariations directly and can be used to separate the crustal signal from the sea surface height variationsin tide gauge measurements. The correction of the tide gauge sea level rates for the sea surface heightcontribution requires collocation of the satellite pass and the tide gauge station. We show that even ifthis is not the case, the satellite altimetric observations enable correction of differential tide gauge ratesfor the effects of sea surface rate inhomogeneities.

We apply the methodology to an area of broad scientific interest, due to its high seismic risk and itslocation as standpoint for a proposed major bridge connecting Sicily to the Italian mainland.

We find that the Southern Calabria and the eastern Sicily tide gauges have a deficit in sea level increaseof 1–2 mm/yr with respect to the north western Sicilian tide gauge. The satellite altimetric observationsshow that this differential movement must be caused by a tectonic component, because the sea surfacerates are higher offshore eastern Sicily compared to offshore western Sicily. The satellite altimetric ratesshow that the sea surface rates are inhomogeneous in the Mediterranean and have larger amplitudes aswe move away from the coast than immediately offshore. Our technique can be applied to any part of the

bserv

world where tide gauge o

. Introduction

The sea level measured by tide gauges is the sum of crustalovement rate and the sea surface height variation. In this papere define sea surface height as the geocentric height as measured

y satellite altimeters (e.g., Chelton et al., 2001). We use the termea level to define the quantity measured by tide gauges, whichust be tied to a geodetic height reference system in order to be

n absolute quantity. Since sea level changes in space and time,single tide-gauge station is insufficient to characterize vertical

rustal movement, as independent information is necessary to sep-rate crustal movement from the sea surface height change. Theifferential rate of two close tide-gauge stations is representative of

ifferential crustal rates, where the differences in sea surface heightates are either negligible or known. The differential rate betweentide gauge and the sea surface height observed by satellite altime-

er is equal to the geocentric crustal movement rate (Cazenave et

∗ Corresponding author. Tel.: +39 0405582258.E-mail address: berg@units.it (C. Braitenberg).

264-3707/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.jog.2010.09.003

ations are available, because satellite altimetric observations are global.© 2010 Elsevier Ltd. All rights reserved.

al., 1999; Fenoglio-Marc et al., 2004; Kuo et al., 2004; Mangiarotti,2007). Ideally the satellite track must fly over the tide gauge sta-tion (or at least close to it). The success of the method has beendemonstrated by Kuo et al. (2004, 2008), where the vertical rateshave been verified by GPS observations.

Along the Italian coast (7570 km length of coastline (Antonioliand Silenzi, 2007)) the number of tide gauges amounts to 26,and therefore is a valuable source of information for estimatingvertical crustal movement rates. The sea level increase rates ofthe Mediterranean have been mapped (Klein and Lichter, 2008;Church et al., 2004; Marcos and Tsimplis, 2008; Pirazzoli, 1996;Douglas et al., 2001) using tide gauge data from the PermanentService for mean Sea Level (PSMSL; http://www.pol.ac.uk/PSMSL(Woodworth, 1991; Woodworth and Player, 2003)). They varybetween −16 mm/yr and 18 mm/yr (Piraeus and Khios, respec-tively, time interval 1990–2003; Klein and Lichter, 2008). The

observed scatter of the rates is due to the combined effect ofcrustal movement, locally varying sea surface height, and biasesintroduced by the time intervals used for determining the rates.The specific time interval used for the calculation affects the ratesbecause there is intrinsic variability in time of the sea surface vari-

234 C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244

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ig. 1. Geographical index map. Italian tide gauges (black triangles), Topex/Poseidorey/white shaded area: distinguishes the Tyrrhenian and Adriatic regions used for

tion (e.g., Marcos and Tsimplis, 2007). Mangiarotti (2007) hasnalysed tide gauges and satellite altimetry for the Mediterraneanith the aim of finding coastal sea level trends, neglecting the

ontribution of vertical crustal movement. Fenoglio-Marc (2002,003) and Fenoglio-Marc et al. (2004) obtained the first interestingesults on crustal rates for the Mediterranean using tide gauges andatellite altimetry.

In our work we investigate the case in which the altimetric andide gauge observations are relatively distant from each other, ass the case for many stations along the Italian coastline. In facthe longest altimetric time series stem from the Topex/Poseidonatellites and the subsequent Jason 1 and 2 missions which crosshe Italian peninsula with only four tracks. The spatial coverages greatly improved with the ENVISAT satellite, which presentlyas only a seven-year data-series, too short to be used for deter-ining sea level trends robustly. Specifically we consider Sicily

nd Calabria which are of general interest, given the high seismicotential of the area (Doglioni et al., 1999; Ferranti et al., 2007)nd the plan to connect Sicily to Calabria by bridge. The Holocenend Late Pleistocene rates are locally known from geomorphologictudies (Antonioli et al., 2006, 2009; Ferranti et al., 2006, 2007)nd demonstrate one of the highest uplift rates in the entire Italianeninsula, with vertical Holocene rates in eastern Sicily (St. Alessiond Taormina) of 2.4 mm/yr and in southwestern Calabria (Scilla)f 2.1 mm/yr. These rates are average rates over a period of up to0 kyr, and it is crucial to compare these with the present instru-ental rates in view of the engineering applications and the seismic

isk. The map in Fig. 1 shows the tide gauges and the satellite passesvailable for one segment of the Mediterranean Sea.

Several continuous GPS stations are presently maintainedn Calabria-Sicily, and in the future it will be possible tobtain GPS-determined vertical movement rates (RING-network,ttp://ring.gm.ingv.it/). In 2010 the Sicilian-Calabria network haseveral stations with at least 4 years of data, which allows us to cal-ulate the vertical rates, although special attention has to be given

o methodology and to the choice of reference stations. The ver-ical rates are under study (personal communication Dr. Federicaiguzzi, Data Analysis for Geodesy, INGV), and have not yet beenublished, except for station Reggio Calabria (TGRC). To be com-

Jason 1 satellites passes (dotted lines), individual satellites points (black crosses).polation.

pared with the tide gauges, the GPS stations must be collocated.The sites of the GPS stations were chosen disregarding the positionof the tide gauges and are quite far from them. In Reggio Calabria theGPS station (TGRC) has 6.5 years of data (2000.5–2007), is mountedon the roof of a building and has some fluctuations in the lineartrend. Serpelloni et al. (2006) report an uplift of 0.97 mm/yr withrespect to the stable Sardinia block on a very limited data set of 3.4years, the calculated GPS velocity being −0.23 ± 1.3 mm/yr (posi-tive upwards). As will be shown later, we calculate the differentialsea level rates between tide gauges. An instrumental validation ofthe results would require the differential rates between GPS sta-tions, with at least one GPS common to the tide gauges. Due tothe present distribution of GPS station and the time windows ofavailable data, the differential GPS vertical movement rates areunavailable (personal communication Dr. Federica Riguzzi). There-fore the rates we derive from the tide gauges are complementaryto the vertical rates that will be obtained by GPS in the next fewyears.

For the sake of simplicity, we choose one particular tide gaugestation as a reference station with respect to which the differen-tial rates are calculated. It is advisable to choose a station that hasbeen classified geologically as the most stable. It should be notedthat the results are also valid in the case that the reference stationis moving. We choose the Palermo tide gauge as the reference sta-tion relying on the information from geological investigations. Theaverage vertical movement rate since the last interglacial period(MIS 5.5: 132–116 kyr) can be determined from observation of theheight and age of geological markers that document the past sealevel. For Sicily Ferranti et al. (2006) report variations of the levelof the MIS 5.5 markers between 2 m and 175 m. The authors com-pare these levels to the expected worldwide global height for astable coast of 6 ± 3 m following Lambeck et al. (2004). They find alevel very close to the expected level for the north-western Sicilycoast near Palermo (7–10 m), levels up to 100 m for the north-eastern Sicily coast, and very high levels for the eastern Sicilian

coast (140–170 m). High levels of the markers of MIS 5.5 highstandare also found for the Calabria coast (85–175 m) (Ferranti et al.,2006; Antonioli et al., 2009). The geological observations give usan average movement over the last 116–132 kyr years, and there-

C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244 235

F ervedl RA). (T al datl

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ig. 2. Tide gauge data in three time intervals for the Sicily-Calabria study area: obsinear trend (dashed line). (A) Recent (1999.5–2009.1) data with daily sampling (ISPhe station Genova is a reference station when Palermo is unavailable. (C) Historicevel record of the 1906 earthquake.

ore not necessarily the present movement. Nonetheless, we mayafely assume that among all tide gauge stations available in Sicilynd Calabria, that located on the western Sicilian coast is relativelytable and has moved with rates that are one to two orders of mag-itude smaller than the ones on eastern Sicily or Calabria. Presentlyhis is the most rational choice we can make, as alternative instru-

ental measurements are unavailable.

. The tide gauge data set

We refer to two oceanographic databases, the ISPRA (2009)Istituto Superiore per la Protezione e la Ricerca Ambien-

ale, http://www.apat.gov.it/site/it-IT) and the PSMSL (Permanentervice for Mean Sea Level, http://www.pol.ac.uk/psmsl/) dataase. The PSMSL and ISPRA stations coincide, but cover differentime periods. The ISPRA data are available with hourly samplingnterval, while the PSMSL data only provide monthly sampling

data (black), the best-fitting oscillation summed to the linear trend (black) and theB) Time interval between 1951 and 1983: monthly (PSMSL) and daily data (ISPRA).a set (1896–1924). The Messina record includes the coseismic and postseismic sea

interval. Because of the higher sampling rate, we use only the ISPRAdata for the recent interval and the comparison to the satellite. InFig. 2 we present the available tide gauge data. Fig. 2A refers tothe recent data (1998–2009) from the ISPRA data base. The hourlydata have been reduced to daily sampling by averaging the valuesover an interval of 24 h. In order to determine a mean linear trendwe fit the observations using a least square approximation witha sequence m(t) composed of a linear function and an annual andsemi-annual oscillation. An alternative two-step procedure couldde-season the data and then fit the best linear trend. The func-tion m(t) is defined as follows, where a0, at, acos 1, asin 1, acos 2, asin 2,are the parameters to be determined, and ω1 and ω2 are the yearlyand half-yearly angular frequencies, respectively (for the yearly and

half-yearly frequencies see, e.g. Maul and Martin, 1993).

m(t) = a0 + att + acos 1cos ω1t + asin 1sin ω1t

+ acos 2cos ω2t + asin 2sin ω2t (1)

2 f Geodynamics 51 (2011) 233–244

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36 C. Braitenberg et al. / Journal o

We must define 6 unknown parameters by a multivariate leastquares adjustment, using about 3600 constraining equations forhe tide gauges (nearly 10 years of daily data samples) and about60 equations (nearly 10 years with 10-day sampling for theltimeter. The average linear trend is given by parameter at and itsncertainty is equal to the standard error of the coefficient estimate.e assume zero mean and Gaussian distribution of the residual

or the standard error estimate (e.g. Parker, 1994). Care should beaken when comparing the estimated errors from different authors,s some papers (e.g., Maul and Martin, 1993; Fenoglio-Marc etl., 2004) scale the errors using a formula that defines signifi-ant degrees of freedom by the auto-correlation analysis of theequences. This procedure does not alter the trend results, butields an increase of the estimated errors of nearly 50%. We prefer toeep the standard errors without inflation, as they are comparableo the majority of uncertainties published. The parameters obtainedrom the regression analysis are found in Table 1. All relevant quan-ities are listed: start and end year of the sequence, the number ofquations for the regression analysis (N), the root mean square ofhe residual (�t), and the regression parameters with the respec-ive errors. The first seven rows of the table refer to ISPRA data, theollowing rows to PSMSL data. Differences of sea level rates mustlways be evaluated using identical time intervals. The linear seaevel rate lies between −4.8 ± 0.4 mm/yr (Catania, 1971.8–1982.8)nd 3.1 ± 1.0 mm/yr (Genova, 1951.2–1968). The amplitude ofhe annual signal (equal to

√acos 1

2 + asin 12) is systematically

reater than the half-year amplitude (equal to√

acos 22 + asin 2

2)y a factor of about 7. The rms of the residual �t is about 70 mmor the ISPRA data, and slightly lower (about 50 mm) for the PSMSLata. The reduction of �t is ascribable to the reduced high fre-uency content in the PSMSL data. We find that all stations exceptorto Empedocle have a systematic rate deficit with respect toalermo over the interval 1999.5–2009.1. In Fig. 2A the black heavyine shows the best fitting annual and semi-annual oscillation andhe dashed line the best fitting linear trend. The numbers indicatehe annual linear trend with the respective error. The time inter-al between 1951 and 1983 is shown in Fig. 2B, and the data areonthly for the PSMSL data base. This interval shows the Genova

ata, as we will use it as a stable reference station when the Palermoata are missing. Genova is the reference tide gauge station of the

talian levelling system and has been shown to be stable (Salvioni,957; Serpelloni et al., 2006). The historic data (1896–1924) arehown in Fig. 2C, and are of interest as they cover the 1908 Messina-eggio Calabria earthquake and span 10 years before and after thereat earthquake of magnitude M = 7.0 (e.g., Bottari et al., 1986;ino et al., 2000; Valensise and Pantosti, 1992; Michelini et al.,006). The step in the Messina data demonstrates a co-seismicubsidence of 43 cm, which is recorded between start and endf the 5 months-long interruption of the data, and a subsequentost-seismic subsidence of 32 cm which continues until the endf the data series in 1922. The lack of subsequent data does notllow estimating the full duration of the post-seismic movement,r determining whether in 1922 it came to an end. The total sub-idence composed of the co-seismic and post-seismic movementmounts to at least 77 cm.

The parameters obtained from the regression analysis are foundn the second half of Table 1. For the older data (1950–1972) sta-ions Catania and Reggio Calabria have a rate deficit with respecto Genova. This is interesting, as it would confirm that these twotations have a rate deficit with respect to a stable station. Due tohe great distance of Genova from the Sicilian island, we do not

ursue the calculation of the differential rates with this station anyurther, as the results are less reliable than the ones obtained withalermo reference station and we do not have the control from theatellite altimetry.

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C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244 237

Table 2Corrections applied to the altimetric signal (DEOS, 2009).

TOPEX-A JASON-1 (A)

Orbit Pseudo EIGEN-GL04C orbital attitude CNES-EIGEN-GL04C orbital attitudeDry tropospheric correction ECMWF dry tropospheric correction ECMWF dry tropospheric correctionWet tropospheric correction ECMWF model wet tropospheric correction ECMWF model wet tropospheric correctionIonospheric correction Smoothed dual-frequency ionosphere correction Smoothed dual-frequency ionosphere correctionInverse barometric correction None NoneSolid earth tide Solid earth tide Solid earth tideOcean tide GOT4.7 ocean tide GOT4.7 ocean tideLoad tide GOT4.7 load tide GOT4.7 load tide

3

g

g

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Wsv

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g

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4

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Pole tide Pole tideSea state bias CLS sea state biasReference surface EGM2008 geoid heightReference frame offset Reference frame offset

. Methodology in retrieving differential sea level rates

Taking the difference between the time series of two tide gauges1(t) and g2(t) we obtain:

1(t) − g2(t) = e1(t) − e2(t) − (c1(t) − c2(t)) (2)

ith e1(t) and e2(t) the sea surface height variation, and c1(t) and2(t) the crustal vertical movement. The difference between theime series of a tide gauge station g1(t) and the collocated altimetricbservation s1(t) is

1(t) − s1(t) = e1(t) − c1(t) − e1(t) = −c1(t) (3)

hen the geocentric sea surface height variation in two tide gaugetations is the same, the difference between the tide gauge obser-ations is equal to the differences in crustal uplift:

1(t) − g2(t) ≈ −(c1(t) − c2(t)) (4)

We model the mean crustal and sea surface height variations toe linear. The problem therefore consists in determining the dif-erential linear trends in sea level variations. We therefore proposehe following relation with linear coefficients bi, i = 0, . . ., 2, bdt thatre determined through fitting a multivariate regression model:

2(t) = b0 + b1g1(t) + b2g1q(t) + bdtt + n(t) (5)

here g1q(t) is the quadrature of g1(t), and n(t) represents the noise.

he parameter bdt represents the differential sea level variation oftation 2 with respect to station 1. Introducing the quadrature ofhe series in the linear equation allows for a possible phase shiftetween the two time series.

An alternative way to obtain differential sea level change, is toake the difference between the sea level rises calculated separatelyt two stations. In this case we would also model the linear sea levelrend in tide gauge and satellite altimetric data as explained abovesing Eq. (1).

. Altimetric satellite observations

The altimetric satellites record sea surface height with respecto an ellipsoidal reference system. There have been three dedi-ated high quality missions. They are the Topex/Poseidon satellite,aunched in 1992, and the Jason 1 and ENVISAT satellites, bothaunched in 2002 (e.g., Chelton et al., 2001). The Jason 1 satelliteeing the follower mission of Topex/Poseidon, it samples the sameracks, and guarantees continuity of the observations. ENVISAT hashigher spatial resolution, but, as mentioned before, a shorter timeeries. Precision of the single observation is estimated to be about

cm (Chelton et al., 2001), with a repeat time for each track of 10ays for Topex/Poseidon or Jason 1 and 35 days for ENVISAT. Theltimetric satellite observations have been corrected for the delaysaused by atmospheric refraction, the sea state bias and the tidess summarised in Table 2. The choices are standard and follow the

Pole tideCLS sea state biasEGM2008 geoid heightReference frame offset

investigations of Fenoglio-Marc et al. (2004). We do not correctthe altimetric data for the inverse barometric effect, because thiscorrection is not applied to the tide gauge data. As the pressurevariations have almost no long term trend (e.g., Braitenberg et al.,2006), an artefact in the calculated linear trends can be excluded.

The satellite tracks available for the southern Tyrrhenian, Adri-atic and Ionian seas are shown in Fig. 1. The satellite tracks areidentified by a specific number, the pass, which in our case (studywindow Lat. 36–40◦, Long. 10–19◦) takes the values 9, 44, 59, 85,120, 135, 146, 161, 196, 211, 222, and 237.

We rely on the sea surface heights stored in the DEOS (2009)database, which publishes the values along track. The data are sealevel anomalies, defined as the difference between the geocentricsea surface height and the best available geoid model (EGM2008).The data are available at 1 Hz, which corresponds to a 7 km dis-tance of each sample along track, each track being repeated after10 days. We construct time series with 10 days sampling intervalat discrete locations with the criterion of covering the Mediter-ranean homogeneously. We could use all data along track, but inthe averaging process the result would be largely biased towardsthe variations along the tracks. This is avoided by choosing discretepoints that homogeneously cover the area of interest. The pointsare at all track crossovers and the midpoints between crossovers,which yields points spaced 130 km apart along the tracks. As weuse one satellite at a time no bias should be expected at crossovers.In coastal areas we increase the number of points to about 5 pointsbetween crossovers. The satellite observations that fall into a spa-tial window of 7 km distance from the discrete location and fall inthe 10 days interval are averaged to produce one data point. Wealso select positions as near to the tide gauges as possible, com-patible with the data availability in coastal areas. In Fig. 3 the dataseries for the stations closest to the tide gauges of Southern Italy areshown. In order to find the valid satellite point closest to the tidegauge, we calculate the histogram of observations during the entire1992–2009 time interval along track. We find that at a distance ofabout 45 km from the coast the number of points is reduced drasti-cally, leading to a time series with many interruptions. Inspectionof Fig. 3 reveals a common sub-annual variability in the series, withsome differences in the multi-annual sea level trend. The next stepfits the time-series in a least squares sense with a model com-posed of a linear trend and the yearly and half-yearly oscillation(see Eq. (1)). The least squares fit is made on 10-year sliding win-dows, each shifted by one year, producing the linear geocentric sealevel change rate in each location. The 10-year interval is justified bythe length of the tide gauge time series. The rates are interpolatedon a regular grid with 0.3◦ grid-spacing applying the nearest neigh-

bour interpolation algorithm (GMT software package, Wessel andSmith, 1998). The algorithm determines the nearest points to eachnode in each quadrant and within a maximum search-radius fromthe node. For the nodes that have a full set of nearest neighbours, aweighted average value is computed. The weighting function used

238 C. Braitenberg et al. / Journal of Geo

FJf

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5. Statistical analysis of tide gauge data

ig. 3. Sea level variations recorded by altimetric satellites Topex/Poseidon andason 1 for 6 representative locations surrounding Calabria and Sicily. See Fig. 1or locations.

s w(r) = 1/(1 + d2), where d = 3r/search radius and r is distancerom the node. The interpolation along the coast of the Italianeninsula presents a problem, as weighting should not be madecross the land areas, as this would imply averaging of points per-aining to different oceanic basins. We therefore accomplish thenterpolation in two distinct areas, the Adriatic and Ionian basinsgrey area in Fig. 1) and the southern Mediterranean Sea (whiterea in Fig. 1), and merge the resulting grids in a second step.

.1. Time evolution of sea level trends

In the following the results for the eight time windows1992–2002, to 1999–2009) are discussed (see Fig. 4). The mostecent interval is chosen to be equal to the tide-gauge data availabil-ty, so it is actually the interval 1999.5–2009.1. For the first interval1992–2002, Fig. 4A) we find that the negative sea level anomalyn the Ionian Sea has its largest extension and most pronouncedegative value, with respect to the following years. Values close toero are found near Malta and along the coastline of Porto Empe-

ocle. The northern sector of the Sicilian coast has positive values,ith greater values close to the Eolian Islands and Messina, smaller

alues near Palermo (2.7 mm/yr) and again greater values towardshe Egadi Islands.

dynamics 51 (2011) 233–244

A very similar pattern is also found for the successive time win-dows: for the 1993–2003 to 1996–2006 intervals (Fig. 4B–D), thenegative Ionian basin rate decreases, as also the rates along theSicily-Malta channel and the south Tyrrhenian Sea. For the time-interval (1995–2005, Fig. 4D) the Ionian area continues to reducethe negative anomaly, the Sicily-Malta channel shows slightly neg-ative values, and the Egadi and Eolian Islands, and the southernTyrrhenian area have low positive values. Starting with the series1997–2007, we find slightly positive values for the Ionian basin,with even more positive values along the gulf of Taranto andeastwards towards Greece. The Sicily-Malta channel has reducedpositive values. The Egadi and Eolian Islands have stable positivevalue, and Palermo a lower positive values. The most recent intervalcoincides with that covered by the tide gauges (Fig. 4H).

Summarising, we note that in the Ionian sea we have a negativeanomaly, which decreases in time between 1992 and 2006, and mayaffect the Sicily-Malta channel, which generally has small positivevalues. The Egadi and Eolian Islands have stable medium to largepositive trend values.

Finally we calculate the average linear sea surface increase forthe entire interval of 17 years (1992–2009, Fig. 4J) and show themap of the errors (1992–2009, Fig. 4K). In the Ionian basin wefind a negative geocentric sea level rate (−6 mm/yr), in agreementwith previous work (e.g., Fenoglio-Marc, 2003; Fenoglio-Marc etal., 2004) which decreases towards the eastern Sicilian coastlineturning into positive values southwards (1–3 mm/yr), and east-wards towards the Greek coastline (4–5 mm/yr). In the Sicily-MaltaChannel we find weakly positive values (1.5–3 mm/yr). The valuesalso increase towards the western coast of Sicily (Egadi Islands:3.3 mm/yr). For the south Tyrrhenian Sea (northern Sicily coast)we find positive values, increasing from west (Palermo: 2 mm/yr)to east (Messina Strait and Eolian Islands: 5 mm/yr). The map ofthe errors in Fig. 4K shows the significance of the analysis, as theuncertainties are mostly uniformly distributed and much smallerthan the observed rates.

In Fig. 5 the time evolution of the linear trends for the selectedaltimetric locations nearest to the Sicilia-Calabria coastline aregraphed (location of points shown in Fig. 5B). Each data pointin Fig. 5A represents the trend of the altimeter data series onthe point of the track closest to the tide gauge in consecutive10 years time intervals. As a reference the trends for the full 17years time interval (1992–2009) are also shown. The change ofthe negative Ionian anomaly to a positive anomaly is clearly seen.The linear trends and the relevant parameters from the regres-sion analysis for the satellite points nearest to the tide gauges areshown in Table 3. The time interval is that for which we havethe tide gauge data. With respect to the tide gauges, the rms ofthe residual is almost the same (between 70 and 80 mm). Thetable is analogous to Table 1. The number of data points usedfor the regression varies, because there are missing data in thetime series. Generally, the trends have greater amplitude (between1.9 ± 1.4 and 11.9 ± 1.5 mm/yr; 1999.5–2009.1) compared to thoseof the tide gauge stations (between −1.0 ± 0.5 and 1.5 ± 0.4 mm/yr;1999.5–2009.1), which presumably is due to the distance of thealtimeter points from the coast. It shows that at the coast the sealevel rates are confined to smaller values compared to the open sea.This could be due to the smaller effects of temperature and salin-ity, wind or current systems and to the particular ocean bottomtopography and coast line geometry.

We dedicate a first analysis to the statistical evaluation of thetide gauge data. The correlation coefficient between two series isa quantitative measure of their similarity. In Table 4 we report the

C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244 239

Fig. 4. Map of average sea surface height change from altimetric satellite for different time intervals. Triangles: location of tide gauge stations: NA: Napoli; SA: Salerno, PL:Palinuro, ME: Messina, PA: Palermo, PE: Porto Empedocle, LA: Lampedusa, CA: Catania, RC: Reggio Calabria, CR: Crotone, TA: Taranto, OT: Otranto, BA: Bari, VIE: Vieste. (A–H)Eight consecutive 10-year time intervals starting with the year 1992. (J) Average sea surface height change from altimetric satellite for time interval 1992–2008 and (K) mapof the rate error for the same time interval 1992–2008.

240 C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244

Tab

le3

Sea

leve

ltre

nd

sob

tain

edfr

omsa

tell

ite

alti

met

erp

oin

tsn

eare

stto

the

tid

ega

uge

sfo

rd

iffe

ren

tti

me

inte

rval

s.A

llre

leva

nt

par

amet

ers

ofth

ere

gres

sion

are

show

n:

star

tan

den

din

gye

ar,n

um

ber

ofeq

uat

ion

s(N

),rm

sp

red

icti

oner

ror

(�t)

,lin

ear

tren

d(a

t),y

earl

y(a

cos

1,a

sin

1)

and

hal

fyea

rly

(aco

s2,a

sin

2)

amp

litu

de

coef

fici

ents

and

resp

ecti

veer

rors

.

Sin

gle

stat

ion

regr

essi

onan

alys

is.S

atel

lite

poi

nts

nea

rest

toti

de

gau

ges

Stat

ion

Yea

r1

Yea

r2

N�

t(m

m)

a t(m

m/y

r)�

a t(m

m/y

r)a c

os1

(mm

)ye

arly

�a c

os1

(mm

)ye

arly

a sin

1(m

m)

year

ly�

a sin

1(m

m)

year

lya c

os2

(mm

)h

alf-

year

ly�

a cos

2(m

m)

hal

f-ye

arly

a sin

2(m

m)

hal

f-ye

arly

�a s

in2

(mm

)h

alf-

year

ly

Cat

ania

-Pas

s12

019

99.5

2009

.131

579

.712

1.7

−60.

36.

3−2

.36.

5−1

66.

48.

16.

4C

roto

ne-

Pass

135

1999

.520

09.1

413

80.2

11.2

1.5

−71.

15.

6−1

1.8

5.6

6.8

5.7

−13.

65.

6Pa

lerm

o-Pa

ss44

1999

.520

09.1

375

74.2

1.9

1.4

−62.

95.

5−1

8.2

5.4

1.8

5.4

1.1

5.5

Port

oEm

ped

ocle

-Pas

s44

1999

.520

09.1

425

70.3

1.4

1.3

−46.

44.

8−1

4.9

2.7

4.9

−4.4

4.9

Reg

gio

Cal

abri

a-Pa

ss12

0I.

1999

.520

09.1

364

77.4

11.9

1.5

−58.

75.

8−6

.85.

7−3

.75.

8−1

6.5

5.8

Reg

gio

Cal

abri

a-Pa

ss59

T.19

99.5

2009

.133

875

.53.

21.

5−6

05.

9−1

6.6

5.8

−0.9

5.9

−31.

65.

8

Fig. 5. Rates of sea surface height change obtained from satellite altimetric observa-tions at selected points surrounding Sicily-Calabria. Rates are calculated on 10-yeartime intervals. (A) Rates for different 10-year time intervals. (B) Location of points.(C) Average rate for the interval 1992–2009.

mutual correlation coefficient between the tide gauges. The timeinterval for the calculation is 1999.5–2009.1, sampling is daily, andall data refer to the ISPRA database. The high values (0.82–0.97) ofthe correlation coefficients demonstrate an excellent similarity ofthe records, which is a guarantee for the good functioning and sta-bility of the tide gauge stations. The lower left corner of the tablegives the distances between stations, which range between 81 km(Catania-Reggio Calabria) and 381 km (Porto Empedocle-Crotone).Generally, smaller distances give rise to higher correlation coeffi-cients. The correlation coefficients between altimeter data and tidegauges are slightly lower (about 0.7), as will be shown in more detailin the next paragraph.

6. Differential sea level trends

Following the procedure explained in Section 3, we calculatedifferential sea level trends between tide-gauges and between tide-gauges and altimetric observations. Differential trends are alwayscalculated on identical intervals, to avoid bias caused by changes ofthe sea level trends (e.g. see Fig. 5).

We first consider the modern tide gauges from the ISPRAdatabase (Fig. 2A) and calculate the differential sea level changewith respect to the geologically stable station at Palermo (Ferrantiet al., 2006). The differential trend is calculated from the differen-tial linear change according to Eq. (5), and also from the differencebetween the linear trends at two stations. In Table 5 all relevantparameters of the differential regression are given: start and end-ing year, number of equations (N), correlation coefficient (�), rms

of the residual (�t), parameters bdt (here named bt-Pal or bt-Gen) b1,b2 from Eq. (5). In the last column the differences between trendsand the error is given.

Let us first consider the differential rates obtained for the recentISPRA data (19995.5–2009.1). The rms of the residual (�tt-Pal) of

C. Braitenberg et al. / Journal of Geodynamics 51 (2011) 233–244 241

Table 4Correlation coefficient between tide gauge stations (upper right triangle of table) and mutual distances (km) (lower left triangle of table).

PA ME CA PE RC CR

Correlation coefficient

PA – 0.89 0.94 0.96 0.91 0.89ME 191.4 – 0.88 0.89 0.89 0.82

tfyssiaiomE

(oCTsdRmpdihbelCr

gsb

TDnd

Distance (km)CA 169.0 89.4PE 89.8 212.9RC 198.7 104.0CR 343.7 170.4

he differential regression is less than half (15–31 mm) the valueound in the regression of a linear function and a yearly and half-early oscillation. The parameter b1 is about 1 in all cases, whichhows that all stations have very similar amplitude. The value b2 isystematically 10% of b1 or less, which shows that the phase shifts very small. The differential rates obtained from the two methodsre very similar, and differ by 0.8 mm/yr at most. The uncertaintys greater than the formal error of the trend, but is representativef the actual uncertainty on the differential trends due to differentethodological approaches. We find that all stations except Porto

mpedocle have negative sea level rates with respect to Palermo.We now consider the older records from the PSMSL database

monthly sampling), that concern Reggio Calabria, Catania and Gen-va (Fig. 2B). We have one result for the differential rate betweenatania and Palermo (1896.5–1920), with the result of 0 ± 1 mm/yr.he Catania data are not completely reliable, because they are clas-ified as “metric” and not “RLR” by the PSMSL database. The PSMSLefines “metric” the raw data received from the local authority. TheLR (Revised Local Reference) data have been reduced to a com-on datum by the PSMSL making use of the tide gauge history

rovided by the supplying authority. For Catania only the “metric”ata are available, which means that only incomplete information

s present on possible datum shifts or other problems. Two moreistoric rates have been calculated (Catania and Reggio Calabria),ut we are obliged to substitute Palermo with Genova as a refer-nce station, because Palermo is unavailable. The differential seaevel rates are given in Table 5. We find again that the two stationsatania and Reggio Calabria have a negative differential sea level

ate with respect to the stable reference station.

We now proceed with the differential rates between the tideauge and the nearest altimeter point. The location of the altimetricampled point is the one shown in Fig. 1. For the mutual analysisoth time series have identical sampling, so we sample the tide

able 5ifferential sea level trends between tide gauges and the stable station Palermo or Genumber of equations (N), correlation coefficient, rms prediction error (�t), linear trend (bd

istance between stations.

Differential rates of tide gauges with respect to Palermo station. ISPRA data

Station Starting year Ending year N values �t-Pal �tt-Pal (mm)

Catania 1999.5 2009.1 3488 0.95 20Crotone 1999.5 2009.1 3488 0.89 31Genova 1999.5 2009.1 3488 0.93 20Porto Empedocle 1999.5 2009.1 3488 0.97 15Reggio Calabria 1999.5 2009.1 3488 0.94 22

Differential rates of tide gauges with respect to Palermo station. PSMSL data

Station Starting year Ending year N values �t-Pal �tt-Pal (mm)

Catania 1896 1920 280 0.78 36

Differential rates of tide gauges with respect to Genova station. PSMSL data

Station Starting year Ending year N values �t-Gen �tt-Gen (mm)

Catania 1960 1972 120 0.80 38 −Reggio Calabria 1951.2 1965.9 178 0.55 60 −

– 0.95 0.97 0.96154.6 – 0.86 0.89

80.8 216.4 – 0.92248.4 381.4 169.5 –

gauge at the time of passage of the satellite with a rate of 1 sampleevery 10 days. We do not average the tide gauge series over the10-day interval, because the altimeter also samples the sea sur-face without any time averaging. In Table 6 we give the results forthe regression parameters between the tide gauge and the altimet-ric points. Again we report the values of the rms residual (�t-A),the differential rate of the tide gauge with respect to the satel-lite altimeter data (bt-A, error �bt-A), and the in phase and out ofphase coefficients and their errors (b1, �b1, b2, �b2). Besides allrelevant regression parameters, we give the correlation coefficient(�t-A) and distance between the tide-gauge station and the satellitealtimeter point. The Reggio Calabria station has a higher correlationcoefficient with the Ionian Sea than with the Tyrrhenian Sea. Themutual distances (D) are between 44 and 126 km. The differentialrates between altimeter and tide gauge are systematically higherthan the differential rates of the tide gauges and the stable Palermoor Genova stations. The tide gauge-altimeter differential rates arebetween 0 and 9 mm/yr, values which are comparable to thosefound in previous works (e.g. Fenoglio-Marc et al., 2004). Assum-ing that Palermo is stable and has no vertical movement, we wouldexpect the differential rates from Tables 5 and 6 to coincide, as theywould both be equal to the local crustal movement. Instead we findsystematically greater values for the altimetric derived differentialtrends, which poses a paradox and may indicate a general problem.Analyzing the different values it is evident that the inconsistenciescannot be relieved by a possible vertical movement of the Palermostation. The differential trends we obtain between tide gauges seemmore realistic, as they compare better to the rates derived from

geological investigations. In our case it is therefore inadvisable tocalculate the differential rates between the tide gauges and the alti-metric observations, but to use the altimetry extrapolated to thecoast to determine the general variation of the rates of sea surfacechange. The altimetric data show that a negative trend of the Reg-

ova. All relevant parameters of the regression analysis: starting and ending year,t), in-phase and out-of-phase amplitude coefficients (b1, b2), correlation coefficient,

Difference of rates

bt-Pal (mm/yr) b1 �b1 b2 �b2 at − aPal (mm/yr)

−1.8 ± 0.1 1.2 0.007 −0.06 0.007 −1.3 ± 0.6−2.5 ± 0.2 1.2 0.010 −0.13 0.010 −2 ± 0.6−1.2 ± 0.1 1.1 0.006 −0.2 0.007 −0.9 ± 0.60.43 ± 0.9 1.1 0.005 0.04 0.005 0.5 ± 0.6−2.0 ± 0.1 1.2 0.007 0.06 0.007 −1.9 ± 0.6

Difference of rates

bt-Pal (mm/yr) b1 �b1 b2 �b2 at − aPal (mm/yr)

0.15 ± 0.33 0.81 0.039 −.0690 0.040 0 ± 0.57

Difference of rates

bt-Gen (mm/yr) b1 �b1 b2 �b2 at − aGen (mm/yr)

1.5 ± 0.95 0.84 0.052 −.065 0.052 −1.5 ± 1.984.0 ± 1.1 0.70 0.073 −0.09 0.072 −4.8 ± 1.60

242 C. Braitenberg et al. / Journal of Geo

Tab

le6

Dif

fere

nti

alse

ale

velt

ren

ds

betw

een

tid

ega

uge

san

dn

eare

stal

tim

eter

poi

nts

.

Tid

ega

uge

stat

ion

Sate

llit

ep

oin

tSt

arti

ng

year

End

ing

year

D(k

m)

N valu

es�

t t-A

(mm

)�

t-A

b t-A

±�

b t-A

(mm

/yr)

b 1�

b 1b 2

�b 2

Tren

dd

iffe

ren

ce

Cat

ania

CA

pas

s120

319

99.5

2009

.112

636

057

0.67

−8.2

±1.

20.

660.

034

−.02

810.

0321

−12.

3±

1.8

Cro

ton

eC

R-P

ass

135

219

99.5

2009

.197

.041

256

0.72

−9.0

±1.

10.

700.

029

−.01

380.

0278

−12.

2±

1.6

Pale

rmo

PA-P

ass

443

1999

.520

09.1

4438

051

0.64

1.0

±1.

00.

490.

030

0.00

268

0.03

05−0

.9±

1.5

Port

oEm

ped

ocle

PE-P

ass

446

1999

.520

09.1

114.

339

254

0.65

−1.6

±1.

00.

590.

035

−.65

70.

0350

0.1

±1.

4R

eggi

oC

alab

ria

RC

-Pas

s12

02

I.19

99.5

2009

.112

6.1

360

530.

68−8

.7±

1.1

0.65

0.03

20.

0612

0.02

99−1

2.8

±1.

6R

eggi

oC

alab

ria

RC

-Pas

s59

3T.

1999

.520

09.1

59.9

404

660.

52−3

.1±

1.2

0.48

0.03

90.

122

0.03

86−4

.1±

1.6

All

rele

van

tp

aram

eter

sof

the

regr

essi

onan

alys

is:s

tart

ing

and

end

ing

year

,nu

mbe

rof

equ

atio

ns

(N),

corr

elat

ion

coef

fici

ent,

rms

pre

dic

tion

erro

r(�

t),l

inea

rtr

end

(bd

t),i

n-p

has

ean

dou

t-of

-ph

ase

amp

litu

de

coef

fici

ents

(b1,b

2),

corr

elat

ion

coef

fici

ent,

dis

tan

cebe

twee

nst

atio

ns.

dynamics 51 (2011) 233–244

gio Calabria and Catania stations with respect to Palermo cannot beexplained by variations in sea surface rates, as the Ionian Sea has ahigher rate than the sea in front of Palermo. Also if the Reggio Cal-abria and Catania stations detect variations of the Tyrrhenian Sea,the altimeter shows that the eastern northern Sicilian coastline hasa higher rise than Palermo, and thus cannot be responsible of therate deficit of the tide gauge.

7. Discussion

Our goal is to use existing historical tide gauge stations todetermine the vertical crustal movements along the Sicilian-Calabrian coastline. The existing continuous data cover the interval1999.5–2009.1 (ISPRA database; stations Crotone, Catania, PortoEmpedocle, Palermo, Messina, Reggio Calabria) and 1950–1970(PSMSL database; Catania, Reggio Calabria, Genova). For the recentdata we have not considered the Messina station for the crustalmovement analysis, because the pier on which the station was setmay be influenced by a land-slide discovered in Messina harbour(Monaco et al., 2008).

Good functioning of the stations can be verified by the calcu-lation of the mutual correlation coefficient of the sea level record,which is high when two stations record the same sea level signal.We find very high correlation coefficients between the stations,with values up to 0.97; we are therefore confident of good dataquality.

For each station we determine a linear rate of sea level change,which is the sum of the geocentric sea surface change and the ver-tical coastal movement. We have shown that the linear sea levelrate varies in time and space, when calculated on a time base of10 years, due to atmospheric and oceanic currents (for a detaileddiscussion see, e.g. Tsimplis et al., 2005).

Geologically, the Palermo station has been shown to be rela-tively tectonically stable (Ferranti et al., 2006), so we calculate thedifferential sea level trends with respect to this station. In the timeintervals in which the Palermo station is unavailable we use theGenova station, which is the reference station for the Italian heightsystem, and tectonically stable (e.g. Salvioni, 1957; Ferranti et al.,2006; Serpelloni et al., 2006). The present day sea level changeis assumed to be caused partly by an isostatic crustal subsidencewhich affects all Mediterranean stations and may account to upto 40% of the signal (Stocchi and Spada, 2009; Lambeck et al.,2004). For the Sicilian-Calabrian stations the difference in the iso-static contribution is 0.1 mm/yr at most (Stocchi and Spada, 2009;Lambeck et al., 2004), so we need not consider it is as a significantdifferential crustal movement in our study.

We find that Catania, Reggio Calabria and Crotone have a sys-tematically negative sea level rate with respect to Palermo. PortoEmpedocle has a slightly positive differential rate. The older recordsconfirm that Reggio Calabria and Catania have negative rates withrespect to the stable Genova station.

The analysis of satellite altimetric data has shown that the Ioniansea is affected by a strong sea level anomaly, that manifested itself inthe first decade of the 1990’s with a negative sea level change up to−21 mm/yr (trend for 1992–2002 time interval). In the subsequentyears the Ionian sea level trends have been steadily rising, invertingthe tendency to a strongly positive sea level rise, when consideringthe last decade (1999–2009). The Ionian sea level anomaly is impor-tant in the context of our study, as it may affect the Reggio Calabriaand Catania stations. The location of the Reggio Calabria station is

close to the Messina straits, so it could also be influenced by sealevel variations in the Tyrrhenian sea. The mutual correlation anal-ysis with satellite altimetry shows that the correlation coefficientfor Reggio Calabria is higher for the Ionian satellite locations thanfor the Tyrrhenian locations, which means that Reggio Calabria fol-

f Geod

lcitaCb(trfptWsdtrttstctoerlCatrR

8

ort(tSicacbbswacrDfvCaot−Rtd

C. Braitenberg et al. / Journal o

ows the Ionian Sea rather than the Tyrrhenian Sea. Presently weannot ascertain whether the negative Ionian anomaly was presentn the years 1950–1970, the interval used for the trend analysis ofhe PSMSL data for the two stations Reggio Calabria and Catania. Were confident that the recent negative differential rates of Reggioalabria and Catania with respect to Palermo cannot be explainedy the different sea surface rates, as in the interval of analysis1999.5–2009.1) the Ionian anomaly was positive and not nega-ive, with high positive increase values (10 mm/yr), whereas theates at the Palermo station are stable in time and lower (3 mm/yror the same time interval). In cases in which the satellite tracksass close to the tide gauge stations, differential trends betweenhe two instruments can be used directly to find the crustal rates.

e have considered a less favourable case, where the satellite mea-urements are far from the tide gauge stations. The distances areictated by two factors: the first one is due to the fixed position ofhe satellite passes, the second one due to the fact that the altimet-ic data are obtainable only up to a distance of 10 km or more fromhe coastline. A closer approach of the remote sea level observa-ion is in theory possible, but requires a retracking of the reflectedatellite signals, which is presently available only for a very limitedime period (e.g. COASTALT, see http://www.coastalt.eu/). In ourase, the difference in the satellite and tide-gauge derived sea levelrends reflects the greater variability of the sea level trends in thepen ocean with respect to the trends observed at the coast. Nev-rtheless the altimetric observations are necessary in estimatingegional characteristics of the sea level rates, which are extrapo-ated to the coastline. We may speculate that Reggio Calabria andatania are only mildly affected by the Ionian sea level anomaly,nd that the observed negative sea level trend is due to an effec-ive tectonic uplift. The uplift would fit the geologic vertical crustalates very well. They predict uplift in both locations Catania andeggio Calabria (Antonioli et al., 2009; Ferranti et al., 2007).

. Conclusions

We examine tide gauge and satellite altimetric observationsf sea level with the aim of obtaining crustal vertical movementates. The methodology is useful as it gives complementary rateso those obtainable from archaeological and geological approachesFlemming and Webb, 1986; Antonioli et al., 2007, 2009) andhe GPS geodetic measurements (Bennett and Hreinsdottir, 2007;erpelloni et al., 2007). We think that the use of these data ismportant, as a multitude of freely accessible tide-gauge data exist,overing several decades of observations. The dedicated satelliteltimeter data are available continuously since 1992. We have exe-uted the data analysis in a specific region of the Mediterranean,ut the methodology we propose is generally applicable and cane used in any other part of the world. The sea level trends vary inpace and in time, with values between −15 mm/yr and 15 mm/yrhen calculated for intervals of a decade in the open sea by the

ltimeter. The scatter is barely smaller when considering the trendsalculated for tide gauges over time intervals of 10 years. The scattereduces greatly when the time interval of calculation is increased.ifferential sea level trends at tide gauges give information on dif-

erential crustal movement at two stations, provided the regionalariations in sea surface rates can be controlled. For the Sicilian-alabrian area we find Reggio Calabria, Catania and Crotone havesignificantly lower sea level rise than that found at Palermo. Thisbservation holds for different time intervals between 1950 and

oday. The differential rates with respect to Palermo range between1.3 ± 0.6 to −1.8 ± 0.1 for Catania and −1.9 ± 0.6 to −2.0 ± 0.1 foreggio Calabria, with an uncertainty of less than 1 mm/yr. As men-ioned above, in principle we cannot exclude the possibility that theeficit in sea level change could be an expression of the lateral vari-

ynamics 51 (2011) 233–244 243

ations of sea surface change, rather than due to a relative tectonicuplift. Contemporaneous satellite altimetric data are available forthe most recent time interval, and show that the deficit cannot beexplained by a slower sea surface change at Reggio Calabria andCatania. We were interested in using the satellite derived sea leveltrends as an absolute reference for the tide gauge derived trends,but find that the short overlapping time interval (10 years) allowsonly qualitative considerations. The satellite derived trends deviatefrom the tide gauge derived trends too much to be used for deter-mination of the vertical crustal movements. One problem may bedue to the fact that our satellite data are only available at distancesgreater than about 44 km offshore, where the sea surface rates seemto be larger offshore than at the coast. In future the comparison maybe made in a more quantitative way, when the data time intervalis larger and the average sea level trends have smaller scatter. Afuture improvement could be achieved with a dedicated and tai-lored data analysis of the reflected satellite altimetric signal thatmay allow us to obtain data nearer to the shoreline. We interpretthe deficit in sea level rise at Reggio Calabria and Catania as due totectonic uplift. Geological studies have shown that at Catania thetectonic Late Holocene rate is 0.6 mm/yr and the Late Pleistocenerate is 1.27 mm/yr (Antonioli et al., 2006); for Reggio Calabria theHolocene rate has been estimated to be 2.1 mm/yr and the LatePleistocene (past 125 kyr) rate to be 1.23 mm/yr (Antonioli et al.,2006). The geologically determined movement can differ from thepresent-day rate, because the current slip rate corresponds to a par-ticular phase of the seismic cycle, whereas the average geologicalrate contains the pre- and post-seismic continuous as well as thecoseismic movement (Ferranti et al., 2007).

Acknowledgements

We thank DEOS, Department of Earth Observation and SpaceSystems of the Faculty of Aerospace Engineering, Technical Univer-sity Delft, PSMSL, Permanent Service for Mean Sea Level and ISPRA,Istituto superiore per la Protezione e la Ricerca Ambientale for dataaccess. We thank Giovanni Arena from ISPRA, Rome for retrievinginformation on the tide-gauge data. We thank Luciana Fenoglio-Marc for discussions on vertical movement rates from tide gaugesand altimeter, and Federica Riguzzi for advice on availability of GPSvertical rates in Calabria-Sicily. We thank Alan Reid for revisingthe manuscript. We thank P. Wessel and W.H.F. Smith for the useof the GMT-mapping software and suggestions on the GMT-helpforum. This research has benefited from funding provided by theItalian Presidenza del Consiglio dei Ministri – Dipartimento dellaProtezione Civile (DPC). Scientific papers funded by DPC do notrepresent its official opinion and policies.

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