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Estuarine, Coastal and Shelf Science 96 (2012) 114e121
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Estuarine, Coastal and Shelf Science
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Seasonal and interannual variability in the Gulf of Cadiz: Validation of griddedaltimeter products
J. Gómez-Enri*, A. Aboitiz, B. Tejedor, P. VillaresApplied Physics Department, University of Cadiz, Av. Republica Saharaui, s/n, 11510 Puerto Real, Cadiz, Spain
a r t i c l e i n f o
Article history:Received 19 November 2010Accepted 18 October 2011Available online 2 November 2011
Keywords:sea-levelseasonal variationsinterannual variabilitytide gaugealtimetryGulf of Cadiz
* Corresponding author.E-mail addresses: [email protected] (J. Gómez
(A. Aboitiz), [email protected] (B. Tejedor), pilar
0272-7714/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.ecss.2011.10.013
a b s t r a c t
Nine years of gridded multi-mission altimeter products have been validated in the Gulf of Cadiz. Thecomparison was performed using two tide gauges deployed in the mouths of two estuaries: the TintoeOdiel system and Guadalquivir River. The averaged seasonal cycle of the sea-level e corrected foratmospheric pressure effect e obtained from the two tide gauges and their closest altimeter pointsshowed good agreement, correlation coefficients being about 0.95 (at both stations). The variability aboutthe mean seasonal cycle observed in the altimeter data is about half that obtained from the tide gauges.The year-to-year analysis of the seasonal cycle confirmed the level of agreement between the twodatasets, and indicated lower amplitude errors in the estimate of the annual harmonic (only significant at95% confidence level in most of the years) in the altimeter data. The estimation of the annual componentis affected by sporadic heavy river discharges only observed in the tide-gauge records. In those cases theriver flow explained more than 50% of the variance associated with the daily mean sea-level.
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1. Introduction
Excluding the semidiurnal and diurnal tides, and the sub-inertial meteorological fluctuations over periods of a few days,the seasonal cycle is the main factor responsible for sea-levelvariability. A better understanding of the contribution of non-tidal processes to the sea-level oscillations on seasonal timescales is useful for a better knowledge of interannual and inter-decade oscillations, and also for analysis of long-term trends(Tsimplis and Woodworth, 1994). A more in-depth monitoring ofthe seasonal sea-level variability in the coastal areas could behelpful for many coastal applications such as marine life studies,shoreline developments (processes like erosion of beaches), studiesof land movement, estuaries (Rossiter, 1967), and definition ofextreme sea-levels and coastal-hazard zones. The interannual sea-level oscillations give information about the year-to-year variabilityof the oceanic conditions and can be used to describe oceandynamics (Srinivas, 2002).
The main contributors to the seasonal and interannual vari-ability are basically oceanographic (e.g. ocean heat content andcirculation), meteorological (e.g. piling-up of water due to local
-Enri), [email protected]@uca.es (P. Villares).
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wind fields, changes in the surface atmospheric pressure field) andhydrological (e.g. changes in river runoff regimes) forcings(Tsimplis and Woodworth, 1994). In addition, astronomical tidescan be considered as a minimal forcing factor and thus negligible incomparison with the others (Pugh, 1987). Most of these primaryforcings affecting the ocean occur at seasonal frequencies (Bell andGoring, 1998). The seasonal cycle and the relative contribution ofeach of the above-mentioned factors has been analyzed in the pastat sites all around the world (Sultan et al., 1995; Bell and Goring,1998; García-Lafuente et al., 2004; Srinivas and Dinesh Kumar,2006; Marcos and Tsimplis, 2007; Vinogradov et al., 2008, andothers).
Traditionally, all the studies mentioned have used time seriesfrom tide gauges deployed at continental boundaries and inshallow waters; this has precluded any attempt to performa regional analysis of the seasonal cycle. Since 1992, radar altimetermissions have provided sea-level observations of high accuracy andprecision. The spatial and temporal resolution of the altimeterrecords is mainly defined by their orbit. In general, the along-trackmeasurements provide near-global coverage of sea-level observa-tions with high accuracy and precision, and thus are suitable foranalysis of the seasonal and interannual variability of the sea-level.
To our knowledge, only a few studies have combined in-situ andalong-track altimeter data for analysis of the seasonal cycle incoastal areas. Fenoglio-Marc et al. (2005) analyzed the sea-levelchange along the Spanish coasts at interannual to inter-decade
J. Gómez-Enri et al. / Estuarine, Coastal and Shelf Science 96 (2012) 114e121 115
time scales. They combined Topex/Poseidon (T/P) altimetry dataand tide gauges and found very good agreement (correlations ofabout 0.7) between the two datasets. Han and Huang (2008)compared T/P and tide-gauge data in the Bohai, Yellow, and EastChina Seas, estimating correlation coefficients of 0.98 (amplitude)and 0.99 (phase) between T/P and tide-gauge annual cycles. Morerecently, Vinogradov and Ponte (2010) used up to 345 tide gaugesand T/P data, and estimated global correlations in annual amplitudeand phases of 0.84 and 0.92 respectively.
In addition to the along-track altimeter measurements of thesea-level oscillations, global sea-level anomaly maps made fromhigh-resolution gridded multi-mission altimeter data (Aviso, 2010)are also available. The study described here was intended to vali-date these gridded maps of sea-level measurements by analyzingthe mean seasonal cycle and its interannual variability. The vali-dation has been performed using two tide gauges deployed in theGulf of Cadiz (GoC, hereinafter) in the vicinity of two estuaries: theTintoeOdiel and Guadalquivir systems, respectively. Secondly, aninvestigate has been made of how extreme changes in river runoffregimes might affect the estimation of the seasonal cycle (ona yearly basis).
The paper is organized as follows. Section 2 gives an overview ofthe study areawith some emphasis on its eastern side. The datasetsused are presented in Section 3. The following section is devoted to:(1) validation of the mean seasonal cycle obtained with altimeterdata; and (2) analysis of the effect of strong river discharges on theestimation of the seasonal cycle. Finally, a short summary and theconclusions are presented in the last section.
2. Study area
The GoC (Fig. 1) is a basin delimited by the southwest coasts ofthe Iberian Peninsula (northern boundaries), the Strait of Gibraltar(eastern boundary) and the Atlantic coast of Morocco (southern
Fig. 1. Location of the study area: the Gulf of Cadiz, southwest Iberian peninsula. The positiothe positions of the valid altimeter measurements (black dots), while the gray dots show t
limit). Its continental shelf is approximately delimited by the 100 misobath where the shelf slopes down into the continental slope. It isdivided by Cape Santa Maria (CSM, see Fig. 1) into two basins withdifferent circulation characteristics (García-Lafuente et al., 2006).West of the cape the shelf is narrow and does not have major inputsof continental fresh waters. East of the cape, where this study isfocused, the shelf is wide (around 50 km) and is subject todischarges from major rivers like (fromwest to east) the Guadiana,TintoeOdiel and Guadalquivir (Fig. 1), that imprint signatures onthe surrounding waters in terms of sea surface temperature, sus-pended sediments, chlorophyll concentration, etc. In this respect,the Guadalquivir river dynamics are reported to affect the surfacecirculation over the eastern continental shelf (see García-Lafuenteand Ruiz (2007) for a thorough review). Although the averagedischarge rate of this river is about 105 m3/s, the normal rate isabout 25 m3/s through the year, but it can reach values as high as1000 m3/s, for periods of days, in spring and autumn (Díez-Minguito et al., 2009).
3. Datasets and methods
3.1. Tide gauge
Two tide gauges situated along the coast of the GoCwere used inthis study. The first one, “Bonanza” (BN), is located in the mouth ofGuadalquivir river (Fig. 1) at 36�4801400N e 6�2001000W. It isa permanent station of the Spanish tide-gauge network REDMAR(Red de Mareográfos de Puertos del Estado: http://www.puertos.es), and it is also included in the Permanent Service for MeanSea-Level (PSMSL) global network. BN is an acoustic sonar gaugemeasuring the sea-level at a sampling period of 5 min. Thisinstrument has an accuracy of 2.5 mm and a resolution of 10 mm,assuming a 5 m tidal range and a 1-min integration period for theaveraged measurements (ESEAS-RI, 2006). The sea-level is
n of the tide gauges “Bonanza” and “Huelva” are denoted by black stars. Also shown arehe location of the closest altimeter points to the tide gauges used for comparison.
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measured with respect to the tide gauge zero. The data are freelyavailable at 5-min intervals and as hourly, daily and monthlymeans. Data undergoes a near-real time quality control (automaticdetection of spikes, interpolation of short gaps and adjustment ofthe time of measurement) (ESEAS-RI, 2006). The second tide gauge,“Huelva” (HU), was moored in Huelva harbor at the mouth of theTintoeOdiel system at 37�0800000N e 6�4905600W (Fig. 1) untilDecember 2008. Like BN, it is an acoustic sonar station (sameaccuracy and precision) managed by REDMAR (also part of thePSMSL network). The data are also freely available at the sameintervals and with the same quality control checks as BN. From theavailable datasets at both stations we selected the monthly meansea-levels (MMSL henceforth) spanning a time period of 9 years(January 2000 to December 2008).
3.2. Altimetry
Radar altimeters are active mono/dual frequency sensorsworking at different microwave bands (basically Ka, Ku and S-band). The radar measures the two-way travel time of the emittedecho at a fixed frequency through the atmosphere and takes intoaccount its interaction with the reflecting ocean surface facets.From this, the distance between the satellite’s center of mass andthe mean sea surface is estimated (Range). The accurate knowledgeof the satellite’s orbit with respect to the reference ellipsoid(WGS84) is known as Altitude. The Range is corrected for instru-mental drifts and failures, atmospheric path delays, the oceaninteraction with the radar signal, the tidal effects (Aviso, 2010) andthe dynamic atmospheric correction: DAC (atmospheric pressure atsea-level and high frequency winds with periods lower than 20days) (Carrère and Lyard, 2003). Once all the corrections areapplied, this corrected Range is subtracted from the Altitude toobtain the instantaneous sea-level (commonly known as SeaSurface Height: SSH). The Sea-Level Anomaly (SLA) is finally ob-tained by subtracting the mean from the SSH (Aviso, 2010).
The altimeter SLA gridded multi-mission products used in thisworkwere produced by Ssalto/Duacs and distributed by Aviso, withsupport from CNES (http://www.aviso.oceanobs.com/duacs/). Wechose the high-resolution “Upd” product (delayed time data), whichuses all available (up to4) altimeters (Ducet et al., 2000), because themesoscale variability can be much better mapped using more thanjust the two altimeters present in the “Ref” product (Pascual et al.,2006). The available weekly maps of SLA have a spatial resolutionof 1/3� �1/3� (Mercator grid).We selected a framewindowcoveringthe study area: [35.5�N e 37.5�N] and [�8.0�W e �5.5�W] (Fig. 1)from January 2000 toDecember 2008. A complete description of theprocedures followed to generate the weekly maps of SLA can befound in Aviso (2010). Monthlymeans of SLAwere estimated for theselected time period and de-corrected by theDAC (auxiliary productavailable in the Aviso archive) correction (Alt_MMSL) in order tocompare themwith the MMSL time series.
3.3. Atmospheric pressure at sea-level
Monthly means of atmospheric pressure at sea-level (MMAP)were obtained from NCEP (National Center for EnvironmentalPrediction) Reanalysis Derived data (Kalnay et al.,1996) provided bytheNOAA/OAR/ESRL PSD, Boulder-Colorado (USA) (http://www.cdc.noaa.gov/). Datawith a spatial coverage of 2.5� � 2.5� global grid arefreely available. We selected the closest data points to the in-situstations. A subset (5 years) of the MMAP was compared againstavailable data from in-situ meteorological stations in the area; thisshowed a very good level of agreement (correlation coefficients of0.99). For the purposes of analysis, the model data was preferred tothe in-situ data, due to the gaps presented in the latter.
3.4. River discharge
For the time period analyzed daily and monthly means of flowrate of the Guadalquivir river were extracted from the AutomaticHydrological Information System hosted by the Andalusianregional authorities (http://www.juntadeandalucia.es). Unfortu-nately, river discharge data for the TintoeOdiel system are notpublicly available to date.
3.5. Methods
The contribution of atmospheric pressure toMMSL andAlt_MMSLwas estimated using the standard inverse barometer correction(Clarke and Liu, 1994), considering that a pure isostatic response ofthe sea-level to atmospheric pressure variations is a reasonableassumption at seasonal and longer time scales (Han et al.,1993). Sea-level anomalies of the pressure-adjusted mean sea-levels (ASL andAlt_ASL) were calculated by subtracting the 9-year mean of sea-levelfrom the monthly means at every location. The mean seasonal cyclefor each time series (ASL, MMAP and Alt_ASL) was obtained by aver-agingall the Januaryvalues for the9-yearperiodof the record to forma January average, and so on for all the other months. All the serieswere de-trended before estimating the seasonal cycle.
The sea-level seasonality in the GoC was analyzed by means ofa least squares fit of the data to annual and semiannual harmonics(Emery and Thomson, 2004) which account for most of theseasonal variability of the series in the study area (García-Lafuenteet al., 2004). The harmonic analysis was applied to the meanseasonal cycles as well as to the yearly data, (to obtain the inter-annual variability of sea-level). The statistical information of theleast squares fit (Foreman and Henry, 1989; Bevington andRobinson, 2003) is taken to ensure the validity of the solutions.The percentage of the variance explained by the fit is expressed bythe goodness of fit (gof).
4. Results
4.1. Validation of altimeter-derived mean seasonal cycle
Atmospheric pressure explained about 34e41% (40e42%) of themonthly mean sea-level variability at the tide-gauge (altimeter)points, in agreement with previous studies carried out with tide-gauge data in the study area (García-Lafuente et al., 2004). Thevalidation of altimeter data was performed by estimating theseasonal cycles obtained from the pressure-adjusted tide gauge andaltimeter data. ASL and Alt_ASL anomalies (Fig. 2) present a similarpattern of variation with maximum/minimum range of about�12 cm. The seasonal cycle is quite evident in most of the years, butwith some exceptions that will be analyzed in further detail later inthe paper. A linear correlation analysis between ASL and Alt_ASLshowed correlation coefficients of 0.84 (HU) and 0.79 (BN), signif-icant at the 95% confident level.
The mean seasonal cycles obtained with ASL and Alt_ASL at HU(Fig. 3A) and BN (Fig. 3B) are of the same order of magnitude. Errorbars represent the variability about the mean, expressed as �1standard deviation (Bell and Goring, 1998). The variability is about50% less in the altimeter data, except during summer when thevariability is relatively small in both datasets. High and significantcorrelations (0.95 and 0.97) were obtained between the seasonalcycles derived from the two datasets for HU and BN, respectively.Results from the harmonic fit applied to the mean seasonal cyclesobtained with ASL and Alt_ASL (both stations) are summarized inTable 1. Both datasets show similar results at two stations. The lowrms and high gof values highlight the high level of agreementbetween the data and the fit independently of the data sources
Fig. 3. In-situ (black solid line) and altimeter (gray solid line) mean seasonal cycle of sea-level at HU (A) and BN (B). Error bars show the variability about the mean (�1 standarddeviation).
Fig. 2. In-situ (black solid line) and altimeter (gray solid line) monthly means of sea-level anomalies from January 2000 to December 2008 at HU (A) and BN (B).
J. Gómez-Enri et al. / Estuarine, Coastal and Shelf Science 96 (2012) 114e121 117
Table 1Amplitude and phase of annual (Sa) and semiannual (Ssa) harmonics (95% confidencelevel) at Huelva and Bonanza for the seasonal cycle of pressure-corrected MMSLanomalies. Root mean squared (rms) and goodness of fit (gof) values are alsoincluded.
Huelva Bonanza
Amplitude (cm) Phase (�) Amplitude (cm) Phase (�)
Sa� 95% CL 4.4� 1.0 281.6� 12.4 4.0� 0.6 308.0� 9.3Ssa� 95% CL 1.7� 1.0 279.8� 32.1 1.7� 0.6 246.1� 22.0rms (cm) 1.2 0.8gof (%) 93.0 96.2
Aviso-Huelva Aviso-Bonanza
Amplitude (cm) Phase (�) Amplitude (cm) Phase (�)
Sa� 95% CL 4.3� 0.7 300.2� 8.9 4.5� 0.7 300.0� 9.1Ssa� 95% CL 1.3� 0.7 286.9� 29.1 1.5� 0.7 281.0� 27.5rms (cm) 0.8 0.9gof (%) 96.3 96.2
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(tide gauge and altimetry) and locations (HU and BN). The meanseasonal cycle at both stations shows a dominant annual harmonicthat peaks in October and a smaller semiannual harmonic thatpeaks in May and November.
Overall, the results indicate the usefulness of gridded maps ofaltimeter data for accurately identifying the mean seasonal cycle inthe study area.
4.2. Interannual variability of the seasonal cycle
The year-to-year repeatability of the seasonal harmoniccomponents was analyzed in detail for ASL and Alt_ASL series at
Fig. 4. Year-to-year variability of the in-situ (black solid line) and
both stations. The annual component is the major contributor tothe seasonal cycle in all cases; in most of the years only theseasonal harmonic is statistically significant at the 95% confidencelevel. For this reason, we focused on the interannual variability ofthis harmonic. The year-to-year amplitude of the annualharmonic (Sa) for ASL and Alt_ASL series at HU (Fig. 4A) and BN(Fig. 4B) present a five-year cycle of variability; this couldcorrespond to the 5.2 yr-period signal detected by Jevrejeva et al.(2005) by analyzing different tide-gauge stations placed along theEuropean coasts. Error bars in the figure represent the errorassociated with the estimation of Sa. It can be observed that thiserror is smaller in the altimeter series (for both stations), indi-cating that, on a yearly basis, the altimeter data give betterstatistical results than the annual harmonics obtained with thetide-gauge series.
4.3. Sea-level response to hydrological forcing: river runoff
The rms of the fit is an indicator of the accuracy in the estimateof the seasonal components. The lowest rms values are obtainedwith the altimeter data both at HU (Fig. 5A) and BN (Fig. 5B), thusshowing a better fit to the harmonic model than tide-gauge data.This could indicate that in a yearly basis the tide gauges, due to theirlocation, are more prone to reflect changes in the sea-level causedby local factors that in turn might affect their accuracy to representthe sea-level seasonal cycle. This could be the case of the tide gaugelocated within the Guadalquivir river mouth (Fig. 5B), since itshows the highest and more variable rms values. In this sense, it isworth noting the peak observed in 2001 at BN. This year presentedseveral episodes of unusually high river flow rates that, in turn,increased the sea-level during those periods of time. The effect of
altimeter (gray solid line) Sa amplitude at HU (A) and BN (B).
Fig. 5. Same as Fig. 4 for the RMS between the data and the fit.
Fig. 6. In-situ (black solid line) and altimeter (gray solid line) monthly mean sea-level anomalies at BN in 2001 (A). In-situ daily mean sea-level in 2001 (solid black line) and dailyriver flow in the estuary (gray solid line) (B).
J. Gómez-Enri et al. / Estuarine, Coastal and Shelf Science 96 (2012) 114e121 119
Table 2Amplitude of the annual harmonic (95% confidence level) at Bonanza in 2001(cases 1e4) and at the closest altimeter point. Root mean squared (rms) andgoodness of fit (gof) values are also included.
Case 1a Case 2b Case 3c Case 4d Aviso
Sa� 95% CL 4.6� 4.6 5.6� 1.9 6.4� 2.8 5.8� 1.8 6.7� 2.0rms (cm) 5.7 4.6 3.4 2.1 2.4gof (%) 39.5 57.6 78.7 87.7 88.2
a Uncorrected monthly mean sea-level (MMSL).b MMSL corrected for the atmospheric pressure.c MMSL corrected for the water discharge effect.d MMSL corrected for atmospheric pressure and the water discharge effects.
J. Gómez-Enri et al. / Estuarine, Coastal and Shelf Science 96 (2012) 114e121120
such heavy and sporadic events on the estimation of the yearlyseasonal cycle is explored here.
The ASL and Alt_ASL anomalies (with respect to their annualmeans) for 2001 (Fig. 6A) show unexpectedly high sea-levels inJanuary and March, more evident in the tide-gauge series, coin-ciding with episodes of very high river discharge rates (Fig. 6B).Correlation analysis carried out between the mean seasonal cyclesof the Guadalquivir river runoff and sea-level at BN showed a low(0.3) but significant correlation coefficient indicating that theannual mean flow rate does not contribute significantly to theannual mean sea-level seasonal cycle. The Guadalquivir riverdischarge is relatively small over the year as a whole (w25 m3/s)and is not related to the mean sea-level, with the exception ofJanuary and March when heavy precipitations in the area lead toextremely high flow rates (w1000 m3/s and 2000 m3/s, respec-tively) that resulted in considerably higher dailymean sea-levels. Inorder to estimate the contribution of the river discharge to the sea-level, we performed a regression analysis between the daily meansea-levels at BN and the river flow for those particular months.Results indicated that the river flowexplainedmore than 50% of thevariance associated with the daily mean sea-levels in both months.The regression models obtained for each month were used tocorrect the sea-level value and to compute newmonthly mean sea-level values for those months.
Results from the harmonic analysis performed on the 2001MMSL time series after corrections had been made for the atmo-spheric pressure and the river discharge effects (Table 2), showinga greater impact on the estimates when correcting for the waterriver discharge (case 3) than when correcting for the atmosphericpressure effect (case 2). The best results (in terms of rms and gof)were obtained when MMSL was corrected for both effects (case 4).Moreover, only case 4 presented a statistically significant (95%)estimation of the annual harmonic. This case also showed the bestcomparison against the Alt_ASL in 2001. The inclusion of the waterriver discharge effect in the altimeter series did not improvesignificantly the estimates of the annual harmonic, suggesting thatthe river discharge effect might be limited to a narrow fringearound the river mouth.
5. Summary and conclusions
Griddedmulti-mission altimeter data have been validated in theGoC using two tide gauges located in the mouths of two estuaries,for a time period of nine years (2000e2008). The validation wasperformed by analyzing the seasonal cycle once the atmosphericpressure effect was removed from both datasets using the standardinverse barometer correction. Finally, it was also analyzed howsporadic river water discharges might affect the estimates of theseasonal cycle on a yearly basis.
The mean seasonal cycle estimated with altimeter gridded datahas the same order of accuracy as that obtained with the ground-
truth data. The variability about the mean presented by the grid-ded altimeter mean seasonal cycle data was approximately 50% ofthat presented by the tide-gauge records. The interannual analysisof the seasonal cycle confirmed the good level of agreementbetween the two data sources, proving that the gridded multi-mission altimeter data is a valuable tool for the analysis of thesea-level seasonal cycle in shallow waters. The amplitude errorassociated with the estimate of the annual harmonic indicateda smaller error for the gridded altimeter data in most of the years.The magnitude of this difference was not uniform for each year ofthe period studied due to the interannual variability of the sea-levelin the study area.
Although the mean seasonal cycle of Guadalquivir Riverdischarges did not contribute significantly to the sea-level meanseasonal cycle, heavy sporadic discharges had a noticeable effect onthe low-frequency local sea-level. This has a major impact on theaccuracy of the estimates of the seasonal cycle observed only in thetide-gauge data. Correcting in-situ data for those specific riverdischarge events improved the statistics associated with the esti-mates of the seasonal cycle and thus the level of agreement withaltimeter (monthly) time series. We have shown how the estima-tion of the annual component on a yearly basis must take intoaccount these sporadic heavy river discharge events. Thus, caremust be taken when using data from tide gauges deployed in themouths of big river estuaries.
Gridded multi-mission altimeter data have proven to be a valu-able tool for the analysis of the seasonal cycle. Global maps of sea-level anomalies increase the availability of accurate information ofsea-level oscillations, especially in shallow waters and continentalboundaries. The spatio-temporal resolution of these maps wouldallow the analysis of the geographical distribution of the seasonalcycle. Thus, a proper understanding of the sea-level variability incoastal areas needs both tide-gauge data and gridded altimeterproducts.
Acknowledgments
This study was carried out under the auspices of the SpanishResearch and Development Programme (Project Code N. CGL2008-04736). Special thanks are due to the Spanish Puertos del Estado forproviding the tide-gauge data and to the Andalusian AutomaticHydrological Information System for the river discharge timeseries. Meteorological in-situ data were provided by the SpanishReal Observatorio de la Armada. The atmospheric pressure datawere obtained from the NOAA/OAR/ESRL PSD, Boulder-Colorado(USA). Altimeter data were provided by Aviso. Special thanks arealso due to Irene Laiz for her help with this manuscript.
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