ANALYSIS OF OCEANOGRAPHIC PROPERTIES OF THE ADRIATIC SEA BY GIS TECHNIQUE

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1 ANALYSIS OF OCEANOGRAPHIC PROPERTIES OF THE ADRIATIC SEA BY GIS TECHNIQUE V. Dadić 1, M. Srdelić 2, ad Z.Gržetić 2 1 Istitute of Oceaography ad Fisheries, Split, Croatia Tel: 385/ , dadic@izor.hr 2 Croatia Hydrographic Istitute, Split, Croatia Tel: 385/ , mlade.srdelic@st.tel.hr Abstract It was aalyzed all historical data of classical oceaographic parameters (temperature, saliity, oxyge, ad ph) have bee stored i Marie Evirometal Database of the Adriatic Sea (MEDAS). As mai figure of data distributio is ot regular i space ad time it was ecessary to estimate the value of the each parameters at the odes of a iterpolated grid. All parameters were iterpolated o the stadard oceaographic levels, ad the semivariogram graphs were used to describe the structural properties of field of each parameter. Geostatistics was used for iterpolatio of data because it estimates values correspod to the average values of the radom fuctio with miimum stadard deviatio error of iterpolated data. GIS techique was used for the fial aalyze ad presetatio of output data (seasoal, mothly, yearly data distributed i differet layers of water colum ad differet sub regios of the Adriatic Sea). I additio, overlayig of differet oceaographic parameters maps shows a ifluece of climate ad evirometal chage o biodiversity i the Adriatic Sea. 1 INTRODUCTION Mai figure of available classical oceaographic data (temperature, saliity, oxyge ad ph) is their statistically radom distributio i space (geographic positio ad layer i the water colum) ad time (year, seaso ad moth). Therefore, it was ecessary to maage of these data i three steps to get output data i suitable form for aalysis: - Iterpolatio of measurig data o the stadard oceaographic levels - Estimatig of iterpolatig data i square odes of geographic grid, ad - Presetatio of output results i GIS form. Other oceaographic data had bee passed a similar cotrol procedure that depedig of ature ad distributio of data. For example, data related fishes caught by trawlers represet bottom layer i water colum but radomly distributed i geographic space. As mai objective of ay spatial ivestigatios is to simplify aalyzig of spatially distributed data to the ed users, the special GIS system was desiged to perform the followig fuctios: data iput, data storage ad database maagemet system, data aalysis ad processig, iteractio of spatial data with GIS basic layers through graphics iterface, ad data output first of all i graphic forms. Maps created by Geographical Iformatio System (GIS) have represeted all measured, aggregated ad estimated data. Based o the maps gathered by these processig of data, various aalyses of oceaographic properties of the Adriatic Sea has bee possible. 2 MATERIAL AND METHODS 2.1 Oceaographic data

Oceaographic ivestigatio i the Adriatic Sea has bee doe for more tha a cetury by may istitutios from Croatia, as well as may iteratioal oceaographic orgaizatios.

2 Oceaographic ivestigatio i the Adriatic Sea has bee doe for more tha a cetury by may istitutios from Croatia, as well as may iteratioal oceaographic orgaizatios. It icludes physical oceaography, chemistry, biology ad fisheries. Classical oceaographic parameters represet the most ofte measured oceaographic parameters. As data had bee collected by may istitutios ad measured at various sea levels usig differet methods ad istrumets (BOT - bottle data, MBT - mechaical bathythermograph, XBT expaded bathythermograph, CTD - multiparametar probe), data quality cotrol (QC) has bee oe of major task. After QC procedure that icluded duplicate elimiatio, rage checkig, statistical checks ad static stability check of data more data were excluded from processig, e.g. 49% of BOT data ad 8% of MBT data (Table 1). Mai figure of data distributio is ot regular i space ad time (Fig. 1). They had bee collected at various (statistically radom) sea levels, geographic positios (oceaographic statios) ad seasos durig year depedig of used methods ad purpose of measuremets. Table 1 Number of classical oceaographic data received from differet coutries /projects ad umber of uique ad correct data Data source Temperature ( o C) Saliity (psu) BOT MBT XBT CTD BOT CTD Oxyge (ml/l) ph Croatia Italy USA Greece MEDAR MODAB Russia Frace Total Uique ad correct data (%) A) B) Fig. 1 Number of measured data by year (A) ad spatial distributio of statio i the Adriatic Sea (B) 2.1 Method of iterpolatio data o stadard levels There are more so call geeral ad local methods of iterpolatio. Local iterpolatio methods take ito accout oly eighborhood data ad it is more suitable for applicatios data o the stadard levels because water has completely differet properties i specific parts of water colum. There are more local methods of iterpolatios (Ratrray, 1958; Reigei ad Ross, 1964, Dadic, 1996). I this paper secod order Newto s method of fiite differeces

3 from three uequally spaced positios istead Lagage s method. This method determies the iterpolated value p ijk (x 0 ) ad its errors R ijk (x 0 ). Let us take values of the depedet variable y 1, y 2, y 3, y 4 to be kow at successive positios x 1, x 2, x 3, x 4 where x 4 >x 3 >x 0 >x 2 >x 1. The the value of y 0 at positio x 0 lyig betwee x 2 ad x 3 is y 0 = p ijk (x 0 ) +R ijk (x 0 ), where p ijk (x)=y i + k 1 (x-x i )+ k 2 (x-x i )(x-x j ), ad k 1 = x j -x i =(y j -y i )/( x j -x i, k 2 = x j,x i,x k = x j -k k - x j -k k / x j -k k, with i, j, k equal to either 1,2,3 or 2,3,4, respectively. This formula was improved accordig Reiiger ad Ros itroducig a referece curve to miimize the effect of uacceptable parabola o the fial iterpolated values. It is created by adjustig the liearly iterpolated value by a amout related to the differece betwee the iterpolated value (two ier poits) ad extrapolated value (two outer poits). It is used a weightig which is iversely proportioal to some power of the separatios betwee the iterpolated ad extrapolated poits. Accordig Dadic, 2001 the best-fitted referece curve for classical oceaographic parameters has the form: y R. (x) = 1 2 y 23+(y 12 -y 23 ) 1.5 y 34+ (y 23 -y 34 ) 1.5 y 12 / (y 12 -y 23 ) 1.5 +(y 23 -y 34 ) 1.5 Iterpolatio y(x) icludes liear iterpolatio betwee the values obtaied from two parabola (Fig. 2.A) ad the referece curve y R. (x) (Fig. 2.B) as i formula: y(x) = (y R -y p1 ) 0.6 y p2 + (y R -y p2 ) 0.6 y p1 / (y R -y p1 ) 0.6 +(y R -y p2 ) 0.6, D e pt h A) B) Measurig levels: 1, 2, Iterpolatio poit Extrapolatio poit Measurig level 3-40 Stadard level Saliity (ppt) Slaost (ppt) Saliity (ppt) Sl t ( t) Fig. 2 Procedure of local iterpolatio o the stadard oceaographic levels usig secod order of Newto s method (A) corrected by liear referece curve (B). For the top ad bottom of water colum where there is oly oe poit above or below stadard level it is ecessary to adopt this formula for liear iterpolatio. This formula gives less reliable results tha i the iterior of water colum because the curve is almost completely domiated by iformatio below or above the regio of iterpolatio. A automatic software tools was developed for iterpolatio data from observed to stadard oceaographic levels with graphic visualizatio of output results (iterpolated value ad expected error). All data profiles were passed through quality cotrol before ad after their iterpolatio o stadard levels. Values of all parameters have to be iside of miimum ad maximum, as well as climatological rage.

4 2.2 Method of geostatistical aalysis ad presetatio of spatially distributed data Geostatistical aalysis represets a applicatio of probability theory to estimate statistics relatig to spatial variables. There are more geostatistical methods for iterpolatio of spatially distributed data. I this paper liear geostatistics, applyig krigig techiques was used for estimatio value of the give variables i each usampled poit (Jourel ad Hjugregts, 1978). Krigig method is optimal method of iterpolatio that provides best liear ubiased estimate of the variable at a give poit. It is a extract iterpolator i the sese that iterpolated values, or best local average, will coicidece with the values at the data poits. I mappig, values have bee iterpolated for poits o a regular grid that is fier tha the spacig used for samplig. Geerally, the spatial distributio of variables related oceaographic parameters i the sea could be represeted as a complex surface (Fig. 3). We used the semivariogram as a useful tool to describe the structural properties of this surface (Fig. 4). It measures the level of dissimilarity betwee poits as a fuctio of the distace betwee them. If the semivariogram icreases as the distace icreases, the closer poits are more similar tha more distat poits ad therefore there is spatial structurig. If, the semivariogram is flat, the close poits have as differet values as distat oes ad therefore there is o spatial structurig. The semivariogram ca be calculated alog several directios to highlight structural aisotropy (Fig. 4). A ) N N H mi H ma E E Fig. 4 Omidirectioal experimetal Isotropic field Aisotropic field variogram (h) a (h) a (h) B ) H h H mi H max h () Area of tolerace Fig. 5 Distace (h) ad agle Fig. 3 Field structure (A) ad semivariogram (B) () tolerace amog radomly iside isotropic ad aisotropic field I homogeeity coditios of the spatial field the semivariace distributed (h) ca spatial be estimated data from sample data: 2 1 ( h ) V ( x i ) V ( x i h ), 2 i 1 where is umber of pairs of sample statios separated by distace h called the lag. Usually, some mathematical models (Gauss, expoetial, spherical, etc) have bee fitted through the experimetally derived data statios i order to be able to describe the way i which semivariogram chages with the lag. Krigig is method of iterpolatio that the values recorded at the sample poits we wish to estimate the value of the variable at the odes of a iterpolatio grid. As for ay iterpolatio method, krigig allows us to move from the radomly samplig cofiguratio to a regular cofiguratio, which automatically provides a map. Krigig allows us to estimate,

5 o the iterpolatio grid, the values of the uderlyig process. The kriged estimated values correspod to the average values of the radom fuctio. We used the values of eighborig sample poits to estimate the values of the desity surface at o-sample poits. The eighborhood is the area aroud the poit to be estimated i which we fid the samples to be used for the estimatio. The krigig weight give to each eighborhood sample depeds o spatial structure, its positio i relatio to the poit to be estimated ad its positio relative to the other eighborhood samples. The krigig weights are fuctios of the semivariogram ad of the samplig cofiguratio. With assumptios of field homogeeity a liear ad statioary geostatistics ca be used for spatial iterpolatio of data. Above defied semivariogram ca be used to determie the weights i eeded for iterpolatio by krigig method i the equatio: V ( x ) * V ( x 0 i i i1 where V(x o ) is estimated value i poit x o ad V(x i ) kow value i poit x i. The weights i are chose so that the estimate value of variable V(x o ) i poit with estimatig values x o is ubiased ad the estimated variace is less tha for ay other liear combiatio of the observed values. The miimum variace of the variable i poit of iterpolatio V(x o ) is obtai i case: j 1 ad it is: j * ( xi, x j ) ( xi, x0 ) i 1,... i 1, 2 e i 1 i * ( x i, x 0 where (x i,x j ) is semivariace of V(x) betwee samplig poits x i ad x j, (x i,x o ) is semivariace betwee samplig poits x i ad poit with estimatio x o, ad is Lagrage multiplier required for miimalizatio. 3 RESULTS AND DISCUSION After QC procedure ad harmoizatio of data umerous of uique ad correct data profiles were icluded i statistical calculatios, e.g. more tha oe hudred thousad of temperature data (Tab. 1). Importace of QC procedure ad harmoizatio of classical oceaographic is show o Fig. 6. Spatial field of temperature gathered from origial data looks like very artificial, while field of temperature gathered from QC data looks like more realistic. Geerally, results obtaied where quite satisfactory except i some areas where the urealistic values of oceaographic parameters have bee obtaied. Examiatio of these features idicated that some of them are due to large spatial ad iteraual variability i a regio where scattered samplig i time has occurred, existig of sprigs ad river mouths i coastal area, ad sparse of measurig data (Fig. 6 ad 7). We had these facts i mid whe acceptig the results of objective aalyze because distributio of data i some regios was urealistic with high gradiets or bulls-eyes. ) ) i 1

6 Fig. 6 Iterpolated field of mea value of temperature at the sea surface layer gathered from the origial data (A) ad data passed quality cotrol ad harmoizatio (B) Based o QC ad harmoized data profiles 41 stadard levels were defied i the Adriatic Sea. Additioally, two maximum depth differece criteria (ier ad outer) were defied for each stadard level as proposed Dadic, The first criterio defied a maximum distace from stadard level to the adjacet shallower ad deeper measured level, ad the secod criterio, that less strict, defied maximum distace to the secod shallower ad deeper measured level. If the ier ad outer distace criteria were violated, o stadard level value was calculated, but if oly outer maximum depth distace was violated the liear iterpolated was used. As there was aisotropy i spatially distributed data i trasversal ad logitudial axes of the Adriatic Sea differet radius ifluece ad grid mesh were used. So, ifluece radius of 7.5 km ad iterpolated data o a 5 x 5 km grid were used i trasversal directio of the Adriatic Sea ad i the coastal area ad ifluece radius of 12 km ad 7.5 x 7.5 km grid i logitudial directio at the ope sea durig krigig iterpolatios. These parameters allowed us to aalyze climatology of all radomly distributed oceaographic parameters i the space at every stadard oceaographic level i the same way. Some results of geostatistical aalysis of data those preseted by GIS tools there are i Fig. 7, ad 8. Aalysis of data of differet parameters shows that the Adriatic Sea is the most coastal basi i the Mediterraea Sea. Therefore, there is strog variability i the classical oceaographic parameters both i space ad time that has a great ifluece o biological aspects. Geerally, climatologically maps show a great seasoal variability of aalyzed oceaographic parameters, especially temperature (Fig. 7) ad saliity. So, sea surface temperature shows higher thermal gradiet alog the logitudial axis of basi i witer, while it is almost flat i summer. There are higher temperatures i the shallow orther part ad alog wester coast ad lower temperature i the souther part ad alog easter coast. I the witer seaso there is o stratificatio of the water masses ad magitude of thermal gradiet is almost same both i surface ad bottom layer alog logitudial axis. Durig witer seaso water masses are cooler i orther part ad alog wester coast tha those alog easter coast ad i souther part. Seaso variatio of temperature i surface layer of the Adriatic Sea is i the rages from 5 o C i the witer ad 27 0 C i the summer. Variatio i temperature is the greatest i coastal area ad orther Adriatic, ad more limited i ope sea ad souther Adriatic. Saliity variatios are also greater i coastal area ad orther Adriatic tha i the ope sea because of seasoal variatios i ruoff of the Adriatic rivers.

the Adriatic Sea ca be divided i four differet areas: 1) chael area alog Croatia coastlie, 2) orther basi with shallow mea depth, 3) south basi with the deepest part

7 A) B) C) D) Fig. 7 Maps of averaged temperature ( o C) at surface layer durig witer (A), sprig (B), summer (C), ad autum (D) for the period Cocerig oceaographic properties the Adriatic Sea ca be divided i four differet areas: 1) chael area alog Croatia coastlie, 2) orther basi with shallow mea depth, 3) south basi with the deepest part of the Adriatic Sea, ad 4) middle trasitio zoe betwee orth ad south basi located aroud lie Split-Mote Gargao (Fig. 8). Water masses with differet properties assiged with differet color (LIW - Levati itermedial water, NaDW - North Adraitic Deep water, SaDW- South Adriatic deep water, MAdW Middle Adriatic deep water).

8 Fig. 8 The most ofte water masses circulatio i the Adriatic Sea derived from temperature ad saliity data collected i the period CONCLUSION ORACLE RDBMS ad ArcView GIS tools orgaized as a automatic system for capturig off differet layers of oceaographic data ad their overlayig with basic cartographic layers performed varies sophisticated aalyses related to marie eviromet, especially i climatologically domai. REFERENCES Artegiai A., D. Bregat. E. Paschii. N. Piardi, F. Raicich ad A. Russo, The Adriatic Sea geeral circulatio. Part I: Air Sea iteractios ad water mass structure. Joural of Physical Oceaography, Vol. 27, No. 8: Boyer, T. ad S. Levitus, QC ad processig of historical oceaographic temperature, saliity ad oxyge data. USA-NOAA techical report NESDIS, No. 81. Dadić, V., Procedures for acquisitio, aalysis ad presetatio of oceaographic data. Thesis. Uiversity of Zagreb, 189 pp. Dadić, V., A iterpolatio of oceaographic data o stadard oceaographic levels. Proc. of 42 d Iteratioal Coferece ELMAR ' 2000, Zadar, 42: Davis J. C., Statistics ad data Aalysis i Geology. Joh Wiley & sos: 410 pp. Zore-Armada M., M. Boe, V. Dadić, M. Morović, D. Ratković, L. Stojaovski ad I. Vukadi, Hydrographic properties of the Adriatic Sea i the period from 1971 through Acta Adriat. 32 (1): Zore-Armada, M., M. Boe, V. Dadić, M. Gačić, V. Kovačević ad Z. Vučak, Ecological study of gas fields i the orther Adriatic, 4.Circulatio. Acta Adriat. 37(1/2):

Example 3.3: Rainfall reported at a group of five stations (see Fig. 3.7) is as follows. Kundla. Sabli

Example 3.3: Rainfall reported at a group of five stations (see Fig. 3.7) is as follows. Kundla. Sabli 3.4.4 Spatial Cosistecy Check Raifall data exhibit some spatial cosistecy ad this forms the basis of ivestigatig the observed raifall values. A estimate of the iterpolated raifall value at a statio is