INSTITUTE OF OCEANOGRAPHY AND FISHERIES SPLIT, CROATIA, since 1930 Ocean Biodiversity Informatics...

Ocean Biodiversity InformaticsInternational Conference on Marine Biodiversity Data Management

Hamburg, Germany: 29 November to 1 December 2004

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MEDAS SYSTEM FOR ARCHIVING, VISUALISATION AND VALIDATION OF

OCEANOGRAPHIC DATA

Dadic, V. and D. Ivankovic

Setaliste Ivana Mestrovica 63, 21000 Split; Damjana Jude 12, 20000 Dubrovnik

INSTITUTE OF OCEANOGRAPHY AND FISHERIESINSTITUTE OF OCEANOGRAPHY AND FISHERIES



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Database users

MEDAS

Application server

Web browser

Croatian Institutions

In Frame of

Croatian National Monitoring Programme

Systematic Research of the Adriatic Sea as a Base for Sustainable Development of the Republic of

Croatia

Use

Database with web interface

Marine Environmental Database of the Adriatic Sea(MEDAS)



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Database structure

MEDASDATABASE

MEDASDATABASE

MARINE PHYSICS• SEA LEVEL•SEA CURRENTS •CTD•METEOROLOGY

•CLASSICAL OCEANOGRAPHY•(T,S,Ph,o2, sea surface meteorology)

MARINE PHYSICS• SEA LEVEL•SEA CURRENTS •CTD•METEOROLOGY

•CLASSICAL OCEANOGRAPHY•(T,S,Ph,o2, sea surface meteorology)

META DATA

•ROSCOP

•INSTRUMENS

•RESPONSIBLE PERSONS

META DATA

•ROSCOP

•INSTRUMENS

•RESPONSIBLE PERSONS

INTERFACE

•GIS LAYERS

•EXCHANGE FORMATS

INTERFACE

•GIS LAYERS

•EXCHANGE FORMATS

FISHERIES•COASTAL FISHERIES•PELAGIC FISHERIES•DEMERSAL FISHERIES•EGGS,LARVES, JUVENILS•CATCH STATISTICS•AQUACULTURE

FISHERIES•COASTAL FISHERIES•PELAGIC FISHERIES•DEMERSAL FISHERIES•EGGS,LARVES, JUVENILS•CATCH STATISTICS•AQUACULTURE

MARINE BIOLOGY

•ZOOPLANCTON

•ZOOBENTHOS

•PHYTOPLANCTON

•MICROBIOLOGY

MARINE BIOLOGY

•ZOOPLANCTON

•ZOOBENTHOS

•PHYTOPLANCTON

•MICROBIOLOGY

BASIC LAYERS•COASTAL LINE•BATHYMETRY•WRECKS …

BASIC LAYERS•COASTAL LINE•BATHYMETRY•WRECKS …

MARINE

CHEMISTRY

•NUTRIENTS

•HEAVY METALS

•P A T

•P A H

MARINE

CHEMISTRY

•NUTRIENTS

•HEAVY METALS

•P A T

•P A H



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Database capability

Online stations (real time)

Cruise summary reports

(near real time – GPRS)

Data in digital form

Manually inserted data

Data visualisation

Search criteria, grouping data,

statistics

Data export:• MEDAR / MEDATLAS• Excel• Ocean Data View• Surfer• Arc GIS 8.1

MEDAS

database

Mappingtool

Data for running models



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MEDAS database - history

Distribution of Distribution of measuring stationsmeasuring stations archived in MEDAS archived in MEDAS databasedatabase

ORACLE 5 + Forms 3 – mapping tool: non ORACLE 7 + Forms 5 - mapping tool : C++ Oracle 9i + Oracle Application server - Mapping tool : Java applet



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Why this technologies?

ORACLE 9i: Stability, security Lack of stuff (few men's show)

ORACLE Application Server: No need for client side software (only browser) Accessibility (different institutions, ship – GPRS) Easy web publishing (default)

Java applet – mapping tool and data visualisation: Portability (cross platform) Use of Client side resources (no need for powerful server )

Disadvantages: Software license cost Instability and bugs of some Java versions Sometimes need for manually JVM install



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MEDAS database web interface



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Importance of good search criteria



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Input forms

Data input trough web forms (Croatian language only)

Authorisation requested

Basic input validation

Secure protocol (https)



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Cruise report example

Cruise information with mapping tool

Frames organised

Form – applet interaction



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Where & When

Who

What

Importance of Where, What and Who

Expended GF3 based organisation



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Real-time circulation model



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Real – time data (automatic stations)

Station PUNTA JURANA in front of the main building of the Institute

12 meteorological - oceanographic parameters (nine air and three sea parameters)

measurements every 10 minutes 24 hours a day Station VELI RAT

Meteorological sensors on the top of lighthouse11 meteorological - oceanographic parameters (nine air and two sea parameters) Measurements every 10 minutes 24 hours a day, data refresh interval – 1 hour



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Bathimetry and coastline of the Adriatic

Sea

Data sources in MEDAS databaseperiod almost a century (1907– 2004)countries: Croatia, Austria, ex Yugoslavia, Italy, USA, Russia, France, Germany and Slovenia Oceanographic data (characteristics):measured by research and other vesselsrandomly distributed stations (not regular in time and space)measured at various sea levelsdifferent methods and instruments (different quality of data)

About data



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Quality Control and Data Processing Data Processing

Data quality control in MEDAS database includes:Duplicate data elimination Range checking of data Statistical checking of dataStability checking of data.

Data Data profiles (profiles (setsets)s) 51769 temperature 32562 salinity profiles 5840 oxygen 2925 pH 13835 nutrients 3345 chlorophill_a

1753 plankton (zoo and phyto) 1218 fish trawler data 785 echosounding samples

Procedure: two steps

QC1 first step

QC2 second step



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DATABASEDATABASE

UNKNOWNDATE

UNKNOWNDATE

WRONG POSITION

OF STATION

WRONG POSITION

OF STATION

NO DEPTHNO DEPTH

EXCLUDE WHOLE DATA CAST

EXCLUDE WHOLE DATA CAST

EXCLUDE A DATA FROM CAST

EXCLUDE A DATA FROM CAST

CRUISECRUISE CASTCAST

Cases when data excluded from processing

0 - No QC1 - Correct value2 - Suspect value3 - Out of climatological range4 - Interpolated value5 - wrong value9 - missing value



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Classical Oceanographic Data Inventory

The most complete data sets: 1907-2004



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Number of measuring

data at different levels

BOT

CTD

XBT

MBT



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An example of spatial distribution of received oceanographic stations stations in wide area City of Split

Split

Ploče

Makarska

Korčula

Hvar

451 stations with451 stations with wrong positionwrong position

327 resolved 124 unresolved

Brač



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Example: Result of converting from pressure to depth and vice-versa



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Number of data of temperature at 5

different levels by season

a) Original data

b) Data in interval of 3 sigma of average

c) Data inside 1 sigma of average

02500

50007500

1000012500

15000

ZI PR LJ JE

Godišnje doba

Bro

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dat

aka 0

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0250050007500

10000125001500017500

ZI PR LJ JE

Godišnje doba

Bro

j po

dat

aka

0

10

50

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500

1000

0

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6000

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10000

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ZI PR LJ JE

Godišnj e doba

Bro

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data

ka

0

10

50

100

500

1000

a)

b)

c)

Season

Season

Season



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WinterWinter

SpringSpring

SummerSummer

FallFall

Seasonal presentation of total amount of temperature data



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Sea surface temperature sorted by time from 1900 up to 2000



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28 one degree squares and 4 sub-regions in the Adriatic Sea

Definition of areas in the Adriatic Sea for calculation climtologcal ranges of classical oceanographic parameters

(I)

(IV)

(III)

(II)



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OK

Calculation of basic statistics (number of samples, mean value, standard deviation, min and max

values)

Interpolation of measuring data on 41 standard oceanographic levels

using Newton method of finite differences modified by reference

RR curves

Calculation of basic statistics on standard levels

Interpolation by kriging method and graphical presentation by

mapping tool

NoYES

Finish

Semiautomatic

Visualisation

Procedure:

Data processing is done in the next steps in aim to get output data in suitable form for analysis



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Standard depth

Max distance between two outer levels

Max distance between two inner levels

Dis

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e be

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n le

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(m)

Dep

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)

Adriatic Sea: 41 standard levels

Number of standard level



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Comparison of the three different methods of interpolation data on

standard levels

Temperature (oC)

Newton interpolation of finite differences (nth order)

Newton interpolation of finite differences (2th order) through 3 points

Newton interpolation of finite differences (2th order) through 3 points modified by R-R reference curve

Dep

th (

m)



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Testing of parameters in reference curves:

m=1-22 n=1,10

Optimal values:

m=1.6, n=0.6

Testing of parameters



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Method of analysis of spatially distributed data

Kriging method has been used as optimal method of interpolation of randomly distributed data in space because it provides the best linear unbiased estimate (BLUE) of the variable at a given point.

It is an exact interpolator in the sense that interpolated values, or best local average, will coincidence with the values at the data points.

The kriging method is valid if there is continuity among dana, which is testing by variogram.



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Distance between pair of stations (*60 Nm)

Se

miv

aria

nce

Nu

mb

er o

f st

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airs

DiscontinuityContinuity

SemivarianceWinterSpring SummerAutumnNumber of pairsWinterSpring SummerAutumn

Investigation of spatial continuity of temperature by variance



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Oxigen stations

37439 drops 7845 drops

All stations(1900-2004)

Spatial distribution of oceanographic measuring stations

Spatial distribution of oxygen stations

Quality controlled data



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Filed of spatially distribute sea surface temperature derived from original data and data partly passed

QC procedure for the period 1907-2003



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Averaged values and standard deviation of salinity in 1 deg. squares (0-5 meters) for the period 1907-2004

Possible choosing different size of

squares

Square includes:

- coastal area- open sea

(need to use smaller size square)



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Result of data analysis of T,S, O2, pH and nutrient parameters

It was concluded: 41 standard levels in the Adriatic Sea was defined as enough for

qualitative climatological analysis of T,S,O2, pH and nutrients in

the Adriatic Sea climatological range of oceanographic parameters in the Adriatic

sea for above mentioned parameters was defined spatial continuity of oceanographic parameter are from 5 to 7.5

km depends of season and depth of standard oceanographic level detail structure of spatial distribution for climatological

properties of oceanographic parameters has been calculated different layers of the water mass in the Adriatic Sea has been

analysed.



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Water mass distribution in the period of low liw inflow (derived from oceanographic data)



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Water mass distribution in the period of liw inflow intensification (derived from oceanographic data)



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Cuper (Cu) u oyster in Dubrovačko-Neretvanska County

0

10000

20000

30000

40000

50000

2000 2001 2002 2003

Year

w C

u (µ

g kg

-1 s.m

.)

OT2

OT3

OT4

OT6

Source: IOR-Split

Cadmium (Cd) in oyster in the Istarska County

0

250

500

750

1000

1250

2000 2001 2002 2003

Year

w C

d (

µg

kg

-1 s

.m.)

OT23

OT24

OT25

OT27

Source: IOR-Split

ZInk (Zn) ikn oyster in the Primorsko-goranska County

0

50000

100000

150000

200000

250000

2000 2001 2002 2003

Godina

w Z

n (µg

kg

-1 s

.m.)

OT21

OT22

Source: IOR-Split

Heavy metals in dry weight of oyster tissue

analysis



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Mean concentration of chlorofill_a

analysis

Arc-ViewArc-ViewMEDAS USA NODC Tax code



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SURFERSURFERMEDAS

H V A R

MEDITS

Example: spatial analysis of distribution commercialy demersal fish population in the

Adriatic SeaCalculation of spatially distribution of

relative abundance (kg/km2) of:

total stock of demersal fish population

stock of main commercial spicies

from two extensive trawl-surveys carried out in two distinct periods shifted 50 years in time:

• Expedition Hvar (1948-1949) • EU project MEDITS (1996-1997)

FAO tax code



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Spatial distribution of total fish stock (kg/km2) during HVAR and MEDITS expedition

HVAR

MEDITS



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HVAR

MEDITS

Spatial distribution of main commercial fishes (kg/km2) during HVAR and MEDITS expedition

Decrease of quantity of total as well as the most important

commercial fishes of the demersal fish stock

The changes in fish abundance are more significant in shallow coastal area then in deeper sea areas

Composition changes of demersal stocks assemblage within two surveyed periods should be foreseen as a consequence, either of fishing pressure or environmental changes in the sea.



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Example: planning of assignment of marine areas in the Split-dalmatia County



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Future Improvements:

Transcode all database thematic parts to web environment

Input forms (ergonomic)

Web visualisation tools

Data quality procedures and tools for biological parameters

Extend taxonomic codes

Add multimedia support

Add GIS support

INSTITUTE OF OCEANOGRAPHY AND FISHERIES SPLIT, CROATIA, since 1930 Ocean Biodiversity Informatics...

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Transcript of INSTITUTE OF OCEANOGRAPHY AND FISHERIES SPLIT, CROATIA, since 1930 Ocean Biodiversity Informatics...