Department of Physical Oceanography

Lab of Remote Sensing and Spatial Analysis

Lab of Sea Dynamic

Lab of Remote Sensing and Spatial AnalysisInvestigations based on:

satellite datadatadatadata (AVHRR, SeaWiFS, Meteosat)

own measured data by

HRPT Data Receiver Sonda STD

Tethered Spectral Radiometer Buoy

Fluorometer Tachymetr

Coulter counter Beam transmittance meter

exchange of data between: IO PAS, MI, MFI, RDANH

ModelsModelsModelsModels (M3D_UG, ICM, ECMWF, HIROMB)

ProjectsProjectsProjectsProjects::::

Analysis of solar energy inflow and temperature distribution at the Baltic Sea surface basing on satellite data

The consequence of coastal upwellings phenomenon for biological productivity along Polish coast

of the Baltic Sea

Application of the SeaWiFS data for studies of the water turbidity in the Baltic Sea

HRPT

Raw AVHRR

data

HRPT

Raw AVHRR

data

ASDIK

System of Automatic

Registration, Geometric

and Geographic

Correction of AVHRR

Data

ASDIK

System of Automatic

Registration, Geometric

and Geographic

Correction of AVHRR

Data

PRODUCTS

Quicklooks & UTM

maps of:

SST, Brightens

temperature, Albedo,

cloudiness

PRODUCTS

Quicklooks & UTM

maps of:

SST, Brightens

temperature, Albedo,

cloudiness

Data base of

raw data

Data base of

product

WWW interfaceWWW interface

The The sattelite monitoringsattelite monitoring

Lab of Sea Dynamic

•Long-term changes hydrometeorological of climate

•Long-term changes of the Baltic Sea level

•Patterns of circulation in the Baltic

•Ecohydrodynamic model of the Baltic Sea

•Coastal upwellings in the Baltic Sea

•Sea state modelling using system identification

methods

•Modelling of nearshore currents induced by wind

waves

•Modelling of the interaction between currents and

surface waves

Investigations focused on:

hydrology, waves and ecohydrodynamichydrology, waves and ecohydrodynamichydrology, waves and ecohydrodynamichydrology, waves and ecohydrodynamic

Correlation between the NAO index and runoff from selected

sub-catchment areas into the Baltic Sea

The examples of many years’ sea level changes of the following stations: Wismar, Warnemunde, Kunkolmsfort, Geteborg, Ratan, Oulu

The time series of the mean annual sea level in the period of 1900-2000

Principal components of time series variability

Spatially averaged changes of sea level in time (a) and the main three principal components of sea level variability, which explains 93.6% of total variance (b-d)

Spatial charge distribution of the three variability Components of mean sea level in 100 years’ period

Sea state modelling using system identification methodsComparison of System Identification modelling (right) and spectral wave model WAM results (left)

for significant wave height (upper) and mean wave period (down) for 1100 hrs UTC on March, 7th, 2000.

Methods are based on finding simple, mathematical transformations between two sets of variables

(e.g. wind field and wave characteristics).

Modelling of nearshore currents induced by wind waves

Example of the longshore current

model results (upper) along multi-

bar bottom crossection (down).

Incoming deep wave water parameters:

Ho = 0.8m, To = 6s, θo = 65o

Significant influence on the

coastal zone circulation has a

wave breaking. Energy, which is

dissipated in this process, causes

coastal wave-driven currents.

Analytical and numerical models

of coastal zone circulation are

based on radiation stress

concept.

Operational System for Coastal Waters

of Gdańsk Region

Hydrodynamic model

M3D

Meteorological forecasts

UMPL Model

ICM

ProDeMO

ecosystem model

Network ofNetwork of seasea level level

river discharges river discharges

chemical and biological chemical and biological

stationsstations

Remote

Sensing

Monitoring

Observations and

hydrological forecasts

IMWM

Operational

observations

BOOS

Ecohydrodynamic modelProcesses included in the ProDeMo:

1) nutrient uptake by phytoplankton,

2) phytoplankton grazing by zooplankton,

3) phytoplankton respiration,

4) phytoplankton decay,

5) sedimentation,

6) nutrients release from sediment,

7) atmospheric deposition,

8) denitrification,

9) mineralisation,

10) zooplankton respiration,

11) sedimentation of phosphorus

adsorbed on particles,

12) detritus sedimentation,

13) zooplankton decay

14) nitrogen fixation

15) nutrient deposition.

influenced the dissolved oxygen:

16) reaeration,

17) flux to atmosphere due to the over saturated

conditions,

18) zooplankton respiration,

19) phytoplankton respiration,

20) assimilation,

21) mineralisation,

22) nitrification,

23 ) denitrification

3D Hydrodynamic

Model

Meteorological Data:

Model UMPL (ICM)

River

Inflows

Data

Production and

Destruction of

Organic Matter

Model (ProDeMo)

Solar

Radiation

Model

Atmos-

pheric

Deposition

NUTRIENTS

N-NO3

P-PO4

Si-SiO4

N-NH4

DETRITUS

CDETR

PDETR

SiDETR

NDETR

ZOOPLANKTON

Zooplankton

C:N:P

PHYTOPLANKTON

Dinoflagellate

NSED PSED SiSED

DISSOLVED

OXYGEN

Water

Atmosphere

Sediment

1

2

3

4 53

6

7

8

7

10

11

12

13

16 17

18

19 20

21

22

23

Spring diatoms

Autumn diatoms

Blue-green algae

Green algae

Inactive layer

Active

layer

14

15 15 15

NH4[gm

-3]

0.00

0.02

0.04

0.06NH4_OBS

NH4_MOD

R=-0.037

Ntot[gm

-3]

0.00

0.10

0.20

0.30

0.40

Ntot_OBS

Ntot_MOD

R=0.480

PO4[gm

-3]

0.00

0.01

0.02

0.03

0.04PO

4_OBS

PO4_MOD

R=0.713

Ptot[gm

-3]

0.00

0.01

0.02

0.03

0.04

Ptot_OBS

Ptot_MOD

R=0.434

SiO

4[gm

-3]

0.00

0.10

0.20

0.30

0.40

0.50

SiO4_OBS

SiO4_MOD

R=0.269

O2[gm

-3]

8.0

11.0

14.0

17.0 O2_OBS

O2_MOD

R=0.852

Tw[oC]

0.0

8.0

16.0

24.0

Tw_OBS

Tw_MOD

R=0.976

P140

P39

P5 P63

Baltic Sea

KNP

P1

P101

P104

P110

P116

R4

ZN2

ZN4

Vistula

Gulf of Gdañsk

Gdañsk

grid step: 5 NM

grid step: 1 NM

Validation Validation

of the of the ProDeMoProDeMo modelmodelS[psu]

6.00

6.50

7.00

7.50

8.00S_OBS

S_MOD

R=0.503

1994 1995 1996 1997 1998 1999 2000 2001 2002

NO3[gm

-3]

0.00

0.04

0.08

0.12NO3_OBS

NO3_MOD

R=0.800

0 10 20 30 40 50

-100

-80

-60

-40

-20

0

Depth [m]

0 10 20 30 40 50

Distance [km]

-100

-80

-60

-40

-20

0

Depth [m]

observed

modelled

0.0

0.1

0.2

0.3

0.4

0.5

0.6

NO3 [g m3]

0 10 20 30 40 50

Distance [km]

-100

-80

-60

-40

-20

0

Depth [m]

observed

0 10 20 30 40 50

-100

-80

-60

-40

-20

0

Depth [m]

modelled

0.00

0.02

0.04

0.06

0.08

0.10

0.12

PO4 [g m3]

5 10 15 20 25

Salinity [PSU]

8 9 10 11

Dissolved oxygen

[g m-3]

0 0.005 0.01 0.01 5 0.02

N-NH4 [g m-3]

0.01 0.03

P-PO4 [g m-3]

0.1 0.4 0.7 1 1.3

Si-SiO4 [g m-3]

11 13 15 17 19 21 23

Temperature [°C]

Spatial Spatial distribution distribution

of the of the nutrientsnutrients –– JuneJune 19991999

0 0.05 0.1 0.15 0 .2 0.25

N-NO3 [g m-3]

The impact of the Vistula river on the

coastal water of the Gulf of Gdansk

Scenarios analysis by ecohydrodynamic

model

N:P 2002 N:P 2015

0 10 20 60 100 140 180 N:P

2002 2015

0 40 80 120 160 200

Primary production

[gC m-2/ year ]

P rimary production [106 kg/year]

800850900950

1000

Reference year

2002

Policy targets

low

Policy ta rgets

high

Deep green

The lowest biological productivity has been The lowest biological productivity has been

obtained for Deep green scenario obtained for Deep green scenario -- 7.5 % 7.5 %

less than in the reference year 2002.less than in the reference year 2002.

Due to reduction of phosphorus loads from Due to reduction of phosphorus loads from

40.9 % for the policy target low to 45.5 % 40.9 % for the policy target low to 45.5 %

for Deep green scenario and nitrogen loads for Deep green scenario and nitrogen loads

less than 10%, the phosphorus becomes less than 10%, the phosphorus becomes

a limiting nutrient in the Gulf of Gdansk a limiting nutrient in the Gulf of Gdansk

in the analyzed scenarios.in the analyzed scenarios.

15 20 25 30

54

56

58

60

62

64

66

Annually averaged circulation pattern in the Baltic Sea

0

0.1

0.2

0.3

0.4

0.5

0.6

Velocity [m/s]

0.05 0.1

15 20 25 30

54

56

58

60

62

64

66

surface

0

0.04

0.08

0.12

0.16

0.2

0.24

0.28

0.32

magnitude[m/s ]

∑= uN

1u

vectorial mean velocities

∑= vN

1v

∑=

+

+=

N

1i

2

1

2

i

2

i

2

122

)vu(N

1

)vu(B

∑=

+=N

1i

2

1

2

i

2

i )vu(N

1V

arithmetic mean velocities

stability

surface

15 20 25 30

54

56

58

60

62

64

66

spring

0

0.1

0.2

0.3

0.4

0.5

0.6

Velocity [m/s]

0.05 0.1

stability

15 20 25 30

54

56

58

60

62

64

66

0

0.1

0.2

0.3

0.4

0.5

0.6

Velocity [m/s]

0.05 0.1

stability

autumn