Lab of Remote Sensing and Spatial Analysis

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 data (AVHRR, SeaWiFS, Meteosat) own measured data byHRPT Data Receiver Sonda STD Tethered Spectral Radiometer Buoy Fluorometer TachymetrCoulter counter Beam transmittance meter exchange of data between: IO PAS, MI, MFI, RDANH

Models (M3D_UG, ICM, ECMWF, HIROMB)

Projects:

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

HRPTRaw AVHRR

data

HRPTRaw AVHRR

data

ASDIKSystem of Automatic

Registration, Geometric and Geographic

Correction of AVHRR Data

ASDIKSystem of Automatic

Registration, Geometric and Geographic

Correction of AVHRR Data

PRODUCTSQuicklooks & UTM

maps of:SST, Brightens

temperature, Albedo, cloudiness

PRODUCTSQuicklooks & UTM

maps of:SST, Brightens

temperature, Albedo, cloudiness

Data base of raw data

Data base of product

WWW interface WWW 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 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 methods

Comparison 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 of sea level Network of sea level river discharges river discharges

chemical and biological chemical and biological stationsstations

Remote Sensing

Monitoring

Observations and hydrological forecasts

IMWM

Operational observations

BOOS

Ecohydrodynamic model Processes 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 decay14) 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 5

3

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

NH

4[g

m-3

]

0.00

0.02

0.04

0.06NH4_OBS

NH4_MOD R=-0.037

Nto

t[gm

-3]

0.00

0.10

0.20

0.30

0.40

Ntot _OBS

Ntot _MOD R=0.480

PO

4[g

m-3

]

0.00

0.01

0.02

0.03

0.04PO 4_OBS

PO 4_MOD

R=0.713

Pto

t[gm

-3]

0.00

0.01

0.02

0.03

0.04

P tot _OBS

P tot _MOD R=0.434

SiO

4[g

m-3

]

0.000.100.200.300.400.50

SiO 4_OBS

SiO 4_MOD R=0.269

O2[g

m-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

Vistu la

G ulf o f G dańsk

G dańsk

grid step: 5 N M

grid step: 1 NM

Validation Validation

of the ProDeMo modelof the ProDeMo model

S[p

su]

6.00

6.50

7.00

7.50

8.00S_OBS

S_MOD R=0.503

1994 1995 1996 1997 1998 1999 2000 2001 2002N

O3[g

m-3

]

0.00

0.04

0.08

0.12NO 3_OBS

NO 3_MOD

R=0.800

0 1 0 2 0 3 0 4 0 5 0

- 1 0 0

- 8 0

- 6 0

- 4 0

- 2 0

0D

epth

[m

]

0 1 0 2 0 3 0 4 0 5 0

D i s t a n c e [ k m ]

- 1 0 0

- 8 0

- 6 0

- 4 0

- 2 0

0

Dep

th [

m]

o b s e r v e d

m o d e l l e d

0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

N O 3 [ g m 3 ]

0 10 20 30 40 50

Distance [km]

-100

-80

-60

-40

-20

0

Dep

th [

m]

observed

0 10 20 30 40 50

-100

-80

-60

-40

-20

0

Dep

th [

m]

modelled

0.00

0.02

0.04

0.06

0.08

0.10

0.12

PO4 [g m 3]

5 1 0 1 5 2 0 2 5

S a l i n i t y [ P S U ]

8 9 1 0 1 1

D i s s o l v e d o x y g e n

[ g m - 3 ]

0 0.005 0.01 0.015 0.02

N -N H 4 [g m -3]

0.01 0.03

P-PO 4 [g m -3]

0.1 0.4 0.7 1 1.3

S i-S iO 4 [g m -3]

1 1 1 3 1 5 1 7 1 9 2 1 2 3

T e m p e r a t u r e [ ° C ]

Spatial distribution Spatial distribution

of the nutrients – June 1999of the nutrients – June 1999

0 0.05 0.1 0.15 0.2 0.25

N -N O 3 [g m -3]

The impact of the Vistula river on the coastal water of the Gulf of GdanskScenarios 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

Prim ary production[gC m -2/ year ]

Primary production [106 kg/year]

800850900950

1000

Reference year2002

Policy targetslow

Policy targetshigh

Deep green

The lowest biological productivity has been The lowest biological productivity has been obtained for Deep green scenario - 7.5 % obtained for Deep green scenario - 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.

1 5 2 0 2 5 3 0

5 4

5 6

5 8

6 0

6 2

6 4

6 6

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]

uN1

u

vectorial mean velocities

vN1

v

N

1i

21

2i

2i

2

122

)vu(N1

)vu(B

N

1i

2

12i

2i )vu(

N1

V

arithmetic mean velocities

stability

surface

1 5 2 0 2 5 3 0

5 4

5 6

5 8

6 0

6 2

6 4

6 6

spring

0

0.1

0.2

0.3

0.4

0.5

0.6

Velocity [m /s]

0 .05 0.1

stability

1 5 2 0 2 5 3 0

5 4

5 6

5 8

6 0

6 2

6 4

6 6

0

0.1

0.2

0.3

0.4

0.5

0.6

Velocity [m /s]

0 .05 0.1

stability

autumn