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