1. technology and computing

Monitoring sea level fluctuation in South Aegean
Vangelis
1
1
Zacharis ,
1
Paradissis ,
2
Drakatos ,
1
Marinou ,
Demitris
George
Aggeliki
Nicolaos
1
1
1
Demitris Anastasiou , Stavroula Alatza , and Xanthos Papanikolaou
2
Melis ,
Higher Geodesy Laboratory and Dionysos Satellite Observatory, School of Rural & Surveying Engineering, National Technical University of Athens
2
Institute of Geodynamics, National Observatory of Athens
[email protected]
Introduction
Data management
Conclusions/Suggestions
The complexity of the geological setting of the South Aegean is
well-known, among the scientific community. The subduction zone
coupled with the latest unrest of the Santorini volcano, as well as
the particular morphology of the earth’s surface and seabed pose
a poorly understood source of tsunami hazard.
A sparse network of tide gauges that operate in the area for varying
periods of time is strengthened by the establishment of new sensors
at selected locations [4], as part of a multiparametric platform that
combines various sensors (eg. GNSS receivers, seismometers, accelerometers, etc.). This platform will provide scientists access to a
unique data and result archive, stemming from an exceptional region of the Earth, which has and continues to draw great interest.
At the moment, various pieces of software are being developed, using high-level languages such as Python [8], to aid data acquisition,
management and modelling. These modules are being thoroughly tested using data from a newly installed tide gauge at Kastelorizo
(Megisti) island in the far eastern edge of the Aegean Sea.
Using these software modules, raw data –that is being recorded in varying intervals– are tested, outliers are filtered and finally are stored
along with suitable meta-data in appropriate database tables (fig. 2, 3).
The sparse network of tide gauges in the South Aegean is being
strengthened; this will provide the scientific community with the
means to better understand the underlying processes of the region,
and reveal means of dealing with their aftermath.
As previously mentioned, a number of techniques are examined
in analyzing the collected data. By utilizing different methods in
the analysis will allow us to benefit from the advantages of each
particular method and avoid their weaknesses.
Finally, since it is recognized that great earthquakes can occur along
the Hellenic plate boundary, and the hazards –particularly the tsunami hazard– associated with this boundary remain unquantified,
more research is needed to place constraints on the seismic and
tsunami hazard associated with this boundary.
Network Description
Currently (April 2015), this network consists of nine tide gauge
sensors installed and maintained by NTUA, NOA, or the Hydrographic Service of the Hellenic Navy, as shown in table 1.
To strengthen this sparse network, nine new sensors are being installed in selected positions (see figure 1).
MEGI
MEGI
water level [cm]
2.0
water level [cm]
2.30
2.25
1.5
2.20
2.15
1.0
19.0
18.5
18.0
17.5
17.0
16.5
16.0
100
90
80
70
60
50
40
30
20
9
8
7
6
5
4
3
2
1
0
2.35
2.10
water temperature [°C]
relative humidity [%]
wind velocity [m/s]
5
5
5
5
5
5
5
5
5
20 1 6 201 9 20 1 2 20 1 5 20 1 8 20 1 1 201 4 20 1 7 20 1
3
0
0
1
1
2
2
0
1
2
Feb Feb Feb Feb Feb Feb Feb Feb Feb
20
18
16
14
12
10
8
6
4
1025
1020
1015
1010
1005
1000
995
990
985
400
350
300
250
200
150
100
50
0
air temperatue [°C]
pressure [mbar]
wind direction [°]
5
5
5
5
5
5
5
5
5
20 1 6 20 1 9 20 1 2 20 1 5 20 1 8 20 1 1 20 1 4 20 1 7 20 1
3
0
0
1
1
2
0
1
2
2
Feb Feb Feb Feb Feb Feb Feb Feb Feb
2.05
18.0
17.8
water temperature [°C]
17.6
17.4
17.2
17.0
16.8
16.6
16.4
100
90
relative humidity [%]
80
70
60
50
40
8
7
wind velocity [m/s]
6
5
4
3
2
1
0
00
00
00
00
00
00
00
00
00: 2:00: 0:00: 2:00: 0:00: 2:00: 0:00: 2:00:
:
0
1
1
1
1
0
0
0
0
Figure 2: Raw data recorded at station MEGI for Feb 2015.
Figure 1: Current and future status of the tide gauges’ network.
CODE
JRC01
JRC02
JRC03
Latitude
36.1429
35.2302
36.7975
Longitude
022.9993
023.6835
021.9563
Location
Kapsali
Paleochora
Koroni
VLYCH
36.3358
025.4353
Vlychada
KALA
KATA
PEIR
SYRO
KAST
37.0215
37.6405
37.9347
37.4380
35.5140
022.1098
021.3192
023.6212
024.9411
023.6370
Kalamata
Katakolo
Peiraias
Syros
Kasteli
Agency
NOA
HL-NTWC
NOA
HL-NTWC
NOA
HL-NTWC
NOA
HL-NTWC
HNHS
HNHS
HNHS
HNHS
NTUA
Sensor Type
1: radar sensor
2: pressure sensor
1: radar sensor
2: pressure sensor
1: radar sensor
2: pressure sensor
1: radar sensor
1: pressure sensor
1: pressure sensor
1: pressure sensor
1: pressure sensor
1: radar sensor
Connection
ADSL/GPRS
(latency: 1min)
ADSL/GPRS
(latency: 1min)
ADSL/GPRS
(latency: 1min)
GPRS
(latency: 1min)
FTP
FTP
FTP
FTP
GTS
MEGI
2.5
References
[1] GILL, S., G. HOVIS, K. KRINER, M. MICHALSKI (2014) Implementation of Procedures for Computation of Tidal Datums in
Areas with Anomalous Trends in Relative Mean Sea Level, US
Department of Commerce, National Oceanic and Atmospheric
Administration, National Ocean Service, Center for Operational
Oceanographic Products and Services, Technical Report NOS
CO-OPS 068
[2] GRINSTED, A., J. C. MOORE, S. JEVREJEVA (2004) Application
of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlinear Processes in Geophysics 11: 561-566,
SRef-ID: 1607-7946/npg/2004-11-561
Figure 3: Raw data recorded at station MEGI
for the period 26-28 Feb 2015.
A number of methods and techniques of tidal analysis are investigated for the modelling of the data. A lot of information is drawn from
the Permanent Service for Mean Sea Level [7], but also from [1], [3] and [6]. In general, these methods fit, in some optimal way, a set of
harmonic constituents to the data, which can be done in several ways. For example applying a Low-Pass filter to remove the tidal energy at
diurnal and higher frequencies, as shown in figure 4. More recent techniques such as the one outlined in [2], are also being tested, giving
promising results.
In a region of such complexity, there are numerous effects that involve crust deformation and sea level fluctuations, and that take place
simultaneously. Combining tide gauges with co-located GNSS receivers will provide additional information to separate these effects.
Additionally, besides long-term variations in currents and the volume of water, other geophysical signals are also present in tide gauge
data, such as earthquake motion and ground water extraction, which can be investigated using these data.
Table 1: Current status of the tide gauges’ network.
EGU 2015, European Geosciences Union, General Assembly 2015, 12-17 April 2015, Vienna, Austria
19
18
air temperatue [°C]
17
16
15
14
13
12
11
10
1024
1022
pressure [mbar]
1020
1018
1016
1014
1012
1010
1008
400
350
wind direction [°]
300
250
200
150
100
50
0
0
00
00
00
00
0
00
00
00
:
00: 0:00: 2:00: 0:00: 2:00: 0:00: 2:00:
00
:
:
2
0
1
1
1
1
0
0
0
0
water level [cm]
2.0
[3] HICKS S.D. (2006) Understanding Tides, US Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Center for Operational Oceanographic Products and Services
[4] IOC Manuals and Guides No. 14: Volumes I - IV, http://www.
psmsl.org/train_and_info/training/manuals/
[5] SZABADOS M (2008). Understanding Sea Level Change, ACSM
Bulletin, No. 236, 10-14
[6] ZERVAS C., S. GILL, W. SWEET (2013) Estimating Vertical Land
Motion from Long-Term Tide Gauge Records, US Department of
Commerce, National Oceanic and Atmospheric Administration,
National Ocean Service, Center for Operational Oceanographic
Products and Services, Technical Report NOS CO-OPS 065
[7] http://www.psmsl.org
1.5
[8] https://www.python.org
1.0
0.5
0
2
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Acknowledgments
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Figure 4: Filtered values for sea level at station MEGI
(Kastelorizo) for Feb 2015.
5
This work is funded through the National Strategic Reference Framework.