81
shown in the Figure . Spatial analysis is carry out by using System for Automated Geoscientific
Analyses SAGA and Quantum Geographic Information System QGS .
Figure 1. Study area; location of gridded Aquarius data set used in this study.
3. Results and discussion
3.1
Monthly average SSS distribution in the Indonesian Seas.
Figure and shows the monthly mean of four years SSS in the ndonesian Seas. Based on monthly mean SSS data, the maximum SSS occurred in Southeast monsoon July to
September and the minimum SSS occurred in Northwest monsoon January to April .The occurrence of spatial variability of SSS in the ndonesian Seas are indicated by
low SSS in the eastern part of inland seas Karimata Strait and Java Sea compare to the western part area. This result indicate that the fresh water flow South China Sea lead to
lower SSS in the Karimata Strait and Java Sea. SSS of September and October has a highest range in spatially with reaching ppt and ppt, respectively.
82 Figure 2. Monthly average of SSS derived from Aquarius; January to June
83 Figure . Monthly average of SSS derived from Aquarius; July to December
3.2. Annual Difference
The annual variation of SSS data in the region is shown inSSS difference from year to
Figure . The results showed Aquarius SSS in is lower than
and . The
SSS shows the fresh water in the most part of ndonesian water Karimata Strait, Makasar Strait, Java, Flores and Banda Seas , except Celebes Sea. The
Java Sea SSS in is . ‐ lower than
. The Banda Sea SSS difference of ‐
is . ‐ . . The saline water in Celebes Sea is recognized in compare to other
area in the inland seas.
84 Figure . Annual differenceof SSS
‐ ;
‐ ;
‐
8
3.3. Seasonal SSS anomaly
The Aquarius SSS anomaly pattern for the northwest monsoonDecember‐January‐ February DJF andsecond transition March‐April‐May MAM seasons, averaged from
to is shown in Figure aand Figure .b.besides the SSS for Aquarius for the
different season; southeast monsoon June‐July‐August JJA and firsttransition September‐October‐November SON . DJF anomaly shows a very clear remarkable
freshening in Java and Celebes Seas. A clear westward propagation of low salinity water form Java Sea into Banda Sea can be seen in the MAM SSS field.The freshen forms a low
salinity also can be seen in Arafura Self. The spatial distribution for the transition period MAM reveals some significant
change in SSS distribution. The pattern shows great range, from a low of ‐ in the Java, Flores and Banda Seas to .8 in the South China Sea, in contrast to only a – for the
SON.
Figure . Seasonal anomaly of SSS
3.4 Time‐latitude diagram of Aqurius SSS inthe ITF region
The transfer of water mass between the Pacific and the ndian Oceans is an important phenomena for regional ocean‐atmospheric circulation in the ndonesian Seas with
potential impacts on the monsoon phenomena and ENSO Webster et al., 8 . Figure
display the SSS variability of Celebes Sea, Makasar Strait, Banda and Java Seas. During south east monsoon the Celebes Sea SSS is above . The lowest SSS observed during
northwest monsoon and mostly on February to April. n the Makasar Strait during south east monsoon the SSS varies between . and , fresher than Celebes Sea. During
north west monsoon n the Banda Sea SSS during south east monsoon the SSS is between and . .
8 Figure . Aquarius SSS of TF channels Celebes Sea, Makasar Strait, Banda Sea and Java Sea
3.5 Time‐latitude diagram of Aquarius SSS in the outland‐sea
SSS variation of TF inflow South China Sea; West Pacific and outflow South Java Sea area are shown in Figure . The West Pacific SSS shows very clear monsoonal features.
The South China Sea SSS do not show very clear monsoonal variation in contrast of SSS in West Pacific and South Java Sea show. Compare to West Pacific and South Java Sea,
the South China Sea has the lowest SSS. t is seem to be freshening in the South China Sea occurred for all season. n the West Pacific during north west monsoon December
to May the SSS varies between and . n the South Java Sea, during transition period September to November the SSS varies between and . .
87 Figure . Aquarius SSS of outland‐seas West Pacific, South China Sea, South Java Sea
3. Conclusion
The seasonal and spatial variability of SSS in the ndonesian Seas were determined. Aquarius displays an annual and seasonalcycle for the ndonesian Seas. Aquarius SSS
feature was characterized with the seasonal cycle such as Southeast monsoon and Northwest monsoon and estimated SSS concentrations during January to May give
fresher lower thanduring June to November due to rainfall effects. The seasonal variability of SSS in the TF channels was also estimated in the Java
Sea, Makassar Strait, Banda and Celebes Seas. As a major transport of TF, these area shows very clear seasonal pattern which also influence by monsoon. These results
showed that SSS patterns in these seas might be influenced by El‐Nino‐ Southern Oscillation ENSO phenomena and La‐Nina duringNorthwestMonsoon period which
indicated by remarkable freshening in the South China Sea then expand to the Java Sea, Makasar Strait and Banda Sea.
88
Acknowledgments
would like to thank the institution that provided the data used in this study: The Aquarius satellite SSS data were supplied through the NASACONAE AquariusSACD
Project and can be downloaded from http:podaac.jpl.nasa.gov. would like to thank the CReSOS, University of Udayana that support this study.
References
Antonov, J. ., D. Seidov, T. P. Boyer, R. A. Locarnini, A. V. Mishonov, . E. Garcia, O. K. Baranova, M. M. Zweng, and D. R. Johnson
, World Ocean Atlas , Volume : Salinity, in NOAA
Atlas NESDS , edited by S. Levitus, 8 pp., U.S. Gov. Print. Off., Washington, D. C. Font, J., G. S. E. Lagerloef, D. L. Vine, A. Camps, and O. Zanife
, The determination of surface salinity with the European SMOS space mission, EEE T. Geosci. Remote, ,
– .
Gierach, M. M., J. Vazquez‐Cuervo, T. Lee, and V. M. Tsontos , Aquarius and SMOS detect
effects of an extreme Mississippi river flooding event in the Gulf of Mexico, Geophys. Res. Lett., , 88–
, doi: . grl.
. Grodsky, S. A., G. Reverdin, J. A. Carton, and V. J. Coles
, Year‐to‐year salinity changes in the Amazon plume: Contrasting
and AquariusSACD and SMOS satellite datas,
Remote Sens. Environ., , – , doi: .
j.rse. . 8.
. Kohler, J., M. S. Martins, N. Sierra, and D. Stammer
, Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic, Journal of Geophysical
Research, , ‐
. Lagerloef, G., et al.
8 , The AquariusSAC‐D Mission: Designed to meet the salinity remote‐ sensing challenge, Oceanography,
, 8–8 . Lee, T., G. Lagerloef, .‐Y. Gierach, M. M. and Kao, S. Yueh, and K. Dohan
, Aquarius reveals salinity structure of tropical instability waves, Geophys. Res. Lett.,
, L ,
doi: . GL
. Locarnini, R. A., A. V. Mishonov, J. . Antonov, T. P. Boyer, . E. Garcia, O. K. Baranova, M. M. Zweng,
and D. R. Johnson , World Ocean Atlas
, Volume : Temperature, in NOAA Atlas NESDS 8, edited by S. Levitus, 8 pp., U.S. Gov. Print. Off., Washington, D. C.
Menezes, V. V., M. L. Vianna, and . E. Phillips , Aquarius sea surface salinity in the South
ndian Ocean: Revealing annual‐period planetary waves, J. Geophys. Res. Oceans, ,
88 – 8, doi: .
JC .
Roemmich, D., and J. Gilson , The
– 8 mean and annual cycle of temperature,
salinity, and steric height in the global ocean from the Argo program, Prog. Oceanogr., 8
, 8 – , doi: .
j.pocean. . .
. Sprintall, J., J. T. Potemra, S. L. autala, N. A. Bray, and W. W. Pandoe
, Temperature and salinity variability in the exit passages of the ndonesian Throughflow, Deep Sea Res., Part
, , 8 – , doi: .
s ‐
‐ . Webster, P., V. Magana, T. Palmer, J. Shukla, R. Tomas, M. Yanai, and T. Yasunari
8 , Monsoons: processes, predictability, and the prospects for prediction, Journal of
Geophysical Research , ,
– , .
Joint Scientific Symposium
IJJSS 2016
Chiba, 20‐24 November 2016
89
Coastal Dynamic, Nitrate NO
3‐
Phosphate PO
4‐
and Phytoplankton
Abundance at Morodemak North Java Sea Indonesia
a
Muh. Yusuf
a
Marine Science Department, Faculty of Fisheries and Marine Sciences, Diponegoro University, Prof. Sudarto, S Street, Tembalang, Semarang.
. Central Java University of Bangka Belitung ‐ ndonesia.
Abstract
Coastal dynamic of North Java sea was the influence of the west and east monsoon as well as interseasonal effect during April‐June and October‐December. Espescialy to
coastal current patern and to nitrate and phosphate variation and ultimately to phytoplankton. Study area focused at
° . E ‐ ° 8 E and °8 . S
‐ °8 . S. Aim of study was to built current spatial model, measure insitu nitrate and phosphate variation and phytoplankton abundance. Coastal current spatial
modelling was done using SMS‐v8. and sampling site based to purposive sampling represetative to the estuary and coastal system. Spatial modelling using Arc.GS
software. The study revealed that nitrate concentration ranged at . ‐ . mgl, phosphate . ‐ . mgl and current speed .
‐ . msec to southeast
direction. About genera of phytoplankton were found, with moderate dominancy of Baccilariophyceae, Dinophyceae and most dominance of Rhizosolenia. Most abundance
of phytoplankton was at the mouth of the river or the estuary with 8, ,
cellm . Lowest abundance at offshore coastal site with
, ,
cellm . The highest diversity index ’ was .
at the estuary and the lowest was .8 at coastal
offshore.
Keywords
Coastal current, nitrate, phosphate, phytoplankton, North‐java sea
1. Introduction
Coastal water regarded as specific ecosystem with many natural and man made influences from upland areas as well from oceans Dahuri et.al,
. Nutrient of phosphate and nitrogen considered as the limiting factor for sewater productivity
Sastrawijaya, . The two nutrients has important role for the life of marine
organisms such as phytoplankton Fachrul et.al. . Nitrogen compound which can
be used are nitrite and nitrate, while phosphorus in the form of ortho phosphat Jones‐ Lee and Lee,
. Semarang, Morodemak and Demak coastal water the main study area was in fact
as fishing gound, auction place and fishermen villages with many kinds of polution to the
90 adjacent water and effect to water quality. More specifically are house hold organic
sewage and detergent, which will affect the concentration of nitrate and phosphate in the seawater. Coastal current will have the influence to the distribution of nitrate,
phosphate and phytoplankton. Aim of study was to measure insitu nitrate, phosphate variation and phytoplankton abundance, coastal current and built spatial model.
2. Method
Primary data of nitrate, phosphate phytoplankton abundance as well as dissolved oxygon DO , p, salinity, sea surface temperature SST and water transparency.
Supporting data are digital map of Semarang and Demak coastal water in a scale of : ,
. Sampling coordinates were based on Purposive Sampling Method as refered to the aim of the research Sudjana,
using GPS Global Positioning System . Total of station were sampled, where station‐ represent for river mouth estuary. Station
, , , and represent for the coastal water and station , , 8 and represent as the fishing ground and station
, , and
more offshore water. Precisely in the coordinate of
° . E ‐ ° 8E and
°8 . S ‐ °8 . S.
Seawater samples were taken with volume of ml and immediately store in a cool
box. Seawater quality parameters such as dissolved oxygen, temperature, p, salinity and transparency were measured insitu. Phytoplankton were sampled using .
micron mesh plankton net and preserve in formaline. Coastal current measured using current meter.
Nitrate measurement in the laboratory using Specthrophotometer after filtered with m mesh, while absorbance reading using
nm and nm wavelength of standard
metode SN ‐ 88. ‐
. Phosphate measurement using standard SN ‐ 8 ‐
. Field data and coordinate were the processed into spatial model using Arc.GS‐
software Education license . Spatial model of coastal current was processed using SMS
8.1 software, as multilayer concept of seawater parameters had been developed by artoko and elmi
, then analised discriptively Suryabrata, 8 .
3.
Result and Discussion
Nitrate concentration at Morodemak‐Demak coastal water ranged of . ‐ . mgl.
ighest concentration found at station‐ and lowest at station‐ as presented in Tabel and Figure . This was assumed that river water bringss high concentration of
nitrate. Phosphate range from . 8 – . mgl with highest concentration at station‐ or at mouth of the river Figure . Coasatal current speed range from .
– . ms with dominant of southeast direction. Which is considered as the tidal current
pattern. n comparison to concentration at the north Papua deepsea water nitrate concentration range of . ‐ . mgl and phosphate concentration range of . – .
mgl artoko and Subiyanto, . Other implication of the current, temperature –
depth interactions. Both water current and depth contribute significantly to the vertical temperature profile of North Molucas and almahera, with the average current velocity
was about . cmsec respectively Robertson and Field, which is much higher
than coastal current of Demak. Related to the productivity processes at coastal water the important parameter is water transparency or turbidity. Where water transparency
range of . – . m should be relative to the coastal depth.