5 research outputs found
Physical mechanisms of nutrient supply and controlling factors of phytoplankton growth in the Arafura Sea
No full textThe Arafura Sea is a relatively shallow basin (< 200 m deep) located in the eastern part of the Indonesian archipelago. Its border on the west is a deeper basin, the Banda Sea, with an average depth of ∼5000 m. Previous studies have shown that circulation in this particular region is mainly influenced by monsoonal winds, which are the northwest (NW) and southeast (SE) monsoons. In addition, previous studies have shown that upwelling is a characteristic phenomenon in this region, induced by the SE monsoon from June to August. During this period, the lower sea surface temperature and elevated chlorophyll-a concentrations are observed near the coast of Papua (northern Arafura Sea). Recent studies have suggested that these features are due to the upwelling of cold and nutrient-rich water masses originating from deeper layers of the Banda Sea.
The present study aims to investigate the mechanism of nutrient supply and its implication on phytoplankton distribution in further detail using a numerical model. A three-dimensional (3D) biogeochemical model ECOHAM (ECOsystem model HAMburg) is utilized in this study. The model domain extends from 122°–139°20’ E and 1°48’–14°19’ S, covering the Arafura and the Banda Sea regions. ECOHAM is forced by ocean current fields derived from HAMSOM (HAMburg Shelf Ocean Model), river run-off, atmospheric nitrogen deposition, wind stress, and solar radiation. Moreover, the initial and boundary values of biogeochemical variables are derived from WOA and GCOMS. Finally, the model results are validated against the in-situ nutrient measurements and satellite-derived chlorophyll-a concentration.
The simulated nutrient (i.e., nitrate and phosphate) concentrations show a good fit with observations, especially in the upper 200 m. Besides nutrients, the simulation overestimates surface chlorophyll-a concentrations in the northern Arafura Sea, but it still represents the seasonal variation quite well. Furthermore, the sensitivity test reveals that a 10% change in temperature factor Q10 for phytoplankton can significantly changes the Redfield net primary production by up to 25%.
This modeling study suggests a different mechanism of nutrient supply between the shallow region in the northern part (Sahul Shelf) and the continental slope area of the Arafura Sea. In the Sahul Shelf, nitrate is primarily transported to the near-surface layer via vertical mixing, which is stronger during the SE monsoon, compared to the NW monsoon. On the other hand, nitrate supply in the continental slope area is mainly regulated by advection. During the NW monsoon, the simulation reveals the horizontal intrusion of nitrate-rich water masses from the eastern Banda Sea in the layer above the nitracline (a layer in which the nitrate concentration increases rapidly with increasing depth). By contrast, during the SE monsoon, the vertical advection transports nitrate to the layer above the nitracline, which is confirmed by nitrate budget analysis.
Furthermore, this study shows that phytoplankton growth is mainly regulated by nitrogen availability. In the Sahul Shelf, the seasonal variations of phytoplankton production and zooplankton grazing indicate the bottom-up control in June-August and top-down control in October-December in the zooplankton-phytoplankton system. In the continental slope area, nitrate concentration in the near-surface layer is depleted, suggesting a strong nitrate limitation, especially for diatoms. In this region, non-diatom production is higher than for diatom because non-diatoms take up ammonium more effectively
Data assimilation technique of 2-dimensional vertical temperature transport model (Case study: Tropical Pacific Ocean)
No full textAnalysis of ocean wave characteristic in Western Indonesian Seas using wave spectrum model
No full textUnderstanding the characteristics of the ocean wave in Indonesian Seas particularly in western Indonesian Seas is crucial to establish secured marine activities in addition to construct well-built marine infrastructures. Three-years-data (July 1996 - 1999) simulated from Simulating Waves Nearshore (SWAN) model were used to analyze the ocean wave characteristics and variabilities in eastern Indian Ocean, Java Sea, and South China Sea. The interannual or seasonal variability of the significant wave height is affected by the alteration of wind speed and direction. Interactions between Indian Ocean Dipole Mode (IODM), El Niño Southern Oscillation (ENSO) and monsoon result in interannual ocean wave variability in the study areas. Empirical Orthogonal Functions (EOF) analysis produces 6 modes represents 95% of total variance that influence the wave height variability in the entire model domain. Mode 1 was dominated by annual monsoon and has spatial dominant contribution in South China Sea effected by ENSO and Indian Ocean influenced by IODM. Java Sea was influenced by Mode 2 which is controlled by semi-annual monsoon and IODM. A positive (negative) IODM strengthens (weakens) the winds speed in Java Sea during the East (West) season and hence contributes to Mode 2 in increasing (decreasing) the significant wave in Java Sea
Analysis of ocean wave characteristic in Western Indonesian Seas using wave spectrum model
No full textUnderstanding the characteristics of the ocean wave in Indonesian Seas particularly in western Indonesian Seas is crucial to establish secured marine activities in addition to construct well-built marine infrastructures. Three-years-data (July 1996 - 1999) simulated from Simulating Waves Nearshore (SWAN) model were used to analyze the ocean wave characteristics and variabilities in eastern Indian Ocean, Java Sea, and South China Sea. The interannual or seasonal variability of the significant wave height is affected by the alteration of wind speed and direction. Interactions between Indian Ocean Dipole Mode (IODM), El Niño Southern Oscillation (ENSO) and monsoon result in interannual ocean wave variability in the study areas. Empirical Orthogonal Functions (EOF) analysis produces 6 modes represents 95% of total variance that influence the wave height variability in the entire model domain. Mode 1 was dominated by annual monsoon and has spatial dominant contribution in South China Sea effected by ENSO and Indian Ocean influenced by IODM. Java Sea was influenced by Mode 2 which is controlled by semi-annual monsoon and IODM. A positive (negative) IODM strengthens (weakens) the winds speed in Java Sea during the East (West) season and hence contributes to Mode 2 in increasing (decreasing) the significant wave in Java Sea.</jats:p
Investigation of environmental factors impact on fish catch in East Java Waters
No full textThis study explores the relationship between environmental parameters and fish catch rates for scad, skipjack, and yellowfin tuna in East Java waters from 2019 to 2022. The environmental factors considered include sea surface temperature (SST), chlorophyll-a concentration, and Ekman pumping velocity (EPV) as indicators of upwelling. SST and chlorophyll-a data were obtained from Aqua MODIS satellite observations, while wind and seawater density data for EPV calculations were sourced from ERA5 and SMOS-OI, respectively. Fish catch data were provided by the Department of Marine and Fisheries of East Java. Cross-correlation analysis revealed a negative correlation between SST and fish catch, with time lags of 2, 4, and 5 months for scad, skipjack, and yellowfin tuna, respectively. Chlorophyll-a concentration showed a positive correlation with fish catch, particularly for scad (0.6 with a one-month lag) and for skipjack and yellowfin tuna (0.62 with a four-month lag). Additionally, EPV exhibited a positive correlation (0.3 to 0.6) with fish catch. These findings emphasize the significance of SST and chlorophyll-a as indicators of fish population dynamics and offer valuable insights for fisheries management.Keywords:East Java WatersSea surface temperatureChlorophyll-aFishcatchUpwellin
