Analisis Multilayer Variabilitas Upwelling Di Perairan Selatan Jawa

Sukresno, Bambang; Jatisworo, Dinarika; Kusuma, Denny Wijaya

doi:10.15578/jkn.v1i1.6619

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…The speed of outflow and entry in the Bali Strait makes the exchange of water masses in these waters more dynamic. The dynamics of waters in the Bali Strait are indirectly affected by the dynamics of waters in the Indian Ocean on intra-annual time scales such as monsoonal, IOD, and ENSO (Iskandar et al 2009;Sartimbul et al 2010;Ningsih et al 2013;Gaol et al 2014;Sukresno et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia

et al. 2020

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Abstract. Wijaya A, Zakiyah U, Sambah AB, Setyohadi D. 2020. Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia. Biodiversitas 21: 5283-5290. The Bali Strait is influenced by seasonal and inter-annual systems. El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are climate variabilities that affect water conditions. The knowledge about influence of ENSO and IOD variations on the fertility of waters in the Bali Strait is still lacking. The purpose of this study was to determine the effect of seasonal and inter-annual variability on the variability of Sea Surface Temperature (SST) and Sea Surface Chlorophyll-a (SSC) in the Bali Strait. This study applied SST and SSC data collected from the Aqua/Terra MODIS satellite, as well as the ENSO and IOD indices during March 2000-December 2019. The results described that the effect of ENSO on SST and SSC was low and IOD on SST and SSC was quite high. The effect was quite high between IOD and SST anomaly of-0.401. Seasonal variations indicate the abundance of high SSC and low SST in the southeast monsoon (JJA) which characterizes upwelling. Meanwhile, in the northwest monsoon (DJF), SSC was low and SST was high which characterizes downwelling. This condition cannot separate from the monsoonal process that occurred in the Bali Strait. The inter-annual variation showed that in the strong El Nino period and IOD (+) triggers a negative SST anomaly and a positive SSC results in strong upwelling, while in the strong La Nina period and strong IOD (-) triggers a positive SST anomaly and a negative SSC results in downwelling. The inter-annual variability of SSC influenced by IOD rather than ENSO. This condition indicates that the ENSO/IOD event changes the period of SSC concentration.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia

et al. 2020

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“…After a strong El Niño event during 2015-2016 [34,35], during the latter half of 2016, extreme wet conditions hampered Indonesia and Australia, which is caused by the strong negative Indian Ocean Dipole (IOD) and weak La Niña [34]. This phenomenon inflicted a lower intensity of upwelling, followed by an increase in sea surface temperature (SST) and a decrease in chlorophyll-a concentration in Southern Java waters [36], which was negatively correlated with the abundance of skipjack [37,38].…”

Section: Discussionmentioning

confidence: 99%

Developing an Abundance Index of Skipjack Tuna (Katsuwonus pelamis) from a Coastal Drifting Gillnet Fishery in the Southern Waters of Indonesia

Novianto

Ilham

Nainggolan

et al. 2019

Fishes

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Skipjack tuna is targeted by various types of fishing gear in coastal countries. Due to itsresilience, it has withstood heavy fishing pressure in the past few decades. Coastal drifting gillnetfleets also mark skipjack as their main target, but it is often overlooked in terms of stock assessment.This study provides new information on an abundance index based on fishery-dependent data from2010 to 2017. Generalized linear models (GLMs) were used to standardize the catch-per-unit-ofeffort(CPUE) using year, quarter, and gross tonnage as the prediction variables. Model goodnessof-fit and model selection were based on the Akaike Information Criterion (AIC), the pseudocoefficient of determination (R2), and model diagnostics with a residual analysis. The finalestimation of the abundance index was calculated by least square means or marginal means. Theresults showed that the index was heavily influenced by the year and quarter, but it did not relateto the vessel’s capacity. While the CPUE series fluctuated greatly, it showed a declining trend overthe years of observation.

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“…phenomenon also affects the deviation, which blows cold and dry air from the Indonesian coast. It also causes upwelling followed by a decrease of not less than 3° C in the South Java Sea, besides this usually occurs from July to August (Sukresno et al, 2018).…”

Section: Land Cover Changesmentioning

confidence: 99%

Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency

2021

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Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predict the associated changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers were dominated by forest with decreasing trend, while the built-up area continuously increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated with the occurrence of land conversion. Land cover predictions for 2028 show similar forest dominance, with a 23,4% built-up area at a surface temperature of 28.9°C. Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface Temperature Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Analisis Multilayer Variabilitas Upwelling Di Perairan Selatan Jawa

Cited by 12 publications

References 22 publications

Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia

Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia

Developing an Abundance Index of Skipjack Tuna (Katsuwonus pelamis) from a Coastal Drifting Gillnet Fishery in the Southern Waters of Indonesia

Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency

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