scholarly journals Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

2022 ◽  
pp. 118040
Author(s):  
Liu Na ◽  
Chen Shaoyang ◽  
Cheng Zhenyan ◽  
Wang Xing ◽  
Xiao Yun ◽  
...  
2020 ◽  
Vol 21 (11) ◽  
Author(s):  
Adi Wijaya ◽  
UMI ZAKIYAH ◽  
ABU BAKAR SAMBAH ◽  
DADUK SETYOHADI

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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.


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