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Title: Statistical tools for studying the temporal variations in chlorophyll-a concentration along the Southwest Bay of Bengal waters
Authors: Poornima, D
Shanthi, R
Thangaradjou, T
Saravanakumar, A
Sarangi, R K
Keywords: Bay of Bengal;Chlorophyll;Multiple linear regression;Nitrate;Principal component analysis;SST
Issue Date: Jun-2021
Publisher: NIScPR-CSIR, India
Abstract: Multivariate statistical analysis such as multiple linear regression (MLR) and principal component analysis (PCA) are used to study the effect of physico-chemical parameters on chlorophyll distribution along the southwest Bay of Bengal from January 2012 to June 2014. Physical properties recorded showed clear seasonal patterns in sea surface temperature (26.2 – 32.8 °C), salinity (24 – 36 PSU), pH (7.808 to 8.428), photosynthetic photon flux (522 – 1220.4 μM m-2s-1) with the minimum and maximum values during monsoon and summer seasons, respectively. In contrast, the chemical variables such as nitrite (0.15 to 2.35 μM), nitrate (1.02 to 6.58 μM), ammonia (0.11 – 5.22 μM), total nitrogen (1.04 to 11.58 μM), inorganic phosphate (0.16 – 2.97 μM), total phosphorus (0.55 – 8.60μM) and reactive silicate (2.00 to 23.95 μM) showed the minimum and maximum concentration during summer and monsoon seasons, respectively. The high and low chlorophyll (0.10 to 6.92 μg l-1) and dissolved oxygen (4.07 and 7.884 mg l-1) concentrations are observed during summer and pre-monsoon seasons, respectively. PCA found that nitrogenous nutrients and chlorophyll are positively loaded and sea surface temperature (SST) was negatively loaded in all the seasons except during summer season. Inter-comparison of modeled and in-situ chlorophyll-a (chl-a) concentration showed a significant correlation during monsoon season by 93 % of matchup with a R2 = 0.930, N = 60 and SEE = ±0.369 compared to other seasons. Regression analysis also predicted the positive influence of nitrate and ammonia and negative influence of SST with chl-a.
Page(s): 454-464
ISSN: 2582-6727 (Online); 2582-6506 (Print)
Appears in Collections:IJMS Vol.50(06) [June 2021]

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