ASSESSMENT OF THE LONG-TERM DYNAMICS OF ALGAL BLOOMS AND THEIR LINKAGES WITH OCEANOGRAPHIC PARAMETERS USING TIME-SERIES REMOTE SENSING DATA

Algal bloom events distress coastal fisheries, tourism, and recreational activities and affect a nation's economy. We have studied the large-scale spatio-temporal dynamics of algal bloom events in Indian waters. We have assessed the algal bloom trend over 20 years using a non-parametric Mann-Kendall test. The cross-correlation test is used to estimate the influence of oceanographic processes on bloom. Besides, temporal prediction of bloom events is carried out using the multivariate autoregression model. Study reveals that algal bloom coverage is declining over the coastal waters in all seasons except post-monsoon. The cross-correlation results indicate significant changes in the regional bloom patterns and are due to the substantial changes in the regional oceanographic processes owing to climate change. We observe that the autoregression models with a variable time lag up to 2 months are more suitable for predicting bloom occurrences. Furthermore, this study is useful in making appropriate adaptation measures against climate change.