The work is to be 9 pages with three to five sources, with in-text citations and a reference page. The variability in landings during the period 1991-2008, which was a period with frequent and intense climatic events, has been investigated. Trend analysis has been conducted in order to reveal, whether the landings keep the short term as well as a long term trend. In the time-series analysis, a statistical prediction model has been validated using ARIMA with the observed landing data. Approximately 90% of the variation in predicted landings can be explained by the observed landings using this model. The outcomes help understand the variability in fish landings and are also important to assist the management of fishery resources.
Fisheries play an important role in the world economy, thus, exploitation of fishery resources is widely discussed on a global scale. About 200 million people worldwide depend on fishing and related industries for livelihood. Now the quantity of landings is declining in thirteen of the fifteen major marine areas (Garcia and Newton, 1994. Bathal and Pauly, 2008. Zeller et al., 2009). Marine fish populations have been known for extensive variation in landings (Ljungman, 1882). For various fisheries in many parts of the world’s oceans the variability in landings has been recognized (Kawasaki and Omori, 1995. Lluch-Cota et al., 1999). In the Indian Ocean, there is deterioration in quality caused by a decline in the sizes of highly valued species and the move to bulk landings of lower-value species due to climate change (Regier and Baskerville, 1986). The fish production of India has increased more than fivefold since 1950. Special efforts have been made to promote extensive and intensive inland fish farming, modernize coastal fisheries, and encourage deep-sea fishing through joint ventures. These efforts led to a significant increase in the fishery. At present, India stands in seventh place among fishing countries.
The South Eastern Arabian Sea (SEAS) undergoes strong upwelling during the southwest monsoon period. .