Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh

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dc.creatorMontes, Carlo
dc.creatorAcharya, Nachiketa
dc.creatorHossain, Peerzadi Rumana
dc.creatorAmjath-Babu, Tharayil Shereef
dc.creatorKrupnik, Timothy J.
dc.creatorHassan, S.M. Quamrul
dc.date2022-04
dc.date2023-01-03T12:13:17Z
dc.date2023-01-03T12:13:17Z
dc.date.accessioned2026-06-27T18:47:42Z
dc.descriptionThe occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite the advances in the seasonal forecasting, the development of operational tools remains a challenge. This work presents the development of a seasonal forecasting approach to predict the number of warm days (NWD) and number of heavy rain days (NHRD) tailored to aquaculture in two locations of Bangladesh (Sylhet and Khulna). The approach is based on the use of meteorological and pond temperature data to generate linear models of the relationship between three-monthly temperature and rainfall statistics and NWD and NHRD, and on the evaluation of the skill of three operational dynamical models from the North American Multi-Model Ensemble (NMME) project. The linear models were used to evaluate the forecasts for two seasons and 1-month lead time: May to July (MJJ), forecast generated in April, and August to October (ASO), forecast generated in July. Differences were observed in the skill of the models predicting maximum temperature and rainfall (Spearman correlation, Root Mean Square Error, Bias statistics, and Willmott’s Index of Agreement,), in addition to NWD and NHRD from linear models, which also vary for the target seasons and location. In general, the models show higher predictive skill for NWD than NHRD, and for Sylhet than in Khulna. Among the three evaluated NMME models, CanSIPSv2 and GFDL-SPEAR exhibit the best performance, they show similar features in terms of error metrics, but CanSIPSv2 presents a lower interannual standard deviation.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/126494
dc.identifier.urihttp://hdl.handle.net/123456789/164631
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceMontes, C., Acharya, N., Rumana Hossain, P., Amjath Babu, T. S., Krupnik, T. J., & Quamrul Hassan, S. M. (2022). Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh. Climate Services, 26, 100292. https://doi.org/10.1016/j.cliser.2022.100292
dc.subjectfishery production
dc.subjectrisk management
dc.subjectaquaculture
dc.subjectearly warning systems
dc.subjectclimate change
dc.titleDeveloping a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh
dc.typeJournal Article

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