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

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Title Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh
 
Creator Montes, Carlo
Acharya, Nachiketa
Hossain, Peerzadi Rumana
Amjath Babu, Tharayil Shereef
Krupnik, Timothy J.
Hassan, S.M. Quamrul
 
Subject fishery production
risk management
aquaculture
early warning systems
climate change
 
Description The 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.
 
Date 2022-04-15
2023-01-03T12:13:17Z
2023-01-03T12:13:17Z
 
Type Journal Article
 
Identifier Montes, C., Acharya, N., Rumana Hossain, P., Amjath Babu, T.S., Krupnik, T.J. and 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://hdl.handle.net/10883/22047
2405-8807
https://hdl.handle.net/10568/126494
https://hdl.handle.net/10883/22047
https://doi.org/10.1016/j.cliser.2022.100292
 
Language en
 
Rights CC-BY-NC-ND-4.0
Open Access
 
Format application/pdf
 
Source Climate Services