Type
Date
Corporate author
Editor
Illustrator
Producer
Photographer
Contributor
Writer
Translator
Journal Title
Journal ISSN
Volume Title
Access Rights
Abstract
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.
Keywords
Citation
APA citation
ISO citation
Copyright
CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Journal
Climate Services
Journal volume
26
Journal issue
Article number
100292
Place of Publication
Netherlands
Publisher
Elsevier
Related Datasets
CGIAR Initiatives
Initiative
Transforming Agrifood Systems in South Asia
Impact Area
Nutrition, health & food security
Climate adaptation & mitigation
Climate adaptation & mitigation
Action Area
Resilient Agrifood Systems
Donor or Funder
CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)
Climate Services for Resilient Development (CSRD)
Bill & Melinda Gates Foundation (BMGF)
Columbia University’s World Projects (ACToday)
Climate Services for Resilient Development (CSRD)
Bill & Melinda Gates Foundation (BMGF)
Columbia University’s World Projects (ACToday)