Md. Rashed Chowdhury

Seasonal Flood Forecasts and Warning Response Opportunities (eBook, PDF)

ENSO Applications in Bangladesh

• Format: PDF

Produktbeschreibung

This book explores the feasibility of using El Niño-Southern Oscillation (ENSO)-based forecasts and early warning systems to prevent losses from floods and droughts in Bangladesh. Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is due to short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts-the demand for which is significantly increasing in Bangladesh- and disseminate these products through appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecasts, depends on consensus among the participating institutions. Therefore, the primary objective of the book is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The book discusses issues related to the 5-stage Flood Forecasts, Warning, and Response System (FFWRS) and emphasizes the role of the seasonal 'Climate Outlook Forum (COF)' in Bangladesh. The book also identifies ways to improve forecasting and early warning systems by utilizing ENSO-based climate data and models, and discusses a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

Several successful case examples of ENSO-based seasonal forecasts and early warning systems from other ENSO-sensitive regions have been documented. The primary audience includes academics and students, government policymakers, engineers, and business leaders.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 201
• Erscheinungstermin: 16. November 2022
• Englisch
• ISBN-13: 9783031178252
• Artikelnr.: 66435537

Autorenporträt

Md. Rashed Chowdhury - currently an adjunct professor affiliated with the School of Sustainable Engineering and the Built Environment (SSBE) at Arizona State University (USA) - is the former Principal Research Scientist of the Pacific ENSO Applications Climate Center (PEAC) at the Joint Institute for Marine and Atmospheric Research (JIMAR) of the University of Hawaii. For the last 25 years, his activities focused on addressing water challenges surrounding emergency management and implementation activities related to climate variability and change, sea level rise, land-use planning and environmental resource management for the El Niño-Southern Oscillation (ENSO)-sensitive countries/islands in the Asia-Pacific region, including Bangladesh, the Maldives, Japan, Hawaii, Guam, Commonwealth of the Northern Mariana Islands, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa. He has been responsible for the translation on the probable impacts of various seasonal climate products through local and international activities, and in the process he is actively involved in the development of adaptation plans regarding climate hazards management.

Inhaltsangabe

TABLE OF CONTENTSPreface: Why ENSO-based seasonal forecasts are important in Bangladesh Acknowledgements.- About the author.- Chapter 1: Introduction-Seasonal Flood Potential in Bangladesh.- Chapter 2: Basin-wide Hydro-Meteorological Variability and Opportunity for Seasonal.- Forecasts in Bangladesh.- Chapter 3: History of Flood Forecasts and Early Warning in Bangladesh.- Chapter 4: Overview of Weather, ENSO, and Climate Scale.- Chapter 5: The El Niño-Southern Oscillation (ENSO) and Seasonal Flooding in Bangladesh.- Chapter 6: Seasonal Flood Potential in Bangladesh.- Chapter 7: Overview of Eastern, Mixed, and Central Pacific El Niño.- Chapter 8: Seasonal Sea Level Information and Flood Forecast Potential in Bangladesh.- Chapter 9: Consensus Seasonal Flood Forecasts and Warning Response in Bangladesh.- Chapter 10: ENSO Impacts, Applications, and Conclusions.