Multi-factor evaluation indicator method for the risk assessment of atmospheric and oceanic hazard group due to the attack of tropical cyclones

Author(s):  
Peng Qi ◽  
Mei Du
Author(s):  
Junxiang Zhang ◽  
Chongfu Huang ◽  
Xulong Liu ◽  
Qinghua Gong

2021 ◽  
Author(s):  
Thanh Quoc Vo ◽  
Trung Hieu Nguyen ◽  
Vo Thi Phuong Linh

Abstract Rice is an important human crop and rice cultivation is threatened due to natural disasters, leading to negative effects on national and global food security. The natural disasters, such as tropical cyclones and saline intrusion, have dramatic influences in coastal regions. To investigate possible impacts of these disasters on rice cultivation, it needs an efficient tool to assess potential disasters impacts and a risk index is highly applicable. Therefore, this study aims at establishing a risk assessment of rice production in coastal areas under effects of tropical cyclones and saline intrusion. We adopted risk definition introduced by IPCC (2014) in which risk is a function of hazard, exposure and vulnerability. Multiple hazards of tropical cyclones and saline intrusion were indicated by their frequency and severity at some critical levels of 25%, 50% and >50% rice yield reduction. Each hazard was weighted by its damage on rice yield. Exposure and vulnerability of rice crops are evaluated at different growing phases. Tropical cyclone hazard index was ranked high and very high in the wet season while salinity hazard index was ranked very high in the dry season. Due to the combined effects of tropical cyclones and salinity, rice crop is highly susceptible during the reproduction phase and at the panicle initiation stage particularly. Based on the cropping calendar of My Xuyen, the period of October-November was the very high vulnerability period since it had the largest rice cultivable area and rice crops were at the reproduction phase. This result shows that rice crops are at high risk in October and November. Noticeably, saline intrusion reaches the highest level in April and May, but no risk is at this period because of no rice crop cultivated. This can reflect a measure to reduce risk by adjusting the cropping calendar.


MAUSAM ◽  
2021 ◽  
Vol 48 (2) ◽  
pp. 305-322
Author(s):  
ANWAR ALI ◽  
JAHIR UDDIN CHOWDHURY

Tropical cyclones are regarded as the most deadly among all natural disasters. They bring catastrophic ravages to life and property as well as to environment. Among all the areas in the world affected by tropical cyclones, the countries along the rim of the Bay of Bengal suffer most and Bangladesh is the worst sufferer. In order to minimise the future loss of life and property, proper cyclone disaster management action is an absolute necessity. This, in turn, requires a better assessment of risks associated with a cyclone. The present paper discusses the major components of risk assessment, viz., (i) inventory of cyclones with associated causes of hazards, (ii) analysis of damages and inventory of elements at risk and (iii) vulnerability analysis with special reference to Bangladesh. Inventory of cyclones deals with the cyclone climatology in the Bay of Bengal region over the period 1881-1990. Discussions on causes of hazards cover strong winds. storm surges, rainfall. socio-economic factors, greenhouse effects, etc. An idea about the degree of cyclone damages and the elements at risks in Bangladesh is given. Some discussions on vulnerability analysis and risk reduction/mitigation with a few case studies in Bangladesh are made. Finally few recommendations are put forward.  


2008 ◽  
Vol 47 (2) ◽  
pp. 361-367 ◽  
Author(s):  
Timothy M. Hall ◽  
Stephen Jewson

Abstract Two statistical methods for predicting the number of tropical cyclones (TCs) making landfall on sections of the North American coastline are compared. The first method—the “local model”—is derived exclusively from historical landfalls on the particular coastline section. The second method—the “track model”—involves statistical modeling of TC tracks from genesis to lysis, and is based on historical observations of such tracks. Identical scoring schemes are used for each model, derived from the out-of-sample likelihood of a Bayesian analysis of the Poisson landfall number distribution. The track model makes better landfall rate predictions on most coastal regions, when coastline sections at a scale of several hundred kilometers or smaller are considered. The reduction in sampling error due to the use of the much larger dataset more than offsets any bias in the track model. When larger coast sections are considered, there are more historical landfalls, and the local model scores better. This is the first clear justification for the use of track models for the assessment of TC landfall risk on regional and smaller scales.


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