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The purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.

Firstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.

The results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.

The systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.

By calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.

The purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.

The grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.

The drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.

It provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.

By studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

All over the world, Bangladesh is well known as a flood- and cyclone-affected country. But in the recent years the slow onset disaster of drought is more frequent in Bangladesh due to climatic as well as nonclimatic variability. As a consequence, agriculture along with its dependent farmers’ livelihoods tremendously experience its adverse impacts. Therefore, the main focus of this chapter is to discuss about drought, its effects on different sectors, and how in different levels a number of drought risk management actions are carried out to cope with this insidious disaster in the context of Bangladesh.

The southwest coastal region is part of an inactive delta of large Himalayan rivers and is protected from tidal surge by the Sundarbans mangrove forest. This area is the hub of all types of disasters such as cyclones, tidal surges, floods, drought, salinity intrusions, repeated waterlogging, and land subsidence. Cyclonic tidal surges and floods are the more common disasters, and their effects are frequently experienced at the local level. But silent and invisible disasters such as increased salinity, arsenic contamination, and drought affect local livelihoods, people, and environments in this region. The vulnerability of southwest region to increased salinity, arsenic contamination, and drought are the result of a complex interrelationship among biophysical, social, economical, and technological characteristics of the country. Moreover, in the current and foreseeable future, the country is likely to be affected by the biggest, most long-lasting, and global scale but silent disaster: increased salinity, natural arsenic contamination, and drought. Therefore, this region is thought to be the most disaster-prone region in Bangladesh because of natural disasters and highly vulnerable to the effects of climate change.

Examine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.

Based on 0.5° gridded rainfall and temperature data (1960–2015) and projections of the WRF-RCM (2016–2040), the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are calculated for Central Java over different time spans. The drought indices are correlated to annual and seasonal rice production, based on which a weather index insurance structure is developed.

The six-month SPI correlates best with the wet season rice production, which generates most output in Central Java. The SPI time series reveals that drought severity increases in future years (2016–2040) and leads to higher payouts from the weather index structure compared to the historical period (1960–2015).

The developed methodology in using SPI for historical and projected periods allows the development of weather index insurance in other regions which have a clear link between rainfall deficit and agricultural production volatility.

Meteorological drought indices are a viable alternative for weather index insurance, which is usually based on rainfall amounts. RCM outputs provide valuable insights into future climate variability and drought risk and prolong the time series, which should result in more robust weather index insurance products.

Among natural disasters, drought affected the most people worldwide during the past few decades (Obasi, 1994). Since the late 1970s, there has been a shift in El Niño-Southern Oscillation toward more warm events, closely related to a worldwide trend for intensified drought (Dai, Trenberth, & Karl, 1998). In particular, this trend was manifested as widespread droughts during 1999–2002 in the northern hemisphere (Lotsch, Friedl, Anderson, & Tucker, 2005), including Asia, and notably in Mongolia (Nandintsetseg, Shinoda, Kimura, & Ibaraki, 2010; Shinoda, Ito, Nachinshonhor, & Erdenetsetseg, 2007). The decade of 2000s has experienced increased vegetation degradation and wind erosion that resulted from decreased summer precipitation in wide areas of East Asia (Kurosaki, Shinoda, & Mikami, 2011a; Kurosaki, Shinoda, Mikami, & Nandintsetseg, 2011b). Furthermore, in general, projections of climate models have suggested that the frequency and intensity of extreme weathers will likely increase in the future (IPCC WG I, 2007). Given this background, it is vital to make an assessment of socioeconomic impacts of the extreme weathers and to develop proactive approaches to mitigating such impacts.

The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province.

The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed.

This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures.

The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

Examines attitudes to disasters in Arab countries and argues that they are frequently not reported and therefore not analysed in sufficient detail. Using data from the US Office of Foreign Disaster Assistance describes the types and frequency of natural disasters throughout the Arab world. Concentrates on drought as this is by far the largest cause of fatalities in the region. Outlines current policies regarding disaster prevention and management and concludes by suggesting ways in which the present situation could be improved.

Novel micro‐insurance schemes are emerging to help the poor better deal with droughts and other disasters. Climate change is projected to increase the intensity and frequency of disasters and is already adding stress to actual and potential clients of these schemes. As well, insurers and reinsurers are increasingly getting worried about increasing claim burdens and the robustness of their pricing given changing risks. The purpose of this paper is to review and suggest ways to methodologically tackle the challenges regarding the assessment of drought risk and the viability of index‐based insurance arrangements in the light of changing risks and climate change.

Based on novel modeling approaches, the authors take supply as well as demand side perspectives by combining risk analysis with regional climate projections and linking this to household livelihood modeling and insurance pricing. Two important examples in Malawi and India are discussed, where such schemes have been or are about to be implemented.

The authors find that indeed micro‐insurance instruments may help low‐income farming households better manage drought risk by smoothing livelihoods and reducing debt, thus avoiding poverty traps. Yet, also many obstacles to optimal design, viability and affordability of these schemes, are encountered. One of those is climate change and the authors find that changing drought risk under climate change would pose a threat to the viability of micro‐insurance, as well as the livelihoods of people requesting such contracts.

The findings and suggestions may corroborate the case for donor support for existing or emerging insurance arrangements helping the poor better cope with climate variability and change. Furthermore, a closer linkage between climate and global change models with insurance and risk management models should be established in the future, which could be beneficial for both sides.

The purpose of this paper is to provide useful information for members of the weather community and policy makers, to help them understand the full scope of drought economic impacts and assessment methodologies, and to help determine the feasibility of future drought mitigation programs.

To accomplish the objective, the paper reviews the literature of drought economic impact studies in both agricultural and non‐agricultural sectors, summarizes the methods and data employed, compares the various results, and investigates the problems and limitations of previous studies.

The paper concludes with a discussion of the challenges and directions of future improvement on drought economic impact assessment.

This paper gives a comprehensive review of drought economic impacts and the associated quantitative assessment methodologies, which provides valuable information to rational decisions supporting drought mitigation policies and programs.