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Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.

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.

The conceptual root of vulnerability dates back to the 1970s in the social science spheres. Vulnerability is a multi-dimensional and determinant precondition for disaster occurrence. The Gamo lowlands are exposed to a wide range of vulnerabilities. Therefore, this study aims to schematize community perceptions and understanding of vulnerability in drought-affected rural Gamo lowlands.

A community-based cross-sectional survey design and the mixed-methods research approach were executed. A four-staged multistage sampling was used to identify the respondent households. Into the four study sites, sample households were allocated proportionally by the lottery method. The survey data were gathered from 285 lowland households. The structured survey questionnaire, key informant interview, focus group discussion, and field observations, and transect walks were the tools used to collect the primary data. Data were analyzed deploying both qualitative and quantitative techniques. The Likert scale is used to analyze households’ vulnerability perceptions in which the item analysis approach was used for detailed analysis of the Likert-type items.

Locally, people perceive and understand vulnerability as exposure to drought hazard, rainfall inconsistency, the prevalence of human and animal diseases, livelihood insecurity, food shortfalls, poor income, lack of access to market, landholding and livestock ownership which are schematized by vulnerability perception pathways that delineate its extent. The findings also showed that the Gamo lowland inhabitants are unequally vulnerable as 96.5% of the studied households stated the differential idiosyncrasy of vulnerability. Old-aged, small-sized and female-headed households with no supportive force were found to be more vulnerable.

For better resilience, enhancing communities’ perceptions and understanding of vulnerability via continuous awareness creation by all the concerned stakeholders is recommended as the majority was lowly educated. It also yields input for policy debates and decision-making in the drought-prone lowland setup for building a resilient community.

To the best of the authors’ knowledge, this is an original work pursued by using a household survey with empirical data sourced from drought-prone rural lowland communities.

The purpose of this paper is to apply the concept of “Interconnected Geoscience” to a disaster and risk reduction (DRR) case study at SECMOL College, near Leh, Ladakh, N. India. Interconnected geoscience is a model that advocates holistic approaches to geoscience for development. This paper reports research/practical work with Ladakhi students/staff, undertaking community-oriented DRR exercises in hazard awareness, DRR themed village/college mapping, vulnerability assessments and DRR management scenario development. The geoscientific hazard analysis work is published within a separate sister paper, with results feeding into this work. This work addresses aspects of, and contributes to, the DRR research(science)-policy-interface conversation.

Interconnected geoscience methodologies for DRR here are: the application of geoscience for hazard causality, spatial distribution, frequency and impact assessment, for earthquakes, floods and landslides, within the SECMOL area; the generation of community-developed DRR products and services of use to a range of end-users; the development of a contextual geoscience approach, informed by social-developmental-issues; and the active participation of SECMOL students/teachers and consequent integration of local world-views and wisdom within DRR research. Initial DRR awareness levels of students were assessed with respect to earthquakes/floods/landslides/droughts. Following hazard teaching sessions, students engaged in a range of DRR exercises, and produced DRR themed maps, data, tables and documented conversations of relevance to DRR management.

Students levels of hazard awareness were variable, generally low for low-frequency hazards (e.g. earthquakes) and higher for hazards such as floods/landslides which either are within recent memory, or have higher frequencies. The 2010 Ladakhi flood disaster has elevated aspects of flood-hazard knowledge. Landslides and drought hazards were moderately well understood. Spatial awareness was identified as a strength. The application of an interconnected geoscience approach immersed within a student+staff college community, proved to be effective, and can rapidly assess/build upon awareness levels and develop analytical tools for the further understanding of DRR management. This approach can assist Ladakhi regional DRR management in increasing the use of regional capability/resources, and reducing the need for external inputs.

A series of recommendations for the DRR geoscience/research-policy-practice area include: adopting an “interconnected geoscience” approach to DRR research, involving scientific inputs to DRR; using and developing local capability and resources for Ladakhi DRR policy and practice; using/further-developing DRR exercises presented in this paper, to integrate science with communities, and further-empower communities; taking account of the findings that hazard awareness is variable, and weak, for potentially catastrophic hazards, such as earthquakes, when designing policy and practice for raising DRR community awareness; ensuring that local values/world views/wisdom inform all DRR research, and encouraging external “experts” to carefully consider these aspects within Ladakh-based DRR work; and further-developing DRR networks across Ladakh that include pockets of expertise such as SECMOL.

The term “interconnected geoscience” is highly novel, further developing thinking within the research/science-policy-practice interface. This is the first time an exercise such as this has been undertaken in the Ladakh Himalaya.

The recently released fifth IPCC report indicates a high agreement among global actors on the need to integrate climate change adaptation (CCA) and disaster risk reduction (DRR). However, there remains little local level evidence on how DRR and CCA could be linked, the sorts of adjustments that are required for the two concepts to be integrated and the challenges ahead. This paper aims to provide an empirical insight on the possible links and departures between DRR and CCA.

The study used a qualitative case study approach to excavate lessons from an existing DRR intervention for CCA using a local-level adaptive capacity assessment framework as a normative criteria. Data was collected both from primary and secondary sources. The primary data collection involved the use of participatory rural appraisal techniques with village communities in Chifra District, Afar Regional State, Ethiopia.

The findings showed that the DRR interventions studied addressed parts of the elements of adaptive capacity at the local level. The findings also showed the limitation of the DRR intervention, which could be attributed to both the nature of the DRR interventions in general and implementation problems of the case study intervention in particular. The limitations show cases where full integration of DRR with CCA could be challenging.

The paper argues why the two approaches may not be integrated fully and also shows the need to focus on the design of DRR interventions in achieving both short-term (reducing disaster risks) and long-term objectives (enhancing adaptive capacity).

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.

A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.

A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.

The results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.

The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

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.

The Arab world is made up of 22 countries in the Middle East and North Africa. These countries are subjected to many social, economic, political and geographical vulnerabilities contributing to increased risks or ineffective emergency and disaster management. This paper examines these vulnerabilities, how they may impact the country's ability to face disasters, and how they can improve disasters' overall management.

The author selected Qatar, Oman to represent the Arab oil-rich countries, while Jordan, Egypt and Morocco to represent non-oil rich countries. The research was conducted in a qualitative, inductive systematic literature review based on a well-established systematic literature review methodology. Selected literature was based on its recency and the countries in question.

The review reveals population gaps that could threaten the social system in the event of a disaster in countries like Qatar and Oman. The majority of the countries lack community engagement and pre-planning for emergency preparedness due to social and cultural barriers. Other nations like Jordan, Egypt and Morocco are prone to long-lasting economic challenges due to lack of resources, mismanagement or corruption. The paper also highlights the need to raise the educational attainment among citizens to understand disaster risk reduction.

This study utilized the research method developed by Williams et al. (2017) to present a comprehensive systematic and comparative review of disaster management in the Arab world. Considering that disaster and emergency management has remained disproportionately unexplored in the Arab world, this paper reviewed several vulnerabilities and how those vulnerabilities may affect disaster and emergency management efforts in the Arab countries.

With the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.

The linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.

By adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.

This paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.

Based on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.