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Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). [ABSTRACT FROM AUTHOR]

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

Agro-pastoral ecotone of northern China is the prominent area for agricultural production, but it is also the most typical ecological fragile area with frequent drought disasters. Taking Yulin City at Shaanxi Province in China as the case area, the paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought in China's agro-pastoral ecotone in the period 2000 to 2020. The results show that: 1) the agricultural vulnerability to drought in Yulin City has shifted from high vulnerability in the period 2000–2010 to low vulnerability in the period 2011–2020. 2) There exist obvious spatio-temporal differences of the agricultural vulnerability to drought in Yulin City during the research period. 3) Four sensitive events and 14 resilient events were identified in the research and the crops of Yulin had become more resilient to drought. Finally, the paper put forward with policy implications to make adaptive strategies of agriculture to climate change in China's agro-pastoral ecotone in the future, e.g., carrying out agricultural zoning based on agricultural production conditions, intensifying the construction of disaster prevention and relief system, and integrating with modern agricultural technology to develop new type agriculture. [ABSTRACT FROM AUTHOR]

Studies about disasters have focused on large-scale and extreme weather events. However, slow-onset hazards such as drought-like seasons and monsoons also pose challenges since they are dynamic and experienced differently from place to place. This paper shows how difficulties in livelihood of the agricultural sector can be made evident using a placebased approach for identifying vulnerability in an island setting. A household survey was conducted to gather perceptions of hazard impacts and coping strategies for extreme weather events and pervasive hazards. Results show that the perceived impacts of hazards differ by events, and respondents cope with extreme weather events and pervasive hazards in almost the same ways. The coping strategies include diversification of livelihood and mutual help, a common tradition among Philippine villages. Community-based disaster risk management strategies through indigenous ways also enabled the island community to bridge the interventions of the national government to the local context in terms of reducing risks. In conclusion, a place-based approach adds value to the current way of assessing vulnerability as it shows that social vulnerability is more dynamic in the local context, and social bonding is crucial for coping during difficult times. [ABSTRACT FROM AUTHOR]

Responding to disasters triggered by natural hazards is a deeply political process, but it is usually presented by practitioners as an apolitical endeavour. This is striking when disasters occur in authoritarian and politically highly polarised conflict‐affected settings. Although the literature provides leads as to why and how humanitarians depoliticise aid, there has been little empirical research on the implications of depoliticisation, especially at the community level. Based on qualitative fieldwork that focused on the drought responses that overlapped with the 2016–19 sociopolitical crises in Zimbabwe, this paper details the practices, motivations, and implications of humanitarian depoliticisation. It differentiates between strategic, coerced, and routine managerial depoliticisation, and argues that, in an authoritarian conflict‐affected setting, depoliticisation strategically allows state and non‐state actors to defuse sensitive issues and actor relations and to remain safe. However, depoliticisation can also have implications for information management, monitoring, accountability, and protection, and thus ultimately for upholding humanitarian principles. [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen's slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally. [ABSTRACT FROM AUTHOR]

The summer monsoon–affected area is a suitable area for agricultural production in China. Against the background of climate warming, the activity of the summer monsoon is changing significantly. The drought and flood disasters in this region are becoming more and more frequent and have a greater and more serious impact on agricultural production. Therefore, based on observational data from meteorological stations and yield loss data in agriculture caused by drought and flood disasters in summer monsoon–affected areas, this paper studied the characteristics of the hazard of disaster-causing factors, the risk of losses to agriculture caused by droughts and floods, and their correlation. Moreover, it compared and analyzed the variability of drought and flood hazard and risk to agriculture before and after a temperature rise. The results showed that the precipitation in summer monsoon–affected areas is obviously affected by monsoon activities, and there is an overall though non-significant positive correlation between annual average standardized precipitation evapotranspiration index (SPEI) and the summer monsoon activity index (MI). The drought disaster comprehensive loss rate (DCLR) was generally higher than the flood disaster comprehensive loss rate (FCLR). This shows that the impact of drought on agriculture production is significantly greater than that of floods in this area. The high hazard and risk areas for drought were mainly distributed along the edge of the summer monsoon area and in its vicinity, whereas the high hazard and risk areas for flooding were mainly distributed in the lower reach of the Yangtze, Songhua, and Liaohe Rivers. Among them, Liaoning, Jilin and Heilongjiang are high-risk superposition areas of drought and flood disasters. The hazards of drought and flood dominate the risk of disaster loss. Climate warming has significantly expanded the high-hazard and high-risk ranges in China for drought and flood disasters and has also changed their spatial distribution patterns. The high-hazard area for drought has shifted to the monsoon edge area, the high-risk area for drought has moved northward, and both the high-hazard and the high-risk areas for floods have clearly moved southward. The impact of climate warming on drought was more significant than on floods. This study provides important scientific guidance for formulating drought and flood disaster prevention planning and agricultural production development patterns in China. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to provide a scientific reference for drought prevention and disaster avoidance of sugarcane production, the temporal and spatial characteristics of sugarcane drought in different growth period in Guangxi were analyzed in recent 60 years. [Method] Firstly the development period of sugarcane, soil data and meteorological data from 1961 to 2020 in Guangxi were collected for the study. Under major concentration for effectiveness of precipitation and dynamic change characteristics of crop coeffi cient, the temporal and spatial variation characteristics of sugarcane drought in growth period were analyzed by linear and periodic analysis method and spatial station technology basing on crop water deficit index. [Result] The interannual drought incidence was the highest in maturity stage, followed by stem elongation stage, and the least in seedling stage and in tillering stage. Light and medium drought are mainly in seedling stage, tillering stage and stem elongation stage. Heavy and extreme drought are mainly in maturity stage. During the study period, there was no obvious linear change trend in the interannual drought incidence at all levels and with a periodic change on the scale of 2 - 5 years. The years of occurrence were mainly in 1970s and 1980s. In terms of spatial distribution, in the seedling stage light drought and medium drought were mainly distributed in the west and south of Guangxi. The occurrence frequency of light drought was less than 40%. The occurrence frequency of medium drought was less than 30 % . The occurrence frequency of heavy drought was less than 10 % and its distribution range was small. In the tillering stage, light drought occurred in all over the region. The occurrence frequency in the southwest of Guangxi was the highest, generally between 21 % and 40%. Medium drought was mainly distributed in the southwest of Guangxi and the occurrence frequency was less than 10% . The occurrence frequency of severe drought was less than 10 % and its distribution range was small. In the stem elongation stage, mild drought was distributed in the whole region with the occurrence frequency between 11 % and 70%. Medium drought was mainly distributed in the northeast of Guangxi with the occurrence frequency between 21 % and 60%. Heavy drought was mainly distributed in the northeast of Guangxi with the occurrence frequency less than 20%. There was no extreme drought in the seedling stage, tillering stage and stem elongation stage during the study period. In the maturity stage, light drought, medium drought, heavy drought and extreme drought were distributed in whole study region. The occurrence frequency of light drought was less than 30%. The occurrence frequency of medium drought and severe drought were less than 40%. The occurrence frequency of extreme drought was the most and its distribution was wide. The regions with high extreme drought occurrence frequency were mainly distributed in the center, south and east of Guangxi with the occurrence frequency between 31 % and 50%. [Conclusion] The light and medium drought are the main drought in the seedling stage, tillering stage and stem elongation stage of sugarcane in Guangxi during the sugarcane development. The heavy and extreme drought is the main drought in the maturity stage. In the maturity stage of sugarcane, the heavy and extreme drought occur once every 2 - 3 years in the center, south and east of Guangxi. The regions above should be get more attention for drought prevention and disaster avoidance. [ABSTRACT FROM AUTHOR]

The cultivation of apples plays an important role in China's fruit production. Climate change has brought about more frequent and severe drought and waterlogging disasters, which have negatively impacted apple yield quantity and quality. Through the summary of previous research studies done on the impact of drought and waterlogging disasters and its effect on apple production, methods for disaster prevention and mitigation can be provided to reduce the impact of these disasters to the industry. In this paper, the methods of classification and systematic analysis combined with studying actual disaster cases were used to review the latest research into the effects of droughts and waterlogging disasters on apple production in China and identify the next steps for future research and development into this subject. The intensity and impacts of droughts and waterlogging disasters on apple production are determined by weather, regional climate, topography, soil composition, and several other factors. Adopting the morphological indicators to study these disasters, most existing research into this area only focuses on meteorological factors and rarely consider agrometeorological factors such as soil composition, current crop conditions, and more. The distribution of apple drought disasters in China decreases from north to south. The prevention measures of apple drought and waterlogging included irrigation, strengthening orchard soil management, implementing the integration of soil and fertilizer, etc. More of the current research done into this subject takes place in the Northern parts of China and often lack the necessary information to be put into use in disaster monitoring and early warning applications. In the future, research into apple droughts and waterlogging disasters should focus on increasing in orchard observation data collection, constructing a disaster risk monitoring and early warning model, and implementing the model into apple growing. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) with high precision and high temporal resolution can be obtained based on the global navigation and satellite positioning system (GNSS) technique, which is important for GNSS in disaster prevention and mitigation. However, related studies on drought monitoring using PWV have rarely been performed before, which becomes the focus of this paper. This paper proposes a novel drought monitoring method using GNSS-derived PWV and precipitation, and a multi-time-scale standardized precipitation conversion index (SPCI) is established. This index is different from the traditional index in terms of expression, standardization, and time scale. The proposed SPCI is then compared with the standardized precipitation index/standardized precipitation evapotranspiration index/self-calibrating Palmer drought severity index (SPI/SPEI/scPDSI) and applied to local and global drought monitoring. Validated results show that multi-time-scale SPCI has good consistency with the corresponding SPI/SPEI/scPDSI. The correlation between SPCI and SPEI is the strongest (more than 0.96) on a 12-month scale, which indicates the application potential of SPCI in drought monitoring. In addition, applications for regional (Queensland, Australia) and global drought/wet monitoring further verify the capability of the proposed SPCI. The average percentage deviations of drought/wet monitoring between SPCI and SPEI are 2.77% and 3.75%, respectively on a global scale. The above results show that the SPCI developed in this study is efficiently applied to global flood/wet studies. [ABSTRACT FROM AUTHOR]

Frequent drought disasters have significant impacts on socio-economic and ecosystems, and it is crucial to explore the propagation characteristics of drought for disaster prevention and mitigation. In this paper, based on 0.25° × 0.25° grid-point Meteorological hydrological and large-scale climate factor data, the start-to-start, peak-to-peak, and end-to-end drought propagation times, and the periods and rates of drought development and recovery were estimated for 11 regions in the Luanhe River Basin under stationary and non-stationary conditions. The Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework was used to construct stationary and non-stationary models to capture the stationary and non-stationary drought characteristics respectively, and then the drought development and recovery patterns, and drought propagation times were assessed. The results showed that under the influence of climate factors, severe drought events occurred in most study areas, the duration of meteorological drought was prolonged by 0.1–0.6 months, and the peak intensity was increased by about 0.05. The severity of hydrological drought increased by 0.2–0.8, and the peak intensity decreased by 0.1–0.2. The rate of development and recovery of hydrological drought was accelerated by 0.1–0.2 per month. The propagation time of start-to-start was shortened by 0.2 months, and the propagation time of end-to-end was extended by 1–2 months. These mean that water resource control authorities need to propose responses to droughts more quickly. • The non-stationary drought indices considering climatic factors were constructed. • The drought propagation time was calculated from start to start, peak to peak, and end to end. • Quantification of the effects of climate change on drought propagation [ABSTRACT FROM AUTHOR]

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

As one of the natural disasters, drought has caused a large amount of financial loss in the past few centuries. It is quite essential to reveal the variation of extreme drought event in Wei River Basin (WRB) of China. This paper investigated the change patterns of extreme drought event using Standardized Precipitation Evapotranspiration Index (SPEI). Furthermore, the SPEI is predicted by combining different influencing factors in the WRB using Long Short-Term Memory (LSTM) model. The spatiotemporal variation characteristics were examined using non-parametric Mann-Kendall test, and the nonlinear relationships between El Niño-Southern Oscillation (ENSO) and SPEI were quantified using wavelet coherence analysis (WTC). Results showed that midland of the WRB have the highest probability of extreme drought events. Meanwhile, changes in SPEI in the northeast were more erratic than in other regions. The area with extreme drought had increased at a rate of 2.4% per decade. The prediction result of SPEI-24 was the best by LSTM model, and the prediction result of SPEI-3 was the worst. The R-square between the predicted value and the actual value of SPEI-24 is 0.87. The results help to realize the characteristics of extreme drought in the last hundreds of years, which can provide scientific basis and reference for drought emergency response and management in the WRB. [ABSTRACT FROM AUTHOR]

Due to climate change, the frequency and intensity of droughts are expected to increase. To improve resilience to droughts, proactive drought management is essential. Economic assessments are typically included to decide on the drought risk-reducing investments to make. The choice of both methods and scope of economic assessments influences the outcome, and thus the investment choice. This paper aims to identify how comprehensively economic assessments are applied in practice. Through a systematic literature review, 14 actual economic assessments are identified and their methods are evaluated based on seven criteria for economic assessments as derived from the United Nations Framework Convention on Climate Change (UNFCCC). The results show that in practice, economic assessments rarely address all criteria. Applying a limited number of criteria reduces the scope and narrows the approach, possibly leading to the underestimation of drought risk reduction approaches' related benefits. Applying the seven criteria in practice will improve the results of economic assessments of drought risk reduction measures, allowing for optimal investment selection. Based on the different criteria, a Framework for Economic Assessments of Drought Risk-Reducing Applications (FEADRRA) is proposed. Applying the criteria of the framework can support decision-makers in drought risk management and in carrying out the most fitting drought interventions. • Criteria of the economics of climate change rarely translate into practice. • Limited criteria included can underestimate risk-reducing measures' attractiveness. • A framework for the economic assessment of drought mitigation measures is proposed. [ABSTRACT FROM AUTHOR]

Drought early warning systems (DEWS) have been developed in several countries in response to high socioeconomic losses caused by droughts. In Europe, the European Drought Observatory (EDO) monitors the ongoing drought and forecasts soil moisture anomalies up to 7 days ahead and meteorological drought up to 3 months ahead. However, end users managing water resources often require hydrological drought warning several months in advance. To answer this challenge, a seasonal pan-European DEWS has been developed and has been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) project. The ANYWHERE DEWS (AD-EWS) is different than other operational DEWS in the sense that the AD-EWS provides a wide range of seasonal hydrometeorological drought forecasting products in addition to meteorological drought, that is, a broad suite of drought indices that covers all water cycle components (drought in precipitation, soil moisture, runoff, discharge, and groundwater). The ability of the AD-EWS to provide seasonal drought predictions in high spatial resolution (5 km × 5 km) and its diverse products mark the AD-EWS as a preoperational drought forecasting system that can serve a broad range of different users' needs in Europe. This paper introduces the AD-EWS and shows some examples of different drought forecasting products, the drought forecast score, and some examples of a user-driven assessment of forecast trust levels. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

Natural hazards are a long existing threat to human and their surroundings which may occur throughout the world. Tamil Nadu is one of the Indian States with a number of natural hazard incidences. The occurrence of natural hazards, such as cyclone, storm surge, flood, drought, landslide, forest fire etc., has increased manifold in the recent decades. The multi-hazard zonation is one of the preliminary studies in disaster management scenario, which is used to understand the product of all prominent natural hazards. At the state level, it is imperative for the government to know the regions affected by multiple hazards to help them prepare the management plans appropriately to protect the local communities and infrastructures. However, such systematic hazard assessment and integration in an administrative unit is largely missing in Tamil Nadu. Further, the utilisation of geoinformatics in the preparation of multi-hazard zonation helps to identify the most endangered areas of the State precisely and offers insights to detailed studies in highly risk zones. This paper attempts on these lines to prepare Multi Hazard Zones (MHZ) based on natural hazards viz. earthquake, landslide, cyclone, storm surge, flood, drought and forest fire of Tamil Nadu. The data for the study were generated from multiple sources, which were all generalised and integrated in a normalised scale. The occurrences, intensities and frequencies of hazards, namely seismic, landslide, and forest fire are the reasons for a very high multi-hazard in hilly tracts of the Nilgiris and parts of Shayadhri hills in Coimbatore and Theni Districts, whereas cyclone, storm surge, and flood caused a very high risk along the coastal stretch of Chennai, Kancheepuram, Cuddalore and Ramanathapuram Districts. The segregation of the results into administrative division's which was then categorised in an order of high risk zones may provide a powerful tool to the State authorities to allocate fund and resources. The output of this study also offers zonation for immediate knowledge, policy briefs, and proper disaster management plan at State level. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) play vital roles in the western United States and related regions globally, not only producing heavy precipitation and flooding, but also providing beneficial water supply. This paper introduces a scale for the intensity and impacts of ARs. Its utility may be greatest where ARs are the most impactful storm type and hurricanes, nor'easters, and tornadoes are nearly nonexistent. Two parameters dominate the hydrologic outcomes and impacts of ARs: vertically integrated water vapor transport (IVT) and AR duration [i.e., the duration of at least minimal AR conditions (IVT ≥ 250 kg m–1 s–1)]. The scale uses an observed or predicted time series of IVT at a given geographic location and is based on the maximum IVT and AR duration at that point during an AR event. AR categories 1–5 are defined by thresholds for maximum IVT (3-h average) of 250, 500, 750, 1,000, and 1,250 kg m–1 s–1, and by IVT exceeding 250 kg m–1 s–1 continuously for 24–48 h. If the AR event duration is less than 24 h, it is downgraded by one category. If it is longer than 48 h, it is upgraded one category. The scale recognizes that weak ARs are often mostly beneficial because they can enhance water supply and snowpack, while stronger ARs can become mostly hazardous, for example, if they strike an area with antecedent conditions that enhance vulnerability, such as burn scars or wet conditions. Extended durations can enhance impacts. Short durations can mitigate impacts. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to demonstrate how the contingency approach to disaster preparedness inhibits proactive management of slow-onset disasters, such as El Niño, with the purpose of advancing disaster risk theory.Design/methodology/approach This study draws on fieldwork data from Nariño, Colombia, combined with secondary data and a review of the literature on El Niño and disaster preparedness.Findings Disaster managers in Nariño do have contingency plans for El Niño events at their disposal. Yet, these plans do not come into play before impacts reach a certain severity. This “contingency approach” to disaster preparedness appears to stem from the assumption that disaster must come before response, effectively inhibiting proactive responses to El Niño impacts.Research limitations/implications Attributing observed cases of droughts and oral accounts of impacts to the El Niño phenomenon is methodologically challenging. To overcome this, the aim of this study is not the documentation of subjective attributions. Instead, the focus is on bringing to the fore key dilemmas that preparedness professionals may face when they prepare for disasters with a slow onset.Practical implications Developing prevention and preparedness conceptualisations that focus on preemptive measures should ensure a more proactive response to slow-onset disasters.Originality/value Whether slow-onset disasters lend themselves to the same types of risk reduction strategies applied to rapid-onset disasters is a theoretical and practical issue that has not been explored sufficiently in the disaster risk literature. [ABSTRACT FROM AUTHOR]

Natural disasters are a major concern in Bangladesh, particularly drought which is one of the most common disaster in Bangladesh. Drought needs to be explained spatially to understand its spatiotemporal variations in different areas. In this paper, the meteorological drought has been shown by using the Standardized Precipitation Index (SPI) method and illustrated through the Inverse Distance Weighted (IDW) method across Bangladesh. We used rainfall data of 30 meteorological stations in Bangladesh during the study period of 1981–2010. The results indicate that drought has been fluctuating and it has become a recurrent phenomenon during the study period. The SPI depicted the drought conditions that plunged dramatically in 1981, 1982, 1985, 1987, 1989, 1992, 1994, and 1996 and then gradually improved in 2004, 2006, and 2009 in the country. The present study demonstrated that drought occurred in Bangladesh on an average of 2.5 years. Drought was more prominent in the northern, south-western, and eastern regions in Bangladesh compared to the rest of the areas of the country. The outcomes of the present study will help in during disaster management strategies, particularly drought, by initiating effective plans and adaptation remedies in different areas of Bangladesh. [ABSTRACT FROM AUTHOR]

Drought plans depend on indicators and triggers to characterize drought conditions and guide drought responses. Yet indicators and triggers often suffer from deficiencies, such as temporal and spatial inconsistency, statistical incomparability, and operational indeterminacy. Further, even though indicators and triggers are vital to drought hazard reduction, they are often selected and used arbitrarily, undermining the potential value of drought plans. Addressing these concerns, this paper provides a process and analytic methods for the development, analysis, and evaluation of indicators and triggers. In addition, this paper details their application to Georgia’s first state drought plan. Results are transferable to other drought plans, offering scientific justification, operational relevancy, and guidance for drought mitigation and response. [ABSTRACT FROM AUTHOR]

Due to the national nutrition crisis and the adjustment of planting structure, millet has once again become a widely planted cash crop in Northern China. However, as the climate warms in recent years, drought has occurred frequently and repeatedly in Northern China, which has seriously threatened millet production safety in this area. Therefore, it is urgent to clarify the hazard of millet drought disaster and provide reference for the formulation of millet disaster prevention and reduction measures. In this paper, combined with the water demand of millet in each growth period, Standardized Precipitation Requirement Index (SPRI) was selected to identify and quantify the drought disaster of millet. Considering the impact of different degrees of drought in different growth periods, a hazard assessment model is constructed to evaluate the hazard of millet drought disaster in Northern China under the background of climate change. The results show that: (1) SPRI can well characterize the drought of millet in Northern China. (2) In the context of climate change, millet drought in Northern China shows a trend of increasing intensity and decreasing frequency, and it is possible that millet drought will be even more severe in the future. (3) Combined with the hazard assessment model, millet drought in Northern China will still be dominated by middle hazard in the future, but the areas with high hazard will gradually increase, mainly distributed in northeast China, central and western Inner Mongolia and northern Gansu. The research results can be of great practical significance for the adjustment of agricultural production structure in the future, especially for the sustainable development of agriculture and the formulation of measures to prevent and resist agricultural disasters. [Display omitted] • We apply SPRI to identify millet drought by considering actual water demand. • Future changes of millet drought evaluated by CMIP6-GCM models. • Hazard assessment model was presented combined with disaster loss coefficient. • The areas with high hazard of millet drought will gradually increase in future. [ABSTRACT FROM AUTHOR]

Risk assessment of drought disaster is necessary for the sustained agriculture development under the background of global climate change, and, meanwhile, it is an urgent scientific issue needed to be solved in agricultural risk assessment discipline. In this paper, collecting the areas covered by drought disasters, areas affected by drought disasters, areas with no harvest by drought disasters, areas planted, and yield per unit area data, agricultural drought disasters losses of the southern five provinces in China were calculated. The best probability density function was obtained according to the goodness of fit test results. Then, the value at risk (VaR) from financial market risk research method was used to the effective measure of agricultural drought risk. The results show that, in southern five provinces of our research region, risk of agricultural drought disaster in Yunnan and Guizhou is greater than other three provinces. In the scenario of 50 years and 100 years for a return, the crop loss caused by drought will reach 13.6% and 17.4% in Yunnan province, and the crop loss will be more serious in Guizhou province and be up to 15.3% and 18.1%. The above results can provide multifaceted information about drought risk that can help to guide management of drought disasters. [ABSTRACT FROM AUTHOR]

The start and duration of a drought are often not easy to define. The advance and onset are usually slow and the social and economic impacts can then extend over a long period. In this study, the Standardized Precipitation Index (SPI) was used to describe and compare droughts in different periods and regions under different climatic conditions. The importance of the index lies in its simplicity and in its ability to identify the beginning and end of a drought event. Thus it may be a useful tool for drought contingency planning. This paper examines the SPI by evaluating its application using historical precipitation data from nine weather stations in China. Different time series data covering the period 1980 to 2011 and for differing timescales of 3, 9 and 24 months were used. The results underline the potential of the SPI to inform drought alerts and forecasting efforts as part of a drought contingency planning strategy. [ABSTRACT FROM AUTHOR]

In order to solve the fuzzy uncertainty problems of droughts and floods disaster, and to overcome the drawbacks of previous evaluation on floods and droughts which mainly used subjective weighting method to determine the weight of each index, the paper proposed fuzzy variable model with combined weights to evaluate the degree of droughts and floods. The results showed that this method used fuzzy variable model to calculate objective weight and adjusted it by AHP after satisfying the intended accuracy so as to achieve the integration of subjective and objective weight, which was more scientific than simple subjective methods. Also, making use of fuzzy variable model's parameter transformation under the combined weights to evaluate the disaster increased the stability and reliability of the conclusions. The results make instructive sense of disaster prevention and mitigation practice. [ABSTRACT FROM AUTHOR]

Since early 2018 the "Natural Hazards" Section of Geosciences journal has aimed to publish pure, experimental, or applied research that is focused on advancing methodologies, technologies, expertise, and capabilities to detect, characterize, monitor, and model natural hazards and assess their associated risks. This stream of geoscientific research has reached a high degree of specialization and represents a multi-disciplinary research realm. To inaugurate this section, the Special Issue "Key Topics and Future Perspectives in Natural Hazards Research" was launched. After a year and half since the call for papers was initially opened, the special issue is now completed with the editorial introducing the collection of 10 selected papers covering the following hot topics of natural hazards research: (i) trends in publications and research directions at international level; (ii) the role of Big Data in natural disaster management; (iii) assessment of seismic risk through the understanding and quantification of its three components (i.e., hazard, vulnerability and exposure/impact); (iv) climatic/hydro-meteorological hazards (i.e., drought, hurricanes); and (v) scientific analysis of past incidents and disaster forensics (i.e., the Oroville Dam 2017 spillway incident). The present editorial provides a summary of each paper of the collection within the current context of scientific research on natural hazards, pointing out the salient results and key messages. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) changes the air flow in the tropics, causing global droughts, floods, typhoons, and other natural disasters. Therefore, monitoring and forecasting of ENSO events effectively play an important role in disaster prevention and mitigation. This paper proposes a novel ENSO monitoring index using GNSS-derived zenith tropospheric delay (ZTD) and temperature (T), which is called the standardized ENSO monitoring index (SEMI), and validated its performance by introducing the standardized precipitation evapotranspiration index (SPEI). Multichannel singular spectral analysis (MSSA) and Lomb-Scargle periodogram are first applied to determine the periodic signals of ZTD and T. Empirical Orthogonal Function (EOF) is then used to analyze the spatio-temporal characteristics of ZTD and T. Finally, the response thresholds of ZTD and T to ENSO events are determined using the moving window correlation analysis (MWCA) method, and the SEMI is established based on the relationship between abnormal changes of sea surface temperature anomaly (SSTa) and ZTD/T. ZTD and T of 97 GNSS stations and the corresponding SPEI over the period of 2006–2016 in Taiwan province has been obtained to validate the capacity of the proposed SEMI for ENSO monitoring. Experimental results reveal that (1) annual and semi-annual periods existed in ZTD and T with the values of 365.25 and 182.625 days, respectively. (2) ZTD and T have good correlations with SSTa, and the coefficients are 0.64 and 0.70, respectively. (3) The response thresholds of ZTD and T to ENSO events in Taiwan province are [−1.13 °C, 0.84 °C] and [−1.09 °C, 0.69 °C], respectively. (4) The proposed SEMI has a good relationship with SSTa with a correlation coefficient of 0.77 and has been proven to have good ENSO monitoring capacity by comparing with SPEI with a correlation coefficient of 0.83. This result is of great significance for drought and flood disaster monitoring by the GNSS technique. • A standardized ENSO monitoring index (SEMI) is proposed. • SEMI only considers the parameters of GNSS-derived ZTD and temperature. • Response thresholds of ZTD and T to ENSO events are determined. • The performance of SEMI is validated by drought monitoring index (SPEI). [ABSTRACT FROM AUTHOR]

Limited studies have shown that disaster risk management (DRM) can be cost-efficient in a development context. Cost-benefit analysis (CBA) is an evaluation tool to analyse economic efficiency. This research introduces quantitative, stochastic CBA frameworks and applies them in case studies of flood and drought risk reduction in India and Pakistan, while also incorporating projected climate change impacts. DRM interventions are shown to be economically efficient, with integrated approaches more cost-effective and robust than singular interventions. The paper highlights that CBA can be a useful tool if certain issues are considered properly, including: complexities in estimating risk; data dependency of results; negative effects of interventions; and distributional aspects. The design and process of CBA must take into account specific objectives, available information, resources, and the perceptions and needs of stakeholders as transparently as possible. Intervention design and uncertainties should be qualified through dialogue, indicating that process is as important as numerical results. [ABSTRACT FROM AUTHOR]

With the advancement of computer science, Artificial Intelligence (AI) is being incorporated into many fields to increase prediction performance. Disaster management is one of the main fields embracing the techniques of AI. It is essential to forecast the occurrence of disasters in advance to take the necessary mitigation steps and reduce damage to life and property. Therefore, many types of research are conducted to predict such events due to climate change in advance using hydrological, mathematical, and AI-based approaches. This paper presents a comparison of three major accepted AI-based approaches in flood and drought forecasting. In this study, fluvial floods are measured by the runoff change in rivers whereas meteorological droughts are measured using the Standard Precipitation Index (SPI). The performance of the Convolutional Neural Network (CNN), Long-Short Term Memory network (LSTM), and Wavelet decomposition functions combined with the Adaptive Neuro-Fuzzy Inference System (WANFIS) are compared in flood and drought forecasting, with five statistical performance criteria and accepted flood and drought indicators used for comparison, extending to two climatic regions: arid and tropical. The results suggest that the CNN performs best in flood forecasting with WANFIS for meteorological drought forecasting, regardless of the climate of the region under study. Besides, the results demonstrate the increased accuracy of the CNN in applications with multiple features in the input. [Display omitted] • Performances of three AI models were compared for flood and drought forecasting. • Comparisons were done for an arid and a tropical basins separately. • Statistical measures and event indicators were used to determine the best model. • WANFIS model showed the highest accuracy for drought forecasts in both regions. • CNN model was the best forecast regardless of the climate of the region. [ABSTRACT FROM AUTHOR]

The dominant climatic mode responsible for seasonal rainfall variability across central southern Africa has been well-established as ENSO. Hence, the El Niño signal of the equatorial Pacific has been used extensively to predict droughts in this sub-region. Although this paper acknowledges that El Niño influences rainfall deficits over eastern southern Africa, an earlier signal of extreme positive sea level pressure (SLP) anomalies at Darwin for the averaged March to June period (MAMJ Darwin) has proved to have a superior remote connection to droughts in the sub-region. Simple linear statistical tools including composite techniques and correlation methods have been employed on century long data sets (1901–2000) to identify the emerging paramount connection between MAMJ Darwin SLP anomalies and southern African rainfall. Both MAMJ Darwin SLP anomalies and the Zimbabwe seasonal rainfall time series are significantly correlated (above the 95% significant level) with sea surface temperature anomalies. These represent the Indian Ocean Dipole mode in the tropical Indian Ocean and ENSO in the tropical Pacific for the averaged September to December period. ‘Pure’ MAMJ Darwin (that occur in the absence of El Niño in the Pacific) coincide with droughts more significantly (83% hit rate) than ‘pure’ El Niño events (not preceded by a high MAMJ Darwin) (38% hit rate). Co-occurrences (MAMJ Darwin preceded by El Niño) do not only have the highest hit rate of 93% but subsequent droughts are noticeably more severe. The ‘pure’ El Niños however, are not only poorly related to Zimbabwe seasonal rainfall deficits, but are apparently not connected to extreme droughts of the 20th century. Thus, MAMJ Darwin is a good simple predictor of droughts associated with or without ENSO in the Pacific. The high prediction skill of these results, especially the inherent longer lead-time than ENSO, makes MAMJ Darwin SLP anomalies an ideal additional input candidate for sub-regional drought monitoring and forecasting schemes. In this way, drought early warning and disaster preparedness activities can be enhanced over the sub-region. [ABSTRACT FROM AUTHOR]

Developed countries such as the United States have proposed planning frameworks for managing the impacts of drought. This paper describes a process that is a modification to these frameworks for application to developing countries in Africa. A case study for the Niger River in Mali, Africa, is used to illustrate the process. A spreadsheet based multicriterion decision analysis model is presented. The model is used to analyze the sensitivity of management alternative rankings to the type of planning, that is, strategic, tactical, and emergency. It is also used to analyze the sensitivity of the alternative rankings to various stages of a drought and to priorities of different decision makers. The results show the importance of using an approach that integrates the tactical and emergency management with the strategic objectives of drought impacts management. [ABSTRACT FROM AUTHOR]

The proactive approach to drought management is based on measures devised and implemented before, during, and after the drought event, according to a planning strategy rather than within an emergency framework. The measures taken before the initiation of a drought event consist of long-term measures oriented to improve the reliability of the water supply system to meet future demands under drought conditions. The measures taken after a drought is forecasted or starts are short-term measures that try to mitigate the impacts of the particular drought on the basis of a contingency plan. Selection of the preferable mix of long-term and short-term measures can be accomplished through multicriteria decision analysis (MCDA), which enables the comparison of alternatives on the basis of appropriate quantitative and/or qualitative assessment criteria. In this paper, a methodology for the assessment of drought mitigation measures based on a combined use of simulation models and MCDA is applied to a water supply system located in eastern Sicily, Italy. The system comprises two reservoirs and several diversion dams and hydropower plants, and its main uses are irrigation and municipal water supply. First, a simulation model is applied to evaluate the effects of several drought mitigation alternatives consisting of a mix of long- and short-term measures. Then, MCDA is applied to rank the different alternatives on the basis of economic, environmental, and social criteria, taking into account the process of coalition formation among stakeholders on the basis of their different point of view. The results confirm the applicability of the proposed multicriteria methodology for a transparent comparison of drought mitigation measures to be adopted as a support for the decision making process. [ABSTRACT FROM AUTHOR]

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages. [ABSTRACT FROM AUTHOR]

• Drought loss models of wheat are built using two different statistical models. • The important SPEI variables recognized by two methods are different. • The random forest-derived model outperforms the stepwise regression model. Agricultural drought loss models provide services for the rapid risk assessment of agricultural disasters, and regional disaster prevention and mitigation efforts. This paper takes wheat as an example, and chooses counties dominated by rain-fed farmland in Henan Province as the study area. Counties dominated by rain-fed farmland are determined by setting a rain-fed threshold that is related to the proportion of the effective irrigation area to the cultivated land area. Modeling samples are screened by considering both drought occurrence time and wheat yield reductions. Under different thresholds (30%, 40%, 50% and 60%), we use the yield loss ratio as the dependent variable and 24 standardized precipitation evapotranspiration index parameters as independent variables to build drought loss models using both a multivariate stepwise regression model and a random forest model. Yield loss ratio from 1990 to 2015 is calculated by decomposing historical wheat yield time series. 24 standardized precipitation evapotranspiration index variables are 1–3 months' time scale standardized precipitation evapotranspiration index during the growth period (from October to May of the following year) of winter wheat in Henan Province. The results show that the random forest-derived model outperforms the stepwise regression model in all tests. The accuracy of all the models increases with an increase of the proportion of the rain-fed threshold. When the rain-fed threshold is 60%, the R2 values of the random forest model and the multivariate stepwise regression equation are 0.720 and 0.523, respectively. The validation results show that the mean absolute error and the root mean square error of the multivariate stepwise regression are 1.38 times and 1.31 times larger than the mean absolute error and the root mean square error from the random forests model. Moreover, both models identify that standardized precipitation evapotranspiration indices in October (sowing/planting stage) and February (overwintering stage) are important variables. However, the multivariate stepwise regression model fails to recognize the importance of standardized precipitation evapotranspiration indices during April–May (filling stage). [ABSTRACT FROM AUTHOR]