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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]

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]

Mozambique is highly vulnerable to clime change induced hazards. The extreme weather impacts are directly related to the temperature and precipitation variations leading to more frequent and devastating events as floods, droughts and cyclones. Even though Mozambique has committed to international policies and has adopted mitigation measures, still it is lacking of sufficient capacity on various levels to lower the country's vulnerability level. A consortium of eight partner countries, along with Mozambique, commenced a Climate Change Induced Disaster Management in Africa (CIDMA) project which aims at building education capacity through implementing geospatial information technology for improved disaster management in Mozambique. The core of the project is in developing three 10 ECTS courses that will implement state-of-the-art techniques and methodologies for dealing with climate change induced hazards. The courses are intended from one hand to university students and staff, but on the other to local authorities, organisations and companies occupied with disaster management, and local communities. As one of course, "Public Participatory GIS and Spatial Data Infrastructure in Disaster Management" is designed to prepare students to be able to produce thematic maps through GIS and crowdsourced data, as well as various EO data. With the presented course it is expected for the students to gain valuable theoretical and practical knowledge of GIS, VGI and SDI for exploiting, managing and processing geospatial data for risk mitigation and hazard mapping. Moreover, they will be skilled in using free and open-source GIS software, desktop and mobile mapping techniques, and free web-based dissemination and processing services. In addition, it is expected for the students to develop critical judgement for analysing data with the correct tools in case of climate induced disasters. This paper describes the design, structure and topics of the "Public Participatory GIS and Spatial Data Infrastructure in Disaster Management". [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

It is important to consider extreme climate events, reduce disaster losses, and formulate effective disaster prevention and mitigation countermeasures. Based on the daily data from 36 meteorological stations of Xinjiang from 1960 to 2020, in order to analyze the temporal–spatial variations and influencing factors of extreme dry and wet events in Xinjiang, a number of methods were used including climate trend, the Mann–Kendall test, the Fourier power spectrum, the contribution rate, partial least squares and cross-wavelet analysis. Results indicate that the annual average frequency of extreme dry/wet events has a decreasing/increasing trend, at the rate of 0.26 times/decade and 0.19 times/decade, respectively; the variation trend in extreme dry and wet events of four seasons are consistent with the annual counterpart, at the rate of −0.04 times/decade and 0.02 times/decade (spring), −0.08 times/decade and 0.05 times/decade (summer), −0.05 times/decade and 0.06 times/decade (autumn), and −0.1 times/decade and 0.08 times/decade (winter). Fe fluctuation is greatest in winter and the smallest in spring, so the transition to warm and wet is obvious in winter and spring drought is easy to occur; the variation extent of extreme dry and wet events in northern Xinjiang exceeds the counterpart in southern Xinjiang; 1986 and 1987 witnessed abrupt variation in extreme dry and wet events in Xinjiang, with indication of distinct periodic oscillations of 2.44, 2.94, and 5.69 years and 2.94 and 5.69 years, respectively; the extreme dry (wet) events are determined by meteorological factors, comprising precipitation, relative humidity and temperature, and the circulation factors constituted by Western Pacific Subtropical High-Intensity Area (East Asian Trough Intensity, Westerly Circulation and Western Pacific Subtropical High Area) and El Niño events. [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]

Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the U.S. Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the state of Ohio: standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer's Z index, and Palmer hydrological drought index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring. [ABSTRACT FROM AUTHOR]

Excessive water and water deficit are two important factors that limit agricultural development worldwide. However, the impact of waterlogging on winter wheat yield on a large scale, compared with drought caused by water deficit, remains unclear. In this study, we assessed the waterlogging damage to winter wheat yield using the downscaled and fused TRMM 3B42 from 1998 to 2014. First, we downscaled the TRMM 3B42 with area-to-point kriging (APK) and fused it with rain gauge measurements using geographically weighted regression kriging (GWRK). Then, we calculated the accumulated number of rainy days (ARD) of different continuous rain processes (CRPs) with durations ranging from 5 to 15 days as a waterlogging indicator. A quadratic polynomial model was used to fit the yield change rate (YCR) and the waterlogging indicator, and the waterlogging levels (mild, moderate, and severe) based on the estimated YCR from the optimal model were determined. Our results showed that downscaling the TRMM 3B42 using APK improved the limited accuracy, while GWRK fusion significantly increased the precision of quantitative indicators, such as R (from 0.67 to 0.84), and the detectability of precipitation events, such as the probability of detection (POD) (from 0.60 to 0.78). Furthermore, we found that 67% of the variation in the YCR could be explained by the ARD of a CRP of 11 days, followed by the ARD of a CRP of 13 days (R2 of 0.65). During the typical wet growing season of 2001–2002, the percentages of mild, moderate, and severe waterlogged pixels were 5.72%, 2.00%, and 0.63%, respectively. Long time series waterlogging spatial mapping can clearly show the distribution and degree of waterlogging, providing a basis for policymakers to carry out waterlogging disaster prevention and mitigation strategies. [ABSTRACT FROM AUTHOR]

Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate. [ABSTRACT FROM AUTHOR]

Crop production security is an essential guarantee for the prosperity of country and the well‐being of people. In the context of frequent occurrence of extreme weather events, how to comprehensively evaluate the risk of multiple hazards and quantitatively describe spatial differences will be one of the significant topics in agricultural disaster mitigation. In this study, based on the gridded meteorological data and other datasets (crop, socioeconomic, disaster loss, and actual planting location) from 2000 to 2020, multihazard risk model for different growth period was built to assess the comprehensive risk of winter wheat in the upper Huaihe River basin (UHRB), China. The conclusions were as follows: (a) The spatial distribution of continuous rain risk increased gradually from north to south; the area of the highest lodging risk was located in the east; the spatial trend of drought risk was the highest in the south and the lowest in the northeast. (b) The weights of continuous rain, lodging, and drought disasters were 41, 31, and 28%, respectively, which indicated that winter wheat in UHRB was most affected by continuous rain during the harvest period. (c) The spatial distribution of comprehensive disaster risk assessment showed that the higher risk areas were located in the central and southern parts (Huaibin, Linquan, Luoshan). After verification by historical disasters information, the results are credible, providing a concrete scientific basis for implementing regional agricultural disaster response measures. [ABSTRACT FROM AUTHOR]

Agricultural drought is a major problem facing China's agricultural production. In this study, the cash crop 'peanut' was used as an example to explore vulnerability. Through the atmosphere–plant–soil continuum system, a single index that could represent different types of droughts affecting peanuts was selected and weighted using the CRITIC weighting method to construct a multi-source data fusion drought index (MFDI). Then, Pearson correlation analysis between the comprehensive drought index and relative meteorological yield and the Mann–Kendall trend test for different growth periods of peanuts were used to verify MFDI and analyze the variation over time. A three-dimensional vulnerability assessment method of drought intensity–drought duration–yield reduction rate was established based on the run theory and trend surface analysis. The results show that the constructed multi-source data fusion drought index (MFDI) can more accurately characterize the actual drought conditions of peanuts in Shandong Province. The MFDI results showed that the drought severity in the coastal areas of the study area decreased with the growth and development of peanuts, while the drought became more severe in the western and northern parts during the late growth period of peanuts. The vulnerability surface of the drought intensity–drought duration–yield reduction rate showed that when the drought intensity was < 0.8 and the duration was < 3.5 months, the vulnerability of peanut crops was low, and then with the increase in drought intensity or duration, the vulnerability increased. The impact of drought duration cannot be ignored. In contrast to traditional vulnerability assessment methods, this study established a three-dimensional vulnerability surface, which provides a new approach for agricultural drought vulnerability assessment. The research results are helpful for a deeper understanding of the relationship between drought and crop vulnerability and provide scientific support for local governments in formulating disaster prevention and mitigation policies. [ABSTRACT FROM AUTHOR]

The refined assessment of the spatiotemporal characteristics of droughts is of great significance for drought evaluation. Based on monthly precipitation and temperature grid data (1961–2019) in the middle reaches of the Yellow River basin (MYRB), the standardized precipitation evapotranspiration index (SPEI) was calculated at monthly, seasonal, and annual scales. The run theory was used to extract the drought features at the monthly scale, and the spatiotemporal characteristics of different drought levels were analyzed using Mann–Kendall mutation tests and spatial interpolation. The Moran' I was used to analyze the spatial heterogeneity of droughts. The results showed that the drought trend in the MYRB increased from 1961 to 2019, with the SPEI exhibiting an overall decreasing rate of − 0.1145/decade. Decreasing rates were observed in spring (− 0.1356/decade), summer (− 0.0362/decade), and autumn (− 0.0745/decade), whereas an increasing rate was observed in winter (0.0781/decade). Only extreme droughts were long term, with an intensity as low as − 22.29. The highest frequencies were observed for mild–moderate droughts, which mainly showed high-value clusters in the western and central regions. The frequencies of severe–extreme droughts mainly presented low-value clusters in the northern and southwestern areas. The frequencies of mild and severe droughts exhibited significant spatial cluster characteristics, while the drought intensity showed non-significant spatial clusters and a random distribution. The high and low values of drought intensity were mainly clustered in the middle–upper reaches. The research results provide reference for disaster prevention and mitigation, agricultural planning, and water resource allocation in the MYRB. [ABSTRACT FROM AUTHOR]

The literature on the costing of mitigation measures for reducing impacts of natural hazards is rather fragmented. This paper provides a concise overview of the current state of knowledge in Europe on the costing of mitigation measures for the reduction of natural hazard risks (droughts, floods, storms and induced coastal hazards as well as alpine hazards) and identifies knowledge gaps and related research recommendations. Furthermore, it provides a taxonomy of related mitigation options, classifying them into nine categories: (1) management plans, land-use planning, and climate adaptation; (2) hazard modification; (3) infrastructure; (4) mitigation measures (stricto sensu); (5) communication in advance of events; (6) monitoring and early warning systems; (7) emergency response and evacuation; (8) financial incentives; and (9) risk transfer (including insurance). It is found that the costing of mitigation measures in European and in other countries has almost exclusively focused on estimating direct costs. A cost assessment framework that addresses a range of costs, possibly informed by multiple stakeholders, would provide more accurate estimates and could provide better guidance to decision makers. [ABSTRACT FROM AUTHOR]

Understanding household disaster risk perception is crucial to formulate and apply disaster risk reduction strategies. Using survey data from 300 households from three highly disaster-prone areas of the lower Teesta River basin in Bangladesh, this study explores households' risk perception of drought, earthquake, and flood at the local level. The ordered probit regression model was applied to identify the factors influencing household disaster risk perception. Most of the respondents perceived the likelihood of occurring drought, earthquake, and flood hazards on a large scale in the selected areas which cause negative impacts on their quality of life and financial losses. They have lack knowledge on mitigation actions which makes them unable to control the devastating impacts of disasters. Econometric results show that households' age, gender, education, and income-generating sources had significantly influenced the respondent's drought, earthquake, and flood risk perception. Female participants have less knowledge on mitigations actions and are less capable of controlling the hazards than their counterparts making them more vulnerable to the impacts of hazards. Urgent action is required to improve their socio-economic conditions, and to reduce the knowledge gap between males and females as well as to improve the household's understanding of mitigation and preparedness for disaster risk. [ABSTRACT FROM AUTHOR]

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

With rapid economic development and population growth, the impacts of drought continue to affect China and the situation will probably be worsened further under projected climate warming. Therefore, understanding the characteristics of drought conditions and its actual impacts is especially important for disaster prevention and mitigations, under climatic warming. Here, we investigated the spatiotemporal variations of drought, using the self-calibrating Palmer Drought Severity Index (scPDSI) based on two evaluation methods, i.e., scPDSITh (Thornthwaite method for calculating evapotranspiration) and scPDSIPM (Penman–Monteith method for calculating evapotranspiration). Results show the drought severities characterized by these two methods are highly consistent, except that the scPDSITh shows more drought severity than the scPDSIPM in some regions, e.g., Xinjiang, Tibet, Qinghai, and Inner Mongolia. Based on the scPDSIPM and drought disaster statistics, we adopted a downscaling approach to obtain the county level dataset of drought-affected area from the provincial level. Using this finer-scale dataset, we observed spatial agglomeration characteristics of drought-affected area during the study period, denoting by the Moran's I. Moreover, results show that the high-high clusters (high values surrounded by high values) of the drought-affected area are mainly distributed in northern China, and the moving direction of high-high clusters presents the East–West pattern. Combining the meteorological data and disaster statistics, this study can improve the accuracy of drought detection, which is important for disaster prevention and mitigations in China. [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]

Under the background of climate warming, drought events occur frequently. Generally, meteorological drought leads to agricultural drought. Understanding the spatiotemporal distribution, characteristics of drought and the relationship between meteorological and agricultural drought are important for early drought warning. Northeast China (NEC) was selected as the study area. The spatiotemporal characteristics of meteorological drought and agricultural drought in different seasons in NEC were analyzed. Correlation analysis was employed to analyze the relationship between meteorological and agricultural drought in different vegetation types. Furthermore, cross-wavelet analysis was employed to further analyze the relationship between meteorological drought and agricultural drought, explore the teleconnection and large-scale climate patterns and investigate possible causes of drought variations in this region. The results showed that (1) the frequency of mild meteorological drought and low and that of moderate agricultural drought was high; (2) there was a significant positive correlation between meteorological drought and agricultural drought, while the change in agricultural drought lagged behind that of meteorological drought. (3) A strong correlation between meteorological drought and agricultural drought was identified in cropland areas. (4) The El Niño Southern Oscillation and Pacific interannual oscillation were important factors affecting the changes in meteorological drought and agricultural drought NEC. The results provide scientific ground for the sustainable development of agriculture, drought monitoring and early warning, disaster prevention and mitigation in NEC. [ABSTRACT FROM AUTHOR]

Drought is one of the major natural disasters in northern China. Therefore, monitoring and analyzing the changes of drought indices can provide scientific evidence for disaster assessment and for instituting policies for disaster prevention and mitigation. In this work, we used the gridded precipitation data set of a semi-arid steppe region in the Inner Mongolian Plateau from 1962 to 2017 to calculated the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). By comparing the spatiotemporal distribution of the two indices with the historical drought records, we found that the SPI was more suitable for the drought description in the study area. Based on this, we calculate the SPI at different time scales (annual and seasonal), then depict the spatiotemporal variation of drought during the past 56 years by analyzing the frequency of drought events, drought frequency, and the station frequency ratio. The results indicate that in recent years, the degree of drought in the study area has increased, and the south-central regions are the highest occurrence area for different types of drought events. In addition, light drought occurs mostly in the autumn and is mainly distributed in the central and southern parts of the study area. The most extreme drought events often occur in the summer, and the highest frequency area is located in the western part of the study area. Large-scale light drought occurred in all seasons in the 1990s, while the domain moderate drought and the domain extreme drought occurred in the summers of 2001 and 2010, respectively. [ABSTRACT FROM AUTHOR]

Virtually all climate monitoring and forecasting efforts concentrate on hazards rather than on impacts, while the latter are a priority for planning emergency activities and for the evaluation of mitigation strategies. Effective disaster risk management strategies need to consider the prevailing "human terrain" to predict who is at risk and how communities will be affected. There has been little effort to align the spatiotemporal granularity of socioeconomic assessments with the granularity of weather or climate monitoring. The lack of a high-resolution socioeconomic baseline leaves methodical approaches like machine learning virtually untapped for pattern recognition of extreme climate impacts on livelihood conditions. While the request for "better" socioeconomic data is not new, we highlight the need to collect and analyze environmental and socioeconomic data together and discuss novel strategies for coordinated data collection via mobile technologies from a drought risk management perspective. A better temporal, spatial, and contextual understanding of socioeconomic impacts of extreme climate conditions will help to establish complex causal pathways and quantitative proof about climate-attributable livelihood impacts. Such considerations are particularly important in the context of the latest big data-driven initiatives, such as the World Bank's Famine Action Mechanism (FAM). [ABSTRACT FROM AUTHOR]

Debris-flow disasters occurred frequently after the Mw 8.0 Wenchuan earthquake on 12 May 2008 in Sichuan Province, China. Based on historical accounts of debris-flow disaster events, it found that debris flow occurrence is closely related to the impact of earthquakes and droughts, because earthquake-drought activities can increase the loose solid materials, which can transform into debris flows under the effect of rainstorms. Based on the analysis of historical earthquake activity (frequency, magnitude and location), drought indexes and the trend of climate change (amount of rainfall), a prediction method was established, and the regional debris flow susceptibility was predicted. Furthermore, in a debris flow-susceptible site, effective warning and monitoring are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. The advantages of the prediction and early monitoring include (1) the acquired results being sent to the central government for policy making; (2) lives and property in mountainous areas can be protected, such as the 570 residents in the Aizi valley, who evacuated successfully before debris flows in 2012; (3) guiding the government to identify the areas of disasters and the preparation for disaster prevention and mitigation, such as predicting disasters in high-risk areas in the period 2012–2017, helping the government to recognize the development trend of disasters; (4) the quantitative prediction of regional debris-flow susceptibility, such as after the Wenchuan earthquake, can promote scientific and sustainable development and socioeconomic planning in earthquake-struck areas. [ABSTRACT FROM AUTHOR]

Natural disaster vulnerability can intuitively reflect the susceptibility of an area to environmental changes. Better understanding the spatial distribution of natural disaster vulnerability is a critical process for taking effective adaptation and management. Although significant achievements have been made in disaster vulnerability, few studies are known about natural disaster vulnerability at the national scale, especially from the typical natural disaster events in China. In this study, with normalizing selected indicators and calculating vulnerability index, we analyzed the spatial distribution of natural disasters vulnerability during 2010–2017 using the geospatial techniques. The results showed that natural disaster vulnerability has certain spatial differences, but different natural disaster can occur in the same area during the study period. Drought disaster can occur in all regions of China, especially in Inner Mongolia. Flood disaster is mainly concentrated in the middle and lower reaches of the Yangtze River and the Yellow River Basin. The wind and storm disaster is chiefly in the northern regions in China. The freezing disaster is widely distributed in China. Furthermore, the regions with low vulnerability were primarily distributed in the eastern coastal region, indicating that the rapid development of economy and technology can resist or mitigate natural disaster to a certain extent. This study offers a solution to study natural disasters and provides scientific basis for disaster prevention and mitigation actions. [ABSTRACT FROM AUTHOR]

Environmental change is a reason of relevant concern as it is occurring at an unprecedented pace and might increase natural hazards. Moreover, it is deemed to imply a reduced representativity of past experience and data on extreme hydroclimatic events. The latter concern has been epitomized by the statement that 'stationarity is dead.' Setting up policies for mitigating natural hazards, including those triggered by floods and droughts, is an urgent priority in many countries, which implies practical activities of management, engineering design, and construction. These latter necessarily need to be properly informed, and therefore, the research question on the value of past data is extremely important. We herein argue that there are mechanisms in hydrological systems that are time invariant, which may need to be interpreted through data inference. In particular, hydrological predictions are based on assumptions which should include stationarity. In fact, any hydrological model, including deterministic and nonstationary approaches, is affected by uncertainty and therefore should include a random component that is stationary. Given that an unnecessary resort to nonstationarity may imply a reduction of predictive capabilities, a pragmatic approach, based on the exploitation of past experience and data is a necessary prerequisite for setting up mitigation policies for environmental risk. [ABSTRACT FROM AUTHOR]

Recent severe drought events across the nation have raised our concerns over society’s changing and increasing vulnerability to droughts. All levels of governments have taken actions to plan for the slow-onset, long-lasting and spatially extensive drought hazard. The progress of drought planning at the state level is especially impressive over the past decade due to the dramatic growth of drought plans. To date, almost all states have drought plans, but previous studies indicated these plans are still heavily relying on the reactive crisis management approach to deal with ongoing droughts rather than the proactive risk management approach toward building drought resilience. No study so far has empirically examined how well all of these state plans are and to what extent these plans incorporated risk management theory and practices on a national basis. Thus, this study develops a drought risk coding protocol to systematically assess the 44 latest state drought plans’ quality in risk management. An inventory of the state drought plans is also established to demonstrate their quality, content and characteristics. The results indicate that state drought plans typically address emergency responses well, while they are generally weak in establishing strong goals, mitigation and adaptation, public involvement, plan updates and implementation. Lastly, recommendations are provided for drought officials to develop, enhance or revise drought plans toward a risk management approach. [ABSTRACT FROM AUTHOR]

In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting Lake basin. Afterwards, by means of the variance and autocorrelation coefficient that characterize the phenomenon of critical slowing down, the early-warning signals indicating the drought-flood state in the Dongting Lake basin are explored. The results show that an obvious drought-to-flood (flood-to-drought) event occurred around 1993 (2003) in the Dongting Lake basin in recent 60 years. The critical slowing down phenomena of the increases in the variance and autocorrelation coefficient, which are detected 5–10 years in advance, can be considered as early-warning signals indicating the drought-flood state transition. Through the studies on the drought-flood state and related early-warning signals for the Dongting Lake basin, the reliabilities of the variance and autocorrelation coefficient-based early-warning signals for abrupt changes are demonstrated. It is expected that the wide application of this method could provide important scientific and technological support for disaster prevention and mitigation in the Dongting Lake basin, and even in the middle and lower reaches of the Yangtze River. [ABSTRACT FROM AUTHOR]

Several American Indian Tribes are currently leading the way in developing drought mitigation and response plans. These tribes are undertaking the process of identifying and implementing actions that will help them better prepare for and respond to future drought events. The Hualapai Tribe in Arizona is one such group that has recently completed a drought plan with assistance provided through the Bureau of Reclamation’s States Emergency Drought Program. The National Drought Mitigation Center has helped provide technical assistance in developing the Hualapai Tribe’s plan and recently conducted interviews with tribal representatives to better understand lessons learned during the planning process. The efforts of the Hualapai Tribe provide an important case study for the development of similar plans for other tribal entities. [ABSTRACT FROM AUTHOR]