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Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001-2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011-2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI. [ABSTRACT FROM AUTHOR]

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale. [ABSTRACT FROM AUTHOR]

Global warming has altered the uniformity of precipitation in Inner Mongolia, China, eventually leading to droughts. Further studies are necessary to determine the relationship between the concentration of precipitation and drought. Therefore, we assessed the spatial and temporal characteristics of the precipitation concentration degree (PCD), precipitation concentration period (PCP), and standardized precipitation evapotranspiration index (SPEI) in Inner Mongolia in the past (1961–2017) and predict changes in the three indices under different scenarios in the future (2018–2100) using measured model data and Sen's slope and Mann–Kendall trend analysis. The correlation between PCD/PCP and SPEI was explored using Pearson's correlation coefficient. The results showed that the spatial distribution of PCD and PCP in Inner Mongolia exhibited significant east–west differences. The PCD values were 0.42–0.76, with high‐value areas in the east. PCD showed a decreasing trend in both historical and future scenarios, indicating an even distribution of precipitation and an increased risk of drought. The PCP values were 190°–226°, with high‐value areas mainly in the western region. Except for in the Representative Concentration Pathways RCP4.5 and RCP8.5, PCP values in the historical and RCP2.6 scenarios showed a decreasing trend, indicating an earlier onset of maximum precipitation. SPEI values ranged between −1.23 and 1.17, with all future scenarios showing a decreasing trend and the historical scenario showing an increasing trend. The stations with positive correlation between SPEI and PCD accounted for 89.13, 67.39, 91.3, and 95.65% of Inner Mongolia, while those with positive correlation with PCP accounted for 43.47, 60.87, 56.52, and 4.35%, indicating that the correlation between drought variation and precipitation concentration is strong. These results can help reduce and prevent droughts and floods caused by changes in precipitation patterns and provide a basis for the rational use of water resources for preventing droughts and making relief decisions. [ABSTRACT FROM AUTHOR]

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research. [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]

Global warming has increased the prevalence and severity of natural disasters, such as the increased frequency and intensity of drought events. In this study, we employed the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) to analyze and predict the temporal and spatial variation of drought and its characteristics in the Songliao Plain under two global warming scenarios (1.5 and 2 °C). We used climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project. The results show that drought will become more frequent in the future; it is more serious at representative concentration pathway (RCP) 8.5 than RCP4.5 and under global warming of 1.5 °C than 2 °C. Geographically, the SPEI indicates that there are signs of drought in the northwest and northeast of Songliao Plain while the SPI indicates that drought decreases from north to south. In terms of drought characteristics, the drought barycenter expressed by the SPEI moves to the northeast, while the drought barycenter expressed by the SPI moves to the northwest. The SPI relies on a single meteorological factor, making the SPI mutation test more stable. Finally, compared to the historical period (1976–2005), the frequency and duration of drought have increased, and it is more reliable to use the SPEI to monitor drought in this area. [ABSTRACT FROM AUTHOR]

In the context of global climate change, drought has become an important factor that affects the maize yield in China. To analyse the impact of drought on maize yield loss in Northeast China in current and future climate scenarios, the Composite Meteorological Drought Index (CI) is introduced to reconstruct the following drought indicators: drought accumulative days (DAD), drought accumulative intensity (DAI), and consecutive drought days (CDD). These three drought indicators are used to describe the three-dimensional characteristics of drought in this study. Sen’s slope method and three-dimensional copula functions are adopted to analyse the variety of drought features, and Ensemble Empirical Mode Decomposition (EEMD) is used to analyse the variations in maize yield. A temporal assessment of the standardized yield residuals series (SYRS) of maize from 1961 to 2014 is conducted. A panel regression model is applied to demonstrate the drought impact on maize yield at various growth stages under the RCP4.5 scenario. The results show that the drought risk level for midwest Jilin Province, western Liaoning, and eastern Heilongjiang increase with global warming in the current scenario. The shorter three-dimensional joint return periods, 44–80 yr, were mainly located in western Jilin Province, Liaodong Peninsula, and northwestern Liaoning. Eastern Heilongjiang has a slightly longer joint return period of 80–100 yr. The SYRS shows a strong statistical correlation with drought indicator variations; drought-prone regions exhibit strong positive correlations. In comparison, excess precipitation regions show strong negative correlations with drought indicators in most growth stages. Drought indicators have a relatively strong association with SYRS at the milky–mature maize growth stage, and the occurrence of drought during this period primarily determines the maize yield changes in the future. Maize yield changes are −2.04%, −2.65% and −1.57% for Liaoning, Jilin, and Heilongjiang Province under the RCP4.5 scenario. These results can be used as a tool for early warning of drought risk to maize, and will accelerate the progress of drought disaster risk management. [ABSTRACT FROM AUTHOR]

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world. [ABSTRACT FROM AUTHOR]

The traditional studies on drought disaster risk were based on the ground point data, which were unable to realize the continuity of space and the timeliness. It is shown that the monitoring and evaluation precision on drought were reduced significantly. However, remote sensing data in adequate spatial and temporal resolution can overcome these limitations. It can better monitor the crop in large area dynamically. This study presents a methodology for dynamic risk analysis and assessment of drought disaster to maize production in the northwest of Liaoning Province based on remote sensing data and GIS from the viewpoints of climatology, geography and disaster science. The model of dynamic risk assessment of drought disaster was established based on risk formation theory of natural disaster, and the expression of risk by integrating data came from sky, ground and space. The risk indexes were divided into four classes by data mining method, and the grade maps of drought disaster risk were drawn by GIS. It is shown that the spatial and temporal risk distributions of maize at each growth stage changed over time. The model has been verified against reduction in maize yield caused by drought. It demonstrated the reasonability, feasibility and reliability of the model and the methodology. The dynamic risk assessment of regional drought disaster for maize can be used as a tool, which can timely monitor the status (the possibility and extent of drought) and trends of regional drought disaster. The results obtained in this study can provide the latest information of regional drought disaster and the decision-making basis of disaster prevention and mitigation for government management and farmers. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a "west−central−southwest" pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively. [ABSTRACT FROM AUTHOR]

• Prolonged drought had a greater positive impact on pine caterpillar outbreaks than short-term drought. • Pine caterpillar outbreaks in Shandong Province predicted to decrease gradually to 2100. • Modeling scenarios differed with respect to latitudinal shifts in outbreaks. Drought is an important driver of forest pests, and reports of climate change are becoming more conspicuous as a result of enhanced drought. These projected changes have a positive impact on forest insect pests that result in substantial forest damage, adverse economic impacts, and losses of ecosystem resources. Therefore, we explored the impact of drought on the outbreak of pine caterpillars and predicted the future trend of pine caterpillar outbreaks under different emission scenarios by constructing a drought-pine caterpillar model (using a greenhouse gas emission scenario: Representative Concentration Pathways, RCP4.5 and RCP8.5 from 2016 to 2099). The results showed that: (1) Long-term or prolonged drought had a greater positive impact on pine caterpillar outbreaks than short-term drought; (2) The drought was more severe in the RCP 8.5 scenario than that in RCP 4.5; (3) The outbreaks of pine caterpillars in Shandong Province will gradually decrease approaching 2100 under the RCP4.5 and RCP8.5 scenarios. There is a trend of cyclical changes of approximately 30 years under the RCP4.5 scenario, while these changes primarily occurred before the 2050s and then decreased significantly and even became almost zero in the 2090s. (4) Under the two scenarios, light infestations comprised the largest area, followed by moderate and severe. Our study is important to promote the understanding of climate change and provide references and direction for forest managers. [ABSTRACT FROM AUTHOR]

• A quantitative assessment framework for ecosystem drought risk was established. • Nearly 45.4% of ecosystem vegetation in NEC has a moderate or high risk of drought. • Quantitative assessment of vulnerability is the key point to drought risk assessment. • The drought risk patterns driven by climate variability and human activities are different in farmland and natural vegetation. Given the significant increasing trend of droughts in Northeast China (NEC) under climate change, it is of particularly urgency to quantify the drought risk to vegetation in different ecosystems. Based on the theory of risk formation, we developed a quantitative framework for the assessment of ecosystem drought risk that integrates both drought hazard and vulnerability (sensitivity and adaptability). To quantitatively assess the drought risk to different vegetation types in NEC, we used long-term (1982–2015) normalized difference vegetation index (NDVI) data and standardized precipitation evapotranspiration index (SPEI) data. The effects of climate change and non-climatic factors (mainly human activities) on the NDVI dynamics and drought risk were analyzed using the partial least squares regression method to explore key drivers of risk formation. Our findings indicate that almost 45.4% of ecosystem vegetation in NEC is at a moderate or high risk of drought. Our findings indicate that vulnerability is key to NEC drought risk assessment, and adaptability plays an important role in regulating sensitivity. Land cover is the main driving factor for the spatial pattern of drought vulnerability in NEC, and forests have the lowest vulnerability. The impact of climate change on vegetation dynamics in dry years (R2 dry = 0.35) is significantly higher than over the entire study period (R2 all = 0.20). Cropland drought risk is dominated by human activities, whilst woodland and grassland drought risk is controlled by temperature and precipitation variability, respectively. The driving force analysis of vegetation dynamics and drought risk improves our understanding of the mechanisms behind the formation of risk patterns in NEC. Our study provides a basis for land use management and the formulation of drought adaptation options for vegetation types at different growth stages. [ABSTRACT FROM AUTHOR]

Due to global warming, it is necessary to study the influence of extreme climate and concurrent events on crop growth. The study area was the Songliao Plain, where drought events frequently occur. First, the daily meteorological data of 14 meteorological stations from 1981 to 2016 were collected to analyze the temporal and spatial changes in the crop water surplus and deficit index, extreme growing degree-days, and heat stress intensity during different growth stages of maize. Second, the cloud model was used to describe the fuzziness of concurrent events (simultaneous drought and heat), and mutual mapping between qualitative and quantitative data was undertaken. The fuzzy certainty degree of the influence of different degrees of concurrent events on maize was calculated. Third, the copula function was used to describe the randomness of concurrent extreme events and calculate the joint probability distribution and return period. An assessment method was proposed for concurrent events from the perspective of system uncertainty. Finally, we analyzed the relationship between concurrent events and maize yield, which showed different degrees of water deficit and warming trends during each growth period. Crops were most affected by extreme weather during the reproductive growth period (RGP). During the vegetative growth period (VGP), the temperature increase was higher than in other periods, especially in the high-latitude region. The frequency of mild concurrent events was higher during the VGP and RGP. During the vegetative and reproductive period, the average occurrence probability of mild, moderate, and severe concurrent events was 21.9%, 1.7%, and 0.35%, respectively, whereas during the RGP, it was 23.1%, 8.2%, and 0.12%, respectively. The present study provides a meaningful reference for a better understanding of the occurrence laws of drought, heat, and concurrent events during crop growth periods and how to optimize the agricultural management of maize. • Cloud model qualitative and quantitative mapping of drought and heat events. • Copula function was used to describe the randomness of concurrent extreme events. • Vegetative growth periods (VGP) and reproductive growth periods (RGP) were modelled. [ABSTRACT FROM AUTHOR]