Agrometeorological Disaster Research Articles

Hazard assessment of rice cold damage based on energy balance in paddy field

Cold stress seriously affects rice yield in Northeast China and, as a result of climate change, there are new trends in the characterization of cold damage. Accurate simulation of water temperature in paddy fields and assessment of cold damage hazard can contribute to improving the accuracy of agrometeorological disaster risk assessment in the context of climate change. Hence, in this study, first, we simulated the water temperature of paddy fields based on the paddy field energy-balance model and evaluated the simulation results. Subsequently, a composite cooling-degree-day (CCDD) indicator for rice fields was constructed based on water and air temperatures to characterize cold stress and identify cold damage events. Finally, a cold damage hazard-assessment model was constructed for rice based on the frequency and intensity of cold damage to assess cold damage hazard in Northeast China from 1979 to 2018. The results showed that the root-mean-square error between simulated and observed water temperatures of the rice field energy-balance model was 0.45, which means that the model accurately simulated the water temperature of the rice field on a large spatial scale. In 2004, cold damage to late-maturity rice accounted for 30% of the total study area, which was one of the years with most severe cold damage in late-maturity rice-growing areas thus far in the 21st century. Water temperature affected 76.4% of the area in 2004, and was related to radiation and saturated vapor pressure. The low-hazard areas accounted for more than 20% in 2009–2018, and the high-hazard areas were still higher than 10%, indicating that the threat of cold damage in the northeastern region of China remained severe as the climate has become increasingly warm.

Higher risk of spring frost under future climate change across China's apple planting regions

Spring frost is one of major agro-meteorological disasters impacting apple production. Previous studies focused on the impacts of spring frost during the flowering period on apples, however, the frost events occurring at both pre- and post-flowering phases have significant impacts on apples. Our study employed four phenology models (parallel model, M1 model, sequential model, and growing degree hour model), along with their ensemble model driven by five global climate models (GCMs) to predict the key spring phenphases of apple across China’s apple planting regions under two emission scenarios (SSP245 and SSP585) during the 2050 s (2040–2069) and 2080 s (2070–2099). A new spring frost assessment index (FI) combining frequency and intensity of spring frost was developed to evaluate the impacts of climate change on spring frost risk of apple. The results showed a large spatial variation in projected spring phenophases across China’s apple planting regions. Projected budbreak (BBCH 09), initial flowering (BBCH 60), and final flowering (BBCH 69) would advance 5.6–15.9 days, 5.7–14.8 days, and 5.6–14.5 days, respectively, in regions I, II, and V of northern China apple planting regions. However, in regions III and IV of southern China apple planting regions, projected budbreak, initial flowering, and final flowering would delay –9.2–21.5 days, –7.5–20.6 days, and –6.8–20.8 days in the future climate scenarios, compared with the baseline (1980–2009). Projected last spring frost date considering the entire spring phenphases would advance 3.7–11.8 d in the future climate scenarios, compared with the baseline. Projected spring frost risk would increase in all the three growth stages including budbreak to initial flowering, flowering period, and final flowering to the last spring frost date across all the China's apple plating regions in future climate scenarios except southward China's apple plating region under the SSP585 in the 2080 s. Our study provides an important reference for early warning, prevention and mitigation of spring frost disasters, and the layout of the apple industry.

Temporal and Spatial Variability of Ground Frost Indices in Northeast China

Frost is one of the most frequent, intense, and influential agrometeorological disasters that occurs frequently in Northeast China. The study of the spatiotemporal changes of ground frost is of great significance for farmers and policymakers in Northeast China, as it can inform decisions related to crop selection, planting schedules, and the development of regional climate adaptation plans. In this study, the spatiotemporal changes of frost indices (last spring frost (LSF), first fall frost (FFF), and frost-free period (FFP)) in Northeast China were analyzed from 1961 to 2020. Then, we investigated the mutation characteristics of the frost indices and their correlation with geographical factors. The results revealed that (1) the LSF, FFF, and FFP in Northeast China were concentrated at 120–140 DOY, 260–280 DOY, and 110–170 days, respectively. The spatial distribution of frost indices exhibited significant spatial heterogeneity. (2) The LSF, FFF, and FFP showed significant trends of advancement, delay, and extension, with trends of −1.94 days/10 a, 1.72 days/10 a, and 4.21 days/10 a, respectively. (3) More than 80% of the LSF, FFF, and FFP of the sites showed trends of advancement, delay, and extension, with greater variability in the central part of Heilongjiang Province. (4) The FFF and FFP experienced an abrupt change in the late 1990s. (5) The correlation between latitude and LSF, FFF, and FFP was the strongest, with correlation coefficients of 0.77, −0.79, and −0.78, respectively. This study provides a comprehensive understanding of the changing characteristics of ground frost indices that impact agricultural production in Northeast China against the backdrop of climate change. The findings hold significant scientific value in guiding the adaptation of agricultural production layouts in Northeast China to the evolving climatic conditions.

Farmstead-Specific Weather Risk Prediction Technique Based on High-Resolution Weather Grid Distribution

In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology.

Large-Scale Climate Factors of Compound Agrometeorological Disasters of Spring Maize in Liaoning, Northeast China

Co-occurring extreme heat, drought, and moisture events are increasing under global warming and pose serious threats to ecosystem and food security. However, how to effectively link compound agrometeorological disasters (CADs) with climate change has not been well assessed. In this study, we focus on the comprehensive influence of large-scale climate factors on CADs rather than extreme meteorological elements. The results indicate that there are two main CADs of spring maize in Shenyang, Northeast China (NEC), including concurrent drought and cold damage (DC) and drought in multiple growth periods (MD). The related circulation anomalies at mid–high latitudes are identified as four patterns, namely, the Northeast Asia Low (NEAL) and Ural High (UH) patterns affecting DC, the Baikal High and Okhotsk Low (BHOL), and the Northeast Asia High (NEAH) patterns leading to MD. The vertical profile and water vapor transport anomalies further demonstrate the influence mechanism of large-scale circulation on compound heat–moisture stresses. This study highlights the role of atmospheric circulation, which can provide effective predictors for these synergistic agrometeorological disasters.

Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.

Determining the critical threshold of meteorological heat damage to tea plants based on MODIS LST products for tea planting areas in China

Tea (Camellia sinensis (L.) Kuntze) is a daily drink in modern life throughout the world. Global warming increases the exposure of tea plants to elevated heat conditions during the summer picking period. Therefore, it is a fundamental function of the meteorological service to determine the critical temperature threshold that triggers heat damage (HD) to tea plants. In this study, MODIS daytime land surface temperature (LST) products obtained from the Aqua satellite (MYD11A1), annual land cover type products (MCD12Q1), and actual HD records were jointly used to determine the critical LST threshold resulting in HD to tea planting areas in China. An LST dataset of HD and HD-free samples was constructed based on actual HD records. The box-plot method was then used to delineate the range of LST outliers in HD to tea plants. The cumulative frequency distribution (CFD) method was adopted to determine the critical LST threshold by searching for the maximum slope of CFD within the range of LST outliers based on 2901 actual HD samples from 42 agrometeorological stations in 11 provinces in China. Results showed that the critical LST threshold triggering HD to tea plants was 30.1 °C. Two different HD data sources (i.e., 68 counties in eight provinces from the Meteorological Disaster Management System (MDMS) and 37 counties in six cities from a published book) were used as HD validation samples. Validation accuracy analysis revealed that a critical LST threshold of 30.1 °C was reasonable, with an average accuracy of between 60% and 90% for the published-book dataset, and 75.7% in the northern Yangtze River area and 86.3% in the southern Yangtze River area for the MDMS dataset. This study confirmed that using remotely sensed LST products is an effective method for identifying temperature-related agrometeorological disasters owing to the method's ability to intuitively capture the surface temperature distribution of the disaster-affected object. The findings will be helpful for identifying the timing and location of HD to tea plants, and thereby potentially preventing HD to tea plants.

Spatiotemporal distribution of agrometeorological disasters in China and its impact on grain yield under climate change

Agrometeorological disasters (AMDs) in the context of climate change pose a great threat to agricultural production and food security in China. This study analyzed spatiotemporal characteristics of five types of AMDs (drought, flood, hail, low-temperature freezing and snow (LTFS), and typhoon) in China, assessed the impacts of AMDs on meteorological yield, and explored the influences of extreme climate on AMDs and meteorological yield. The results demonstrated that the total covered rate of AMDs generally showed a decreasing trend from 1978 to 2020 in China. The covered rates of drought, flood, and hail had significant downward trends, while the covered rates of the LTFS and typhoons had insignificant increasing trends. The AMDs’ structure in different regions obviously changed after 2010. In addition, Pearson correlation analysis revealed that the covered rates of drought, flood, and typhoon were affected by the extreme precipitation indicators, and hail and LTFS were affected by the extreme temperature indicators. The increases of extreme temperature led to the decrease of the meteorological yield, while the effect of extreme precipitation on meteorological yield varied from region to region. This study provides significant references for guiding disaster mitigation.

Construction and empirical testing of comprehensive risk evaluation methods from a multi-dimensional risk matrix perspective: taking specific types of natural disasters risk in China as an example

Global climate change, continuous economic development and accelerated urbanisation have increased the frequency and complexity of various incidents. Risk assessment is an important means to deal with different crises and incidents. Based on the systematic analysis of the basic connotation, attribute relationship and main contents of risk assessment, this study extends the traditional two-dimensional risk matrix to multi-dimensional risk matrix. On this basis, this study further proposes four comprehensive risk assessment methods, including total ordinal, weighted average, Euclidean distance and two-norm methods. Empirical research was conducted by selecting three types of natural disasters, regional agro-meteorological disasters, geological disasters and forest fires, in 30 related regions in China between 2010 and 2019. The results indicated that compared to the two-dimensional risk matrix, the multi-dimensional risk matrix can intuitively characterise and compare the all-round risk situation of different regions, periods, types and dimensions, which is more helpful for a systematic analysis and risk comparison. All four evaluation methods proposed in this paper proved able to effectively conduct comprehensive regional risk evaluations with consistent results. However, when considering the fitting coefficient between the results obtained by various evaluation methods and the comprehensive evaluation results, it can be found that the accuracy (i.e. fitting coefficient) of Euclidean distance method is significantly higher than that of the other three methods. In the last 10 years, the temporal order of natural disaster risk in China has been more volatile, with Beijing, Shanxi, Tianjin, Hebei, Liaoning, Ningxia, Zhejiang, Shaanxi, Fujian and Gansu experiencing higher volatility in the ranking of natural disaster risk.

Grain Risk Analysis of Meteorological Disasters in Gansu Province Using Probability Statistics and Index Approaches

With global warming, agrometeorological disasters are also rising, posing a severe threat to China’s food security. Risk assessment serves as a bridge from disaster crisis management to risk management. Gansu Province is geographically crucial, so we performed a refined assessment of grain production risk for this province using multiple features of disaster loss data recorded at the county level. Analyses were performed for each district and county with a probability approach and an index system. We found that grain trend yields in each district and most counties in Gansu Province are increasing. Wuwei and Linxia districts had higher yearly growth rates, of more than 120 kg/(ha·year). However, there are considerable differences in risk levels among counties, even within the same district. Huating and Jinchang counties are high risk locations, while Cheng, Diebu, Jinta, and Xiahe counties are low risk zones. In 39.2% of counties, the fluctuation tendency rate of relative meteorological yield was positive. The average yield reduction rates of grain in the 1980s, 1990s, 2000s, and 2010s were 5.5%, 6.6%, 8.1%, and 4.2%, respectively, and the average fluctuation coefficients were 5.0%, 5.5%, 7.1%, and 3.8%, respectively. After 2010, most regions’ average yield reduction rates fell dramatically, and grain output progressively stabilized. Counties prone to heavy disasters are primarily spread along the Hexi Corridor, with the probability exceeding 8%. However, 27.9% of counties were spared from severe calamities, which were mainly distributed in southwestern Gansu Province. Crop disaster conditions significantly positively correlated with grain risk. Drought is the primary cause of grain yield decline in Gansu Province. The findings can provide essential policy advice for the government in disaster prevention.

The optimal time-scale of Standardized Precipitation Index for early identifying summer maize drought in the Huang-Huai-Hai region, China

Study regionThe Huang-Huai-Hai region (HHH). Study focusThe optimal time-scales of Standardized Precipitation Index (SPI) for early drought identification was determined by using meteorological data and agrometeorological disaster observation records of summer maize in HHH in this study. Two performance measures were used, one is accuracy, defined as the proportion of the drought samples identified by SPI in the total samples, and the other is sensitivity, defined as the ratio of the number of early recognized samples to the number of identified samples. New hydrological insights for the regionThe SPI can be calculated with only precipitation at any time-scales, mainly including 3-, 6-, 12-, or 24-month periods in previous studies. However, the growth period of maize is only 3–4 months, and the SPI with more than 3-month time-scales may not be applicable for maize drought identification. Additionally, meteorological drought is considered to be the origin for agricultural drought. It is important to identify meteorological drought signals in advance by using applicable time-scale SPI to avoid crop yield reduction. Results showed that 10-day SPI (SPI10) was the optimal index for drought identification with the highest accuracy (95.8 %) and sensitivity (77.72 %). Additionally, most of the drought samples identified by SPI10 were 0–16 days ahead of the observed onset date. The findings can provide theoretical guidance for early warning and near real-time monitoring for summer maize drought disasters in HHH.

Assessing Drought, Flood, and High Temperature Disasters during Sugarcane Growth Stages in Southern China

As a globally important sugarcane-producing region, Southern China (SC) is severely affected by various agrometeorological disasters. This study aimed to comprehensively assess multiple sugarcane agrometeorological disasters with regards to sugarcane yield in SC. The standardized precipitation evapotranspiration index and the heat degree-days were employed to characterize drought, flood, and high temperature (HT) during sugarcane growth stages in three provinces in SC in the period 1970–2020. Moreover, the relationships between sugarcane climatic yield and disaster intensities were investigated. The results indicated that the most recent decade witnessed the most intensive sugarcane agrometeorological disasters; sugarcane drought and HT intensities significantly (p < 0.05) increased in one and two provinces, respectively. Central and western SC was most drought-prone, while eastern SC was most flood-prone; sugarcane HT was concentrated in southwestern SC. The mature stage exhibited the greatest monthly intensities of drought and flood; the most HT-prone growth stage varied with provinces. The relationships between drought/flood intensity and sugarcane climatic yield were significant in seven districts; the yield-reducing effect of sugarcane flood was more obvious than that of drought. In conclusion, this study provides references for agrometeorological disaster risk reduction for sugarcane in SC.

Tracking the influence of drought events on winter wheat using long-term gross primary production and yield in the Wei River Basin, China

The Wei River Basin (WRB) is the main winter wheat-producing area in Northwest China and plays a pivotal role in national food security and economic development. Drought is the main agro-meteorological disaster affecting winter wheat yield in this region, and the accurate assessment of the drought impacts on crop growth and yield is an important part of maintaining food security. However, commonly used assessment methods, such as correlation analysis and crop model simulation, have some limitations. In this study, based on standardized precipitation evapotranspiration index (SPEI) at multiple time scales, gross primary production (GPP) and yield data from 1990 to 2018, sensitivity analysis, Pearson correlation, and superposed epoch analysis (SEA) were used to analyze the spatiotemporal characteristics of drought in the WRB and its influence on winter wheat growth and quantitatively assess the impact of drought at different levels on winter wheat yield. The results were as follows: 1) Overall, the WRB was in a dry state with a drying trend during 1990–2018, and the droughts in spring, summer, and winter were the main driving forces of annual drought that seriously threatened the growth and yield of winter wheat, especially in spring (March–May), which is the key growth period of winter wheat. 2) The average annual GPP of winter wheat in the WRB showed a significant upward trend of 0.465 · 10a−1, and the central part of the WRB was the region where winter wheat GPP had higher sensitivity to drought. In general, winter wheat GPP during the growing season was most sensitive to SPEI-3, indicating that seasonal water deficits had the greatest impact on winter wheat growth. 3) The yield reduction of winter wheat caused by drought presented a spatial distribution that is heavy in the north and light in the south, and as the drought grade increased, the winter wheat yield decreased more significantly, even in irrigated areas. Therefore, relevant government departments still need to strengthen the risk management of agricultural droughts in the WRB and formulate reasonable policies to maintain food security.

Study on the different responses of different winter wheat cultivars to dry hot wind.

Dry hot wind (DHW) is one of the main agro-meteorological disasters that occur during the grain filling stage of winter wheat in northern China. In this study, three major winter wheat cultivars planted at the Mazhuang experimental station, Xinji city, Hebei Province, including Henong 6119 (HN6119), Gaoyou 5218 (GY5218), and Jimai 325 (JM325), were analyzed. Through natural DHW and artificially simulated DHW experiments, we investigated how the physiological parameters of the three cultivars were affected on the day with DHW and the day before and after DHW occurred. Comparative analysis of the different responses among the physiological parameters of the three cultivars demonstrated that HN6119 experienced less leaf water loss by reducing its stomata conductance and transpiration rate under natural DHW conditions, while GY5218 and JM325 experienced more leaf water loss by increasing their stomata conductance and transpiration rates under natural DHW conditions. The net photosynthetic rate, transpiration rate, and stomata conductance of HN6119 recovered after the DHW conditions, while those of GY5218 and JM325 showed a continuously decreasing trend. The leaf photosynthetic water use efficiency decreased on DHW days because the net photosynthesis rate was reduced for HN6119, but the transpiration rate increased for GY5218 and JM325. HN6119 showed a significant positive correlation between physiological parameters, while GY5218 and JM325 showed a poor correlation after being affected by DHW conditions. The effect of artificial simulation under mild and severe DHW stress on the thousand kernel weight (TKW) of HN6119, GY5218 and JM325 was 0.01%, 3.51%, 3.57% and 0.36%, 8.12%, 8.84%, respectively. HN6119 showed better resistance to DHW, followed by GY5218, and JM325 showed the weakest resistance.

Quantifying the impact of frost damage during flowering on apple yield in Shaanxi province, China

Frost damage during flowering is recognized as one of the most serious agro-meteorological disasters affecting apple production in Shaanxi province, a typical apple producing area in China. Quantitative assessments of flowering frost damage to apple yield are critical for the development of strategies on mitigating yield losses, but have been rarely conducted. For the first time, our study used the process-based STICS model and statistical methods based on Lagrange interpolation method to assess the impacts of frost damage during flowering on apple yield at five study sites over the past three decades. Four canopy temperature thresholds were set as 0 °C, − 2 °C, − 5 °C, and − 35 °C for 0, 10%, 90%, and 100% frost damage on fruit number, respectively. The study results showed that STICS could effectively simulate the phenology and yield of apple, with the simulation errors less than 15%. Simulated yield losses caused by frost damage during flowering by STICS model were consistent with that estimated by statistical methods. Average yield loss due to frost damage during flowering estimated by STICS model was 6.35–29.27% in frost years during past three decades at five study sites. In general, both the frost occurrence days and intensities were the highest in the recent ten years. Our study provides a method to quantitatively assess the impacts of frost damage during flowering on apple yield for the prevention and mitigation of frost disasters in apple production.

GIS-based risk assessment of agrometeorological disasters of summer maize in northern mountainous area of Sichuan Basin: A case study of Wangcang County, Sichuan Province, China

Meteorological disaster is one of the main factors restricting agriculture development in China. It is important to clarify the risk of summer maize agrometeorological disaster for disaster prevention and mitigation. Based on the natural disaster risk theory, we collected meteorological data and maize yield data from 1981 to 2018 in a typical area in the northern mountainous area of Sichuan Basin (Wangcang County). The main disaster-causing factors affecting summer maize production were determined. A comprehensive agro-meteorological disaster risk assessment model for summer maize was constructed in combination with the sensitivity of pregnant environment and vulnerability of disaster bearing to evaluate the agrometeorological disasters risk of summer maize production in the northern mountainous area of Sichuan Basin. The results showed that during the study period, high temperature in mature period, rainstorm in flowering period, rainstorm in mature period, continuous rain in filling period and drought in booting stage were the main agrometeorological disasters affecting the growth and development of summer maize. The agrometeorological disaster risk of maize generally distributed in a southwest-northeast pattern, with the distribution areas of high-risk and higher-risk areas accounting for half of the total area of Wangcang County. The high-risk areas were mainly located in the southwest of the county, which was basically consistent with the high-value areas of hazard-pregnant environment sensitivity. The low-risk areas were mostly concentrated in the western part of county territory, which were also low-risk areas of high temperature in mature period, rainstorm in mature period, and rainstorm in flowering period disasters.

Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas.

The Spatio–Temporal Variation of Spring Frost in Xinjiang from 1971 to 2020

Under the background of intensifying global change, the frequent occurrence of agrometeorological disasters has an adverse impact on the social economy. Low-temperature weather in spring is one of the main agrometeorological disasters in Xinjiang. Studying the spatial and temporal characteristics of low temperatures in spring in Xinjiang is of great significance. However, research on the spatiotemporal variation of spring frost in arid areas is currently lacking, which limits our understanding of the occurrence and development mechanism of typical disastrous weather events in arid areas. Therefore, on the basis of the daily meteorological data of 40 meteorological stations in the Xinjiang Province of China from 1971 to 2020, we studied the spatiotemporal heterogeneity of spring frost in Xinjiang through trend analysis, the Mann–Kendall test, Kriging interpolation, and wavelet analysis. Results showed that the frequency of spring frost had the spatial trend from Northern Xinjiang to Southern Xinjiang. The occurrence frequency of spring frost in the entire and northern region of Xinjiang had an upward trend, whereas that in Southern Xinjiang showed a downward trend. Severe and moderate spring frost events mainly occurred in Xinjiang. The duration of spring frost had the characteristics of Northern Xinjiang to Southern Xinjiang. The spring frost in Northern Xinjiang mainly occurred in April, and that of Southern Xinjiang was in March. Obvious 15-, 10-, and 20-year oscillation cycles were observed in the occurrence frequency of spring frost in the entire, southern, and northern regions of Xinjiang, respectively. This study can provide a useful reference for the prediction and research corresponding to the occurrence mechanism of spring frost in arid areas.

Sustainable Livelihoods in Rural Areas under the Shock of Climate Change: Evidence from China Labor-Force Dynamic Survey

The threat of climate change to the sustainability of farmers’ livelihoods is becoming more significant. Research on the impact of climate change on the sustainability of farmers’ livelihoods could provide a scientific basis for enhancing farmers’ adaptability to climate change, reducing farmers’ livelihood vulnerability, and promoting the formulation of governmental adaptation strategies. Although studies have assessed the impact of climate change on the sustainability of farmers’ livelihoods, their analysis units have been aggregated. Therefore, this study was grouped based on geographical location (north and south regions), and then an additional grouping was conducted according to the internal economic factors of each region. Using data from China’s labor-force dynamic survey as our sample, this study measured the sustainable livelihood in agricultural households. This research provided a method to quantify the sustainability of farmers’ livelihoods based on measurements of poverty vulnerability. Additionally, using the annual average temperature as the core explanatory variable to describe climate change, this study evaluated the impact and heterogeneity of climate change on the sustainability of farmers’ livelihoods and replaced the annual average temperature with the normalized vegetation index to conduct a robustness test. The empirical study showed that the average annual temperature significantly decreased the sustainability of farmers’ livelihoods. The average annual temperature change had a greater impact on farmers in the southern provinces as compared to those in the north. Southern coastal regions, eastern coastal regions, the middle reaches of the Yangtze River, and the northeast regions were the key areas of concern. Finally, considering the current risk vulnerability of farmers, we concluded that crop breeding should be oriented to the trend of climate change, farmers’ risk prevention awareness should be increased, financial tools should be enhanced to mitigate the impact of meteorological disasters, an appropriate sustainability developmental evaluation index should be implemented, and the construction of agrometeorological disaster prevention and mitigation infrastructure should be advanced.

Effect of agricultural meteorological disasters on the production corn in the Northeast China

<p id="C2">Agricultural meteorological disasters are the main natural disasters that threaten grain output. In recent years, with climate change, agricultural meteorological disasters are more and more frequent, and their impact on northeast China is also increasing. In this paper, to make better use of advantages and avoid disadvantages in actual production to ensure food production safety, spring corn in three provinces of northeast China was taken as the research object to discuss the impact of major agricultural meteorological disasters in this region on spring corn yield and its degree. The results showed that agricultural meteorological disasters could explain about 50% of the fluctuation of spring corn yield in Northeast China, and the main agricultural meteorological disasters that affected spring corn production had changed from cold damage in the traditional sense to drought. Under the background of climate warming, the main factor affecting spring corn production was changed from thermal condition to water condition, and drought had become the primary disaster threatening spring corn production. The agricultural meteorological disasters that affected the spring corn yield in Liaoning province were drought, flood, and wind in order of the degree of influence. The agricultural meteorological disaster that affected spring corn yield in Jilin province was only drought. The agricultural meteorological disasters affecting the yield of spring corn in Heilongjiang province were ranked as flood, drought, and cold damage according to the degree of impact. In conclusion, due to geographical differences, the main agro-meteorological disasters affecting the corn production in the three provinces of Northeast China were also different, and their complexity was not the same, which cannot be generalized in the specific study.