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High temperatures significantly injure the flowering, pollination, fruit growth, and quality of plants. Photosynthesis, the fundamental process supporting plant life, is crucial. Nevertheless, the quantitative evaluation of the physiological activity of the photosynthetic system of Nanfeng tangerine (NT) plants under high-temperature conditions remains a challenge. This research utilized NT plants, a distinctive citrus variety in Jiangxi Province, as the experimental subject. The study investigated the effects of varying degrees of high-temperature stress and duration on 16 photosynthetic physiological parameters of NT plants. The study examined the impact of four varying high-temperature treatment levels (32/22 °C, 35/25 °C, 38/28 °C, and 41/31 °C) for durations of 2, 4, 6, and 8 days, respectively. Principal component analysis was utilized to identify the key indicators of photosynthetic physiological activity in NT plants, with Fv/Fm, Pmax, LCP, H2O2, MDA, and POD being selected as key parameters. The high-temperature stress index model previously constructed was used to calculate the high-temperature stress index value of the NT plants exposed to varying degrees and durations of high temperature, in order to provide a comprehensive assessment of the photosynthetic system of NT plants under high-temperature stress. Subsequently, the high-temperature stress levels were categorized into five levels based on the calculated values: Level 0 for 0 < HSI ≤ 2, Level 1 for 2 < HSI ≤ 4, Level 2 for 4 < HSI ≤ 6, Level 3 for 6 < HSI ≤ 8, and Level 4 for HSI > 8. The research results provide valuable data for agricultural meteorological departments to carry out disaster risk zoning and risk assessment in the future. [ABSTRACT FROM AUTHOR]

Spring frost is an extreme temperature event that poses a significant threat to winter wheat production and consequently jeopardizes food security. In the context of climate change, the accelerated phenology of winter wheat due to global warming advances the frost-sensitive stage, thereby escalating the risk of spring frost damage. Present techniques for monitoring and assessing frost damage heavily rely on meteorological data, controlled field experiments and crop model simulations, which cannot accurately depict the actual disaster situation for winter wheat. In this study, we propose a novel method that utilizes remote sensing index and statistical data to ascertain the spatial distribution of spring frost damage to winter wheat and evaluate the extent of damage. This method was employed to monitor and assess the spring frost damage event that occurred in Shandong province from 3 to 7 April 2018. The result shows that beginning on 3 April, the daily minimum temperature in western Shandong Province dropped significantly (decreased by 17.93 °C), accompanied by precipitation. The daily minimum temperature reached the lowest on 7 April (−1.48 °C). The growth of winter wheat began to be inhibited on 3 April 2018, and this process persisted until 13 April. Subsequently, the impact of spring frost damage on winter wheat ceased and growth gradually resumed. The affected area of winter wheat spanned 545,000 mu with an accuracy rate of 89.72%. Severely afflicted areas are mainly located in the cities of Jining, Zaozhuang, Dezhou, Heze, Liaocheng, Jinan and Tai'an in western Shandong province, and the yield reduction rates were 5.27~12.02%. Our monitoring results were consistent with the distribution of county-level winter wheat yield in 2018 in Shandong province, the daily minimum temperature distribution during spring frost and severely afflicted areas reported by the news. This method proves effective in delineating the spatial distribution of agricultural disasters and monitoring the extent of disaster damage. Furthermore, it can provide reliable information of disaster area and geospatial location for the agricultural department, thereby aiding in disaster damage assessment and post-disaster replanting. [ABSTRACT FROM AUTHOR]

In this study, we have investigated changes in precipitation extremes and the population's exposure to these extremes during 2091–2099 in China's Beijing–Tianjin–Hebei (JJJ) region relative to the historical period of 1991–1999. First, the regional climate model RegCM4, with a hydrostatic dynamic core, was run for east Asia, including China, at a 12 km resolution for 1990–1999 and 2090–2099. This model is forced by global climate model (GCM) MPI-ESM1.2-HR under the middle shared socioeconomic pathways (SSP245). The first year was used as a model spinup. Then, the 12 km results were used to force RegCM4 with a non-hydrostatic dynamic core (RegcM4-NH) at a 3 km convection-permitting scale over the JJJ region during the historical and future periods. Future precipitation extremes were predicted to increase over the whole of China and its four subregions, while decreases were predicted over the JJJ region. This may partly be caused by lower increases in specific humidity over the JJJ region. The percentage contributions of the three components of total population exposure, i.e., changes in exposure due to changes in the population, precipitation extremes and the joint impact of the population and extremes, were then analyzed. Changes in the population and wet extremes were closely related to changes in the total exposure over the JJJ region. The population is the dominant factor that most impacts the total exposure to dry extremes. Finally, changes in future population exposure to precipitation extremes per degree of warming were quantified for the JJJ region. [ABSTRACT FROM AUTHOR]

Inner Mongolia Autonomous Region (IMAR) has become the sixth-largest grain-producing province of China, contributing 5.6% of the total national grain output in 2021. Grain production increased by around 821% from 1980 to 2019. The contributions of structure adjustment, sown area, and yield to the increase in grain production was analyzed for IMAR. For yield, the Cobb–Douglas production function was estimated, to analyze the contribution of agricultural inputs (fertilizer, irrigation, seed, pesticide, agriculture film, and machinery operation) and the impact of natural hazards on yield. Results from our study showed that the grain production and yield in IMAR increased by 81.40 × 104 t and 0.11 t·ha−1 annually from 1980 to 2019. The grain-crop sown area increased by 73.65 × 103 ha annually, and the sown area of maize constitutes 16.82% to 55.30% of the total sown area of the IMAR in 1980–2019. In IMAR, sown area, yield, and structure adjustment can explain 54.06%, 33.31%, and 12.63% of the increase in the total grain production, respectively, from 1980 to 2019. The input per unit area of fertilizer, irrigation, and other inputs (seed, pesticide, agricultural film, and mechanical operation inputs) increased by 362.16%, 259.66%, and 405.55%, respectively, from 1981 to 2019, which contributed 160.44%, 15.06%, and 53.13% for the main cereal grain (maize and wheat) yield, respectively, while the decrease in the comprehensive loss rate from agrometeorological hazards contributed 7.76% to the main cereal grain yield in IMAR. Technologies such as water-saving irrigation, high-efficiency fertilizer application, and agrometeorological-hazards mitigation measures should be adopted in the future to increase production, considering the stable sown area and environmental and resource constraints. [ABSTRACT FROM AUTHOR]

China has a marked continental monsoon climate characterized by dry and wet hazards that have destructive impacts on grape yields and quality. The purpose of this study was to analyze the spatiotemporal characteristics of dryness/wetness in the wine regions of China and explore the links between these variations and large-scale climatic factors. The crop-specific standardized precipitation evapotranspiration index (SPEI) was used to characterize the dryness/wetness using meteorological data collected at 168 meteorological stations located in or near the wine regions from 1981–2015. Results showed that most wine regions of China experienced a wetting trend. The drought and wet event characteristics were region- and site-specific. The main wine regions of China (e.g., Xinjiang, Helan Mountain and Hexi Corridor) were characterized by relatively high drought severity; the extreme drought frequencies of the three regions were higher as well (11.5%, 3.3%, and 3.6%, respectively). Xinjiang was also characterized by a high wetness severity and an extremely high wetness frequency of 16%, but the wetness severity decreased over time. A 4–6-year periodical oscillation was commonly detected over the wine regions. The dryness/wetness characteristics were highly associated with the Southern Oscillation Index, Niño 3.4 and the Indian Ocean Dipole, with highest correlation coefficients of −0.40, 0.36 and 0.43 at lag times of 11, 8, and 11 months, respectively. The serious dry and wet events that occurred in 2001 and 1998, respectively, were speculated to be associated with anomalous atmospheric circulation patterns. These results can be used to inform grapevine stakeholders at various levels (e.g., farmer and industry) for developing strategies to mitigate and adapt dryness/wetness events in the wine regions of China. It is expected that the approach proposed in this study can also be applicable to wine regions of other countries. [ABSTRACT FROM AUTHOR]

As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia. [ABSTRACT FROM AUTHOR]

High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data. [ABSTRACT FROM AUTHOR]