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The aims of this paper is to illustrate the importance of grain production in China's food security strategy based on the three Northeastern provinces (NEP) in the new period, which quantitative assessment of climate change under the background of the loss caused by meteorological disasters of regional food production is clarify. In this paper, using grain planting area, yield and agricultural disaster statistics from 1981 to 2020, comparison analysis grain production and disaster characteristics between NEP and the whole country respectively. The disaster-yield assessment model was used to estimate the disaster-yield loss and final yield of the NEP by inputting disaster data in recent 10 years, and the sensitivity and stability of the disaster-yield assessment model were re-tested. The results showed that :(1) from 1981 to 2020, the grain planting area and total output in NEP increased significantly, and the proportion of the total output of NEP increased steadily, and the proportion of the total output of the three northeast provinces reached 1/5 of the total output of the whole country by 2020. (2) The disaster situation in NEP showed a significant trend of first increasing and then decreasing. The incrasing of average grain yield during the past 40 years was 65.96kg⋅ha-1 and 252.5kg⋅ha-1 per year for NEP and the whole country, while the incrasing of average grain yield during the past 10 years was 52.6kg⋅ha-1 per year for NEP significantly. (3) From 2011 to 2020, the average affected area and affected area in China were 23704.5x10³ha and 11204.7x10³ha, respectively, and 3899.1x10³disasters of the whole country and the three northeastern provinces were significantly lower than those of the previous three years, making it the decade with the least severe disasters in 40 years. (4) The simulation accuracy of the disaster-yield assessment model was high. The linear regression coefficients (R²) of simulated grain yield and measured grain yield in Heilongjiang, Jilin and Liaoning provinces were 0.98, 0.90 and 0.88, respectively. The slopes were 1.05, 1.02 and 0.98 (P < 0.01), respectively. The loss rate of grain yield due to agro-disaster was 10.4%, 17.9% and 18.0% respectively in the three province, while which was more than 8.0%, 17.0% and 16.0% for 20a.years. (5) The model slightly overestimated the grain yield of Jilin and Liaoning in recent 10 years due to the overall light disaster situation. Based on the data from 1981 to 2010, the regional grain disaster loss assessment model was proved to be able to evaluate the loss of grain yield caused by meteorological disasters well, and had the performance of predicting grain yield, and has the feasibility of operational application. The impact of meteorological disasters on the grain output in northeast China is higher than the average level of disasters in the whole country. Considering that the grain output in Northeast China accounts for a high proportion of the national grain output, preventing the risk of agro-meteorological disasters in Northeast China in the new period is of great importance to guarantee the national food security. [ABSTRACT FROM AUTHOR]

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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

• An innovative conceptual model for assessing flood risk. • The FVI increases flood risk by 33 %, while FAI reduces flood risk by 45 %. • PD, DVP and DFS are important responsibilities for urban flood risk. • Reducing socioeconomic disparities among cities is essential for flood risk assessment. Risk assessment and adaptation have become key focuses in the examination of urban flooding risk. In recent decades, global climate change has resulted in a high incidence of extreme weather events, notably flooding. This study introduces a spatial multi-indicator model developed for assessing flood risk at the urban agglomeration scale. A crucial addition to the model is the incorporation of an adaptive capacity within the IPCC risk framework. The model systematically considers various flood risk indicators related to the economic, social, and geographic environments of the central and southern Liaoning urban agglomeration (CSLN). It generates a spatial distribution map of integrated flood risk for multiple scenario combinations. Furthermore, the intricate relationship between different risk indicators and flood risk was analyzed using correlation analysis and the Light Gradient Boosting Machine model (Light GBM). The findings reveal notable variations in flood risk under different scenarios. The inclusion of vulnerability indicators increased flood risk by 33 %, while the subsequent inclusion of adaptive indicators decreased flood risk by 45 %. Dense populations and assets contribute to high flood risk, while adaptive capacity significantly mitigates urban flood risk. The framework adopted in this paper can be applied to other areas where urban agglomeration-scale flood risk assessment is needed, and can contribute to advancing scientific research on flood forecasting and mitigation. [ABSTRACT FROM AUTHOR]

A long time series in crop yield is usually expressed as a long-term trend and a short-term fluctuation due to agricultural technological advance and climatic anomaly. The real climate risk is related to the short-term fluctuation in crop yield. In the paper, the climate risk of maize yield response to long-term climate variables is tested with the long time series (1961–2015) by a trend base line method. The long time series of maize yield is divided into short-term fluctuating meteorological yield and long-term trend yield. The long time series of climate variables are also divided into fluctuating variables and trend variables. After that, Pearson correlation analysis between fluctuating maize yield and fluctuating climate variables is used to identify risk factor causing maize yield fluctuation. Our results reveal that the main risk factors are night-time precipitation and extreme high temperature in growing season. Comparing climate risks in maize-producing provinces, much more climate risks are identified in some regions such as Liaoning province. The results provide useful information for reducing maize yield loss under climatic change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.)

Building zero-waste cities is an important initiative. It helps China to meet the challenges of climate change, build an ecological civilization, and build a beautiful and high-quality China. Through the recycling and safe disposal of waste, the ultimate goal of the construction of zero-waste cities is to minimize the amount of solid waste. Municipal Solid Waste Resource Symbiosis Network (MSWRSN) is the best way to achieve zero-waste cities. However, this system is vulnerable to many factors. These factors will restrict the construction and development of zero-waste cities. This study proposes a vulnerability measurement method of MSWRSN based on energy exchange. Panjin, a city in Liaoning Province, is used as an example to simulate it. The vulnerability measurement method of MSWRSN is composed of three parts. First, the study adopts a directional weighting approach to design the topologic structure of MSWRSN. Second, Dijkstra's algorithm is used to analyze two topological parameters, node mesonumber and edge mesonumber. It also focuses on single-node failure mode and edge failure mode. Finally, the study uses a functional measure function to calculate the vulnerability of each node and each edge in symbiotic networks. The results of the study show that (1) MSWRSN in Panjin has not yet formed a nested pattern. The symbiotic patterns of different industrial chains are also different. (2) Node failure has a greater impact on MSWRSN. (3) There are differences in the vulnerability of the industry chain in the network. Based on the findings of the study, this study advises managing the vulnerability of MSWRSN from the following aspects. It includes increasing the diversity of symbiotic units, promoting the development of symbiotic networks to nested types, and strengthening risk monitoring and management of core enterprises. [ABSTRACT FROM AUTHOR]

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management. [ABSTRACT FROM AUTHOR]

Climate change and the growing-demand for food security force growers to identify ways to improve both food production and resource-utilization effectiveness. Liaoning province is one of the major rice (Oryza sativa L.) production regions of China (CL), accounting for about 10% of the total national japonica cultivation area. In this study, the experimental regions were divided into five rice agroecological zones based on the difference in the planting area and climate conditions. Four nitrogen (N) management levels were designed by the application amount of N fertilizer. ORYZA (v3) model was calibrated, validated and applied to simulate yields under four N fertilizer levels. The results showed that ORYZA (v3) can describe grain yield and above-ground biomass well, and it can be applied to simulate growth in CL region. Scenario-analysis indicated that the simulated grain-yield responded strongly to a low nitrogen level. However, there was no significant grain-yield increase under more N. Significant spatial variation in the grain-yield was observed at different levels. Compared to regions with limited temperature and light resources during the rice growing-periods, the regions with more optimal conditions showed higher grain-yields under all four N levels. [ABSTRACT FROM AUTHOR]

Protected areas and ecotourism are both conservation measures for plant resources, but for one specific plant species, the priority of them needs a reasonable planning. The suitable habitat distribution of species is the basis for planning, and the impact of climate change on habitat distribution should also be considered. Based on maximum entropy model (MaxEnt), we assessed the current and future suitable habitat distributions of seven species of genus Rhododendron in Northeast China. Combined with the distributions of existing protected areas and tourist attractions, the conservation planning is recommended. Our results showed that: (1) the suitable habitat areas of R. redowskianum and R. aureum were mainly distributed in the Changbai Mountains covering only 2.05% and 3.84% of the total area of Northeast China, and showed decreasing trends in the future. (2) The suitable habitat areas for R. schlippenbachii were mainly distributed in the southeast and southwest of Liaoning Province, with a cover of 4.29% in Northeast China. For R. mucronulatum and R. micranthum, suitable habitat areas were mainly distributed in the east and west of Liaoning Province, covering 14.27% and 7.95% respectively. For R. dauricum and R. lapponicum, suitable habitat areas were mainly distributed in the Greater Khingan Range, Lesser Khingan Range, and Changbai Mountains, covering 43.76% and 22.24% respectively. Moreover, they all showed increasing trends in the future. (3) Therefore, protected area was recommended for R. redowskianum and R. aureum, while ecotourism or both protected area and ecotourism was recommended for R. schlippenbachii, R. mucronulatum, R. micranthum, R. dauricum, and R. lapponicum. This study provides planning suggestions for the conservation of Rhododendron and is a reference for the conservation of other plant species. [ABSTRACT FROM AUTHOR]

Faced with severe and growing social inequality among elderly individuals, a more complete understanding of the interconnection between the seasonal migration of the elderly and climate change is clearly needed. This paper tries to clarify the relation between the seasonal migration of the elderly and climate change from a multidimensional perspective. Our study adds to the research on population aging against the background of climate change. This study shows that climate change has a significant impact on regional population aging and that there are significant internal differences between the distribution and scale of the migratory elderly stemming from unequal environmental conditions. Advanced economic conditions and relatively comfortable environments have put some pressure on social endowment services in the three provinces of Northeast China, especially Liaoning Province. With their complex migration mechanism, the fate of the elderly in mainland China is closely intertwined with climatic factors, individual and socioeconomic conditions, traditional cultural backgrounds, etc. A comparatively advanced economy, well-developed endowment resources, and a comfortable environment will help in the development of regional aging resources. Regional inequality and the pursuit of a meaningful life have created different categories of migratory elderly. • Climate change makes the seasonal migration characteristics of the middle-class elderly more distinct. • The migratory elderly in mainland China is closely intertwined with climatic, individual and socioeconomic conditions. • Regional inequality and the pursuit towards value and spirit in life have formed different kinds of migratory elderly. [ABSTRACT FROM AUTHOR]

In this article, the Multi-Fractal Detrended Fluctuation Analysis (MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature (Tmax), mean temperature (Tavg) and minimum (Tmin) air temperature, multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study. Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation (LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmaxand Tmin of the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning > temperature series multifractality of Guangdong > temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu. Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal strength, and the weakest Tmaxmultifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas. [ABSTRACT FROM AUTHOR]

In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change. [ABSTRACT FROM AUTHOR]