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Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

• The extent of flooding in China is greater than in any other country. • Nearly 66% of China's landmass is submerged by flooding. • Financial toll of flooding accounts for approximately 1.42% of the annual GDP. • Various forms of climatic change contribute to flooding. The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China's landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China's flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding. [ABSTRACT FROM AUTHOR]

The abnormity of climate change poses environmental concerns and indirectly promotes the development of low-carbon economic growth pattern. Climate change mitigation technology innovations contribute significantly to achieve environmental goals in China, and thus, technological cooperation has become a strategic choice for the Chinese provinces to achieve technological breakthroughs. Based on 3, 280, 808 patents, this study uses social network analysis (SNA) method and similarity measurement to discuss the evolution process and structural features of the spatial correlation network to analyze the patent data from 1985 to 2020. The process for the research is as follows. First, a form solution of the correlation problem to calculate the minimum Euclidean distance is available in the field of social network. Second, the European distance is used to measure the similarity between provinces, and then, SNA is used to construct the spatial correlation network. Finally, the similarity of growth trend of provincial patent applications in different years from a nonlinear perspective is described, so as to calculate the potential provincial linkage strength. The results demonstrate that low-carbon technology innovation in China takes the form of a network structure. The network exhibits significant characteristics of regional agglomeration, and several central provinces exert the role of mediation and transmission, such as Tianjin, Hubei, and Henan. The study contributes by illustrating the current situation of green technology innovation and provides a critical theoretical foundation for the government when formulating future low-carbon economy development policies. [ABSTRACT FROM AUTHOR]

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China's foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world. [ABSTRACT FROM AUTHOR]

• Tree ring is an ideal indicator of annual total grain production parameters. • Tree growth synchronized with grain production, and concurrent response to climate. • Reconstruction of total grain productivity based on dendrochronology. • To explore the relationship between total grain sown area and climate change. Climate change affects the growth of regional vegetation; both trees and grain crops often change concurrently, such that the annual radial growth of trees shows consistent inter-annual variations with the total grain crop productivity. However, it remains unclear whether they exhibit concurrent responses to climate factors, and that there lacks of the study on long-term high-resolution variations of grain crops productivity. This paper employs a Pinus massoniana tree-ring series from the Tongbai Mountains to analyze the correlations between tree-ring chronology, local climate data and grain productivity indicators (i.e., total sown grain areas (TSA), total grain outputs (TGO), and average grain yield per hectare (YPH)) of Henan Province in central China. The results indicate that temperature in March and August was the main limiting factor on tree growth, and the best concurrent relationship with tree growth was TSA, which has a correlation of 0.747 (p < 0.001) during 1959–2020. Therefore, a 124-year TSA series in Henan Province was reconstructed using tree-ring data from the Tongbai Mountains, which reveals there were two distinct low periods of total grain sown area in the 1920 s-1930 s and 1980 s-2000 s. There are significant cycles of about 2.57a (p < 0.01), 2.89a (p < 0.05), and 10.95a (p < 0.1), indicating that vegetation growth might be affected by large-scale climate forcing, such as ENSO (2-7a cycle) and sunspot activity (11a cycle). Overall, this study outlines a new approach to understand long-term changes in grain production, which is conducive to grain management and socioeconomic sustainability. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]

Henan area is climatologically situated in the transition between northern and southern China and the transition is reported to be highly sensitive to climate changes during the Holocene. To better understand the Holocene climatic and environmental changes in Henan area where high-quality data are lacking, this paper first reviewed those published palaeoenvironmental records in attempt to provide a low-resolution framework of the Holocene environmental changes in Henan area, and then synthesized the moisture proxy records from the surrounding areas in attempt to define a high-resolution trend of the Holocene moisture variation for our target site (i.e., Zhengzhou within Henan area). Two major conclusions can be drawn. First, the high-resolution Holocene moisture curve for our target site well follows the general moisture trend of China and the curve is well corroborated by two recently-reported data sets: one from Mangshan loess-palaeosol sequence and another from Laomu-Dongshiya Cave. Second, our synthesis supports an earlier notion stating that the "Holocene Moisture Optimum" in China was a delayed response to the oceanic "Holocene Thermal Optimum". The latter might in turn be a delayed response to the summer insolation peak. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter closely related to various land surface processes and surface-atmosphere interactions. Analysis of spatiotemporal variation of time-series LST may provide useful information to understand eco-climatic characteristics. In this study, the spatiotemporal pattern of LST and its trend characteristics in Henan Province were examined based on MODIS LST products from 2003 to 2021. In addition, the influences of land cover types, Nighttime Light data (NTL) and Normalized Difference Moisture Index (NDMI) on LST variation were analyzed. The results indicated that: (i) The LST showed slight and rapid decreasing trend for 2004–2010 and 2018–2020, respectively, whereas an obvious increasing and slight increasing trend occurred for 2010–2013 and 2014–2018. In terms of spatial pattern, high-temperature, and sub-high-temperature were mainly distributed in the central part of the province with higher level of industrialization and urbanization at the annual, spring, summer, and daytime scales. While in fall, winter, and the nighttime, the spatial distribution of LST exhibited decreased trend from the southern part to the northern part of the province, the largest Standard Deviation (STD) was observed in summer. (ii) The interannual variation rate of LST was 0.08 °C/Y. The increasing trend mainly occurred in urban and built-up areas. At the seasonal scales, the rising rate decreased sequentially in the order of fall, winter, spring, and summer. In addition, the rising rate in the daytime was higher than that in the nighttime. (iii) LST increased along with the expansion of urban and built-up lands, except in winter. At the annual scales, 84.69% of areas with NTL data exhibited a positive correlation with LST, and NDMI in the western part with high elevation presented a significantly positive correlation to LST, while a significantly negative correlation occurred in urban and built-up areas. The cooling effect of NDMI on LST in the daytime was greater than that in the nighttime. In cropland areas, LST showed a non-significant correlation with NDMI at the annual scale, and a significantly negative correlation with NDMI in spring, summer, and fall. The influence mechanism of cropland on the variation of LST at different timescales needs to be further explored. These findings might provide some hints to understand climate change and its causes in the province. [ABSTRACT FROM AUTHOR]

Climate change affects many aspects of the physiological and biochemical processes of growing maize and ultimately its yield. A comprehensive climate suitability model is proposed that quantifies the effects of temperature, precipitation, solar radiation, and wind in different phenological stages of maize. It is calibrated using weather and yield data from China's Henan Province. The comprehensive suitability model showed the capability of correctly hindcasting observed temporal and spatial changes in maize phenology in response to climatic factors. The predicted yield based on the suitability model can well match the recorded field yield very well from 1971–2020. The results of correlation showed that the yields are more closely related to multi-weather factors, temperature and precipitation than to solar radiation and wind. The sensitivity analysis illustrates that temperature and precipitation are the dominant weather factors affecting yield changes based on a direct differentiation method. The comprehensive suitability model can provide a scientific support and analysis tool for predicting grain production considering climate changes. [ABSTRACT FROM AUTHOR]

In China, historical documents have recorded large quantities of information related to natural disasters, and these disasters have had long-lasting effects on economic and social activities. Understanding the occurrence of the natural disasters and their spatio-temporal variation characters is crucial for sustainable of our society. Therefore, based on the collection and collation of historical documents, and adopting mathematical statistics, Kriging interpolation, correlation analysis and other methods, we systematically explored the meteorological disasters in Henan Province during the past two millennia in analyzing their spatio-temporal distribution characters and driving forces. The results demonstrate that there were five major types of meteorological disasters in Henan Province, including drought, flood, hails, low temperature and frost and insect pests, which presented obvious spatio-temporal variations and have occurred frequently during the past two millennia. According to the historical documents, the major meteorological disasters occurred 1,929 times in Henan from 221 BCE to 2000 CE. On the whole, the disaster frequency show that the occurrence cycle of the meteorological disasters has obvious changes, which mainly occurred in the middle and late stages during the past two millennia, especially after 1300 CE. Furthermore, we also find that the variation of meteorological disaster events is consistent with the variation of temperature in eastern China and the frequency of meteorological disaster increases in the cold period, but decreases in the warm period. In addition, there are obvious differences in the spatial distribution of the major meteorological disaster, which were mainly distributed in the northwest and southern part region of the Henan Province before 1911 CE. While after 1911 CE, the northern and southeastern parts were the meteorological disaster-prone areas in this region during this period. Spatial correlation analysis of each meteorological disaster before and after 1911 CE points out the droughts disaster frequency-occurring district has transferred in different periods, while the hail and low temperature and frost disasters just have a smaller transferred during these two periods. Conversely, the frequency-occurring districts of floods and insect pest disasters have no obviously transferred in different periods. These results can provide an important scientific basis for governmental decision makers and local people to prevent and mitigate meteorological disaster in the future. [ABSTRACT FROM AUTHOR]

Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC) for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China. [ABSTRACT FROM AUTHOR]

Based on Chinese historical sources, the possible climatic impact in China of the prolonged Eldgjá eruption starting around 934 AD was investigated. An extremely hot summer was reported in 934 AD; hundreds of people died of the intense heat of this summer in Luoyang, the capital of the Later Tang Empire (923-936 AD). Snowless (and possibly also mild) winters probably occurred successively following the Eldgjá eruption until 938 AD. In 939 AD, cold weather set in abruptly and lasted for about 3 years; whereas peak cooling occurred in 939AD. In the summer of 939 AD, it snowed in the southeast of the Inner Mongolia Plateau (about 40-44°N, 113-123°E). From 939AD to 941 AD, hard winters occurred successively in China. Worse, unprecedented drought and plague of locusts broke out in 942 AD and persisted in 943 AD. More than several hundred thousand people were starved to death. This catastrophe was at least partly responsible for the collapse of the Later Jin Dynasty in China. By comparison with the tree-ring evidence and uncovered European historical evidence, the spatial response to the Eldgjá eruption appeared to be complex, whereas hemispheric or global cooling occurred in 939-942 AD. [ABSTRACT FROM AUTHOR]