Study region: Shenzhen, located in southern Guangdong Province, is a core city within the Guangdong-Hong Kong-Macao Greater Bay Area in China. Study focus: This study aims to assess the efficacy of SynxFlow, a newly developed open-source hydrodynamic model, by conducting an urban pluvial flood simulation and sensitivity evaluation in the central urban area of Shenzhen across six rainfall return periods (2-, 5-, 10-, 20-, 50-, and 100-year). The research methodology employs SynxFlow's 2D hydrodynamic engine, which utilizes a Godunov-type shock-capturing scheme to solve the Shallow Water Equations (SWE) to robustly simulate complex inundation dynamics. Model validation was rigorously conducted using observed depths from six flood events and media-reported data from the "9 & sdot;7 Rainstorm" event. The results demonstrate SynxFlow's robust performance, characterized by high precision in depth estimation (average deviation < 0.04 m) and strong spatial consistency (90.24% capture rate). Comparative simulations with the widely used Lisflood-FP model showed broad agreement. Additionally, a slight systematic deviation was observed, with SynxFlow generally yielding marginally lower inundation depths than Lisflood-FP. This discrepancy, corresponding to an average bias of -0.021 m, is likely attributable to differences in the drainage modules of the two models. New hydrological insight: This study addresses a key gap in the application of the emerging SynxFlow model through a benchmark application in a high-density urban setting. Results indicated significant sensitivity to rainfall intensity: the inundated area expanded from 4.66% (2year) to 13.74% (100-year), while the 99.5th percentile depth rose from 0.72 m to 1.88 m. Notably, the inundation extent exhibits a non-linear response to rainfall intensity, with the expansion being most rapid between 2- and 20-year return periods. Ultimately, this study underscores the critical role of integrating explicit drainage modules within open-source hydrodynamic frameworks, offering a scalable and cost-effective pathway for enhancing flood resilience planning in high-density mega-cities.

Tropical cyclones pose a significant threat to coastal regions through hazard-inducing factors such as wind, rainfall, and storm surge, whose interactions often lead to amplified impacts. Existing studies often fail to capture the complex dependence among these factors. This study focused on the coastal counties of Zhejiang Province, utilizing numerical simulation data of tropical cyclone-induced winds, rainfall, and storm surges from 1979 to 2022. A joint probability model based on the C-vine copula function was developed to characterize the synergistic mechanisms among these factors, and to analyze return periods and failure probabilities of engineering structures under different hazard scenarios. Furthermore, a comprehensive hazard index was introduced to assess the hazard of tropical cyclone events. The main findings are as follows: (1) The simulated data agreed well with observations, with root mean square errors below 4 m/s for wind and 0.2 m for storm surge, and correlation coefficients all above 0.75. (2) Neglecting multiple factors and their dependence introduced bias in the return period and failure probability estimates. For example, when the exceedance probability for each single factor was 0.05, the mean return period for the three factors under the independence assumption (1.760 years) was 35% shorter than that considering dependence (2.698 years). (3) The comprehensive tropical cyclone hazard in the coastal counties of Zhejiang exhibited a distinct spatial pattern, with higher values in the south and lower values in the north. This study provides a scientific basis for disaster risk management and the design of tropical cyclone protection infrastructure in coastal areas.

Compound extreme events, including extreme wet conditions and extreme heat, exhibit distinct temporal sequences, yet their characteristic differences remain systematically understudied. In this study, we attempt to reveal the main types and dominant factors affecting compound rainstorm and heatwave (CRH) events in the southern region of China. Firstly, we define CRH events and then classify them into two categories (binary and multiple) according to the number of individual events. Then, we analyze the spatiotemporal changes in the frequency of each kind of CRH event during the period from 1981 to 2020. Finally, the contribution of heatwaves and rainstorms to CRH events and to the frequency change is assessed. The results show that CRH events mostly occurred in southeast China and eastern Sichuan. The contribution of heatwaves is larger for most multiple CRH, while heatwaves and rainstorms contributed equally for most rainstorm-heatwave events. In addition, the frequencies of binary CRH events increased rapidly after 2000. Heatwaves are the main dominant factor leading to the rapid increase in the frequency of CRH events. The results help improve our understanding of the combination of rainstorms and heatwaves and provide a justification for studying compound climatic hazards.

Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.

Context: Population expansion has become a trend. To meet the growing demand for crops, the area required for planting crops will also increase in the future. Current research generally assumes that land suitable for crops will generally shrink under global change, which, together with the increased crop demand, poses challenges to food security.Objective: The objective of this study is to access the global maize planting distribution by improving the crop distribution model with a special focus on both environmental and socioeconomic factors under the global change scenarios of SSP245 and SSP385.Methods: We constructed a crop distribution model that considers both environmental and socioeconomic factors. The environmental factors include topography, soil, and climate variables, while socioeconomic factors include supply-demand balancing, planting inertia, trade, irrigation ratio, and yield progress variables. Through those two aspects, we conducted a comprehensive prediction for the maize planting distribution.Results and conclusion: Compared with the statistics, the modelling results of most countries differ slightly from the statistical data (within 20%), which shows that the model has good performance. Based on the projections of the model, the predicted planting area under the SSP245 and SSP385 scenarios in the near-term (2016-2035) increases by 39.86% and 48.70%, and in the mid-term (2046-2065) increases by 56.17% and 75.73%, respectively. In addition, the maize planting zone has decreased in size in the United States, Mexico, northern Brazil, South Africa, and southern Europe.Significance: Our study provided a comprehensive crop distribution model and projected the global maize planting distribution, emphasizing the importance of socioeconomic factors in crop distribution modelling. Our research could benefit agricultural risk and regional field management.

The rapid and effective identification of the spatial distribution of rock deserts in ice–snow melting areas can provide useful information for the prevention of natural disasters. In this study, Landsat TM/OLI were used to track the evolution of snow lines in the snow melting areas of Yushu Tibetan Autonomous Prefecture of the Qinghai-Tibet Plateau (hereinafter referred to as Yushu Prefecture) during the last 30 years. A total of seven extraction schemes were used to classify the rock desert in the ice–snow melting area by remote sensing. Our results show that: (1) The accuracy of the multi-index factor compound analysis and object-oriented classification compound method was the highest. This method can provide a rapid and efficient reference scheme for rock desert extraction in the plateau ice–snow melting area. (2) The combinations of two single methods with higher accuracy can further improve the total accuracy. If a single method with lower accuracy is involved in the multi-method fusion, the accuracy of the method with lower accuracy can be improved. (3) In the past 30 years, there has been a large amount of ice–snow melting in Yushu Prefecture. The ice–snow melting area accounts for 53.78% (1451.04 km2) of the ice–snow area, and the bare rock and bare stone in the rock desert account for 63.77% of the total area of the ice–snow melting area. Bare sand and bare soil area account for 30.27% of the total area of ice and snow melting area.

Background Uncertainties exist in the magnitude and outbreak of debris flow disasters, resulting in significant loss of lives and property to human society. Improved identification of debris flow susceptibility areas can help to predict the location and sphere of influence of debris flow disaster, thus accurately assessing the risk of debris flow disaster and reducing losses caused by such a disaster. The dry-hot valleys of Basu County in the Eastern Qinghai-Tibet Plateau are typical areas of high debris flow incidence, mapping of debris flow susceptibility identification and regional risk assessment is needed in this area. Results The parameters improved Flow-R model was first applied to identify debris flow susceptibility areas in Basu county using the digital elevation model, flow accumulation, slope, plan curvature, and land use data, followed by debris flow risk assessment. The Flow-R model can output high result accuracy of high-resolution susceptibility to debris flow identification on a regional scale with less data, and its accuracy value is 87.6%, indicating that the susceptibility to regional debris flow disaster is credible. This study provides a useful basis for effective prevention of regional debris flow disasters in the future, and provides a useful method for effectively identifying the debris flow susceptibility areas and assessing the related risk in large-scale areas. Conclusions (1) The debris flow susceptibility areas in Basu County covered 97.04 km 2 (0.79% of the study area), distributed mainly in the Nujiang River Valley, Lengqu tributaries, and both sides of National Highway 318. (2) The debris flow susceptibility areas were dominant in zones characterized by an altitude range of 3000–4000 m, a plane curvature of − 2/100 m −1 to 1/100 m −1 , and a low slope of 20°–40°. In addition, the susceptibility areas were dominant in the unused land and less prevalent in the water area. The highest and lowest susceptibility values were observed for cultivated and unused lands, respectively. (3) The debris flow risk in the study areas accounted for 0.82 km 2 and revealed a distribution of high-risk debris flow along roads. The areas with a high debris flow risk were mainly distributed along the mainstream of the Nujiang River, which is the main future protected area.

The increasing temperature with global warming will have great impacts on major cereal crop cultivation (Peng et al. 2004; Fahad et al. 2019). Among various impacts, the crop exposure to extremely high temperature, which is based on the land suitability for crop cultivation, may ultimately pose a great threat to food security.

AbstractThe latest special report from the Intergovernmental Panel on Climate Change (IPCC), published in 2018 (Anandhi et al. 2016), estimates a 1.5 °C increase in global temperature in 2040 at the current rate of global warming. Such a rise has serious implications for major cereal crop cultivation: unless crop varieties adapted to higher temperatures become available, the areas suitable for cropping are bound to shift in the future. Therefore, to safeguard food security, we need to predict such changes in spatial and temporal terms, which can intuitively reflect the potential distribution of crops under different climate change scenarios and for different time periods, helping to reduce losses according to local conditions (Deng et al. 2009).

Suitable land is an important prerequisite for crop cultivation and, given the prospect of climate change, it is essential to assess such suitability to minimize crop production risks and to ensure food security. Although a variety of methods to assess the suitability are available, a comprehensive, objective, and large-scale screening of environmental variables that influence the results—and therefore their accuracy—of these methods has rarely been explored. An approach to the selection of such variables is proposed and the criteria established for large-scale assessment of land, based on big data, for its suitability to maize (Zea mays L.) cultivation as a case study. The predicted suitability matched the past distribution of maize with an overall accuracy of 79% and a Kappa coefficient of 0.72. The land suitability for maize is likely to decrease markedly at low latitudes and even at mid latitudes. The total area suitable for maize globally and in most major maize-producing countries will decrease, the decrease being particularly steep in those regions optimally suited for maize at present. Compared with earlier research, the method proposed in the present paper is simple yet objective, comprehensive, and reliable for large-scale assessment. The findings of the study highlight the necessity of adopting relevant strategies to cope with the adverse impacts of climate change.

In this paper, we use the extraction method of multi-factors fusion to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. The study results indicate that: (1) the method (extracting through multi-factors fusion) is efficient during the extracting process and is highly accurate in extraction results. This extraction scheme allows for not only the spatial heterogeneity of different physical geographic units, but also the impact of multi-factors on crop cultivation; (2) according to our research, the total Highland barley cultivation area on Qinghai–Tibet Plateau is about 2.74 × 105 ha. Based on the statistics, we draw the first distribution map of the Highland barley cultivation area on Qinghai–Tibet Plateau, which upgrades its spatial distribution pattern from administrative unit to patch unit; (3) Highland barley in various divisions has a distinct spatial heterogeneity in elevation. On the whole, the Highland barley on the plateau is planted at an elevation of 2500–4500 m, up to 5200 m. Due to the impact of topography diversity, temperature, moisture, light, arable land and irrigation conditions, its cultivation area at the same elevation varies in different divisions.

Exposure to extreme heat can severely harm crop growth and development, and it is essential to assess such exposure accurately to minimize risks to crop production. However, the actual distribution of crops and its changes have neither been examined in sufficient detail nor integrated into the assessments of exposure to ensure their accuracy. By examining the distribution of maize at a high resolution through species distribution modeling, we assessed the past and future exposure of maize to temperatures above 37°C worldwide. Such exposure is likely to be widespread and severe, mainly in the subtropics, and may even expand to the mid-latitudes to encompass some major maize-producing areas. Many areas at both high and low latitudes may become exposed for the first time in the next 20 years. By the 2050s, the total area exposed could increase by up to 185% to 308.18 million ha, of which the area exposed for over 60 days may increase nearly sevenfold. The average length of exposure may increase by 69% to 27 days, and areas optimally suited to maize planting may see the fastest increase by up to 772%. Extreme heat can threaten global maize production severely, and measures to mitigate that threat and to adapt to it are urgently needed.

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.

Extreme temperature events, which are part of global climate change, are a growing threat to crop production, especially to such temperature-sensitive crops as rice. As a result, the traditional rice-growing areas are also likely to shift. The MaxEnt model was used for predicting the areas potentially suitable for rice in the short term (2016–2035) and in the medium term (2046–2065) and under two scenarios developed by the Intergovernmental Panel on Climate Change, namely representative concentration pathway (RCP) 4.5 (the intermediate scenario) and RCP 8.5 (sometimes referred to as the worst-case scenario). The predictions, on verification, were seen to be highly accurate: the AUC—area under the curve—value of the MaxEnt model was > 0.85. The model made the following predictions. (1) Areas highly suitable for rice crops will continue to be concentrated mainly in the current major rice-production areas, and areas only marginally suitable will be concentrated mainly in the rainforest region. (2) Overall, although the current pattern of the distribution of such areas would remain more or less unchanged, their extent will mainly decrease in the subtropics but increase in the tropics and in high-latitude regions. (3) The extent of such areas will decrease in the short term but increase in the medium term.

青稞是青藏高原特有的农作物,是世居民族依赖的主要食物,全球变化背景 下自然灾害的加剧,使青稞的灾害暴露加大,威胁到高原世居民族的粮食安全.在梳理高原青稞种植范围、 品种变化、 产量需求等研究基础上,界定青稞面向自然灾害风险的暴露内涵及时空表达,并对基于现代技术条件下青稞暴露的定量分析方法和未来可能技术进行了总结和展望,以期从青稞暴露研究的角度为农作物自然灾害风险评价提供参考.

Due to the effects of global warming, extreme temperature events are posing a great threat to crop yields, especially to temperature-sensitive crops such as rice. In the context of disaster risk theory, exposure is central to disaster prevention and reduction. Thus, a comprehensive analysis of crop exposure is essential to better reduce disaster effects. By combining the maximum entropy model (MaxEnt) and a multiple-criteria decision analysis (MCDA), this paper analyzed the global distribution and change in rice exposure to high temperature. The results showed the future states of rice after exposure to high temperatures. Our results are: (1) the areas of potential rice distribution zones decreased within the representative concentration pathway (RCP) scenarios RCP2.6 to RCP8.5 in MaxEnt, where the long-term (2061–2080) decreases are greater than those seen in the medium term (2041–2060). (2) In the future, the number of high temperature hazards in potential rice distribution areas increased. In the RCP8.5 scenario, the intensities of global high temperature hazards on rice were reduced because the total area of potential rice distribution zones decreased. (3) Through the view of barycenter shift, the barycenter of the global potential rice and high temperature hazard distributions showed a trend of backward motion, which meant the global rice exposure to high temperature was in a downward trend. With the background of global change, this paper has great significance for the mitigation of high temperature risk in rice and its effect on the potential security of future global rice production. Future research is warranted to concentrate on discussing more socioeconomic factors and increasing rice exposure change from the temporal vision.

China has been suffering from serious air pollution for years due to rapid industrialization. However, the relationship between China’s air pollution and pollution control schemes remains understudied in the Chinese air pollution literature. Using the Environmental Theory Model, this paper studies the relationship between air pollution and control investment in China for the period 2005–2014 and applies the inverse distance weighted interpolation (IDW) and Pearson correlation methods. As a result, this paper puts forward some policy suggestions for the governance of air pollution in China. The results are as follows: (1) The Pearson correlation coefficient between China’s air pollution index (API) and control investment is 0.466 (p < 0.01). This shows that the phenomenon of ‘Treatment after Pollution’ still exists in China’s economy; (2) there is an inverted U-shaped relationship that is in accordance with the environmental Kuznets curve between China’s air pollution control investment and air pollution; (3) the high-effectiveness of governmental air pollution control is reflected in the 2013–2014 period, where the API of year 2014 decreased by 10.45 from 2013. This shows a one-year lag between exposure to air pollution and the start of control investment. Future research is warranted to concentrate on establishing a model of the relationship between air pollution and control investment or an air pollution risk assessment based on the proportional hazards model (COX) model.