Spatial Distribution Characteristics Research Articles

A study on the spatial and temporal evolution patterns of water resource–ecology–social economy composite system resilience in the Huaihe River Basin

ABSTRACT Water resources are important supporting elements in the ecological environment and socio-economic system of sustainable development. The purpose of this study is to evaluate the comprehensive resilience level of the water resources–ecology–socioeconomic system against external disturbances in the Anhui and Shandong sections of the Huaihe River Basin. The comprehensive index system is constructed with the characteristic dimensions of resistance, resilience, and adaptability. Use the game theory combination weighting-VIKOR method to quantify the resilience level, and use the kernel density analysis and Markov chain to explore the spatial and temporal evolution characteristics and transition probability. The results show that (1) the resilience index of the composite system in the Huaihe River Basin shows an increasing trend, and the resilience level of the Anhui section is generally higher than that of the Shandong section. (2) The resilience level gradually evolved into the spatial distribution characteristics of high in the west and low in the east, high in the south and low in the north during the study period. (3) The resilience grade has strong robustness, and the probability of transferring to the higher grade is greater than that of transferring to the lower grade.

Characterising global ocean mesoscale eddy by AVISO and Haiyang-2 altimeter

ABSTRACT This work compares the Haiyang-2 (HY-2) altimeter data with the well-recognized satellite oceanographic data (AVISO) product in the global ocean in their capability of detecting mesoscale eddy. A well-established method to detect mesoscale eddies from HY-2 and AVISO was applied to the sea level anomaly (SLA) data on a global scale in 2021−2022. It is found that the number of mesoscale eddies derived from the HY-2 dataset is three times more than that from the AVISO dataset and with larger eddy kinetic energy (EKE), shorter lifespan and smaller amplitude. Besides, the monthly-averaged number of generated and terminated mesoscale eddies is similar and the temporal variation of SLA exhibits strong lunar oscillation for both datasets. Most mesoscale eddies are generated in the mid-latitude oceans due to the Coriolis force and the strong El Niño. In the western boundary of the global ocean, equator, Antarctic Ocean and other areas with strong currents, the mesoscale eddies have stronger SLA amplitudes, EKEs and vorticity. Furthermore, EKE is low at the centre and high at the eddy meander. The spatial distribution characteristics of vorticity are the same as those of EKEs in the western boundary of oceans, but the vorticity value of HY-2 data near the equator is larger than that of AVISO data. However, considering the large detection error of eddy near the equator, no further exploration will be conducted in this paper. Furthermore, mesoscale eddies tend to move in the east–west direction due to Coriolis force and wind (Ekman transport) effects and large-scale ocean circulation or the pathway of drifters also have a certain impact. Overall, similar to AVISO data, the HY-2 data is also reliable for mesoscale identification in global oceans.

A multidisciplinary approach to describe population structure of Solea solea in the Mediterranean Sea

Investigating marine species population structure in a multidisciplinary framework can reveal signatures of potential local adaptation and the consequences for management and conservation. In this study we delineate the population structure of common sole (Solea solea) in the Mediterranean Sea using genomic and otolith data, based on single nucleotide polymorphism (SNPs) markers, otolith shape and otolith trace element composition data. We correlated SNPs with environmental and spatial variables to evaluate the impact of the selected features on the actual population structure. Specifically, we used a seascape genetics approach with redundancy (RDA) and genetic-environmental association (GEA) analysis to identify loci potentially involved in local adaptation. Finally, putative functional annotation was investigated to detect genes associated with the detected patterns of neutral and adaptive genetic variation. Results from both genetic and otolith data suggested significant divergence among putative populations of common sole, confirming a clear separation between the Western and Eastern Mediterranean Sea, as well as a distinct genetic cluster corresponding to the Adriatic Sea. Evidence of fine-scale population structure in the Western Mediterranean Sea was observed at outlier loci level and further differentiation in the Adriatic. Longitude and salinity variation accounted for most of the wide and fine spatial structure. The GEA detected significant associated outlier loci potentially involved in local adaptation processes under highly structured differentiation. In the RDA both spatial distribution and environmental features could partially explain the genetic structure. Our study not only indicates that separation among Mediterranean sole population is led primarily by neutral processes because of low connectivity due to spatial segregation and limited dispersal, but it also suggests the presence of local adaptation. These results should be taken into account to support and optimize the assessment of stock units, including a review and possible redefinition of fishery management units.

Inversion of electron densities in plasma wakes of hypersonic targets

Focusing on the plasma wakes of hypersonic targets (PWHT), this work provides a method for the inversion of electron densities in the PWHT by integrating electromagnetic radiation theory and three-wave coupling theory (TWCT). Utilizing the Computational Fluid Dynamics (CFD) method, taking a blunt cone as an example, the spatial distribution characteristics of electron densities in the plasma wake are simulated and analyzed in detail. The Zakharov system of equations (ZSE) is applied to the PWHT for the discussion about the electromagnetic radiation generated by plasma parametric instability. Combining the wavenumber spectrum of electromagnetic radiation and the TWCT, an inverse method of the electron densities of plasma wakes is proposed and applied to a blunt cone model. The results indicate that the inversion error rate is effectively held below 1.37 %. Furthermore, reasons for errors are elucidated through the utilization of the wavenumber spectrum structure. The proposed method may contribute to the development of hypersonic wake diagnostic techniques, and offer new insights and directions for wind tunnel experiments, shock tube experiments, as well as research on hypersonic target detection.

Analysis on characteristics of extreme precipitation indices and atmospheric circulation in Northern Shanxi

This article utilizes daily precipitation data from 28 national meteorological stations in northern Shanxi Province spanning from 1972 to 2020, and the US NCEP/NCAR monthly average reanalysis and ERA5 monthly average reanalysis data. The study employs techniques such as empirical orthogonal function (EOF) decomposition, Mann-Kendall mutation and other methods to investigate the spatiotemporal distribution of extreme precipitation index in northern Shanxi and their correlation with atmospheric circulation. The research results show that: the absolute index, relative index, intensity index and sustained dry period index (CDD) in the continuous index appear from southwest to northeast. The spatial distribution characteristics of the central region decrease, while the continuous wet period (CWD) decreases from the central to the east and west. The three indices Rx1day, Rx5day, and CWD mutated in 1978, 1975, and 1983 respectively, and other extreme precipitation indices all appeared in a sudden change from a low-value period to a high-value period occurred around 2010. In the high-value years of the summer extreme precipitation index, there is a significant negative anomaly in the height field in the mid-high latitude regions of Eurasia. Northern Shanxi is controlled by a broad low-pressure trough in the Lake Baikal area. Water vapor transported via the east, west, and south routes converges in the northern Shanxi region and encounters cold air from the north. There is a strong upward motion anomaly at 500 hPa in the troposphere, and the dynamic conditions of upper-level divergence and lower-level convergence lead to more summer extreme precipitation in the northern Shanxi region. Conversely, in the low-value years of the summer extreme precipitation index, northern Shanxi is affected by a strong high-pressure ridge north of Lake Baikal. There is a downward motion anomaly at 500 hPa, and the northern Shanxi region lacks water vapor. The cold and warm air cannot converge, and both the water vapor conditions and dynamic conditions are poor, which is not conducive to the production of extreme precipitation in northern Shanxi.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

Evaluating and simulating the impacts of land use patterns on carbon emissions in coal resource-based regions: A case study of shanxi province, China

It is essential for sustainable development worldwide to reduce carbon emissions (CE), however, how land use patterns in coal resource-based regions (CRBRs) affect CE remains less explored. Based on socioeconomic data and GlobeLand30, this study evaluated and predicted the influence of land use patterns on CE in Shanxi Province from 2000 to 2030. The key findings are as follows: (1) From 2000 to 2020, the area of cropland in the study area decreased by 4.85 × 105 ha and the area of construction land increased by 4.38 × 105 ha, with the increase in construction land coming mainly from cropland. (2) There was an increasing trend in the overall net CE, from 3.17 × 107 t in 2000 to 3.89 × 107 t in 2010 and 6.38 × 107 t in 2020. The land use CE had a spatial distribution characteristic of being high in the west and south and low in the east and north. The total CE was the largest in 2030 in business-as-usual scenario (BAUs), and the CE class of the cities in the study area was mainly high CE, while the ecological and economic balance scenario (EEBs) was higher CE. In 2030, the BAUs will have the largest total CE, and the CE levels of the cities will mainly high CE. (3) The decoupling state between the land use mix degree (LUM) and CE in the study area was mainly an expansionary negative decoupling over the past 20 years and changed from long negative decoupling to weak decoupling in the economic development priority scenario (EDPs) and strong decoupling in EEBs, which might be better suited to the future development of Shanxi Province. This study makes it easier to comprehend how different land use patterns affect the number of CE in CRBRs. Additionally, it provides other CRBRs with a theoretical framework and instances needed to research CE.

Spatial Analysis of Dengue Fever Incidence and Serotype Distribution in Vientiane Capital, Laos: A Multi-year Study

Laos is a hyperendemic country of all 4 dengue serotypes. Various factors contribute to the spread of the disease including viral itself, vectors, and environment. This study aims to analyze dengue data and its incidence in nine districts of Vientiane Capital, Laos spanning from 2019 to 2021 by data collected from Mittaphab Hospital. The Maximum Entropy algorithm (MaxEnt) was applied to assess spatial distribution and identify high-probability locations for dengue occurrence by analyzing crucial environmental and climatic conditions. Dengue cases were more prominent in female (54.88%) and highest case number was found in worker group (29.02%) followed by student (28.47%) and officer (16.92%). In this study, the age group 21-30 years old had the highest infection rate (42.23%), followed by 10-20 years old (24.21%). Most of dengue cases was primary infection (91.61%). Dengue serotype 2 predominated in 2019 and 2020 and substitute by serotype 1 in 2021. Across the nine districts of Vientiane Capital, the highest incidence of dengue was found in Xaythany district population in 2019, shifting to Chanthabouly district in 2020 and 2021. The MaxEnt revealed potentially most suitable areas for dengue were widely distributed central south part of Vientiane, Laos. Additionally, the best predictive variable for dengue occurrence was normalized difference vegetation index. Understanding of case characteristics and spatial distribution features of dengue will be helpful in effective surveillance and disease control in the future.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

In Situ Test and Numerical Analysis of the Subway-Induced Vibration Influence in Historical and Cultural Reserves.

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle-track-tunnel-stratum-structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures.

Achieving EEG-based depression recognition using Decentralized-Centralized structure

Currently, electroencephalography (EEG)-based depression recognition using deep learning methods has become an important approach for early diagnosis and treatment. Due to the functional differences and activation differences among various brain regions, the channels in EEG data may potentially exhibit a certain degree of local correlation. Furthermore, several studies have indicated that there are significant differences in the synchronization for multiple brain regions between patients with depression and healthy controls. However, few researchers have designed depression recognition model architectures according to the spatial distribution characteristics of EEG data. In multiple existing EEG-based classification models, the channels of EEG data are directly merged. This approach hinders the model from effectively capturing the spatial information of EEG data and leads to a partial loss of synchronized information of several brain regions. In light of this, we propose a novel attention network named Decentralized-Centralized Structure Transformer (DCST), which can achieve effective depression recognition utilizing the spatial distribution characteristics of EEG data. The leave-one-subject-out cross-validation is employed to evaluate the classification performance for all models. The experimental results show that our proposed model achieves the highest classification accuracy, 89.8%.

Spatial and Temporal Distribution Characteristics and Potential Sources of Microplastic Pollution in China’s Freshwater Environments

Microplastic pollution is a research hotspot around the world. This study investigated the characteristics of microplastic pollution in the freshwater environments of 21 major cities across China. Through indoor and outdoor experimental analysis, we have identified the spatial and temporal distribution characteristics of microplastic pollution in China’s freshwater environments. Our findings indicate that the average concentration of microplastics in China’s freshwater environments is 3502.6 n/m3. The majority of these microplastics are fibrous (42.5%), predominantly smaller than 3 mm (28.1%), and mostly colored (64.7%). The primary chemical components of these microplastics are polyethylene (PE, 33.6%), polyvinyl chloride (PVC, 21.5%), polypropylene (PP, 16.8%), and polystyrene (PS, 15.6%). The abundance of microplastics in China’s freshwater environments generally tends to increase from west to east and from south to north, with the lowest concentration found in Xining, Qinghai (1737.5 n/m3), and the highest in Jiamusi, Heilongjiang (5650.0 n/m3). The distribution characteristics of microplastics are directly related to land use types, primarily concentrated in areas of intense human activity, including agricultural, transport, and urban land. Seasonal changes affect the abundance of microplastics, peaking in summer, followed by spring and autumn, mainly due to variations in rainfall, showing a positive correlation.

Analysis of the Spatial Distribution Characteristics and Influencing Factors of Traditional Mosque Architecture in the Hehuang Area (China)

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China’s Hehuang region has significant positive implications for the overall protection and development of the region’s architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a “central clustering, peripheral dispersal” distribution pattern, forming a spatial structure of “two cores, one belt, multiple points”, with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22 °C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors.

Temporal and Spatial Distribution Characteristics of Torrential Flooding Disasters with Different Intensities in Tarim Basin

As torrential flooding often cause huge economic losses and casualties, analyzing the spatio-temporal variation characteristics of torrential flooding events is of great significance to disaster prevention and reduction. Based on five indicators for torrential floodingin the Tarim Basin in 1990-2019, ratio weighting and non-dimensional linear summation were employed to calculate disastrous loss indicators that represent disaster intensity. Afterwards, percentile method was used to divide disasters into four levels, i.e., general, relatively severe, severe and extremely severe. The results showed that the regions where Level-1 to Level-4 disasters frequently and recurrently occur are concentrated in Kizilsu Kirghiz Autonomous Prefecture, Aksu Prefecture and Kashgar Prefecture and that such disasters often take place from April to July. The interannual variation of the frequency and intensity of Level-1 disasters presented a linear upward trend, and the frequency and disastrous loss indicator increased by 14.6 and 0.8 per 10a, respectively. The interannual variation of the frequency and intensity of Level-2 to Level-4 disasters did not show a linear increase or decrease trend. The threshold for 12-hour precipitation that may cause torrential flooding in the basin from March and October is 10mm. The annual frequency of 12-hour precipitation exceeding the threshold increased year by year, so did the frequency and intensity of Level-1 disasters.

Study on Transportation Green Efficiency and Spatial Correlation in the Yangtze River Economic Belt

The Yangtze River Economic Belt (YREB), a crucial transportation corridor spanning China’s east and west and linking coastal and inland regions, is not only pivotal in the nation’s strategic development but also drives regional economic and social progress through its transportation industry. Despite rapid growth, the industry faces challenges such as low efficiency, resource supply–demand imbalances, and environmental issues. To advance green and sustainable progress, this study establishes a regional transportation green efficiency evaluation system. Using principal component analysis (PCA) to refine input data, the undesirable super-SBM model quantitatively assesses green transportation efficiency (GTE) in YREB provinces and cities, revealing regional disparities. The study also explores spatial correlations and distribution characteristics of GTE. Results indicate that ① YREB’s GTE shows a U-shaped trend, with significant differences between upper, middle, and lower reaches, being stronger in the east and weaker in the west (lower &gt; middle &gt; upper reaches); ② GTE exhibits spatial correlation in YREB regions, with clear clustering; and ③ cold and hot spots of GTE in the middle reaches are relatively stable, with upstream areas generally cold or sub-cold, and hot spots mainly downstream.

Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.

Prediction of hydrogen leakage location and intensity in hydrogen refueling stations based on deep learning

The common risk of high-pressure hydrogen leakage seriously affects the safety of hydrogen refueling stations (HRS). In this study, we have developed a hybrid model (WD-KNN-CNN) based on wavelet denoising (WD) and deep learning to predict the hydrogen leakage location and intensity in HRS. The raw data were calculated using validated Computational Fluid Dynamics (CFD), and the WD-processed raw data were utilized as input data for the hybrid model. The K-nearest Neighbors (KNN) is employed to identify the leak location, while a two-dimensional Convolutional Neural Network (CNN) is utilized to extract temporal and spatial distribution features from the concentration sequence for intensity prediction. Bayesian optimization is applied to determine the optimal hyperparameter combination for the model. The results demonstrate that the model achieves a prediction accuracy of 99.14% for leak location and 97.42% for intensity level. Furthermore, through the reduction of sensor quantity, we tested the model's performance and found that utilizing 14 sensors as input resulted in 91.3% accuracy for leak location and 90.8% for intensity level prediction. This hybrid model provides a method for safety monitoring in HRS and offers guidance for personnel response during leak incidents.

Climate change impacts on precipitation and water resources in Northwestern China

A statistical analysis is conducted on the hourly precipitation at 105 national meteorological stations in the Xinjiang region of China from 2011 to 2020. The purpose of this analysis is to determine the total quantity of precipitation during the warm and cold seasons, as well as the precipitation frequency at each station. The results of the analysis indicate that there is a more pronounced inter-annual variation in precipitation during the warm and cold seasons in Xinjiang, where the total amount of precipitation during the warm season is significantly higher than that during the cold season, with the warm season experiencing at least twice as much precipitation. In terms of the inter-annual incremental amount of precipitation, both the warm and cold seasons exhibit a trend of increasing and then decreasing over the years. However, the curvature change is more prominent in the cold season compared to the warm season, suggesting that the inter-annual variation in precipitation during the warm season remains relatively stable. The spatial distribution characteristics of the total precipitation in Xinjiang indicate that warm-season precipitation is more scattered, while cold-season precipitation is mainly concentrated in the northern region of the territory. This pattern holds true at low, medium, medium–high, and high altitudes. The cumulative precipitation during the warm season is always higher than that in the cold season, and this difference becomes more prominent with increasing altitude. This indicates that warm-season precipitation in Xinjiang is closely related to its complex topographic structure, particularly the convective weather generated by topographic clouds. On the other hand, precipitation in the cold season is mainly concentrated in the low- and medium-altitude stations, and there is not much correlation between precipitation and altitude. Precipitation is mainly affected by the mesoscale and the direct influence of large-scale weather systems. Analysis of precipitation in extreme moments reveals that during the warm season, extreme precipitation events occur mainly from the evening to early morning (19:00–03:00 the next day), accounting for 75.24% of the 105 stations. In contrast, during the cold season, extreme precipitation events occur between 03:00 and 18:00, corresponding to 74.29% of the stations. The relationship between precipitation and altitude differs in the warm and cold seasons. In the warm season, there is a linear relationship between precipitation and altitude, with a slope of 0.72 and a correlation coefficient of 0.52, indicating that precipitation increases with increasing altitude. However, in the cold season, the linear relationship between precipitation and altitude is very inconspicuous, with a slope of −0.05 and a correlation coefficient of −0.05, suggesting that there is no significant relationship between precipitation and altitude during this season. These findings provide valuable insights into the changing patterns of precipitation gradients at different altitudes and seasons in Xinjiang. This information can be used for the site selection of smokestack operations in high-altitude areas and for ground and air weather modification operations in low-altitude areas. Additionally, these data contribute to the understanding of precipitation patterns in Xinjiang, further improving the effectiveness of weather modification efforts and increasing the utilization of airborne water resources.

Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land

ABSTRACT The Medium Resolution Spectral Image (MERSI) is a MODIS-like sensor aboard Fengyun-3 satellite. The first version of MERSI aerosol algorithm has been developed based on MODIS dark target (DT) algorithm, with modified models for estimating surface reflectance and an adjusted inland water masking method to release haze aerosols. This study applies MERSI DT algorithm to the global observations from the upgraded MERSI sensor (MERSI-II) on Fengyun-3D. And then, the Aerosol Optical Depth (AOD) results from the year of 2019–2020 are validated against the Aerosol Robotic Network (AERONET) data. In addition, analyses of the spatial distribution and error characteristics of MODIS and MERSI-II retrievals are presented. The overall validation demonstrates that MERSI-II retrievals perform well globally, with a correlation coefficient of 0.877 and 67.1% of matchups within the Expected Error envelope of ± (0.05 + 0.2τ), which are close to the statistic metrics of MODIS products. In addition, MERSI-II and MODIS AODs exhibit similar error trends and error dependence. Moreover, the similar global distribution characteristics of the two AODs are revealed in the retrieval performance at site and regional scales, as well as in the analysis of monthly averages. These findings indicate the success of the ported MERSI algorithm.

Study on the Geographical Spatial Characteristics of Forest Health Resorts in Fujian Province, China

Forest health tourism is an increasingly popular new form of tourism. Fujian Province, with its abundant forest resources, ranks among the top provinces in China in terms of forest coverage and holds significant development potential. The study focuses on 80 provincial-level forest health resorts in Fujian Province, China. Software such as the ArcGIS geographic information system is used to analyze the resorts’ nearest neighbor index, geographic concentration index, kernel density, etc., thus indicating their spatial distribution characteristics. Overlay analysis, correlation analysis, and fitting analysis are employed to explore the spatial distribution specificity and its influencing factors. The research results indicate that forest health resorts in the area exhibit an aggregated distribution with a higher overall concentration. They are influenced by factors such as the distribution of river systems, the accessibility of transportation, distance to central cities, and socio-economic conditions. The geographic concentration index (G) is 50.28 and the unevenness index (S) is 0.640, both indicating that the forest health resorts in Fujian Province demonstrate a distribution pattern of “overall dispersion with multiple core aggregations”. Based on the research findings, the article puts forward some policy insights. Taking into account the local geographical characteristics, it advocates for the rational development of forest health resort industry models suitable for the region. It suggests incorporating innovative ideas into the forest health resort industry in order to broaden development channels. Additionally, it emphasizes the importance of close collaboration between the government and the market for mutual development.