Kunming Area Research Articles

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district

Abstract Exploring the pattern of block vitality within cities and its influencing factors is an important topic in urban vitality research, which has great significance in promoting high-quality urban development. However, existing research has paid little attention to the spatial difference characteristics of daytime and night urban vitality and the differences in their influencing factors. Using the urban area of Kunming, China, as a case study, this study explores the differences in spatial patterns of daytime and night vitality at the intra-city block scale and analyses the differences in the main influencing factors of daytime and night block vitality using spatial regression models. The study obtains the following findings: (1) daytime block vitality in Kunming’s urban district (the urban Dist. of KM) is higher than night block vitality. The First Ring Area has the highest daytime block vitality, whereas the Second Ring Area has the highest night block vitality. (2) Block vitality in the urban Dist. of KM presents a polycentric structure. (3) There are obvious differences in the factors influencing urban vitality between daytime and night, with work accessibility, functional mixing, daily consumption accessibility, floor area ratio, and average building age, having different directions of influence on daytime and night urban vitality. (4) Daytime urban vitality in the urban Dist. of KM relies on work activities, while night urban vitality relies on residential activities. The results provide a scientific basis for urban land use layout, facility construction, and higher-quality urban planning and management, thus promoting sustainable urban development.

Assessment of urban flood resilience based on the socio-ecological composite index model: a case study in Wuhua District, Kunming City, China.

Global climate change and rapid urbanization have increased the frequency of flooding, making urban flood resilience a critical objective. This article introduces a methodology for assessing urban flood resilience, utilizing a social-ecological synthesis index that integrates geographical and temporal data with Geographic Information System (GIS). The study focuses on ten administrative subdistricts in Wuhua District, Kunming City, China, and selects 18 social-ecological indicators. These indicators, chosen from social and ecological perspectives, are weighted using the entropy weight method to determine their significance in the assessment system. By combining scores for each subdistrict, the study quantifies flood resilience and creates a spatial distribution map using ArcGIS. Key findings reveal that out of the ten administrative subdistricts, five in Wuhua District, particularly in the core urban area of Kunming, demonstrate strong overall flood resilience. Influenced by social-ecological indicators, there is significant spatial differentiation in flood resilience within Wuhua District, with a decreasing trend radiating from the city center to areas farther from the urban core. The research indicates that regions with well-established transportation infrastructure, a wide distribution of government institutions, improved water management facilities, and a substantial population with higher education levels contribute significantly to enhancing urban flood resilience.

Characterization and Source Analysis of Pollution Caused by Atmospheric Volatile Organic Compounds in the Spring, Kunming, China

The surface atmospheric O3 concentration in Kunming shows a significant upward trend, with high values mainly occurring in March–May. Volatile organic compounds (VOCs) are one of the most important precursors of O3. However, the sources of VOCs are complex and difficult to identify. In order to understand the pollution levels, the spatial distribution characteristics, and possible sources of VOCs, we conducted simultaneous offline sampling at representative sites in six different functional areas of Kunming using SUMMA canisters for one week. The VOC samples were analyzed via GC/MS. The VOC data were analyzed (using the feature ratio method, ozone formation potential (OFP), and Positive Matrix Factorization (PMF) model). Some important conclusions were drawn. Firstly, VOCs during the spring in Kunming were mainly derived from oxygenated VOCs, aromatic hydrocarbons, and halogenated hydrocarbons, with significant spatial differences. Secondly, we found that the potential for atmospheric ozone formation is higher in Kunming for aromatic hydrocarbons and oxygenated VOCs. Finally, the results of the Positive Matrix Factorization model (PMF) showed that Kunming’s ambient atmospheric VOCs mainly originate from anthropogenic source emissions. These conclusions can provide useful reference information for O3 pollution control in Kunming.

Spatial differentiation characteristics of regional self-driving tourism flow: A case study of central Yunnan urban agglomeration

The aim of this study was investigate the spatial effects of A-class scenic spots and the spatial distribution of highway networks on the influence of self-driving tour behavioral patterns in China at the urban agglomeration scale, based on big data of road traffic during three holidays. A spatial analysis method and a geographically weighted regression model were used to analyze the spatial distribution differences and influencing factors of self-driving tourism flows in the central Yunnan urban agglomeration. The results showed that holiday self-driving tourism in the central Yunnan urban agglomeration presented a typical core-edge spatial pattern. The mean value of the spatial autocorrelation coefficient was 0.54, indicating significant spatial autocorrelation. The influence of tourism resources and traffic conditions on self-driving tourism flow showed a decreasing trend from the center of the high positive value to the periphery of the main urban area of Kunming. This study reveals the spatial differentiation characteristics of self-driving tourism flows in urban agglomerations and lays a theoretical foundation for understanding flow pattern.

Analysis and optimization strategy of residential buildings in the main urban area of Kunming

With the rapid development of China's social economy, the number of housing construction has been increasing and the quality of housing has always been improving. At the same time, people's requirements for residential construction are also higher. Aiming at the residential buildings in the main urban area of Kunming, this paper analyzes the current situation of residential buildings in the main urban area of Kunming by using the methods of literature analysis, field investigation, induction and comparative analysis. It is found that the existing residential forms in Kunming mainly include multi-storey residence, villa and high-rise residential buildings, and analyze the problems of these three kinds of buildings.

Ostracods from the Kunming area of SW China, with description of two new species and male records of Cypridopsis vidua (O.F. Mller, 1776).

Previous studies of the Recent non-marine ostracods of Yunnan Province in southwestern China yielded 42 species, among which only 18 species were reported based on living material. Such a low number is likely to underestimate the species diversity of ostracods in Yunnan, considering the geographical disparity among its different parts. In this study, we report 11 nominal species and one species left in open nomenclature from 22 samples collected from the Kunming area in eastern-central part of Yunnan. Among these species, seven are new to the province, including Notodromas semiovata n. sp., Chrissia acuminata n. sp., and the first records of the male of Cypridopsis vidua (O.F. Mller, 1776) outside the USA. A review of previous literature indicates that, with the exception of the two new species, all the other 23 species have Palaearctic (PA) distributions, while only 14 have Oriental (OL) distributions. Such a composition contradicts previous vertebrate-based zoogeographical subdivisions of China in which Yunnan was included in the OL region, and adds to the argument that the geographical distributions of various invertebrate groups need to be investigated to map the PAOL boundary in China more appropriately.

Spatial distribution and suitability evaluation of nighttime tourism in Kunming utilizing multi-source data

In the post-pandemic era, nighttime tourism is vital in promoting the diversified development of tourism, enhancing the vitality of cities, and improving reemployment rates. Using Kunming City, China, as an example, this study used multi-theory and multi-source data to construct an evaluation model of the spatial distribution and suitability of nighttime tourism. The projection pursuit model and spatial analysis method were used to reveal spatial distribution and explore the suitability characteristics and spatial differences of nighttime tourism development. Our results revealed the following: (1) nighttime tourism resources showed a ‘large aggregation, small dispersion’ spatial distribution pattern, which is characterized by a distribution along the railway line; (2) at present, the spatial distribution of nighttime tourism in Kunming displays a pattern of ‘taking a high-density large gathering area as the center and extending around; ’ and (3) the most suitable nighttime tourism area accounts for 11.83% of the total land area of Kunming; highly suitable area accounts for 17.53%. The general suitable and unsuitable areas accounted for 43.29% and 27.35%, respectively. The results of this study assist in providing a scientific basis for the strategic planning and development of the nighttime tourism industry in Kunming.

Spatial Accessibility of Urban Emergency Shelters Based on Ga2SFCA and Its Improved Method: A Case Study of Kunming, China

Emergency shelters are important public facilities for urban disaster prevention and mitigation. Measuring their spatial accessibility provides a valuable reference for urban modernization and a balanced layout of emergency shelters. In previous studies, the Gaussian two-step floating catchment area (Ga2SFCA) method has the shortcoming of ignoring the effect of a fixed search radius on accessibility. In this study, an improved Ga2SFCA method was proposed to evaluate emergency shelter accessibility in the main urban area of Kunming, China. The parameters of the serviceability of the shelters and the degree of population aggregation were introduced to calculate their effect on the search radius. The traditional Ga2SFCA method and the improved method were compared and analyzed. The results showed that the improved method is more reasonable and realistic in the search radius of densely populated areas and areas lacking shelters, thus providing highly objective and accurate accessibility to emergency shelters. Further, the spatial distribution of the emergency shelters in the main urban area of Kunming was unbalanced and did not match the population density distribution. The accessibility of shelters in parts of the northwestern, eastern, and central parts of the city was low. The method presented in this study could be used to provide reference and support for the government and planners to reasonably plan shelter facilities and promote equal access for all residents to shelter resources.

Spatial and component analysis of urban flood Resiliency of kunming city in China

In the context of global climate change and impending urbanization, improving the ability of cities to withstand rainstorms and floods has become a significant challenge in the global urbanization process. Many countries have put forward flood prevention and control measures to cope with global climate change. In this context, the flood toughness analysis and evaluation have been conducted in the Kunming urban area in China as a primary research method, based on urban flood toughness construction, quantitative and visual expression. An evaluation model of urban flood resilience was established by considering the research objects based on the theories and methods of system dynamics, social ecosystems, and the current situation of the research region. The data service consciousness ability, traffic facilities, flood control ability, and the social factors such as comprehensive thinking of natural factors, economic factors, social and cultural factors in the study region. The quantitative factor weighting and GIS visualization methods were used to establish a research area of 100 m * 100 m fishing nets. For the first time, a high-precision spatial distribution map of flood toughness was generated for the main urban area of Kunming. The spatial differentiation characteristics of flood toughness in the area were further analyzed to comprehensively evaluate flood control capacity by a comparative study of historical records of flood disaster points in the region in 2020. The findings of this paper reveal and quantify the influence and factors in urban flood disasters and provide support for subsequent urban construction and flood planning decision-making.

Modified Cam-clay Model Parameters M of Kunming Red Clay

Red clay is widely distributed in Kunming. At present, there are many engineering projects in Kunming, and many engineering projects are built on red clay. Therefore, it is very important to study the mechanical properties of red clay. Kunming red clay was remolded and prepared, and triaxial consolidation drainage tests of remolded saturated clay with the same water content and different dry densities were designed. Through the triaxial consolidation drainage test, the shear strength parameters of Kunming saturated red clay and the modified Cam-clay model parameters are obtained. The experimental results show that the modified Cam-clay model parameters of saturated red clay with the same water content and dry density are basically equal, and the larger the dry density is, the larger the modified Cam-clay model parameter M of saturated red clay is. The test results can provide reference data for engineering design in the Kunming area and modified Cam-clay model simulation in the Kunming area.

Study on the Spatial Distribution Pattern of Catering Industry in the Urban Center of Kunming from 2012 to 2022

Taking the urban center of Kunming as the study area, this paper extracts the POI data of the catering industry in 2012, 2016, 2020 and 2022 of Kunming by adopting web crawler technology, and then the average nearest neighbor index, geographic concentration index, kernel density analysis and spatial autocorrelation index are used by this study to conduct a comparative study on the spatial distribution pattern of the catering industry in the urban center of Kunming so as to explore its spatial clustering and evolutionary characteristics. The study results show that: (1) The development trend of the spatial pattern of the catering industry in the urban center area of Kunming is consistent with the urban development planning of Kunming, with the center clustering and expanding outward. (2) Although relying on commercial centers, tourist hotspots and university campuses, catering points form a multi-center distribution and "large aggregation, small dispersion" spatial pattern, the spatial distribution is unbalanced. (3) The spatial distribution pattern of catering industry is consistent with the overall spatial distribution pattern of catering industry, showing a multicenter gathering trend.

Sliding Window Detection and Analysis Method of Night-Time Light Remote Sensing Time Series—A Case Study of the Torch Festival in Yunnan Province, China

The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership’s visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities’ NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world.

Spatial structure characteristics and effects of self-driving tourism flows before and after the new crown epidemic: Taking Yunnan Province as an example

<p>To obtain the impact of the new crown epidemic on the spatial structure of self-driving tourism flow during the Spring Festival Golden Week, social network and spatial statistical analysis methods are used to integrate road traffic flow big data and travelogue data to analyze the spatial structure characteristics of self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2018 and 2021. The results show that: 1) The self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2021 shows the “dragonfly” spatial clustering characteristics of “two centers, one axis and two wings”, and the new sub-core area of Qujing is added to the core area of Kunming in 2018, and the self-driving tourists are affected by the epidemic. 2) During the Spring Festival Golden Week in 2021, compared with 2018, there is no significant change in the spatial structure of tourism flow into Yunnan from outside the province, but the degree of intermediary centrality of provincial boundary nodes is significantly weakened, and the self-driving tourism flow at Wenshan junction and Lijiang junction decreased by 72.54% and 87.26%. The New Crown epidemic hindered the development of self-drive tours into Yunnan from Guangxi and Sichuan. 3) During the New Crown epidemic, tourists were less willing to visit hotspot cities, and showed overall behavioral preference characteristics of avoiding crowd gathering and focusing on health and safety. Under the New Crown epidemic, the tendency of long-distance self-driving shifts to close distance self-driving between neighboring cities centered on Kunming.</p><div><p> </p></div>

Taxonomic and Phylogenetic Insights into Novel Ascomycota from Forest Woody Litter.

Simple SummaryStudies suggest that fungi belonging to Ascomycota are sensitive to environmental changes which are disrupting ecosystems globally, with numerous extinction events, imbuing fungal diversity research with a sense of urgency. Thus, it is crucial we assess the diversity of Ascomycota across different habitats and substrates, and include programs to seek out novel taxa within Ascomycota. Our study provides some insights into the woody-based saprotrophic microfungi in Yunnan, China by introducing two novel Ascomycota species. Furthermore, these species were discovered by a 5-year-old boy, thus highlighting how young enthusiasts can be involved in field studies and make a significant impact.While surveying the mycobiomes of dead woody litter in Yunnan Province, China, numerous isolates with affinity to Pleosporales (Dothideomycetes, Ascomycota) were recovered. The present work characterizes two species associated with dead woody twigs found in terrestrial habitats in the Kunming area of Yunnan. The novel taxa were recognized based on a polyphasic approach, including morphological examination and multiple gene phylogenetic analyses (non-translated loci and protein-coding regions). Neokalmusia jonahhulmei sp. nov. is introduced in Didymosphaeriaceae (Pleosporales) as a woody-based saprobic ascomycete that possesses multiloculate ascostromata immersed under a black clypeus-like structure, and three-septate, brown, fusiform, guttulate ascospores. Thyridaria jonahhulmei (Thyridariaceae) is introduced with teleomorphic and anamorphic (coelomycetous) characteristics. The teleomorph has the following characteristics: globose to subglobose ascomata with an ostiolum, a pruinose layer of yellow to reddish- or orange-brown material appearing around the top of the ostiolar necks, and brown, ellipsoid to fusoid, two-to-three-septate, euseptate, rough-walled ascospores; the anamorph features pycnidial conidiomata, phialidic, ampulliform to doliiform, conidiogenous cells, and brown, guttulate, ellipsoidal, aseptate conidia.

Constructing a Large-Scale Urban Land Subsidence Prediction Method Based on Neural Network Algorithm from the Perspective of Multiple Factors

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm⋅a−1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3). The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction.

Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model

Urbanization had a huge impact on the regional ecosystem net primary productivity (NPP). Although the urban heat island (UHI) caused by urbanization has been found to have a certain promoting effect on urban vegetation NPP, the factors on the impact still are not identified. In this study, the impact of urbanization on NPP was divided into direct impact (NPPdir) and indirect impact (NPPind), taking Kunming city as a case study area. Then, the spatial heterogeneity impact of land surface temperature (LST) on NPPind was analyzed based on the geographically weighted regression (GWR) model. The results indicated that NPP, LST, NPPdir and NPPind in 2001, 2009 and 2018 had significant spatial autocorrelation in Kunming based on spatial analytical model. LST had a positive impact on NPPind in the central area of Kunming. The positively correlation areas of LST on NPPind increased by 4.56%, and the NPPind caused by the UHI effect increased by an average of 4.423 gC m−2 from 2009 to 2018. GWR model can reveal significant spatial heterogeneity in the impacts of LST on NPPind. Overall, our findings indicated that LST has a certain role in promoting urban NPP.

Correlation Between Microplastics Pollution and Eutrophication in the Near Shore Waters of Dianchi Lake

Microplastics have been found in many environmental media such as sea water, coastal tidal flats, terrestrial water, sediments, and organisms. Microplastics pollution in inland freshwater lakes have received extensive attention; however, the correlation between eutrophication and microplastics pollution in freshwater lakes remains unclear. In this study, 24 sampling sites were set up in the near shore surface waters of Dianchi Lake, and the pollution characteristics of microplastics such as abundance, composition, particle size, color, and form were evaluated. Water quality parameters related to eutrophication state were analyzed, and the eutrophication indices were further calculated. Specifically, sample pre-treatment was conducted according to the method issued by National Oceanic and Atmospheric Administration (NOAA) of the United States. The color and morphological characteristics of microplastic samples were observed using a stereoscopic microscope, and counts and particle size measurements were performed using Nano Measure 1.2 software. Parts of the samples were selected, and the polymer composition analysis was performed using micro-Fourier Transform infrared (μ-FTIR) spectroscopy. The indices related to eutrophication level evaluation were tested according to the experimental standard methods issued by the Ministry of Ecology and Environment of China. The results showed that the abundance of microplastics in the near shore waters of Dianchi Lake was between 800 and 6000 n·m-3, with an average value of 2867 n·m-3. The types of polymers detected were polyethylene terephthalate (PET), polyetherurethane (PEU), polypropylene (PP), polyethylene (PE), and polyvinyl acetate (PVAc), respectively. The diameter proportion of microplastics in the range of 0.2-0.5 mm was the highest. Fiber microplastics accounted for the most observed type, followed by fragments and films. Among the 24 monitoring sites, it was found that proportions of severe, moderate, and mild eutrophication and mesotrophication sites accounted for 8.33%, 58.33%, 29.17%, and 4.17% of the total sampling sites, respectively, and the main pollutant was total nitrogen (TN). Microplastics abundances in the near shore waters of Dianchi Lake were significantly positively correlated with TN concentrations (P<0.01), whereas they were negatively correlated with chlorophyll a(Chl-a)concentrations, not reaching a significant level (P>0.05). The microplastics abundance and TN concentrations in the north bank water near the main urban area of Kunming were significantly higher than those in the other three banks. Microplastics and TN were considered to potentially have the same origin and be attributed to the tail water discharge from WWTPs.

Study on Land Use and Precipitation Changes in the Main Urban Area of Kunming in the Past 40 Years

Based on the land use data of Kunming main city in 1980, 1990, 2000, 2010 and 2020 and the precipitation data of Kunming meteorological station in 1980-2019. Used anomaly analysis and Manner-Kendall methods to analyze land use changes and precipitation trends in the main urban area of Kunming. The results show that under the background of urbanization, the land use change in Kunming main city is obvious, and the construction land area is obviously increased. The annual precipitation level showed a downward trend, the annual precipitation days decreased significantly, and the extreme precipitation days increased. The change of land use has obvious influence on precipitation events.

Brachiopod-dominated communities and depositional environment of the Guanshan Konservat-Lagerstätte, eastern Yunnan, China

The Guanshan Biota is an unusual early Cambrian Konservat-Lagerstätte from China and is distinguished from all other exceptionally preserved Cambrian biotas by the dominance of brachiopods and a relatively shallow depositional environment. However, the faunal composition, overturn and sedimentology associated with the Guanshan Biota are poorly understood. This study, based on collections through the best-exposed succession of the basal Wulongqing Formation at the Shijiangjun section, Wuding County, eastern Yunnan, China recovered six major animal groups with soft tissue preservation; brachiopods vastly outnumbered all other groups. Brachiopods quickly replace arthropods as the dominant fauna following a transgression at the base of the Wulongqing Formation. A transition from a botsfordiid-, eoobolid- and acrotretid- to an acrotheloid-dominated brachiopod assemblage occurs up-section. Four episodically repeated lithofacies reveal a relatively low-energy, offshore to lower shoreface sedimentary environment at the Shijiangjun section, which is very different from the Wulongqing Formation in the Malong and Kunming areas. Multiple event flows and rapid obrution are responsible for faunal overturn and fluctuation through the section. A detailed lithofacies and palaeontological investigation of this section provides a better understanding of the processes and drivers of faunal overturn during the later phase of the Cambrian Explosion. Supplementary material: Composition and comparison of the Malong Fauna and the Guanshan Biota is are available at: https://doi.org/10.6084/m9.figshare.c.5080799