Spatial-temporal Patterns Research Articles

Dynamic functional network connectivity and its association with lipid metabolism in Alzheimer's disease.

The study aims to examine the changing trajectory characteristics of dynamic functional network connectivity (dFNC) and its correlation with lipid metabolism-related factors across the Alzheimer's disease (AD) spectrum populations. Data from 242 AD spectrum subjects, including biological, neuroimaging, and general cognition, were obtained from the Alzheimer's Disease Neuroimaging Initiative for this cross-sectional study. The study utilized a sliding-window approach to assess whole-brain dFNC, investigating group differences and associations with biological and cognitive factors. Abnormal dFNC was used in the classification of AD spectrum populations by support vector machine. Mediation analysis was performed to explore the relationships between lipid-related indicators, dFNC, cerebrospinal fluid (CSF) biomarkers, and cognitive performance. Significant group difference concerning were observed in relation to APOE-ε4 status, CSF biomarkers, and cognitive scores. Two reoccurring connectivity states were identified: state-1 characterized by frequent but weak connections, and state-II characterized by less frequent but strong connections. Pre-AD subjects exhibited a preference for spending more time in state-I, whereas AD patients tended remain in state-II for longer periods. Group difference in dFNC was primarily found between AD and non-AD participants within each state. The dFNC of state-I yielded strong power to distinguish AD from other groups compared with state-II. APOE-ε4+, high polygenic score, and high serum lipid group were strongly associated with network disruption between association cortex system and sensory cortex system that characterized elevation of cognitive function, which may suggest a compensatory mechanism of dFNC in state-I, whereas differential connections of state-II mediated the relationships between APOE-ε4 genotype and CSF biomarkers, and cognitive indicators. The dysfunction of dFNC temporal-spatial patterns and increased cognition in individuals with APOE-ε4, high polygenic score, and higher serum lipid levels shed light on the lipid-related mechanisms of dynamic network reorganization in AD.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

Increase in marginal sea alkalinity may impact air–sea carbon dioxide exchange and buffer acidification

AbstractTotal alkalinity (TA) has increased in the Baltic Sea, with implications for atmospheric CO2‐induced acidification and CO2 uptake. We compiled extensive data of TA in surface waters of the Baltic Sea, aiming to (i) identify new tendencies in the relationship between TA and salinity (TA–S relationship), (ii) update the TA trend analysis, (iii) investigate spatial–temporal patterns, and (iv) discuss potential drivers and implications. We observed a progressive decrease in the slopes and increase in the intercepts of the TA–S overtime due to the persistent process of TA enhancement. A weak seasonal pattern was identified, with warmer months presenting lower salinity and TA. Lower rates of TA increase were observed in high salinities (Skagerrak–Kattegat; +1.00 to +2.20 μmol kg−1 yr−1), intermediate trends in low salinities (Gulf of Bothnia; +3.28 to +3.57 μmol kg−1 yr−1), and maximal trends in the Central Baltic Sea (+3.70 to +4.57 μmol kg−1 yr−1) and Bornholm Basin (+4.82 to +5.32 μmol kg−1 yr−1). The increase in the intercept of the TA–S in the Gulf of Bothnia suggests a progressive increase in the external supply of TA, although lower than previously thought. The maximum trend in the Bornholm Basin suggests an increase in external supply from the Southern catchment and/or the accumulation of internal production. The positive TA–phosphorus correlations underscore a significant internal source. The TA increase amplifies the CO2 uptake by 1.8–7.8% during spring/summer and reduces the CO2 outgassing by 3.4–7.7% in autumn/winter. The TA enhancement has the potential to buffer CO2‐induced acidification by 39–60% by 2050.

Spatial–Temporal Pattern Evolution and Differentiation Mechanism of Urban Dual Innovation: A Case Study of China’s Three Major Urban Agglomerations

Breakthrough innovation and incremental innovation have different impacts on economic development. For regional development, it is important to find a balance in dual innovation, which entails effective coordination of allocating innovation resources and managing risks. However, little attention has been given to the spatial relationship and differentiation mechanisms between breakthrough innovation and incremental innovation. Therefore, our research takes China’s three major urban agglomerations as examples, aiming to explore the spatial–temporal pattern evolution, influencing factors, spatial relationship, and spatial organizational patterns of breakthrough innovation and incremental innovation from 2000 to 2021. The research found that the spatial distribution of urban dual innovation is affected by the law of distance decay, and the spatial distribution of incremental innovation is more polycentric than that of breakthrough innovation. In terms of the differentiation mechanism, breakthrough innovation is more affected by the innovation atmosphere, while incremental innovation is more likely to be affected by the economic foundation and built environment. Our research effectively supplements the shortcomings in the spatial relationship research of breakthrough innovation and incremental innovation and provides references for formulating innovation policies.

Spatial-temporal patterns and evolution of carbon storage in China’s terrestrial ecosystems from 1980 to 2020

Spatial-temporal patterns and evolution of carbon storage in China’s terrestrial ecosystems from 1980 to 2020

Complements or competitors? Equity implications of taxis and ride-hail use in Chicago

Taxis have provided on-demand mobility for decades, including to people earning the lowest incomes. However, studies reveal gaps in taxi service across many US cities due to factors such as fleet size limitations, information asymmetry, and discriminatory practices. Since 2010, the emergence of ride-hail services such as Uber and Lyft has revolutionized the transportation landscape, offering faster and more affordable alternatives to traditional taxis. Yet it remains unclear the extent to which ride-hail services may fill spatial-temporal gaps in taxi services or how car access shifted after the arrival of ride-hail services. Additionally, taxis may still play an important role in car access for people who rely on cash payments. This study uses longitudinal trip-level ride-hail and taxi data in Chicago, IL, to examine the shifting trip patterns across the two modes. We compared the spatial-temporal pattern of taxi and ride-hail trips, as well as the share of taxi trips paid for in cash across three periods; we then developed spatial regression models to investigate what factors relate to taxi cash payments. Results show that ride-hail services may, directly and indirectly, fill taxi service gaps. Additionally, population density, land use mix, and the proportion of people of color and zero-car households are positively related to the share of taxi trips paid in cash, while the number of ride-hail trips and banking density are negatively associated with the percentage of cash payments. Policymakers should consider the evolving interplay between taxi and ride-hail services and the significance of cash payments to ensure equitable access to transportation options for individuals without cars.

Adenovirus infections: new insights for the clinical laboratory.

Since their discovery in 1953, research on human adenoviruses (HAdVs) has had diverse foci, resulted in groundbreaking discoveries, such as gene splicing, and generated powerful oncolytic constructs and expression vectors for vaccine development and gene therapy. In contrast, virologists working in this field have made relatively little progress toward the prevention and treatment of the wide spectrum of HAdV-associated diseases. The understanding of species-specific features of viral pathogenesis, or of the mechanisms underlying the establishment of latency and reactivation, is still limited. This group of viruses currently comprises 7 species, 51 serotypes, and 116 unique genotypes. This complexity manifests with a challenging pathophenotypic diversity. Some types are highly virulent, and others do not seem to cause disease in immunocompetent hosts. The assessment of viral load in blood and respiratory specimens has well-acknowledged clinical utility, but the lack of virus typing capabilities easily implementable in clinical laboratories represents a lingering major limitation to the interpretation of positive tests. Some HAdV infections do have severe consequences for both immunocompetent and immunocompromised patients, and the understanding of why this is the case will require more research. Clinical isolates and collections of positive specimens can provide unique resources to investigate the molecular bases of viral virulence and fitness and also help gather information of spatial-temporal patterns of viral circulation in susceptible communities, but they are extremely scarce. Clinical laboratories are underutilized interfaces between patients and academic scientists and have, therefore, a high potential to become valuable collaborators in research moving forward.

Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration

The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.

Temporal-spatial Patterns and Mechanisms of Illegal Street Vending from the Social Sensing Perspective: A Comparison between Law-enforcement-reported and Resident-complained Events

Temporal-spatial Patterns and Mechanisms of Illegal Street Vending from the Social Sensing Perspective: A Comparison between Law-enforcement-reported and Resident-complained Events

Spatial spillover effects of ecosystem service values in northeast China tiger and leopard national park based on spatial Durbin model

National parks are public green spaces planned and managed by the state, with the primary purpose of protecting natural ecosystems and cultural heritage. Evaluating the Ecosystem Service Value (ESV) of national parks and clarifying their ecological benefits can provide important scientific basis for the ecological protection and restoration of national parks. Therefore, taking the Northeast China Tiger and Leopard National Park (NCTLNP) as the study area, this research constructs an ESV assessment model based on Land Use and Land Cover Change (LUCC), using vegetation net primary productivity（NPP）as a biomass correction factor to modify the Equivalent Factor Approach (EFA). It aims to explore the spatial–temporal pattern changes of the ESV in the NCTLNP from 2000 to 2022. Additionally, the Future Land Use Simulation (FLUS) model is utilized to simulate the future LUCC pattern and ESV evolution trend under ecological protection. Finally, the spatial Durbin model (SDM)is employed, combining socio-economic and environmental factors such as population, GDP, and PM10 and so on, to reveal the driving mechanism of spatial spillover effects of ESV in the NCTLNP. The results indicate that:①The accuracy of the FLUS model is 93.12% with a Kappa coefficient of 0.87. The main LUCC of the NCTLNP is forest. Before 2020, the ecological land had a shrinking trend, while impervious is in a significant increasing trend. After 2020, there is an opposite trend.The most significant changes in LUCC were found in Wangqing County and Hunchun City. ②The ESV of the NCTLNP shows a fluctuating and increasing trend from 2000 to 2022. It increased from 69.315 billion yuan to 69.809 billion yuan. The main service function of the ecosystem in the study area is the regulatory services, which provide 58.03% of the ESV to the NCTLNP. The Hunchun City and Wangqing County have the fastest increasing rate. ③The ESV of the NCTLNP had a significant negative spillover effect, with spatial spillover coefficients of −0.135 and −0.236, respectively, which led to a siphoning phenomenon in the NCTLNP. Overall, by assessing the ESV of the NCTLNP and revealing the spatial spillover effects in the study area, it can provide a vital scientific foundation for the conservation and management of the national park, which will help to fulfill the sustainable development of the ecosystem and the conservation of biodiversity.

The impact of digital financial inclusion on bank performance: An exploration of mechanisms of action and heterogeneity.

The use of digital technology by banks and other financial institutions to facilitate financial inclusion is referred to as digital financial inclusion. This fusion of digital finance and traditional banking methods has the potential to impact banks' operational effectiveness. This study uses the panel effects model to examine the link between digital financial inclusion and bank performance in 30 Chinese provinces from 2012 to 2021. This research uses kernel density estimation to examine the spatial-temporal growth patterns of both variables. The mediator variable in examining how digital financial inclusion affects bank performance is risk-taking. Finally, the paper analyses the regional heterogeneity of the impact. It presents the following conclusions: (1) In China, digital financial inclusion and bank performance have constantly increased, with noticeable regional variances in their development levels. This regional inequality has widened gradually since 2018, yet it has not resulted in polarization. (2) The significant positive correlation between digital inclusive finance and banking performance indicates that banking performance tends to increase with the enhancement of digital inclusive finance. (3) Digital financial inclusion impacts bank performance, with risk-taking as a moderator. The spread of digital financial inclusion services enhances banks' willingness to take risks, enhancing overall efficiency. (4) Digital financial inclusion boosts bank performance in the Northwest, South, North, and East regions while lightly inhibiting it in the Central region. Based on the findings, this study makes bank and government suggestions.

Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China

Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China

Coupled space-time scale analysis for obtaining enhanced accuracy in species dynamics

Scale effects significantly affect the rationality and accuracy of ecological models, so temporal and spatial scales are included in the construction and application of ecological studies. However, few ecological pattern analyses focus on both of these scales simultaneously, which is especially important in terms of synchrony. In this study, based on fishery catch data and simulated data, we propose a framework for coupled space-time scale analysis and reveal possible deficiencies in conventional scale studies. We verified the feasibility and reliability of the scheme using simulated data with different combinations of temporal and spatial scales. The results showed that our scheme can simultaneously identify and localize scale features in the variation in species spatial-temporal patterns, specifically regarding the synchrony and amplitude of temporal dynamics (or spatial patterns) of multiple sites (or times). Our scheme revealed that spatial scales had an important effect on the intrinsic 13-year scale features, which significantly improved our understanding of global Rajiformes fishery dynamics. This showed that the spatial-temporal patterns and scale features obtained based on the coupled space-time scale analysis were complete and more accurate. At the same time, our scheme correctly identified complex pattern structures, such as data stratification and multiple combinations of time and space scales, which can reduce possible errors in practical applications. This scheme can be applied to the identification and prediction of the spatial-temporal patterns in biodiversity in the future and will help in formulating effective policies promoting sustainable fishery resources management and comprehensive conservation of endangered marine species.

Spatial-temporal dynamic evolution of lewy body dementia by metabolic PET imaging.

Lewy body dementia (LBD) is a neurodegenerative disease with high heterogeneity and complex pathogenesis. Our study aimed to use disease progression modeling to uncover spatial-temporal dynamic evolution of LBD in vivo, and to explore differential profiles of clinical features, glucose metabolism, and dopaminergic function among different evolution-related subtypes. A total of 123 participants (31 healthy controls and 92 LBD patients) who underwent 18F-FDG PET scans were retrospectively enrolled. 18F-FDG PET-based Subtype and Stage Inference (SuStaIn) model was established to illustrate spatial-temporal evolutionary patterns and categorize relevant subtypes. Then subtypes and stages were further related to clinical features, glucose metabolism, and dopaminergic function of LBD patients. This 18F-FDG PET imaging-based approach illustrated two distinct patterns of neurodegenerative evolution originating from the neocortex and basal ganglia in LBD and defined them as subtype 1 and subtype 2, respectively. There were obvious differences between subtypes. Compared with subtype 1, subtype 2 exhibited a greater proportion of male patients (P = 0.045) and positive symptoms such as visual hallucinations (P = 0.033) and fluctuating cognitions (P = 0.033). Cognitive impairment, metabolic abnormalities, dopaminergic dysfunction and progression were all more severe in subtype 2 (all P < 0.05). In addition, a strong association was observed between SuStaIn subtypes and two clinical phenotypes (Parkinson's disease dementia and dementia with Lewy bodies) (P = 0.005). Our findings based on 18F-FDG PET and data-driven model illustrated spatial-temporal dynamic evolution of LBD and categorized novel subtypes with different evolutionary patterns, clinical and imaging features in vivo. The evolution-related subtypes are associated with LBD clinical phenotypes, which supports the perspective of existence of distinct entities in LBD spectrum.

Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.

Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios

Revealing the spatial-temporal pattern of extreme heat on staple crops is crucial for proposing adaptation strategies to mitigate climate change-related agricultural risks. Studies in this field generally focus on the reproductive stage and rely on a single-staged threshold temperature to construct extreme heat indicators, which particularly neglect the vegetative stage of wheat. Therefore, to measure the extreme heat risks more scientifically across the entire life cycle of wheat, our study defines a new comprehensive extreme heat index (CEHI) that considers specific thresholds in both the reproductive and vegetative stages. In general, under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), approximately 20 % of the wheat-planting regions in China, especially in winter wheat regions such as the North China Plain, the Sichuan Basin, and the Xinjiang Tarim Basin, are projected to face high levels of extreme heat. Meanwhile, from 2010 to 2099, the average growth rates of extreme heat in China under RCP2.6, RCP4.5, and RCP8.5 scenarios are approximately 0.08, 0.06, and 0.1, respectively. By the century's end, the proportion of wheat-planting regions experiencing high and very high levels (CEHI≥0.4) of extreme heat is projected to increase from 18.0 %, 17.9 %, and 18.4 % to 21.4 %, 25.1 %, and 28.9 % under RCP2.6, RCP4.5, and RCP8.5 scenarios. Among them, RCP8.5 has the highest extreme heat severity on wheat in China, followed by RCP4.5, while RCP2.6 has minimal severity. Under the RCP8.5 scenario, the proportions of very high, high, moderate, low, and very low levels of extreme heat are 3.4 %, 18.5 %, 16.7 %, 14.9 %, and 46.5 %, respectively. Meanwhile, our study also emphasizes that although higher-latitude spring wheat regions will experience a significantly increasing trend in extreme heat, this may not spell long-term damage to wheat. Therefore, with consideration of varied temperature sensitivities across wheat growth stages, our study indicates that CEHI serves as an effective method to comprehensively and scientifically assess extreme heat on wheat. Furthermore, based on the regional and varietal differences in extreme heat under climate change, our study highlights the importance of developing region- and variety-specific policies to ensure the sustainability of wheat.

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.

Global value chains and carbon emission efficiency: evidence from 30 provinces in China

ABSTRACT The severity of global environmental change is a growing issue, primarily as an obvious result of carbon dioxide emissions. This article aims to recognize the causal relationship between Global Value Chains (GVCs) and Carbon Emission Efficiency (CEE) by using 30 provinces from 2000 to 2014 in China. This study calculates the GVCs level in the provincial level considering general trade, process trade, and intermediate trade agents; Besides, a model for the slacks-based measure of super-efficiency in data envelopment analysis (SBM-DEA) was used to measure the CEE. By applying fixed effect model techniques, our estimates reveal critical findings. First, we confirm the existence of the spatial-temporal evolution pattern of CEE in 30 provinces, showing an increased trend and noticeable east-west difference. Second, the econometric results suggest that the GVCs involvement is beneficiary for the CEE. Third, our results confirm that technological innovation and environmental regulation effects contribute to GVCs’ influence on CEE. These findings contribute to the formulation of appropriate carbon policies by the Chinese government, thereby advancing the objective of achieving carbon peaking by 2030 and carbon neutrality by 2050.

Spatial and Temporal Variations of Vegetation Phenology and Its Response to Land Surface Temperature in the Yangtze River Delta Urban Agglomeration

In the Yangtze River Delta urban agglomeration, which is the region with the highest urbanization intensity in China, the development of cities leads to changes in land surface temperature (LST), while vegetation phenology varies with LST. To investigate the spatial and temporal changes in vegetation phenology and its response to LST in the study area, this study reconstructed the time series of the enhanced vegetation index (EVI) based on the MODIS EVI product and extracted the vegetation phenology indicators in the study area from 2002 to 2020, including the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL), and analyzed the temporal–spatial patterns of vegetation phenology and LST in the study area, as well as the correlation between them. The results show that (1) SOS was advanced, EOS was postponed, and GSL was extended in the study area from 2002 to 2020, and there were obvious differences in the vegetation phenology indicators under different land covers and cities; (2) LST was higher in the southeast than in the northwest of the study area from 2002 to 2020, with an increasing trend; and (3) there are differences in the response of vegetation phenology to LST across land covers and cities, and SOS responds differently to LST at different times of the year. EOS shows a significant postponement trend with the annual mean LST increase. Overall, we found differences in vegetation phenology and its response to LST under different land covers and cities, which is important for scholars to understand the response of vegetation phenology to urbanization.

The Impact of Model Horizontal Resolution on Simulating Regional Climate Over East Asia Using Variable‐Resolution CESM2

AbstractIn this study, a variable‐resolution version of the Community Earth System Model (VR‐CESM) with mesh refinement (∼0.125°) over East Asia is used to simulate the regional climate in this region. For the evaluation of model performance and sensitivity to model resolution, the simulated near‐surface temperature and precipitation are compared with observations and simulation results from a globally quasi‐uniform (∼1°) CESM (UN‐CESM). Results show that VR‐CESM better simulates the spatial patterns and seasonal variations of mean temperature and precipitation than UN‐CESM over China. For extreme events, VR‐CESM improves the simulation of the occurrence frequency of wintertime daily minimum temperature and heavy precipitation. In regions with complex terrains, VR‐CESM better resolves the topographic forcing and captures the observed fine‐scale spatial patterns of temperature and precipitation, although precipitation is still overestimated. For East Asian summer monsoon precipitation, both UN‐CESM and VR‐CESM tend to overestimate (underestimate) the precipitation over northern (southern) China, which is associated with too strong meridional water vapor transport in the models and biases in the large‐scale circulation in the middle and upper troposphere. Different from previous studies with different physics parameterizations and refined domains, as the model resolution increases, simulated monsoon precipitation evolution is not obviously improved, and convective precipitation intensity decreases over eastern China. Despite this, our results indicate that VR‐CESM simulates regional climate, topographical forcing, and large‐scale circulations over East Asia reasonably well, and thus it can be applied for the future climate projection in the region.