Spatial Correlation Patterns Research Articles

Effect of Single and Double Moment Microphysics Schemes and Change in Cloud Condensation Nuclei, Latent Heating Rate Structure Associated with Severe Convective System over Korean Peninsula

To investigate the impact of advanced microphysics schemes using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted using Weather Research and Forecasting (WRF) model for a severe mesoscale convective system (MCS) formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation linked with the convective system are improved in the WDM6 simulation. During the developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with the mixing ratio of hydrometeors (cloud, rain, graupel, snow, and ice), the number concentration of cloud and rainwater are also predictable in WDM6. To understand the differences in the vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with the Nr readily available in WDM6. Varied vertical distribution and large differences in rain number concentration and rain particle mass is evident between the schemes. Inclusion of the number concentration of rain and cloud, CCN, along with the mixing ratio of different hydrometers has improved the storm morphology in WDM6. Furthermore, the latent heating (LH) profiles of six major phase transformation processes (condensation, evaporation, freezing, melting, deposition, and sublimation) are also computed from microphysical production terms to deeply study the storm vertical structure. The main differences in condensation and evaporation terms are evident between the simulations due to the varied treatment of warm rain processes and the inclusion of CCN activation in WDM6. To investigate cloud–aerosol interactions, numerical simulation is conducted by increasing the CCN (aerosol) concentration in WDM6, which simulated comparatively improved pattern correlation for rainfall simulation along with intense hydrometer distribution. It can be inferred that the change in aerosol increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution, and subsequent rainfall. Relatively, the WDM6 simulated latent heating profile distribution is more consistent with the ERA5 computed moisture source and sink terms due to the improved formulation of warm rain processes.

Spatio-temporal relationship between carbon emission and ecosystem service value under land use change: a case study of the Guanzhong Plain Urban Agglomeration, China

Changes in land use restructuring significantly impact carbon emissions and the provision of ecosystem service value (ESV). This study focuses on the Guanzhong Plain Urban Agglomeration, and accounts for carbon emissions and ecosystem service values caused by land use in the years 2000, 2005, 2010, 2015, and 2020. Carbon sources, carbon sinks, net carbon emission intensity, and ESV intensity were introduced as research variables and the spatial and temporal divergence and correlation patterns between them were examined. The results show that: 1) The carbon emission intensity of land use in the Guanzhong Plain Urban Agglomeration has increased significantly over the study period, showing a distribution pattern of high intensity in the centra regions and low intensity in the surrounding regions. Construction land was identified as the largest carbon source. 2) The overall ESV follows an increasing trend, with the total value increasing from 215,263.7 million to 216,776.2 million. The distribution of ESV intensity is low in the central regions and high in the surrounding regions, and significant changes were observed in the ESV loss and gain intensity of farmland and water body. 3) Carbon emissions and ESV show a significant negative spatial correlation, and both are dominated by low-high and high-low aggregation patterns. A spatial spillover effect of carbon emissions on ESV was observed. Through the correlation analysis of carbon emissions and ESV, theoretical support is provided for promoting regional low-carbon green development and eco-economic synergistic development.

Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes.

Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.

Decoupling the response of vegetation dynamics to asymmetric warming over the Qinghai-Tibet plateau from 2001 to 2020

Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001–2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.

Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero.

Effects of Moso Bamboo Expansion on the Spatial Pattern of Coarse Woody Debris in Secondary Coniferous and Broad-Leaved Mixed Forest in Wuxie National Forest Park, China

The spatial pattern of coarse woody debris (CWD) within the surrounding forest changes continuously during the expansion of Moso bamboo (Phyllostachys edulis), which partly reflects the death process of trees within the community. In this paper, we sampled an area of 0.72 hm2 within the transition zone of Moso bamboo expansion, which included continuous secondary coniferous and broad-leaved mixed forest (SF), transitional forest (TF), and Moso bamboo forest (MF) in Wuxie National Forest Park, Zhejiang Province. The spatial pattern and spatial correlation of CWD with different diameter classes, different decay classes, and different types in the three forests were analyzed using Ripley’s L function. The results showed that, with the expansion of Moso bamboo, the volume of CWD in TF was higher than those in SF and MF, and the total density of CWD in the three forests varied, with a decreasing order of MF &gt; TF &gt; SF. The spatial pattern of CWD in the three forests was aggregated on a relatively small scale, but the pattern changed from aggregated to random distribution with the increase in scale. Moreover, the CWD of Moso bamboo in TF showed aggregated distribution, and the aggregation intensity increased with the increase in scale. With the expansion of Moso bamboo, the spatial pattern and spatial correlation of CWD with different diameter classes, decay classes, and types have changed significantly in the three forests. Our study suggests that the expansion of Moso bamboo is increasingly strengthening the intraspecific and interspecific competition and accelerating the death of neighboring trees.

Study on the Spatiotemporal Evolution Characteristics and Influencing Factors on Green Building Development of City Clusters in the Yangtze River Delta Region in China

This paper explores the spatiotemporal evolution characteristics and spatial correlation patterns of green building development differences in 41 cities in the Yangtze River Delta region from 2012 to 2020 by means of the gravity center analysis model and spatial autocorrelation analysis. In addition, it further clarifies the impact factors of the spatial differentiation pattern of green building development in combination with GeoDetector based on four dimensional factors of population and economy, market environment, policy, and other factors. The results showed that: (1) According to the analysis of the number of green buildings in each city from 2012 to 2020 and the natural discontinuity method, the development pattern of green buildings in the Yangtze River Delta region city clusters shows an imbalance, being highly concentrated in the eastern coastal areas with Suzhou (1) and Shanghai as the core. The overall trajectory of the center of gravity shows the development from southeast to northwest. (2) The global Moran’s I of green buildings in the Yangtze River Delta region city clusters is greater than 0, and all passed the significance test (Z &gt; 1.96, p &lt; 0.01), indicating that the green buildings in the Yangtze River Delta region city clusters show typical spatial aggregation characteristics. By using the local LISA index, it is found that in the H-H spatial autocorrelation distribution pattern with Suzhou (1) and Shanghai as the core, the core city has a strong attraction ability and relatively low radiation ability. (3) Based on the explanatory power mean, the main driving factors of the spatial differentiation pattern of green building development in the Yangtze River Delta region city clusters are education level (0.6656), technical level (0.6269), and the gross domestic product (0.6091). The factor interaction shows a two-factor enhancement and nonlinear enhancement effect, and there is neither a weakening nor an independent relationship.

Spatial association network of economic resilience and its influencing factors: evidence from 31 Chinese provinces

The spatial correlation pattern of economic resilience is an important proposition for China’s sustainable economic development. This paper measures the economic resilience of 31 provinces in China from 2012 to 2020, and explores the spatial correlation of economic resilience from the overall, group and individual perspectives and its influencing factors. The results show that first, a tightly ordered hierarchy of economic resilience formed in each province of China after 2016. Among them, Jiangsu, Shandong, Guangdong, Hubei, and Shaanxi are the most important clustering points and radiation centers in the spatial correlation framework of economic resilience. Second, being adjacent to marginal and core provinces will maintain the province’s centrality index category to the greatest extent, while being adjacent to sub-core and general provinces leads the province to gain more opportunities for upward transfer. Third, the essence of the interprovincial economic resilience subordination linkage in China is manifested in the aggregation of city clusters or economic circles. The northern economic resilience linkage system with the Bohai Rim as the core contains more provinces but is less stable. Provinces located in the Yangtze River Delta region are the opposite. Fourth, the proximity of geographical location and the difference in human capital level drive the formation of spatial association networks, while the difference in external openness and the difference in physical capital inhibit the formation of networks.

The Study of Wind Field ERA-20C in Monsoon Domains for Rainfall Predictor in Indonesia (Java, Sumatra, and Borneo)

In recent years, various research institutions have developed diverse global data reanalysis projects. This provides an opportunity to gain long-term of meteorological data for local scale. This study aims to select the potential predictor of wind fields u and v of the ERA-20C dataset, a reanalysis dataset, at 850 mb from seven domains or windows of Asian, Maritime Continent, Australian, and Western North Pacific monsoon related physically to rainfall anomaly patterns in Indonesia. The vector wind velocity scalar was obtained by using a Helmholtz decomposition to separate the total circulation v = (u,v) into the divergent component/velocity potential (χ) or Phi and rotational component/stream function (ψ) or Psi for obtaining the scalar variable of vector wind velocity. The method applied Singular value decomposition (SVD) to identify pairs of spatial patterns (expansion coefficients) between the predictors of Phi and Psi in seven domains, with rainfall data from 48 stations in Java, Sumatra, and Borneo Islands from 1981 to 2010. The results revealed that spatial patterns correlations of Java Islands were the highest in the Maritime Continent monsoon domain (80o−150o E and 15oS−15o N), while Sumatra and Borneo Island were in the Western North Pacific monsoon domain (100o–130o E and 5o–15o N) with predictor Psi. The lowest correlation for Java, Sumatra, and Borneo was the Australian monsoon domain (110o E–130o E and 5o S–15o S) with predictor Phi. In general, spatial pattern correl-ations of Java Island were higher than others, agreeing with monsoonal rainfall type dominantly in the region.

Tropical Surface Temperature and Atmospheric Latent Heating: A Whole-Tropics Perspective Based on TRMM and ERA5 Datasets

Tropical surface temperature (TST) and its connection with atmospheric heating, including tropical latent heating (TLH), is essential to the interannual variability of tropical atmospheric circulation and global teleconnection. Utilizing seasonally averaged satellite-based TRMM precipitation data as a proxy of TLH and ERA5-based TST data from 1998 to 2018, we reveal some new features in terms of cross-hemispheric connection in the TLH and TST variability by decomposing them into equatorially symmetric and antisymmetric components. We find surprisingly that the spatial patterns of TLH projected upon the first principal components (PC1) of symmetric and antisymmetric TSTs over the whole-tropics, are very similar to each other, seemingly at odds with the classic Mastuno–Gill theory. The similarity in the projected TLH patterns is mainly because the PC1s of symmetric and antisymmetric TSTs co-vary temporally with a very high correlation. We use the spatial pattern of local correlation between symmetric and antisymmetric components, for both TST and TLH to depict geographic dependence of the symmetric–antisymmetric connection. We suggest that a whole-tropics perspective, which takes the different but connected nature of equatorially symmetric and antisymmetric modes across the whole-tropics into consideration, may well be useful in understanding and predicting tropical climate variability because clarifying the puzzle raised in this research from such a perspective about the consistency between the observation and the classic Mastuno–Gill theory is directly related to the fundamental dynamics of tropical systems, such as Walker circulation, monsoons, and their relationship with underlying land and sea conditions.

Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.

Provincial Inclusive Green Growth Efficiency in China: Spatial Correlation Network Investigation and Its Influence Factors

Inclusive green growth efficiency (IGGE) analysis is an effective tool for improving coordinated economic, social, and environmental development. This study incorporated the game cross-efficiency DEA to measure the IGGE of 30 provinces in China. Then, the modified spatial gravity model and social network analysis model were applied to construct and analyze the spatial correlation network structure of the IGGE. The quadratic assignment procedure was used to mine the influencing factors that affect the formation and evolution of the spatial correlation network of the IGGE. The results are as follows. (1) During the study period, there were significant differences in the IGGE among the 31 provinces, among which the eastern provinces were higher than the central and western provinces. (2) The spatial correlation of the IGGE presented a complex and multi-threaded network structure, indicating that the IGGE has a noticeable cross-regional spillover effect. Beijing, Tianjin, Zhejiang, Shanghai, Jiangsu, and Guangdong played the role of the “net spillover” block. Qinghai, Guizhou, Guangxi, and the surrounding provinces played the role of the “primary beneficial”. The Yangtze delta and Pearl River Delta economic zone (primarily including Shanghai and Guangdong) acted as a “bridge” to the Yunnan–Guizhou region and the surrounding provinces. (3) The spatial adjacency, degree of openness, economic development, and environmental governance were the prominent factors influencing the formation and evolution of the IGGE spatial correlation network. This work provides an example of constructing an IGGE correlation network while considering various factors, such as the economy, population, and distance. It also could help policymakers clarify the IGGE spatial correlation pattern and the provinces’ roles and potential for IGGE synergic improvement.

The Spatio-Temporal Pattern of Regional Coordinated Development in the Common Prosperity Demonstration Zone—Evidence from Zhejiang Province

Achieving common prosperity is the essential requirement of socialism and promoting regional coordinated development (RCD) is an important path to achieving common prosperity. This study uses data from Zhejiang Province from 2011 to 2020, a demonstration zone of common prosperity, to construct an evaluation model of RCD, assess the regional development level and coordinated development degree, and then analyze the regional differences and spatial correlation pattern of RCD. The following results were obtained: (1) The economic, social, and ecological subsystems of all cities or counties show a continuous or fluctuating rise, and the regional coordinated development level of each study unit also shows a rising trend. This shows that steady regional development is the fundamental material basis for common prosperity. (2) The level of economic and social development shows a pattern of high in the north and low in the south, while the level of ecological development shows a pattern of high in the south and low in the north. The level of RCD evolves from a very uneven spatial distribution to a good level of coordinated development in most cities. It shows that the equalization of development among regions is a realistic manifestation of common prosperity. (3) The level of RCD in Zhejiang Province has greater intra-regional than inter-regional differences, and the differences in RCD in the north are greater than those in the south. The differences between regions have been narrowing. It shows a significant positive spatial correlation, with high-value regions tending to be adjacent to high-value regions and low-value regions tending to be adjacent to low-value regions. In sum, the development of Zhejiang Province in the last decade provides evidence of its role as a demonstration zone for common prosperity. It confirms that coordinated regional development is the fundamental way to achieve common prosperity.

Correlation of spatial patterns of endocardial pace mapping to underlying scar topography in patients with scar-related ventricular tachycardia.

Endocardial pace mapping (PM) can identify conducting channels for ventricular tachycardia (VT) circuits in patients with structural heart disease (SHD). Recent findings show the temporal and spatial pattern of PM may aid identification of the surface harboring VT isthmii. The specific correlation of PM patterns to scar topography has not been examined. To correlate the pattern of endocardial PMs to underlying scar topography in SHD patients with VT. Data from patients undergoing VT ablation from August 2018 to February 2022 were reviewed. Sixty-three patients with SHD-related VT (mean age 65 ± 14 years) with 83 endocardial PM correlation maps were analysed. Two main correlation patterns were identified, an "abrupt-change correlation pattern (AC-pattern)" and "centrifugal-attenuation correlation pattern (CA-pattern)." AC-pattern had lower scar ratio (unipolar/bipolar % scar area; 1.1 vs. 1.5, p < .001), had longer maximal stimulus-QRS intervals (97.5 vs. 68 ms, p = .002), and higher likelihood of endocardial dominant scar (11/21 [52%] vs. 3/38 [8%], p < .001) than CA-pattern seen on intracardiac echocardiography (ICE). In contrast, CA-pattern was more likely to have epicardial dominant scar or mid-intramural scar on ICE (epicardial dominant scar; CA-pattern: 12/38 [32%] vs. AC-pattern: 1/21 [5%], p = .02, mid-intramural scar; CA-pattern: 15/38 [39%] vs. AC-pattern: 1/21 [5%], p = .005). The spatial pattern of endocardial PM in SHD-related VT directly correlates with scar topography. AC-pattern is associated with endocardial dominant scar on ICE with lower scar ratio and longer stimulus-QRS intervals, whereas CA-pattern is strongly associated with epicardial dominant or mid-intramural scar with higher scar ratio and shorter stimulus-QRS intervals.

Spatiotemporal Dynamic Evolution and Gravity Center Migration of Carbon Emissions in the Main Urban Area of Chongqing over the Past 20 Years

Global warming caused by carbon emissions is an environmental issue that is of great concern to all walks of life. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important part of achieving the regional double-carbon goals. Taking the main urban area of Chongqing as an example, based on the data of land use and energy consumption, this study estimated the carbon emissions of 153 townships and streets in the main urban area of Chongqing from 2000 to 2020 by using the carbon emission coefficient method. Additionally, using the ESTDA framework to pass the LISA time path, spatiotemporal transition, and the standard deviation ellipse model from the perspective of spatiotemporal interaction, the spatiotemporal dynamic evolution of carbon emissions in the main urban area and the shift in the center of gravity over the past 20 years were analyzed. The results showed that: ① in the past 20 years, the carbon emissions in the main urban and rural areas have had a significant positive spatial correlation, and the spatial convergence showed a trend of first decreasing and then increasing. ② In the past 20 years, there were 126 township streets with low and medium relative lengths (accounting for 82%), indicating that the local spatial structure of township carbon emissions in the main urban area had strong stability; the total number of township streets with low and medium curvatures was 138 (accounting for 90%), indicating that the volatility of the main urban and rural carbon emissions in the direction of spatial dependence was relatively stable; there were 113 township streets (accounting for 74%) of the synergistic growth type, indicating that the main urban and rural carbon emissions were relatively stable. The emission pattern had strong spatial integration. ③ In the past 20 years, the spatiotemporal agglomeration index was greater than 70%, indicating that the local spatial correlation pattern and agglomeration characteristics of carbon emissions in the main urban and rural areas had strong stability. 4 In the past 20 years, the center of carbon emission in the main urban area had been distributed between 106°30'43″-106°32'42″E, 29°33'34″-29°35'56″N, and the center of gravity shifted to the northeast as a whole. The spatial distribution changed from the "northwest-southeast" pattern to the "northeast-southwest" pattern. These results can provide reference for the green and low-carbon sustainable development of Chongqing and the formulation of differentiated emission reduction policies, as well as provide reference for other similar mountain cities in western China.

The Coupling and Coordination Characteristics and Influencing Factors of Green Innovation Efficiency (GIE) and Economic Development Levels in China

Green innovation exchanges low emissions, low pollution and low output for economic development. At the same time, economic development can provide important economic support for green innovation, and managing the connection between green innovation and economic development is significant for the balanced development of a region. There is no unified definition of green innovation efficiency in academic circles, but the definitions can be divided into three types: innovation aimed at minimizing environmental damage; innovation of environmental performance; and innovation for environmental development or environmental improvement. Based on relevant data from 30 provinces and cities in China from 2008 to 2019, this paper uses the coupling coordination model and spatial autocorrelation analysis to investigate the coordination status of green innovation efficiency (GIE), the economic development standard, and their evolution over time and space. We also use the Tobit model to analyze influential factors in coupling coordination. The results show that the overall development trend of coupling coordination is rising, and the gap between the coordination standard between the east and west is obvious. Considering the spatial correlation pattern, the high innovation area is mainly centered in the east. It is empirically demonstrated through the Tobit model that patented technology facilitates the balanced development of regional GIE and economic development. These conclusions provide a new viewpoint for the projection of green innovation policies, help to combine green innovation with economic development, and afford an academic foundation for the government to mark out future development directions and policies.

Spatio-temporal analysis of b-value prior to 28 April 2021 Assam Earthquake and implications thereof

Here, we report a Spatio-temporal analysis of the frequency magnitude distribution of earthquakes (b-value) before the 28th April 2021 (Mw 6.4) earthquake event observed in northeast India. To esti- mate the average b-value for the study region, a data set of 750 earthquake events with magnitude Mw ≥ 3.9 is extracted from the homogenous part of the earthquake catalog (1950-2021) documented by the United States Geological Survey (USGS) and International seismological center (ISC) in the region. For spatial analysis of the disparities in b-value, the whole study region is subdivided into 16 square grids of dimension 1o×1o and the b-value is calculated for each subsection. In congruence with other studies, this work yields b-values ranging from 0.66 to 1.25. After the calculation of the b-value for each grid, it is observed that the grid with the epicentral location of the 28th April 2021 (6.4) earthquake has a low b-value. Accordingly, the spatial correlation and aberrant pattern between b-value and focal depth have been comprehensively explored. It is observed that the b-value sig- nificantly dips within a depth range of ~15-35 km which implicates high-stress accumulation and crustal homogeneity. The depth-wise variation in b-value infers the antithetical relationship between b-value and crustal stress. Mostly interplate earthquakes are observed in the study region; thereby hinting at intense seismicity at the upper crust.

Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Ecosystem service value (ESV) is a crucial indicator for evaluating ecosystem health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study took the Liuxi River Basin in China as the research object and used the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area was spatially stratified by using the local bivariate spatial correlation pattern to mine the potential driving factors of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV had high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation patterns were of statistical significance. (2) The spatial distribution characteristics of ESV were affected by various driving factors to varying degrees. The order of their degree of influence on ESV was per capita green area &gt; slope &gt; the proportion of urban and rural human settlements &gt; river distance accessibility &gt; population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers spatial autocorrelation and spatial heterogeneity. It effectively captured the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

Demography-Oriented Urban Spatial Matching of Service Facilities: Case Study of Changchun, China

People-oriented urban planning requires that service facilities should efficiently meet individual and community activity needs across the demographic landscape that defines a city. To develop a conceptual basis for urban spatial infrastructure optimization, we empirically studied existing population activities and service facilities in Changchun, China, using kernel density estimation, bivariate spatial autocorrelation analysis, and other models. The spatial relationships we derived from multiple sources of big data such as mobile phone signaling and POI data indicated that the intensity of population activity has obvious temporal regularity, and its spatial distribution is “center-periphery.” Service facilities display a “One main and two subs” distribution with no obvious spatial dependence between the core’s density and diversity. Population activities and service facility diversity show a high-high spatial correlation and multiple matching patterns. At the same time, a certain degree of spatial mismatch between different age groups and service facilities was also observed. Our research suggests several urban renewal actions to rectify this mismatch, such as: decentralizing the core area medical service facilities; reducing the attractiveness of the core area and its traffic pressure; and renewing and renovating old facilities to reduce construction costs. At a government planning level, construction along the periphery of the urban can enrich the diversity of its service facilities to improve the efficiency of spatial allocation.

Diagnostic evaluation of extreme winter rainfall events over the Arabian Peninsula using high‐resolution weather research and forecasting simulations

AbstractThe sensitivity of different cumulus physical parameterization schemes for simulating extreme winter precipitation events over the Arabian Peninsula (AP) is investigated using a high‐resolution weather research and forecasting (WRF) model. For winters in 2001–2016, the following three parameterization schemes are examined: (i) Kain–Fritsch (KF), (ii) Betts–Miller–Janjić (BMJ), and (iii) Grell–Freitas (GF). The simulation results suggest that the AP extreme winter rainfall events are best simulated using the KF, followed by the BMJ, in terms of spatial distribution and intensity. The spatial pattern correlation coefficient between the model‐simulated and observed rainfall is highest with KF (0.94), followed by BMJ (0.91) and GF (0.76). These results are attributed to a better representation of the moisture transport associated with upper‐tropospheric cyclonic circulation and potential vorticity intrusions. By contrast, the GF scheme fails to simulate moisture convergence and updrafts, leading to an unrealistic representation of cloud hydrometeors and an improper organization of convection and associated extreme rainfall intensities. Meanwhile, the KF and BMJ also successfully simulate the dynamics and thermodynamics of extreme rainfall events that are usually driven by synoptic forcing. The study results suggest that the choice of cumulus parameterization schemes in the WRF model is critical for reliable simulation of extreme rainfall in the hyperarid AP region.