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Keywords = spatiotemporal homogeneity

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18 pages, 22954 KiB  
Article
Spatiotemporal Analysis of Drought Variation from 2001 to 2023 in the China–Mongolia–Russia Transboundary Heilongjiang River Basin Based on ITVDI
by Weihao Zou, Juanle Wang, Congrong Li, Keming Yang, Denis Fetisov, Jiawei Jiang, Meng Liu and Yaping Liu
Remote Sens. 2025, 17(14), 2366; https://doi.org/10.3390/rs17142366 - 9 Jul 2025
Viewed by 365
Abstract
Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East [...] Read more.
Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article
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21 pages, 3527 KiB  
Article
Effects of Environmental Temperature Variation on the Spatio-Temporal Shoaling Behaviour of Adult Zebrafish (Danio rerio): A Two- and Three-Dimensional Analysis
by Mattia Toni, Flavia Frabetti, Gabriella Tedeschi and Enrico Alleva
Animals 2025, 15(14), 2006; https://doi.org/10.3390/ani15142006 - 8 Jul 2025
Viewed by 344
Abstract
Global warming is driving significant changes in aquatic ecosystems, where temperature fluctuations influence biological processes across multiple levels of organisation. As ectothermic organisms, fish are particularly susceptible, with even minor thermal shifts affecting their metabolism, behaviour, and overall fitness. Understanding these responses is [...] Read more.
Global warming is driving significant changes in aquatic ecosystems, where temperature fluctuations influence biological processes across multiple levels of organisation. As ectothermic organisms, fish are particularly susceptible, with even minor thermal shifts affecting their metabolism, behaviour, and overall fitness. Understanding these responses is essential for evaluating the ecological and evolutionary consequences of climate change. This study investigates the effects of acute (4-day) and chronic (21-day) exposure to three temperature regimes—18 °C (low), 26 °C (control), and 34 °C (high)—on the spatio-temporal shoaling behaviour of adult zebrafish (Danio rerio). Groups of four fish were tested for six minutes in water maintained at the same temperature as their prior acclimation. Shoaling behaviour was assessed by analysing shoal structure—encompassing shoal dimensions and cohesion—as well as spatial positioning. Parameters measured included inter-fish distance, shoal volume, shoal area, homogeneity index, distance to the centroid, and the shoal’s vertical and horizontal distribution. Results revealed complex behavioural changes influenced by both temperature and duration of exposure. At 18 °C, zebrafish showed a marked preference for the bottom zone and exhibited no significant temporal modulation in exploratory behaviour—patterns indicative of heightened anxiety-like responses. In contrast, exposure to 34 °C resulted in increased shoal cohesion, particularly under chronic conditions, and a progressive increase in environmental exploration over the six-minute test period. This enhancement in exploratory activity was especially evident when compared to the first minute of the test and was characterised by greater vertical movement—reflected in the increased use of the upper zone—and broader horizontal exploration, including more frequent occupation of peripheral areas. These findings align with previous research linking thermal variation to neurobiological and proteomic alterations in zebrafish. By elucidating how temperature modulates social behaviour in ectotherms, this study offers valuable insights into the potential behavioural impacts of climate change on aquatic ecosystems. Full article
(This article belongs to the Section Aquatic Animals)
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16 pages, 13161 KiB  
Article
Experimental Assessment of the Effects of Gas Composition on Volatile Flames of Coal and Biomass Particles in Oxyfuel Combustion Using Multi-Parameter Optical Diagnostics
by Tao Li, Haowen Chen and Benjamin Böhm
Processes 2025, 13(6), 1817; https://doi.org/10.3390/pr13061817 - 8 Jun 2025
Viewed by 473
Abstract
This experimental study examines the particle-level combustion behavior of high-volatile bituminous coal and walnut shell particles in oxyfuel environments, with a particular focus on the gas-phase ignition characteristics and the structural development of volatile flames. Particles with similar size and shape distributions (a [...] Read more.
This experimental study examines the particle-level combustion behavior of high-volatile bituminous coal and walnut shell particles in oxyfuel environments, with a particular focus on the gas-phase ignition characteristics and the structural development of volatile flames. Particles with similar size and shape distributions (a median diameter of about 126 µm and an aspect ratio of around 1.5) are combusted in hot flows generated using lean, flat flames, where the oxygen mole fraction is systematically varied in both CO2/O2 and N2/O2 atmospheres while maintaining comparable gas temperatures and particle heating rates. The investigation employs a high-speed multi-camera diagnostic system combining laser-induced fluorescence of OH, diffuse backlight-illumination, and Mie scattering to simultaneously measure the particle size, shape, and velocity; the ignition delay time; and the volatile flame dynamics during early-stage volatile combustion. Advanced detection algorithms enable the extraction of these multiple parameters from spatiotemporally synchronized measurements. The results reveal that the ignition delay time decreases with an increasing oxygen mole fraction up to 30 vol%, beyond which point further oxygen enrichment no longer accelerates the ignition, as the process becomes limited by the volatile release rate. In contrast, the reactivity of volatile flames shows continuous enhancement with an increasing oxygen mole fraction, indicating non-premixed flame behavior governed by the diffusion of oxygen toward the particles. The analysis of the flame stand-off distance demonstrates that volatile flames burn closer to the particles at higher oxygen mole fractions, consistent with the expected scaling of O2 diffusion with its partial pressure. Notably, walnut shell and coal particles exhibit remarkably similar ignition delay times, volatile flame sizes, and OH-LIF intensities. The substitution of N2 with CO2 produces minimal differences, suggesting that for 126 µm particles under high-heating-rate conditions, the relatively small variations in the heat capacity and O2 diffusivity between these diluents have negligible effects on the homogeneous combustion phenomena observed. Full article
(This article belongs to the Special Issue Experiments and Diagnostics in Reacting Flows)
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31 pages, 13950 KiB  
Article
An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models
by Amir Aieb, Antonio Liotta, Alexander Jacob, Iacopo Federico Ferrario and Muhammad Azfar Yaqub
Remote Sens. 2025, 17(11), 1916; https://doi.org/10.3390/rs17111916 - 31 May 2025
Viewed by 866
Abstract
Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and [...] Read more.
Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article
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24 pages, 22764 KiB  
Article
The TSformer: A Non-Autoregressive Spatio-Temporal Transformers for 30-Day Ocean Eddy-Resolving Forecasting
by Guosong Wang, Min Hou, Mingyue Qin, Xinrong Wu, Zhigang Gao, Guofang Chao and Xiaoshuang Zhang
J. Mar. Sci. Eng. 2025, 13(5), 966; https://doi.org/10.3390/jmse13050966 - 16 May 2025
Viewed by 682
Abstract
Ocean forecasting is critical for various applications and is essential for understanding air–sea interactions, which contribute to mitigating the impacts of extreme events. While data-driven forecasting models have demonstrated considerable potential and speed, they often primarily focus on spatial variations while neglecting temporal [...] Read more.
Ocean forecasting is critical for various applications and is essential for understanding air–sea interactions, which contribute to mitigating the impacts of extreme events. While data-driven forecasting models have demonstrated considerable potential and speed, they often primarily focus on spatial variations while neglecting temporal dynamics. This paper presents the TSformer, a novel non-autoregressive spatio-temporal transformer designed for medium-range ocean eddy-resolving forecasting, enabling forecasts of up to 30 days in advance. We introduce an innovative hierarchical U-Net encoder–decoder architecture based on 3D Swin Transformer blocks, which extends the scope of local attention computation from spatial to spatio-temporal contexts to reduce accumulation errors. The TSformer is trained on 28 years of homogeneous, high-dimensional 3D ocean reanalysis datasets, supplemented by three 2D remote sensing datasets for surface forcing. Based on the near-real-time operational forecast results from 2023, comparative performance assessments against in situ profiles and satellite observation data indicate that the TSformer exhibits forecast performance comparable to leading numerical ocean forecasting models while being orders of magnitude faster. Unlike autoregressive models, the TSformer maintains 3D consistency in physical motion, ensuring long-term coherence and stability. Furthermore, the TSformer model, which incorporates surface auxiliary observational data, effectively simulates the vertical cooling and mixing effects induced by Super Typhoon Saola. Full article
(This article belongs to the Section Ocean Engineering)
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26 pages, 47051 KiB  
Article
Dynamic Light Path and Bidirectional Reflectance Effects on Solar Noise in UAV-Borne Photon-Counting LiDAR
by Kuifeng Luan, Jinhui Zheng, Wei Kong, Weidong Zhu, Lizhe Zhang, Peiyao Zhang and Lin Liu
Remote Sens. 2025, 17(10), 1708; https://doi.org/10.3390/rs17101708 - 13 May 2025
Viewed by 502
Abstract
Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering [...] Read more.
Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering dynamics. Validated via single-photon LiDAR field tests on diverse coastal terrains at Jiajing Island, China, BNR-B reveals the following: (1) Solar zenith/azimuth angles non-uniformly modulate noise fields—higher solar zenith angles reduce noise intensity and homogenize spatial distribution; (2) surface reflectivity linearly correlates with noise rate (R2 > 0.99), while roughness governs scattering directionality through micro-facet redistribution. BNR-B achieves 28.6% higher noise calculation accuracy than Lambertian models, with a relative phase error < 2% against empirical data. As the first BRDF-derived solar noise correction framework for coastal LiDAR, it addresses critical limitations of isotropic assumptions by resolving directional noise modulation. The model’s adaptability to marine–terrestrial interfaces enhances precision in coastal monitoring and submarine mapping, offering transformative potential for geospatial applications requiring photon-counting LiDAR in complex environments. Key innovations include dynamic coupling of geometric optics and surface scattering physics, enabling robust spatiotemporal noise quantification, critical for high-resolution terrain reconstruction. Full article
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21 pages, 4957 KiB  
Article
Cross-Sectional Distribution of Microplastics in the Rhine River, Germany—A Mass-Based Approach
by David Range, Jan Kamp, Georg Dierkes, Thomas Ternes and Thomas Hoffmann
Microplastics 2025, 4(2), 27; https://doi.org/10.3390/microplastics4020027 - 11 May 2025
Cited by 1 | Viewed by 1492
Abstract
The focus in microplastic research has shifted from marine ecosystems towards freshwater ecosystems. Still, most studies are based on small sample numbers, both spatially and temporally. Little is known about the spatiotemporal variability of microplastics (MPs) in large river systems such as the [...] Read more.
The focus in microplastic research has shifted from marine ecosystems towards freshwater ecosystems. Still, most studies are based on small sample numbers, both spatially and temporally. Little is known about the spatiotemporal variability of microplastics (MPs) in large river systems such as the Rhine River, Germany. Within our study, we performed four cross-sectional sampling campaigns at two sites in the Rhine River, at Koblenz and Emmerich, involving depth-distributed sampling over a particle size range from 10 µm to 25 mm. For plastic particle analysis, we used both optical and thermoanalytical approaches to determine mass-based polymer concentrations. Our results show that MP variability within the water column is complex, but mostly follows the particles density: the ratio between superficial MPs concentration and mean concentration of the verticals was >1 for lighter polymers with a density below 1.04 g/cm3 and <1 for polymers with a density above 1.04 g/cm3 among all size classes with only a few exceptions, even though the Rouse theory would indicate a more homogeneous distribution for small particle sizes. Large sampling volumes are essential, particularly for larger MP particles, as the coefficient of variation rises with particle size. At our study sites, no significant lateral variation was apparent, while during a flood event, MP concentrations were significantly higher than during low and mean water stages. This study is the first to (i) gain insights into cross-sectional MPs distribution in the Rhine River and (ii) account for particle mass concentrations, and thus lays the foundation for potential future MPs flux monitoring. Full article
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35 pages, 17136 KiB  
Article
Spatio-Temporal Adaptive Voltage Coordination Control Strategy for Distribution Networks with High Photovoltaic Penetration
by Xunxun Chen, Xiaohong Zhang, Qingyuan Yan and Yanxue Li
Energies 2025, 18(8), 2093; https://doi.org/10.3390/en18082093 - 18 Apr 2025
Cited by 1 | Viewed by 451
Abstract
With the increasing penetration of distributed photovoltaics (PVs) in distribution networks (DNs), issues like voltage violations and fluctuations are becoming more prominent. This paper proposes a spatio-temporal adaptive voltage coordination control strategy involving multiple timescales and multi-device collaboration. Aiming at the heavy workload [...] Read more.
With the increasing penetration of distributed photovoltaics (PVs) in distribution networks (DNs), issues like voltage violations and fluctuations are becoming more prominent. This paper proposes a spatio-temporal adaptive voltage coordination control strategy involving multiple timescales and multi-device collaboration. Aiming at the heavy workload caused by the continuous sampling of real-time data in the whole domain, an intra-day innovative construction of intra-day minute-level optimization and real-time adaptive control double-layer control mode are introduced. Intra-day minute-level refinement of on-load tap changer (OLTC) and step voltage regulator (SVR) day-ahead scheduling plans to fully utilize OLTC and SVR voltage regulation capabilities and improve voltage quality is discussed. In real-time adaptive control, a regional autonomy mechanism based on the functional area voltage quality risk prognostication coefficient (VQRPC) is innovatively proposed, where each functional area intelligently selects the time period for real-time voltage regulation of distributed battery energy storage systems (DESSs) based on VQRPC value, in order to improve real-time voltage quality while reducing the data sampling workload. Aiming at the state of charge (SOC) management of DESS, a novel functional area DESS available capacity management mechanism is proposed to coordinate DESS output and improve SOC homogenization through dynamically updated power–capacity availability (PCA). And vine model threshold band (VMTB) and deviation optimization management (DOM) strategies based on functional area are innovatively proposed, where DOM optimizes DESS output through the VMTB to achieve voltage fluctuation suppression while optimizing DESS available capacity. Finally, the DESS and electric vehicle (EV) cooperative voltage regulation mechanism is constructed to optimize DESS capacity allocation, and the black-winged kite algorithm (BKA) is used to manage DESS output. The results of a simulation on a modified IEEE 33 system show that the proposed strategy reduces the voltage fluctuation rate of each functional area by an average of 36.49%, reduces the amount of data collection by an average of 68.31%, and increases the available capacity of DESS by 5.8%, under the premise of a 100% voltage qualification rate. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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20 pages, 6415 KiB  
Article
Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution
by Shenghao Zhu, Guanyi Yin, Qingzhi Sun, Zhan Zhang, Guanghao Li and Liangfei Gao
Sustainability 2025, 17(7), 3024; https://doi.org/10.3390/su17073024 - 28 Mar 2025
Cited by 2 | Viewed by 416
Abstract
With the development of new agricultural business entities in China, a complex competitive evolutionary dynamic has emerged among diversified agricultural business entities (abbreviated as ABEs), including farmers (traditional ABEs), cooperatives, agricultural enterprises, and family farms (new ABEs). Based on the Lotka–Volterra model, the [...] Read more.
With the development of new agricultural business entities in China, a complex competitive evolutionary dynamic has emerged among diversified agricultural business entities (abbreviated as ABEs), including farmers (traditional ABEs), cooperatives, agricultural enterprises, and family farms (new ABEs). Based on the Lotka–Volterra model, the dominance index, the Shannon–Wiener index of ecological theories, and the geo-detector, this study examines the spatiotemporal evolution and driving factors of ABEs’ structural changes across 286 Chinese cities from 2012 to 2021. Key findings include: (1) Farmers maintain absolute numerical dominance, but their relative advantage has declined. (2) The Shannon–Wiener index of diversified ABEs has increased significantly, indicating that differences between ABEs decreased, which means a trend toward structural homogenization. High Shannon–Wiener index values were observed in the Northeast Plain, Xinjiang, Hebei, Gansu, and Shanxi, while low values were concentrated in Yunnan, Guizhou, and the Guangdong-Guangxi region. Both areas experienced a shrinking trend. (3) Agricultural production factors such as multiple cropping indexes and theindustrial structure strongly explained the structural changes to ABEs, while the explanatory power of socio-economic factors can be enhanced after the interaction with agricultural production factors. (4) The relationship between farmers and new ABEs has shifted from a symbiotic relationship favoring farmers to a symbiotic relationship favoring new ABEs, with a significant spatial heterogenous layout among 286 cities. This study proposes a three-stage differentiation framework for ABEs: a simple structure dominated by traditional farmers, a competitive evolutionary dynamic among diversified ABEs, and a modernized structure led by new agricultural business entities. Based on these stages, this paper provides targeted recommendations for building a high-quality ABE system and advancing agricultural modernization. Full article
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25 pages, 27603 KiB  
Article
Evaluation and Influencing Factors of Coupling Coordination of “Production–Living–Ecological” Functions Based on Grid Scale: Empirical Experience of Karst Beibu Gulf in Southwest Guangxi, China
by Ting Feng, Dong Wu, Xiaodong Yu, Meilin Zhang, Renling Dong and Sihan Chen
Land 2025, 14(3), 614; https://doi.org/10.3390/land14030614 - 14 Mar 2025
Viewed by 608
Abstract
Territorial space (TS) is multifunctional, and exploring the relationships between functions and their influencing factors is key to achieving sustainable development of territorial space. However, existing research mostly focuses on the exploration of administrative units, while the exploration of grid units needs to [...] Read more.
Territorial space (TS) is multifunctional, and exploring the relationships between functions and their influencing factors is key to achieving sustainable development of territorial space. However, existing research mostly focuses on the exploration of administrative units, while the exploration of grid units needs to be improved. This paper takes the Beibu Gulf Economic Zone (BGEZ) in Guangxi as the research object, evaluates the “Production–Living–Ecological” Functions (PLEFs) of territorial space using the land category scoring method and summarizes the evolution characteristics of its spatial pattern. It analyzes the dominant and combined functions of territorial space using the revealed comparative advantage index, explores the relationships between various functions by introducing a coupling coordination degree model, and comprehensively uses Geodetector and Geographically and Temporally Weighted Regression (GTWR) models to analyze the spatiotemporal heterogeneity of influencing factors on the coupling coordination degree of functions. The results indicate that at the grid scale (1) regional territorial space is dominated by ecological space, followed by production space, with living space accounting for the smallest proportion. Production space and ecological space has decreased, while living space has increased, with production and ecological spaces mostly flowing into living space. (2) The spatial distribution of production and ecological functions is relatively homogeneous, while the spatial differentiation of living functions is most significant. The grid can be divided into three function-dominant types and six function-combination types. (3) Living function is primarily disordered with production and ecological functions, while production–ecological function is mainly coordinated. (4) Policy regulation is a key factor affecting the degree of functional coordination, and the degree and scope of influence of each factor show significant spatiotemporal heterogeneity. This study reveals the functional relationships and the mechanisms of temporal and spatial evolution of TS at the grid scale, providing a scientific basis for the efficient and sustainable use of TS. Full article
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17 pages, 3577 KiB  
Article
A Study on the Spatiotemporal Characteristics of the Xi’an Metropolitan Area Based on the Coupling and Coordination of Ecosystem Services and Human Well-Being
by Yunsong Gao, Pei Zhang, Yuqian Xu, Zhijun Li and Kaixi Liu
Land 2025, 14(3), 500; https://doi.org/10.3390/land14030500 - 28 Feb 2025
Viewed by 668
Abstract
The escalating conflict between ecosystem degradation and the rising demands of humanity has rendered the attainment of a scientific balance between ecosystem services and human well-being a critical concern in research on human–environment coupling and sustainable development. Metropolitan areas are pivotal in long-term [...] Read more.
The escalating conflict between ecosystem degradation and the rising demands of humanity has rendered the attainment of a scientific balance between ecosystem services and human well-being a critical concern in research on human–environment coupling and sustainable development. Metropolitan areas are pivotal in long-term sustainable development strategies and regional equity due to rapid urbanization and the tension between ecosystem degradation and human well-being. This study proposes a novel perspective, transitioning from a “cascade” to a “coupling” approach in examining the relationship between ecosystem services and human well-being. Taking the Xi’an metropolitan area as the research subject, the research employs a coupling coordination degree model to analyze the spatiotemporal characteristics of their relationship across multiple scales. The key findings of the paper are as follows: (1) We found a severe shrinkage in the ecosystem service value (2000–2020). The ecosystem services in the Xi’an metropolitan area were significantly compromised under the pressure of homogenized human well-being improvement, resulting in weak coupling and coordination between the two. (2) There was a spatial imbalance between supply and demand. Ecosystem service values displayed a core-to-periphery increasing spatial pattern, while human well-being levels exhibited a core-to-periphery decreasing distribution, indicating a marked spatial mismatch. (3) Diverse coupling dynamics within the region were identified. Driven by factors such as the resource distribution, land use, scale effects, and benefit allocation, the coupling relationships between ecosystem services and human well-being varied across development stages and contexts. Ecosystem services functioned as either flexible facilitators or constraints on human well-being improvement. This research provides a blueprint for sustainable development, offering a framework to balance urban growth with ecological health while ensuring equitable well-being across the Xi’an metropolitan area. The study highlights the need for strict ecological space protection, enhanced urban development quality, and integrated human–environment system management. Efforts should focus on minimizing land use trade-offs and spatial competition, strengthening spatial synergy in supply–demand coupling, and promoting sustainable regional development. Full article
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27 pages, 3542 KiB  
Article
Segmentation of Transaction Prices Submarkets in Vienna, Austria Using Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN)
by Lorenz Treitler and Ourania Kounadi
ISPRS Int. J. Geo-Inf. 2025, 14(2), 72; https://doi.org/10.3390/ijgi14020072 - 10 Feb 2025
Viewed by 748
Abstract
This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal [...] Read more.
This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN), which incorporates the temporal change in transaction prices along with spatial proximity to identify spatial areas with similar transaction prices. It represents an advancement over MDST-DBSCAN for this use case, as it considers the change over time as valuable information rather than a constraint that further splits the clustering groups. The results of the case study in Vienna indicate variations in price growth rates among the submarkets (i.e., contiguous regions with similar prices and price growth rates) that confirm the importance of considering the temporal changes in transaction prices. With respect to the Viennese case study, a lower Moran’s I value was observed for 2022 compared to previous years (2018 to 2021), indicating a higher level of homogeneity in transaction prices. This finding was also supported by the cluster analysis, as less expensive clusters demonstrated higher rates of price increase compared to more expensive clusters. Future research can enhance the algorithm’s usability and broaden its potential use cases to other multidimensional spatiotemporal event data. Full article
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16 pages, 5533 KiB  
Article
Decadal Extreme Precipitation Anomalies and Associated Multiple Large-Scale Climate Driving Forces in the Three Gorges Reservoir Area, China
by Yuefeng Wang, Siwei Yin, Zhongying Xiao, Fan Liu, Hanhan Wu, Chaogui Lei, Jie Huang and Qin Yang
Water 2025, 17(4), 477; https://doi.org/10.3390/w17040477 - 8 Feb 2025
Cited by 1 | Viewed by 662
Abstract
Identifying the relationship between extreme precipitation (EP) and large-scale climate circulation is of great significance for extreme weather management and warning. Previous studies have effectively revealed the influence of single climate circulation on EP, although the influence characteristics of multiple climate circulation are [...] Read more.
Identifying the relationship between extreme precipitation (EP) and large-scale climate circulation is of great significance for extreme weather management and warning. Previous studies have effectively revealed the influence of single climate circulation on EP, although the influence characteristics of multiple climate circulation are still unclear. In this study, seasonal spatiotemporal changes in decadal anomalies of daily EP were analyzed based on quantile perturbation method (QPM) within the Three Gorges Reservoir Area (TGRA) for the period from 1960 to 2020. Sea surface temperature (SST)- and sea level pressure (SLP)-related climate circulation factors were selected to examine their interaction influences on and contributions to EP. The results showed that: (1) Summer EP anomalies exhibited greater temporal variability than those in other seasons, with the cycle duration of dry/wet alternation shortening from 15 years to 5 years. Winter EP anomalies showed pronounced spatial homogeneity patterns, especially in the 1970s. (2) According to the analysis based on a single driver, the Southern Oscillation Index (SOI), the North Atlantic Oscillation (NAO), and the Indian Ocean Dipole (IOD) had prolonged correlations with seasonal EP anomalies. (3) More contributions can be obtained from multiple climate circulations (binary and ternary drivers) on seasonal EP anomalies than from a single driver. Although difference existed in seasonal combinations of ternary factors, their contributions on EP anomalies were more than 60%. This study provides an insight into the mechanisms of modulation and pathways influencing various large-scale climate circulation on seasonal EP anomalies. Full article
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20 pages, 15845 KiB  
Article
A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
by Kai Du, Jingni Song, Dan Chen, Ming Li and Yadi Zhu
Appl. Sci. 2025, 15(1), 156; https://doi.org/10.3390/app15010156 - 27 Dec 2024
Viewed by 1055
Abstract
Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such [...] Read more.
Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization. Full article
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26 pages, 6087 KiB  
Article
Pattern Formation Mechanisms of Spatiotemporally Discrete Activator–Inhibitor Model with Self- and Cross-Diffusions
by You Li, Ying Sun, Jingyu Luo, Jiayi Pang and Bingjie Liu
Fractal Fract. 2024, 8(12), 743; https://doi.org/10.3390/fractalfract8120743 - 16 Dec 2024
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Abstract
In this paper, we aim to solve the issue of pattern formation mechanisms in a spatiotemporally discrete activator–inhibitor model that incorporates self- and cross-diffusions. We seek to identify the conditions that lead to the emergence of complex patterns and to elucidate the principles [...] Read more.
In this paper, we aim to solve the issue of pattern formation mechanisms in a spatiotemporally discrete activator–inhibitor model that incorporates self- and cross-diffusions. We seek to identify the conditions that lead to the emergence of complex patterns and to elucidate the principles governing the dynamic behaviors that result in these patterns. We first construct a corresponding coupled map lattice (CML) model based on the continuous activator–inhibitor reaction–diffusion system. In the reaction stage, we examine the existence, uniqueness, and stability of the homogeneous stationary state and specify the parametric conditions for realizing these properties. Furthermore, by applying the center manifold theorem, we perform a flip bifurcation analysis and confirm that the model is capable of undergoing flip bifurcation. In the diffusion stage, we focus on the analysis of Turing bifurcation and determine the parameter conditions for the emergence of Turing instability. Through numerical simulations, we validate and explain the results of our theoretical analysis. Our study reveals various Turing instability mechanisms by coupling Turing and flip bifurcations, which include pure-self-diffusion-Turing instability, pure-cross-diffusion-Turing instability, flip-self-diffusion-Turing instability, flip-cross-diffusion-Turing instability, and chaos-self-diffusion-Turing instability mechanisms. Under different mechanisms, we illustrate the corresponding Turing patterns and discover a rich variety of pattern types such as labyrinthine, mosaic, alternating mosaic, colorful mottled grid patterns with winding and twisted bands, and patterns with dense patches and twisted bands nested together. Our research provides a theoretical framework and numerical support for understanding the complex dynamical behaviors and pattern formations in activator–inhibitor models with self- and cross-diffusions. Full article
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