Search Results (51)

The East China Sea (ECS) is a globally active region for internal solitary waves (ISWs); however, its overall spatiotemporal distribution remains poorly understood. To address this gap, this study proposes a deep learning method based on multi-source remote sensing imagery (MODIS and SAR) for the intelligent identification and pixel-level segmentation of ISWs in the ECS. We adopted the TransUNet model, which combines the global context-capturing capability of Transformers with the fine-grained segmentation advantages of U-Net to effectively handle the large-scale continuous characteristics of ISWs. The model achieved a Dice coefficient of 71.0% and a precision of 72.7% on the test set, significantly outperforming existing models such as FCN, SegNet, DeepLabV3+, and U-Net. Using this automated framework, multi-source satellite data from 2002 to 2024 were processed to generate the first high-resolution spatiotemporal map of ISWs covering the entire ECS. The map reveals two spatial hotspots: a primary one at the shelf break northeast of Taiwan and a secondary one in the waters southwest of Jeju Island. Furthermore, ISWs exhibit a marked seasonal cycle in both occurrence frequency and properties, peaking in summer and minimizing in winter. This seasonal pattern aligns closely with the physics of internal tide generation via body forcing. By providing the first long-term, high-resolution ISW dataset for the entire ECS, this study demonstrates the potential of deep learning techniques for ISW research in complex marginal seas. Full article

Chengiodendron marginatum, an evergreen tree or shrub belonging to the Oleaceae family, represents a critical germplasm resource with considerable potential for novel cultivar breeding. To elucidate the adaptive responses of C. marginatum to climate change and provide strategic guidance for its conservation, this study investigates the changing patterns in its potential suitable habitats under various climate scenarios. We employed an integrated approach combining maximum entropy (Maxent) modeling with GIS spatial analysis, utilizing current occurrence records and paleoclimatic data spanning from the mid-Holocene to future projections (2041–2060 [2050s] and 2061–2080 [2070s]). Climate scenarios SSP126 and SSP585 were selected to represent contrasting emission pathways. The model demonstrated excellent predictive accuracy with an AUC value of 0.942, identifying precipitation-related variables (particularly the precipitation of driest month and annual precipitation) as the primary environmental factors shaping the geographical distribution of C. marginatum. Current suitable habitats encompass approximately 98.38 × 10⁴ km², primarily located in East, Central, and South China, with high-suitability habitats restricted to southern Hainan, Taiwan, and northeastern Guangxi. Since the mid-Holocene, an expansion of suitable habitats occurred despite localized contractions in Southwest China. Future projections revealed moderate habitat reduction under both scenarios, and high-suitability areas decreased substantially. Importantly, under both scenarios, persistent high-suitability habitats were maintained in southern Hainan, Taiwan, and northeastern Guangxi, which are identified as essential climate refugia for the species. These findings provide a basis for understanding the response of the species to climate change and offer valuable guidance for its conservation. Full article

Sterculia foetida L., commonly known as the Java olive, is a tropical tree species native to regions of East Africa, tropical Asia, and northern Australia. This study employs species distribution modeling (SDM) to predict the potential geographic distribution of S. foetida under current and future climate scenarios. Using 1425 occurrence data and 19 environmental variables, we applied an ensemble modelling approach of three algorithms: Boosting Regression Trees (BRT), Generalized Linear Model (GLM), and Random Forests (RF), to generate distribution maps. Our models showed high accuracy (mean AUC = 0.98) to indicate that S. foetida has a broad ecological niche, with high suitability in tropical and subtropical regions of north Australia (New Guinea and Papua), Southeast Asia (India, Thailand, Myanmar, Taiwan, Philippines, Malaysia, Sri Lanka), Oman and Yemen in the southwest of Asia, Central Africa (Guinea, Ghana, Nigeria, Congo, Kenya and Tanzania), the Greater and Lesser Antilles, Mesoamerica, and the north of South America (Colombia, Panama, Venezuela, Ecuador and Brazil). Indeed, the probability of occurrence of S. foetida positively correlates with the Maximum temperature of warmest month (bio5), Mean temperature of wettest quarter (bio8) and Precipitation of wettest month (bio13). The model results showed a suitability area of 4,744,653 km², representing 37.86% of the total study area, classified into Low (14.12%), Moderate (8.71%), and High suitability (15.02%). Furthermore, the study found that habitat suitability for S. foetida showed similar trends under both near future climate scenarios (SSP1-2.6 and SSP5-8.5 for 2041–2060), with a slight loss in potential distribution (0.24% and 0.25%, respectively) and moderate gains (1.98% and 2.12%). In the far future (2061–2080), the low scenario (SSP1-2.6) indicated a 0.29% loss and a 2.52% gain, while the high scenario (SSP5-8.5) showed a more dramatic increase in both loss (0.6%) and gain areas (3.79%). These findings are crucial for conservation planning and management, particularly in regions where S. foetida is considered invasive and could become problematic. The study underscores the importance of incorporating climate change projections in SDM to better understand species invasiveness dynamics and inform biodiversity conservation strategies. Full article

When exploring interactions between Christianity and other religions in East Asia, the place given to the shamanic tradition remains ambiguous and marginal. This article analyzes the religious encounters between shamanism and Christianity in East Asia through specific and representative case studies. This article is divided into three main parts. Section 1 introduces the core terms “shamanism” and “diffusionism”, explaining their general meanings and the specific ways they are used in this study, and provides a regional overview of the cases analyzed in this paper. Sections 2–4 present the historical context and analysis of religious encounters in regions such as Siberia, Mongolia, China (including Taiwan, Southwest China, and Northeast China), Korea, etc. Sections 5 and 6 seek to demonstrate that shamanism operates according to two models: the first characterized by “segregation” and the second by “diffusion”, noting that these models exist on a dynamic continuum. In most historical situations, this study argues that shamanism initially encountered Christianity in a segregation mode, often leading to significant conflicts between the two. Over time, as shamanism’s religious attributes weakened, it paradoxically adapted to a diffusion model, integrating its ethos into other religions, including Christianity. The diffusion model has thus become an appropriate way to understand the current existent form of shamanism in East Asia. Full article

Amid the global wave of digital transformation, advancing the sustainable growth of the real economy has emerged as a key strategic priority. Drawing on panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) between 2013 and 2021, this study utilizes fixed effects and mediation effect models to investigate both the direct and indirect pathways through which the digital economy drives the sustainable development of the real economy. The results indicate that (1) the digital economy exerts a significant direct positive influence on the real economy, demonstrating its role in spurring growth and innovation while injecting fresh momentum into sustainable development. (2) It also indirectly facilitates the real economy’s sustainability by promoting the coupling and coordination of the manufacturing and service sectors, emphasizing the importance of industrial synergy in achieving sustainable economic growth. (3) Regional analysis reveals that the digital economy’s positive direct effect on the real economy is particularly evident in North China and the Southeast and Southwest regions. Furthermore, in the Southeast and Southwest, the mediation effect of industrial coupling and coordination further strengthens the sustainability of the real economy. This study offers theoretical insights into the integration of the manufacturing and service industries and provides practical guidance for advancing the United Nations’ 2030 Agenda for Sustainable Development. It also highlights policy recommendations for China to build a modern industrial system and achieve high-quality economic growth. Full article

This study employed machine learning, specifically deep neural networks (DNNs) and long short-term memory (LSTM) networks, to build a model for estimating acid rain pH levels. The Yangming monitoring station in the Taipei metropolitan area was selected as the research site. Based on pollutant sources from the air mass back trajectory (AMBT) of the HY-SPLIT model, three possible source regions were identified: mainland China and the Japanese islands under the northeast monsoon system (Region C), the Philippines and Indochina Peninsula under the southwest monsoon system (Region R), and the Pacific Ocean under the western Pacific high-pressure system (Region S). Data for these regions were used to build the ANN_AMBT model. The AMBT model provided air mass origin information at different altitudes, leading to models for 50 m, 500 m, and 1000 m (ANN_AMBT_50m, ANN_AMBT_500m, and ANN_AMBT_1000m, respectively). Additionally, an ANN model based only on ground station attributes, without AMBT information (LSTM_No_AMBT), served as a benchmark. Due to the northeast monsoon, Taiwan is prone to severe acid rain events in winter, often carrying external pollutants. Results from these events showed that the LSTM_AMBT_500m model achieved the highest percentages of model improvement rate (MIR), ranging from 17.96% to 36.53% (average 27.92%), followed by the LSTM_AMBT_50m model (MIR 12.94% to 26.42%, average 21.70%), while the LSTM_AMBT_1000m model had the lowest MIR (2.64% to 12.26%, average 6.79%). These findings indicate that the LSTM_AMBT_50m and LSTM_AMBT_500m models better capture pH variation trends, reduce prediction errors, and improve accuracy in forecasting pH levels during severe acid rain events. Full article

The near-inertial motion in ocean surface currents directly reflects the energy transported by wind towards the surface layer, playing an important role in climate regulation and energy balance. Previous studies have mainly focused on near inertial oscillations (NIOs) induced by tropical cyclones in the Taiwan Strait, with few reports on near inertial oscillations induced by monsoon onset. Using high-frequency radar observations, we detected an amplification of NIOs induced by the winter monsoon onset. While not as strong as NIOs induced by tropical cyclones, the near-inertial current (NIC) induced by winter monsoon onset in the Taiwan Strait has peak speeds reaching up to 5.2 cm/s and explaining up to 0.7% of non-tidal variance. This study presents observational results of NIOs during three monsoon onset events, and analyzes the impact of winds and temperature changes on NIOs. Temporal and spectral analysis reveals that the monsoon onset is the primary driver behind the formation of NIOs. Results indicate that near-inertial kinetic energy is relatively lower in shallower waters, such as the Taiwan Bank, compared to deeper regions. Furthermore, by integrating the air and sea surface temperature from reanalysis products, we have examined the abrupt changes in sea surface temperature (SST) before and after monsoon onset and their correlation with NIOs. The findings suggest that temperature falling favors the intensification of NICs during monsoon onset, and a lack of significant SST changes precludes the triggering of notable NICs. These insights enhance our understanding of the mechanisms driving NIOs and their roles in seawater mixing. Full article

This paper conducts an observing system simulation experiment (OSSE) to assess the impact of assimilating traditional sounding and surface data, along with dropsonde observations over the northern South China Sea (SCS) on heavy rain forecasts in Taiwan. Utilizing the hybrid ensemble transform Kalman filter (ETKF) and the three-dimensional variational (3DVAR) data assimilation (DA) system, this study focuses on an extreme precipitation event near Taiwan on 22 May 2020. The event was mainly influenced by strong southwesterly flow associated with an eastward-moving southwest vortex (SWV) from South China to the north of Taiwan. A nature run (NR) serves as the basis, generating virtual observations for radiosonde, surface, and dropsonde data. Three experiments—NODA (no DA), CTL (traditional observation DA), and T5D24 (additional dropsonde DA)—are configured for comparative analyses. The NODA experiment shows premature and weaker precipitation events across all regions compared with NR. The CTL experiment improved upon NODA’s forecasting capabilities, albeit with delayed onset but prolonged precipitation duration, particularly noticeable in southern Taiwan. The inclusion of dropsonde DA in the T5D24 experiment further enhanced precipitation forecasting, aligning more closely with NR, particularly in southern Taiwan. Investigations of DA impact reveal that assimilating traditional observations significantly enhances the SWV structure and wind fields, as well as the location of frontal systems, with improvements persisting for 40 to 65 h. However, low-level moisture field enhancements are moderate, leading to insufficient precipitation forecasts in southern Taiwan. Additional dropsonde DA over the northern SCS further refines low-level moisture and wind fields over the northern SCS, as well as the occurrence of frontal systems, extending positive impacts beyond 35 h and thus improving the rain forecast. Full article

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need. Full article

A correlation model for the diffuse fraction was recently developed on the basis of a data set obtained in the western part of the Taiwanese mainland. However, it is widely agreed that no existing diffuse fraction correlation model is applicable to all geographical regions and climatic conditions, which is a viewpoint stated from a macro perspective. This study re-justifies this viewpoint through the consideration of a rather small geographical region: Taiwan. The topographic profile of the Taiwanese mainland primarily comprises the high-rise Central Mountain Ranges running from north–northeast to south–southwest, which separate the mainland into eastern and western parts. Furthermore, there are a number of small, remote islands around the Taiwanese mainland. The humidity over the sky dome of these small islands, carried from the moist sea (or ocean) air, is usually greater than that of the Taiwanese mainland. This results in different diffuse fraction patterns between these two geographical regions due to the climatic factor of atmospheric constituents. Two diffuse fraction correlation models for Taiwan were developed using in situ data sets for the eastern part of the Taiwanese mainland and an island in the Penghu archipelago, respectively. In particular, one case considered the topographic effect on modeling the diffuse fraction in Taiwan, while the other considered the geographical effect. Statistical assessments indicate that each correlation model developed in the present study performed better than the previous one developed using the in situ data set for the western part of the Taiwanese mainland, with both applied to the specific site where the data set was used for the model’s development. This work demonstrates the need to consider the effects of topography and geography when modeling the diffuse fraction in Taiwan. Full article

A national cetacean stranding response program in Taiwan has evolved significantly in the past three decades. Initially co-ordinated by National Taiwan University from 1994, the program transitioned to the Taiwan Cetacean Society in 1999, and local governments took on a more prominent role after 2009. A comprehensive stranding database (1994–2018) has been maintained, which documented 1320 stranding events involving 1698 animals from at least 27 species. The most commonly stranded species include finless porpoises, bottlenose dolphins, Kogia spp., and Risso’s dolphins. The stranding rates varied annually and seasonally, with increases noted from an average of 16 events per year for the first 3 years to 44–58 events per year between 1997 and 2015, and a sharp rise to over 90 events per year for the period of the last three years. Seasonal variations were also significant, with higher stranding rates during the northeastern monsoon (NEM, October to next April) than that during southwestern monsoon (SWM, May to September). From the aspect of distribution, more frequent and even strandings occurred along the coast of northern Taiwan, while mass strandings were concentrated in the southwestern counties during NEM. Among all strandings, 390 events (29.5%) and 660 animals (38.9%) were live ones. Under great effort in rescuing and rehabilitating 52 cases, 15 cetacean individuals have been released since 2000. Additionally, there have been 56 mass strandings involving at least 11 species since 1994, predominated by pygmy killer whales, particularly during the NEM season along the southwest coast. This study not only contributes to our understanding of the stranding patterns and diversity of the cetaceans in Taiwan, but also provides valuable insights for future conservation strategies on cetaceans in the western Pacific. Full article

Utilizing panel data pertaining to green building across 333 prefecture-level administrative units in China (excluding Hong Kong, Macao, and Taiwan) during the period spanning 2008–2020, an exhaustive examination of the evolution of China’s spatial pattern in green building is conducted employing the nearest neighbor index method, spatial autocorrelation analysis method, and kernel density analysis method. Furthermore, geographic probes are employed to scrutinize the determinants influencing China’s spatial configuration of green buildings. The findings reveal that: (1) An alteration in the density distribution from a “unipolar nucleus and double sub-nuclei” configuration to a “triple polar nuclei and multiple sub-nuclei” manifestation has been discerned in the spatial agglomeration of green buildings in China, exhibiting annual growth. Additionally, the center of green building development has shifted from the northwest to the southwest. (2) Pronounced agglomerations are predominantly situated in the eastern, central, and western regions of the country. High-high agglomerations have gradually dissipated over time in the central provincial capitals of China, the Yangtze River Delta, the Pearl River Delta, and the city clusters of Beijing-Tianjin-Hebei along the eastern seaboard. The western regions manifest a concentration of low-low and low-high aggregates, with high-low agglomeration primarily observed in the provincial capitals of the western regions. (3) The spatial differentiation of green buildings in China is attributable to a multitude of variables encompassing the environment, economy, society, and policies. Among these factors, economic, social, and innovative elements exert the most significant influence on the explicable degree of spatial differentiation. Full article

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies are active in the deep basin of SCS, and the five selected parameters (number or frequency, lifetime, kinetic energy, amplitude, and area or radius) of anticyclonic eddies have a similar temporal variation and a similar spatial distribution pattern. (1) As for monthly variations, anticyclonic eddies are active in late spring and most active in summer. (2) The El Niño–Southern Oscillation had a stronger impact on the inter-annual variations of anticyclonic eddies in the SCS before 2013, resulting in a significant transition of inter-annual variations of these five parameters in around 2004. After 2013, most of these five parameters had a minimum in 2015 and a maximum in 2017. (3) Analyses show that the eddy activities in the SCS are significantly influenced by the monsoon wind and the western boundary current like Kuroshio. Therefore, the areas southwest of Taiwan Island and east of Vietnam are the two areas where the anticyclonic eddies are most active, with much larger eddy kinetic energy and much higher eddy amplitude. Full article

This study selected three heavy-rainfall events of different types in Taiwan’s Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model’s capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model’s performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable. Full article

This study investigated microplastic distribution characteristics by collecting surface seawater from sea areas to the south of Jeju Island in August 2020. The average microplastic abundance was 0.46 ± 0.27 particles/L (n = 23), and PE had a high ratio, averaging 53%. The levels of fragments and fibers were observed to be 69% and 31% on average, respectively. The most common size of the microplastics was on average 0.02–0.30 mm at a level of 69%. We found a higher abundance of microplastics in the study area than in other open waters such as the Arctic Central Basin and the Atlantic Ocean, whereas the abundance was lower than that in previous studies on coastal areas. We studied an area of open sea connecting China, Japan, and the Pacific Ocean, and, in this region, the microplastic distribution varies depending on sea currents in the surrounding areas. In the summer, the western and central regions of the study sea area have low salinity levels due to discharge from China’s Yangtze River. This generally indicates that high-density plastic deposits are found in the Yangtze River estuary, and low-density plastics are found in the study area. Furthermore, this implies that low- and high-density plastics are transported in water for long periods of time due to the Taiwan Warm Current and because the eastern sea area has high salinity. Full article