Search Results (56)

Biodiversity, as the foundation of life on Earth, sustains the balance of ecosystems and supports human sustainable development. However, the current accelerated decline in biodiversity poses ecological threats that require urgent attention. This research based on the perspective of ethnic ecological wisdom, explores the customary practices of biological conservation among the Miao ethnic group in Southwest China, the Tao ethnic group on Orchid Island (Lanyu), Taiwan, and the Maasai ethnic group on the East African Plateau. By conducting in-depth case studies, combined with literature review and data validation, it investigates their practical value and implementation pathways in biodiversity conservation. By analyzing the ecological conservation wisdom models of the Miao, Tao and Maasai ethnic groups, it is found that the core species populations in each region have shown a positive growth trend since the gradual integration of traditional ethnic customary laws with modern ecological protection systems and practices. Drawing on the extensive experience accumulated in integrating customary law into ecological governance across the three cases, this study proposes a three-dimensional optimization pathway: at the policy level, construct a mechanism integrating customary law and diversified ecological compensation; at the community level, implement a model featuring benefit sharing, patrol mediation and digital management; and at the cultural level, strengthen the development and dissemination of ethnic ecological conservation wisdom through multidisciplinary talent training and IP-based communication of exemplary customary law outcomes. We aspire to slow the rate of global biodiversity loss and achieve a bright future of harmonious coexistence between humans and nature. Full article

Based on ERA5 reanalysis data, best-track data of tropical cyclones, and satellite nighttime light data from 1995 to 2024, this study employs a statistical composite method to analyse spatiotemporal evolution characteristics and impact mechanisms of gale events in the South China Sea. The results indicate: ① The gale days exhibit a pattern of ‘high in the northeast and southwest, low in the middle’ with three high-value regions located in the Taiwan Strait, the Bashi Strait, and the offshore region southeast of Vietnam, where the average wind speed at the centres reaches 8 m/s. Maximum wind speeds show a ‘high in the north, low in the south’ pattern, with the dividing line near 10° N. The number of gale days peaks in winter, while maximum wind speeds are higher in summer and autumn than in winter and spring. ② The spatial distribution of gales is primarily influenced by the combined effects of land–sea topography and weather systems. Cold air masses in winter and spring are the dominant cause of gales in the South China Sea. Although typhoons in summer and autumn occur less frequently, they are more likely to trigger extreme gales. ③ Most regions of the South China Sea show an increasing trend in the gale days, while a few areas in the south and near Guangdong exhibit a decrease. The overall increase is primarily attributed to the intensification of the subtropical high, whereas the reduction near Guangdong is mainly due to increased surface roughness caused by urbanisation, which enhances friction and suppresses wind speeds. Full article

Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated using ERA5 100 m wind data from 1944 to 2023, validated against ASCAT observations. High wind speeds and high wind power density (WPD) are concentrated southwest of Taiwan and southeast of Vietnam. Annual wind availability exceeds 6457 h across most regions, reaching up to 8283 h in optimal locations. WPD and capacity factor peak in winter (up to 2.4 × 10⁸ Wh·m⁻² and >50% capacity factor), with the most stable conditions occurring in the southwestern Taiwan Strait, southeast of the Pearl River Delta, and the Beibu Gulf. Empirical orthogonal function analysis reveals that the first mode of winter WPD accounts for 65.7% of the total variance, with a statistically significant increasing trend since 1990. The interannual variation in wind energy resources in the SCS during winter is controlled by the combined effects of sea surface temperature (SST) anomalies in the tropical Pacific and the Arctic Barents Sea. Specifically, in the years with strong wind anomalies in the SCS, mega-La Niña-type SST patterns in the tropical Pacific trigger anomalous cyclonic circulation in the SCS and the eastern Philippine Sea, while warm anomalies in the Arctic Barents Sea surface drive a wave-like structure of “anticyclone–cyclone–anticyclone” from Siberia to South China. The coupling of the two systems jointly promotes the strengthening of the South China Sea monsoon, leading to increased wind speeds and elevated WPD in the northern SCS. These findings provide a scientific basis for wind farm siting and long-term operational planning in the region. Full article

This study investigates the temporal and spatial variations in sea level anomaly (SLA) and sea surface wind in the East China Sea (ECS) from 1993 to 2021 using AVISO altimetry data and ERA5 reanalysis wind data. Empirical Orthogonal Function (EOF) and trend analyses were applied to identify dominant modes and long-term changes. Results reveal pronounced seasonal SLA variability, with lower levels in winter/spring and higher levels in summer/autumn, strongly modulated by monsoon winds. The first EOF mode of SLA accounted for 52.73% of variance, showing basin-coherent seasonal fluctuations, while the second mode (7.79%) reflected contrasts between coastal and Kuroshio-influenced regions. The ECS experienced an average sea level rise of 3.77 mm/year, exceeding 6 mm/year along the Jiangsu and Zhejiang–Fujian coasts. Sea surface wind stress variability was greatest in the northern Taiwan Strait and southwest of the Ryukyu Islands, but decreased along the Zhejiang coast. Sea level anomalies (SLAs) in the East China Sea exhibit clear multi-scale coupling with the wind field. The seasonal SLA variability in the East China Sea is jointly modulated by local Ekman forcing due to wind stress, while also being potentially linked to the Kuroshio and open-ocean Rossby waves. These findings underscore the role of wind forcing in regional sea level changes and provide insight for coastal management under climate change. Full article

Mangrove ecosystems along Taiwan’s southwest coast have been increasingly stressed by climate change, subsidence, and sea level rise. Between 1897 and 2024, the mean annual temperature rose by 2.0 °C, and rainfall declined by 56.5 mm. Severe subsidence occurred in Taixi Township, Yunlin County (−283.0 cm, 1975–2023), where the gray/white mangrove (Avicennia marina) exhibited reduced growth and mortality. Long-term mangrove area (MA) was reconstructed using quadratic polynomials: Tougang Ditch, MA_TG(t) = −0.0084(t − 21.0)² + 2.8 peaking in 1995 (R² = 0.7274), and Budai Lagoon, MA_BD(t) = −0.0468(t − 12.3)² + 26.1 peaking in 1986 (R² = 0.782). Both sites yielded moderate fits indicating partial but less reliable reconstruction. In contrast, Jishui Estuary subsites displayed distinct maxima with stronger fits (R² > 0.85): JS-C, MA_JS-C(t) = −0.0201(t − 14.3)² + 7.0 peaking in 1996; JS-D, MA_JS-D(t) = −0.0093(t − 15.8)² + 2.2 peaking in 1998; and JS-G, and MA_JS-G(t) = −0.0077(t − 11.6)² + 4.3 peaking in 1994. SPOT-6 satellite imagery (22 February 2025) identified 281.9 ha of mangrove and windbreak forests in Chiayi County and 896.3 ha in Tainan City. By integrating climate records, subsidence data, sea level rise, polynomial modeling, and satellite observations, this study provides a robust framework for anticipating mangrove trajectories, assessing carbon sink potential, and refining carbon credit estimates in vulnerable coastal landscapes. Full article

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