Search Results (247)

River health assessment frameworks play a critical role in guiding restoration planning and watershed management, yet conventional index-based approaches often rely on fixed weighting schemes that limit diagnostic sensitivity and interpretability. This study proposes an integrated assessment framework that enhances the traditional Index of Stream Condition (ISC) by incorporating data-driven structural information while preserving transparency and regulatory relevance. Rather than replacing existing indices, the framework recalibrates sub-index contributions based on intrinsic data patterns derived from nonlinear embedding and density-based clustering. The proposed methodology is applied to the Zhuoshui River basin in Taiwan to demonstrate its capability to improve internal consistency, reduce metric redundancy, and clarify dominant environmental drivers. Results indicate that the recalibrated index provides clearer differentiation among ecological conditions and improves explanatory consistency compared with the original ISC formulation. By balancing methodological innovation with interpretability, the proposed framework offers a practical pathway for strengthening river health assessment and supporting restoration-oriented decision-making. Full article

This study proposes a framework, the RA_WLTE_River model, for quantifying the reliability of flood-altering water-level thresholds, considering rainfall and runoff-related uncertainties. The Keelung River in northern Taiwan is selected as the study area, and associated hydrological data from 2008 to 2016 are applied in the development and application of the model. According to the results from the model development and demonstration, the average and maximum rainfall intensities, roughness coefficients, and maximum tide depths exhibit a significant contribution to the reliability quantification of the estimated water-level thresholds. In addition, empirically based water-level thresholds can achieve the goal of rainfall-induced flood early warning, with a high likelihood of nearly 0.95. Additionally, the probabilistically based water-level thresholds derived from the described reliability can efficiently ensure consistent flood early warning performance at all control points along the river. Full article

Highly accurate quantitative detection of heavy metals is crucial for preventing environmental pollution and safeguarding public health. To address the demand for sensitive and specific detection of Cu²⁺ ions, we have developed carbon dots using a simple hydrothermal process. The synthesized carbon dots are highly stable in aqueous media, environmentally friendly, and exhibit strong blue photoluminescence at 440 nm when excited at 352 nm, with a quantum yield of 5.73%. Additionally, the size distribution of the carbon dots ranges from 2.0 to 20 nm, and they feature excitation-dependent emission. They retain consistent optical properties across a wide pH range and under high ionic strength. The photoluminescent probes are selectively quenched by Cu²⁺ ions, with no interference observed from other metal cations such as Ag⁺, Ca²⁺, Cr³⁺, Fe²⁺, Fe³⁺, Hg²⁺, K⁺, Mg²⁺, Sn²⁺, Pb²⁺, Sr²⁺, and Zn²⁺. The emission of carbon dots exhibits a strong linear correlation with Cu²⁺ concentration in the range of 0–14 μM via a static quenching mechanism, with a detection limit (LOD) of 4.77 μM in water. The proposed carbon dot sensor is low cost and has been successfully tested for detecting Cu²⁺ ions in general water samples collected from rivers in Taiwan. Full article

The use and promotion of reclaimed water have become global trends and have been widely adopted in countries such as Singapore, Israel, Japan, and the United States. In recent years, Taiwan has also been promoting demonstration plants for reclaimed water by enacting the Reclaimed Water Resources Development Act. Since the demonstration plants apply different reclamation processes, the costs and quality of the reclaimed water vary. This study aims to analyze the cost effectiveness of reclaimed water under three different scenarios, based on operational costs and water quality data from three demonstration plants: the Fongshan River Reclaimed Water Plant, the Shui Nan Water Resource Recovery Center, and the Futian Water Resource Recovery Center. The result shows that the most cost-effective scenario is either the high-cost-high-quality scenario or the low-cost-low-quality one. The moderate scenario is not preferred in terms of cost effectiveness. If the consideration is simply the total cost as a society, the high-cost-high-quality scenario might be preferred. But if “who pays for the cost” is taken into consideration, the low-cost-low-quality scenario is preferred since the cost would be mostly shouldered by the industrial users, rather than the government. The result can not only be used as a reference for the determination of the unified price and water quality standard for reclaimed water in Taiwan in the future but also shed light on the determination of water reclamation processes globally. Full article

In China’s rapidly urbanizing coastal areas, inclusive green development (IGD) has become an important way to achieve a reduction in economic development disparities, environmental sustainability, and social equity. This study investigates the spatiotemporal dynamics and structural drivers of IGD across 54 coastal cities within three marine economic zones (MEZs) using a hybrid analytical framework that integrates evaluation techniques, inequality decomposition, spatial factor detection, and spatial econometrics. The result shows that a distinctive “four-pillar” spatial structure has emerged, centered on the Shandong Peninsula, Yangtze River Delta (YRD), West Coast of the Taiwan Strait, and Pearl River Delta (PRD). Spatial autocorrelation has intensified since 2020, indicating the cumulative effect of China’s post-2020 regional integration policies and digital infrastructure investments, which accelerated resource flows between cities. Spatial econometric analysis further reveals that economic development and equitable public service provision are the most influential drivers, while public investment in R&D and digital transformation exhibit significant cross-city spillover effects. The findings highlight the importance of regionally adaptive and digitally integrated strategies to promote inclusive and sustainable urban development in coastal economies. Therefore, efforts should be intensified to strengthen the role of core cities as diffusion engines for neighboring areas, with a strategic focus on regional digital transformation and R&D investment, to advance inclusive and sustainable development in coastal economies. Full article

Understanding the sediment grain size distribution in riverbeds is essential for analyzing sediment transport, riverbed morphology, and ecological habitats. Previous studies have shown that riverbed grain size can be inferred from surface roughness using linear relations between manually sampled grain sizes and percentile roughness derived from point-cloud data. However, these relations are often established within narrow grain-size ranges, causing regression coefficients to vary across percentiles and limiting their applicability to broader grain-size variability. This study conducted field investigations and UAV (Unmanned Aerial Vehicle) surveys to examine grain size–roughness relations across four coarse-grained mountainous river reaches in Taiwan, characterized by a wide grain-size distribution (D₁₆–D₈₄: 2.3–525 mm). High-resolution 3D point clouds were generated using UAV-SfM (Structure-from-Motion) techniques for roughness metric computation. Linear relations between grain size D_i (i = 16, 25, 50, 75, and 84) and their corresponding percentile roughness RH_i were developed and evaluated. Results indicate that D_i-RH_i relations exhibit moderate to strong correlations (R² = 0.60–0.94), and the regression slope increases exponentially with grain size. To address cross-percentile variability, an integrated power-law relation was proposed by pooling all paired D_i-RH_i data from Reach R1, yielding a single, continuous reach-scale grain size–roughness correlation. Applicability tests using data from the remaining three reaches show that the integrated relation performs better for coarser grains (D₅₀–D₈₄) than for finer grains. Future work incorporating more sampling sites across diverse river types will help further refine the integrated relation and improve its cross-reach applicability. Full article

Trifolium repens L. is a protein-rich, versatile Leguminous lawn plant that is widely distributed across global temperate and subtropical regions. As an invasive species originating in Europe, its distribution in China extends from Xinjiang in the West to Taiwan and the Yangtze River Delta in the East, and is widespread throughout Northeast and Central China. However, in recent years, the distribution pattern of T. repens has become increasingly patchy and irregular. Therefore, unraveling the potential distribution and key environmental drivers of T. repens is critical for understanding its ecological role. This study utilized current species distribution data of T. repens and employed the MaxEnt model to simulate its potentially suitable niches across present and future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) in China. This study identified Bio2 (mean diurnal temperature range) and Bio14 (precipitation of driest month) as the key drivers shaping the distribution of T. repens. Its current suitable habitats are primarily concentrated in the coastal, central, and Taiwan regions of China. Under future climates, these areas are projected to contract overall and shift toward lower latitudes and higher longitudes, with substantial suitable areas remaining only in the Eastern, Southern, and Taiwan regions. This study quantitatively assessed the ecological niche breadth of T. repens and its future spatial distribution under climate change, thereby laying a theoretical foundation for describing the ecological characteristics of this invasive species, conducting monitoring, and implementing further invasion risk management. Full article

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. Full article

Accurate estimation of flow velocities in torrents and steep rivers is essential for flood risk assessment, sediment transport analysis, and the sustainable management of water resources. While deep learning models are increasingly applied to such tasks, their evaluation often depends on statistical metrics that may yield conflicting interpretations. The objective of this study is to clarify how different evaluation metrics influence the interpretation of hydrological deep learning models. We analyze two models of flow velocity prediction in a torrential creek in Taiwan. Although the models differ in architecture, the critical distinction lies in the datasets used: the first model was trained on May–June data, whereas the second model incorporated May–August data. Four performance metrics were examined—root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott’s index of agreement (d), and mean absolute percentage error (MAPE). Quantitatively, the first model attained RMSE = 0.0471 m/s, NSE = 0.519, and MAPE = 7.78%, whereas the second model produced RMSE = 0.0572 m/s, NSE = 0.678, and MAPE = 11.56%. The results reveal a paradox. The first model achieved lower RMSE and MAPE, indicating predictions closer to the observed values, but its NSE fell below the 0.65 threshold often cited by reviewers as grounds for rejection. In contrast, the second model exceeded this NSE threshold and would likely be considered acceptable, despite producing larger errors in absolute terms. This paradox highlights the novelty of the study: model evaluation outcomes can be driven more by data variability and the choice of metric than by model architecture. This underscores the risk of misinterpretation if a single metric is used in isolation. For sustainability-oriented hydrology, robust assessment requires reporting multiple metrics and interpreting them in a balanced manner to support disaster risk reduction, resilient water management, and climate adaptation. Full article

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response. Full article

More than half of the world’s rivers are intermittent, and climate change is increasing their intermittency, affecting water resources and ecosystems. In Taiwan, steep topography and uneven rainfall have led to increased intermittency in some areas, reflecting changes in hydrological conditions. Using streamflow data, this study applied intermittency ratio (IR), modified 6-month dry period seasonality (SD6), and trend analysis, as well as watershed properties and climate indices. Results showed that 92% of stations had low flows for less than 20% of the time. The dry season was mainly from November to April, and intermittency was spatially affected mainly by upstream soil moisture, moderately by potential evapotranspiration and infiltration, and less by actual evapotranspiration and catchment area. Intermittency increased in the east and decreased in the west. It was negatively correlated with upstream soil moisture and strongly associated with rainfall frequency, especially the proportion of days with precipitation less than 1 mm. These patterns highlight regional differences in river responses to climate. Full article

Biodiversity conservation plays a pivotal role in achieving sustainable development and fostering harmonious coexistence between humans and nature. This study identifies avian conservation priority areas across China by analyzing multi-dimensional biodiversity, incorporating species diversity, functional diversity, and phylogenetic diversity. Through systematic conservation planning using Zonation version 4 software, we delineated priority areas across these diversity dimensions. Our results demonstrate a distinct south-to-north diversity gradient in China’s avifauna, with functional and phylogenetic diversity hotspots concentrated in Yunnan Province, the Hengduan Mountains, Hainan Island, Taiwan Island, and southeastern coastal regions. The identified priority conservation areas cover 14.6% of China’s terrestrial territory, protecting 89.8% of the country’s bird species—including 93.5% of endemic species and 88.9% of critically endangered species. Notably, existing nature reserves encompass merely 8.1% of these priority areas, revealing substantial conservation gaps within the current protection framework. Building upon China’s 3C Zoning Framework (Cities and farms, Shared landscapes, and Large wild areas), we propose zone-specific conservation strategies, with particular emphasis on strengthening protected area networks in the eastern coastal regions and the middle-lower Yangtze River basin, where urbanization pressures are most acute. These findings highlight the critical importance of incorporating multi-dimensional diversity in conservation planning and offer novel perspectives for optimizing China’s protected area system. Full article

The offshore transport of coastal water masses in the East China Sea is vital for maintaining ecological stability. Understanding its spatial-temporal pathways helps clarify material transport and ecological responses. This study used total suspended sediment (TSS) data from the Korean Geostationary Ocean Color Imager to analyze TSS distribution and anomalies, combined with satellite-derived surface residual currents. Results show significant seasonal variations: coastal water masses expand to the 50 m isobath in winter and contract to the 20 m isobath in summer. Offshore transport pathways vary spatially, extending to the shelf edge north of 28° N but restricted by the Taiwan Warm Current south of 28° N. A persistent transport pathway near 28° N shifts from northeastward to eastward. Other pathways include one south of Hangzhou Bay (spring and autumn) linked to tidal mixing and another north of the Yangtze River estuary (summer) following the Yangtze River Diluted Water. These findings provide crucial observational insights for modeling material cycling in the East China Sea shelf. Full article

The three-spot seahorse (Hippocampus trimaculatus) is an economically important marine species in the northern South China Sea (NSCS). However, due to overfishing and marine environmental changes, its wild populations have been gradually depleted. To investigate the transport and settlement mechanisms of H. trimaculatus larvae in the NSCS, a physical–biological coupled model was developed based on the ocean model CROCO and the biological model Ichthyop for the period 2016–2018. The results indicate that the transport and settlement processes of larvae are primarily regulated by the combined influence of the South China Sea Warm Current, coastal upwelling, and Kuroshio intrusion. The larvae predominantly undergo short distance (0–300 km) and mid-short distance (300–600 km) transport, exhibiting significant spatial aggregation along coastal waters, particularly in the Gulf of Tonkin, the Pearl River Estuary, Shantou, Xiamen, and the western coast of Taiwan. Furthermore, extreme weather events, such as typhoons, significantly enhance larval settlement success rates. Notably, Typhoon Hato in August 2017 increased settlement success by 12.2%. This study elucidates the transport and settlement mechanisms of H. trimaculatus larvae, providing a scientific foundation for the conservation and management of its populations in the NSCS. Full article

High-resolution (2 km), high-frequency (hourly) SST data of the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 geostationary satellite were used to derive the monthly climatology of temperature fronts in the South China Sea. The SST data from 2015 to 2022 were processed with the Belkin–O’Reilly algorithm to generate maps of SST gradient magnitude GM. The GM maps were log-transformed to enhance contrasts in digital maps and reveal additional features (fronts). The combination of high-resolution, cloud-free, four-day-composite SST imagery from AHI, the advanced front-preserving gradient algorithm BOA, and digital contrast enhancement with the log-transformation of SST gradients allowed us to identify numerous mesoscale/submesoscale fronts (including a few fronts that have never been reported) and document their month-to-month variability and spatial patterns. The spatiotemporal variability of SST fronts was analyzed in detail in five regions: (1) In the Taiwan Strait, six fronts were identified: the China Coastal Front, Taiwan Bank Front, Changyun Ridge Front, East Penghu Channel Front, and Eastern/Western Penghu Islands fronts; (2) the Guangdong Shelf is dominated by the China Coastal Front in winter, with the eastern and western Guangdong fronts separated by the Pearl River outflow in summer; (3) Hainan Island is surrounded by upwelling fronts of various nature (wind-driven coastal and topographic) and tidal mixing fronts; in the western Beibu Gulf, the Red River Outflow Front extends southward as the Vietnam Coastal Front, while the northern Beibu Gulf features a tidal mixing front off the Guangxi coast; (4) Off SE Vietnam, the 11°N coastal upwelling gives rise to a summertime front, while the Mekong Outflow and associated front extend seasonally toward Cape Camau, close to the Gulf of Thailand Entrance Front; (5) In the Luzon Strait, the Kuroshio Front manifests as a chain of three fronts across the Babuyan Islands, while west of Luzon Island a broad offshore frontal zone persists in winter. The summertime eastward jet (SEJ) off SE Vietnam is documented from five-day mean SST data. The SEJ emerges in June–September off the 11°N coastal upwelling center and extends up to 114°E. The zonally oriented SEJ is observed to be located between two large gyres, each about 300 km in diameter. Full article