Search Results (1,613)

This study examines the landscape characteristics of high-suitability habitats for the Black-tailed Godwit (Limosa limosa) during the non-breeding season in inland and coastal wetlands of the middle and lower Yangtze River regions, and seeks to elucidate the distribution patterns and their drivers. Using the MaxEnt model and landscape analysis, the following conclusions were obtained: (1) High-suitability habitats for the Black-tailed Godwit cover approximately 128,800 km² and are primarily distributed across the middle and lower Yangtze River regions. (2) The dominant environmental variables were identified as elevation, distance to water source, slope, distance to paddy field, land use classification, and minimum temperature of the coldest month. (3) Landscape fragmentation, habitat connectivity, human disturbance, and climate change were found to be associated with the shift in the Black-tailed Godwit’s distribution from coastal to inland areas. (4) The distribution of the Black-tailed Godwit in the Nanji Wetland showed significant moderate positive correlation with shallow-water area (r = 0.38, p < 0.05) and significant moderate negative correlation with deep-water area (r = −0.48, p < 0.01). (5) At large spatial scales (coastal and inland wetlands), habitat connectivity and fragmentation were found to exert a greater influence, whereas at smaller spatial scales (Nanji Wetland) land use areas (wetlands and shallow-water areas) and food resources were found to exert greater influence on the Black-tailed Godwit’s distribution. This study synthesizes findings from multiple sources and aims to provide a reference for the conservation of the Black-tailed Godwit. Full article

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

Lake Villarrica, located in southern Chile, is a vital freshwater resource whose ecological status requires continuous evaluation. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and estimating it using satellite sensors enables efficient and large-scale monitoring. This study compared the performance of different empirical models based on reflectance data obtained from atmospherically corrected satellite images using ACOLITE software (Generic Version 20231023.0), calibrated with in situ measurements of Chl-a collected during the spring and summer seasons between 2014 and 2024. For each sensor, the best combination of spectral bands was selected, and retrieval models were generated using a bootstrapping procedure with 1000 iterations to obtain robust regression coefficients; the final models were defined using the median of these coefficients. The top-performing model for Landsat-8 and 9 was based on a blue-red band combination (R² = 0.79, RMSE = 2.1 µg·L⁻¹, MAE = 1.2 µg·L⁻¹, n = 74). In contrast, the optimal model for Sentinel-2A utilized green and blue bands, yielding higher precision (R² = 0.75, RMSE = 0.8 µg·L⁻¹, MAE = 0.72 µg·L⁻¹, n = 112). In general, the results obtained through remote sensing reveal a gradual increase in Chl-a levels over the last decade, reflected in recurrent summer biomass increases primarily along the shoreline near the urban area of Pucón and in the vicinity of the Pucón River inflow into Lake Villarrica. These results support the development of an operational satellite-based monitoring framework for inland lake water quality assessment. Full article

Driven by sea-level rise and frequent compound coastal flooding, accurate inundation simulation is essential for disaster mitigation and urban planning. To address the topologically disconnected overestimation errors inherent in the traditional bathtub model, this study proposes a dynamic coastal inundation simulation framework based on an 8-neighbor seed-spread algorithm. Within this framework, a digital elevation model (DEM) is resampled to a 10 m spatial resolution, and a high frequency tidal sequence with a 5-min temporal resolution is reconstructed from typical spring tides. The vertical datums of both the topography and tidal water levels are strictly unified to the Mean Sea Level (MSL) to maintain physical consistency. Comparative experiments across multiple water level scenarios reveal a distinct threshold effect and non-linear expansion characteristics in inundation responses under complex geomorphological conditions. Because the traditional bathtub model fails to account for the blocking effects of inland physical barriers, its overestimation increases significantly once the water level exceeds critical flood protection thresholds. By generating high resolution Time of Arrival (ToA) maps, the proposed framework provides a robust spatial–temporal basis for precise coastal risk assessment, evacuation planning, and defense resource allocation. Full article

High-quality water surface elevation (WSE) measurements are critical in hydrological applications, yet no systematic evaluation of the Surface Water and Ocean Topography (SWOT) mission exists for Brazil’s diverse lake systems, where satellite observations are essential given limited in situ monitoring. We evaluated WSE from SWOT over 132 Brazilian lakes, comparing LakeSP, Raster_250m, and Raster_100m products against field measurements over a 20-month period. The 68th percentile errors were under 29 cm for the full dataset, below 12 cm for Flag = 0, and below 21 cm for Flag = 1, indicating good agreement but also the presence of outliers and the need for data screening. A Random Forest analysis identified quality flags, lake geometry, and cross-track distance as key drivers of WSE precision. Flag = 0 is overly restrictive, retaining only 22% of observations, while Flag = 1 contains anomalous data. The SWOT Quality-Range Threshold for Lakes (SQRTL) filter combines Flag = 0 with cross-track constrained Flag = 1 observations. SQRTL more than triples data availability relative to Flag = 0, maintaining comparable precision (68th percentile below 16 cm) and reducing median revisit from 88–123 days to 16–18 days for raster products and from 25 to 14 days for LakeSP. These results provide the first large-scale SWOT WSE evaluation over Brazilian lakes and a transferable filtering framework applicable wherever SWOT and field observations overlap, with potential to extend monitoring to over 100,000 water bodies in the SWOT Prior Lake Database. Full article

Studies have shown that oceanic surface-water salinity varies across the globe and changes over time, while atmospheric water-vapor levels have also increased in recent decades. Evaporation from ocean and inland waters is controlled primarily by meteorological forcing, but the thermodynamic state of the water body also matters. In saline waters, dissolved solutes reduce water activity and thereby reduce the equilibrium tendency of water molecules to enter the vapor phase. In this study, the authors’ coefficient-less aqueous osmotic potential equation was used to examine the thermodynamic effect of representative oceanic salinity differences on evaporative tendency. Calculations were made for recorded surface-water salinities ranging from 31 to 38 kg·m⁻³ of dissolved solutes at an average temperature of 20 °C. Computed osmotic potentials ranged from −2.257 to −2.708 MPa. The corresponding semi-permeable membrane interface pressures ranged from 8.935 to 8.484 MPa, indicating an approximately 5% difference across the selected oceanic salinity range. The interface pressure calculated for solute-free water (11.192 MPa) was more than 24% higher than for the seawater cases considered. These results suggest that salinity acts as a secondary thermodynamic modifier of evaporation potential, whereas radiative, aerodynamic, humidity, and temperature controls remain dominant in determining actual evaporation fluxes. The results also indicate that freshwater bodies and changing land-based evaporative sources may contribute differently to atmospheric water vapor than saline ocean waters. The framework presented here is intended to complement, rather than replace, established evaporation formulations by clarifying how salinity-related osmotic effects can modify the water-side boundary condition. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article

The field of intelligent transportation on inland waterways is experiencing rapid growth, driven by the global pursuit of enhanced waterway safety, operational efficiency, and environmental sustainability. In real-world autonomous operation scenarios of unmanned surface vehicles (USVs), image-based 2D object detection methods are insufficient to meet the demands of 3D environmental modeling and accurate perception of dynamic objects. Existing 3D perception systems for USVs depend heavily on precise sensor calibration. However, projection offsets between point clouds and images—caused by water surface fluctuations and complex outdoor environments—hinder the practical deployment of these methods. To address these limitations, we propose a weak calibration multi-modal 3D object detection algorithm based on cross-view fusion, termed RCF-Free (Radar-Camera Fusion, Free from precise calibration). Inspired by autonomous driving solutions, we design a Triple-Path Cross-View Fusion module that achieves high-quality cross-view feature fusion without requiring accurate calibration parameters, while simultaneously detecting complete bird’s-eye view (BEV) bounding boxes. We further enhance the spatial layout comprehension of the visual branch through a Mobile Self-Attention Module (MAM) and effectively encode sparse point cloud features in BEV space using a dedicated BEV-Point feature encoder. Additionally, we reconstruct and introduce two water-related 3D object detection datasets, FloW-BEV and WaterScenes-BEV. Experimental results demonstrate that RCF-Free achieves $\mathrm{m}\mathrm{A}{\mathrm{P}}_{\mathrm{B}\mathrm{E}\mathrm{V}}50$ scores of 60.5% and 69.3% on the FloW-BEV and WaterScenes-BEV datasets, respectively, showing the effectiveness in water surface object detection. Moreover, on the DAIR-V2X-I dataset for autonomous driving scenarios, the model attains $\mathrm{m}\mathrm{A}{\mathrm{P}}_{3\mathrm{D}}50$ scores of 73.3%, 61.2%, and 61.2% across three task difficulty levels, illustrating strong cross-domain generalization capability. Full article

There is growing concern about the quality of sand on beaches, as users tend to spend most of their time on the sand rather than in the water. Numerous pathogenic agents have reportedly been isolated from sand, including bacteria, nematodes and opportunistic fungi. The ability of sand to retain pollutants and facilitate the transmission of pathogens raises public health concerns. We analysed sand-monitoring data from the 2024 and 2025 bathing seasons on Blue Flag beaches to find trends and patterns in total fungal counts, enterococci, and E. coli. The values recorded for microorganisms showed considerable variability, which may reflect the possible combined influence of multiple climatic, environmental, and anthropogenic factors contributing to their presence in beach sand. Our findings suggest that the total fungal count on coastal beaches may be influenced by periods of rainfall, which increases the fungal load in the sand. Values recorded from inland beaches vary considerably between beaches which may reflect the influence of local environmental characteristics, particularly vegetation and beach morphology, although the smaller number of inland samples also makes it difficult to define clear patterns and consistent reference values for this parameter. Bacterial indicators may be particularly influenced by occasional anthropogenic disturbances and contamination events. This study adds significantly to the understanding of the microbiological quality of beach sand, encouraging the integration of sand monitoring into environmental surveillance and management programmes. Full article

Flood disasters pose significant threats to human life and infrastructure, creating an urgent need for reliable vision-based environmental sensing technologies for rapid floodwater identification. Vision-based platforms such as unmanned surface vehicles (USVs) provide an effective solution for monitoring inland water environments; however, accurate floodwater segmentation remains challenging due to complex water boundaries, reflections, and background interference. To address these issues, we propose RFD-BiSeNet V2, a lightweight semantic segmentation network. Building upon BiSeNet V2, our model integrates an edge-aware learning strategy to track dynamic contours, a feature refinement module to suppress reflection noise, and a multi-scale feature fusion module to accommodate varying morphological scales. Evaluated on a comprehensive dataset incorporating USV data, UAV imagery, and diverse real-world scenes, RFD-BiSeNet V2 achieves an mIoU of 97.10%, outperforming the baseline by 6.68%. Crucially, the results demonstrate the practical implications of our architectural advancements: the edge-aware and feature refinement modules successfully sharpen ambiguous water boundaries and effectively filter out severe surface reflections, directly driving the segmentation accuracy. With a compact size of 5.95M parameters and real-time inference capabilities, the model offers a robust and highly efficient solution suitable for resource-constrained deployments across diverse intelligent environmental sensing systems. Full article

Floodplains are key components of inland river systems of Australia with floodplain vegetation playing important roles in habitat provision, nutrient cycling, and supporting strong cultural values. These vegetation communities are highly dynamic, particularly in response to flooding. However, decades of water development and highly managed water resources are linked to wetland habitat decline in this region. We explored patterns of vegetation response to flooding over twenty years at the Narran Lakes Ramsar site, a terminal floodplain wetland system in the northern Murray–Darling Basin, Australia. We collated data from previous monitoring efforts and resampled permanent plots for understorey vegetation structure and composition. Three flood events were surveyed over a 20-year period, with each event surveyed on two occasions first, following initial drawdown (minimal standing water) and a second survey under dry or drier conditions (~6 months after the recession of floodwaters). Overall, we observed a high diversity of native plant species (~110 species) in understorey communities across the wetland and high compositional turnover both between flood events and within years (i.e., paired surveys). Notably, vegetation cover, but not species richness, was greatest in the 2023 survey following the largest of the three flood events investigated. Understorey composition was strongly driven by inundation regimes, particularly the duration of recent inundation, and the number of wet and dry years prior. Large flood events are critical for supporting vegetation resilience in these systems, increasingly so under a drier climate and with stretched water resources. Continued long-term monitoring of vegetation through flood cycles at the Narran Lakes will be critical to understanding ecological responses to longer-term changes in climate and hydrology to inform adaptive water management and maintain the values of this Ramsar site. Full article

Blue crab (Callinectes sapidus) populations are of substantial ecological and economic importance. As a keystone species, C. sapidus plays a critical role in maintaining estuarine food webs while also supporting one of the most consumed and economically valuable seafood industries in Delaware and Maryland. This study investigated the presence of Callinectes sapidus reovirus 1 (CsRV1) in C. sapidus collected from Rehoboth Bay, Delaware, USA, using reverse transcription–quantitative polymerase chain reaction (RT-qPCR), and evaluated potential associations between viral occurrence and physicochemical parameters, including temperature, salinity, pH, turbidity, alkalinity, calcium hardness, nitrite, and chlorophyll-a. A total of eighteen traps were deployed across six study sites encompassing oyster aquaculture areas, artificial oyster reefs, and control sites with minimal structural habitat. CsRV1 was detected in blue crabs from Rehoboth Bay, confirming the presence of the virus within the Delaware Inland Bays; however, detections were limited to a small subset of sampled individuals. Among the environmental parameters examined, salinity exhibited the greatest interannual variability, while other physicochemical conditions remained relatively consistent across site types and sampling periods. Overall, environmental conditions during the study period were within ranges considered suitable for C. sapidus, indicating that the population is likely to experience limited environmental stress and minimal disease-related impacts under current conditions. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

Global climate change has altered temperature regimes, hydrological stability, and redox dynamics in inland waters, yet the continental-scale impact of these alterations on sediment microbiomes remains poorly understood. Here, we compiled 562 publicly available 16S rRNA gene datasets from lake sediments across five major climatic zones in China to examine how climatic gradients influence microbial diversity, community assembly, and interaction networks, as well as their associated taxonomic composition and environmental responses. Sediment microbiomes showed clear spatial differentiation in both α- and β-diversity, accompanied by climatic zone-specific taxonomic signatures and biomarker taxa. Community assembly also varied markedly across climatic zones, with stochasticity and dispersal limitation dominating in colder regions, transitional assembly in the south temperate zone, and stronger selective or high-turnover dynamics in the warm subtropics. Importantly, random forest models revealed a clear transition from climate-dominated to anthropogenic-dominated control in sediment microbiome organization: microbial variation in the plateau and temperate regions was primarily associated with climatic and geographic constraints, whereas anthropogenic factors played a more important role in shaping community differentiation in the central subtropical zone. By integrating diversity patterns, taxonomic composition, assembly processes, and network topology, we further propose a three-stage conceptual pattern of sediment microbial community organization along climatic gradients, shifting from a persistence-dominated regime in the cold plateau regions, to an efficiency-dominated regime in the temperate zones, and finally to a plasticity-dominated regime in the warm subtropical regions. These findings would provide a continental-scale framework for understanding sediment microbiome responses to coupled climatic and anthropogenic forcing in inland waters, with implications for future water quality management and ecosystem conservation. Full article