Search Results (3,280)

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity. Full article

Based on monitoring data from 2019 to 2024 at 270 typical surface drinking water sources (SDWS) in the Pearl River Basin (PRB), the occurrence and health risks of metal and metalloid pollutants (MMPs) were analyzed from a large watershed scale and long-term evolution. The results indicated that the overall pollution status of 8 MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) were at a low level and the concentrations of Cd, Pb, Ni, Ba, and V exhibited downward trends from 2019 to 2024. The distribution of MMPs exhibited significant regional differences with the main influencing factors including geological conditions, industrial activities, and urban development. River-type drinking water sources might be more affected by pollution from human activities such as industrial wastewater discharge, and the concentration levels of MMPs were generally higher than those in lake-type drinking water sources. Monte Carlo simulation revealed that 33.08% and 12.90% of total carcinogenic risks (TCR) exceeded the threshold of 10⁻⁶ for adults and children, respectively. Ba and Ni were the main contributors to the TCR, while As posed a certain non-carcinogenic risk to children. Sensitivity analysis indicated that concentrations of As and Ba were the main factors contributing to health risks. Although highly stringent water pollution control and a water resource protection policy have been implemented, it is still suggested to strengthen the control of As, Ba, and Ni in industrial-intensive areas and river-type water sources in the PRB. Full article

This pilot study uses Raman spectroscopy and SERS to monitor monthly water composition changes in two adjacent hypersaline lakes (L1 and L2) at a balneary resort, during the peak tourist season (May–October 2023). In situ pH and electrical conductivity (EC) measurements, along with evaporite analyses, complemented the spectroscopic data. Although traditionally considered similar, the lakes frequently raise public questions about their relative bathing benefits. While not directly addressing the therapeutic effects, the study reveals distinct physicochemical profiles between the lakes. Raman data showed consistently higher sulfate levels in L2, a trend also observed in winter monitoring. pH levels were higher in L1 (8–9.8) than in L2 (7.2–8), except for one October depth reading. This trend held during winter, except in April. Surface waters showed more variability and slightly higher values than those at 1 m depth. SERS spectra featured β-carotene peaks, linked to cyanobacteria, and Ag–Cl bands, indicating nanoparticle aggregation from inorganic ions. SERS intensity strongly correlated with pH and EC, especially in L2 (r = 0.96), suggesting stable surface–depth chemistry. L1 exhibited more monthly variability, likely due to differing biological activity. Although salinity and EC were not linearly correlated at high salt levels, both reflected seasonal trends. The integration of Raman, SERS, and physicochemical data proves effective for monitoring hypersaline lake dynamics, offering a valuable tool for environmental surveillance and therapeutic water quality assessment, in support of evidence-based water management and therapeutic use of salt lakes, aligning with goals for sustainable medical tourism and environmental stewardship. Full article

This study aimed to characterize and analyse the kinematic parameters and muscle activity of swimmers in open water swimming (OWS). Nine male swimmers (age: 25.4 ± 11.9 years; body mass: 75.9 ± 9.0 kg; height: 180.7 ± 6.7 cm; and arm span: 185.6 ± 10.3 cm) were evaluated in an open environment (lake), performing 5 m × 1000 m at maximum intensity, with a rest of 30 s every 1000 m. For kinematical analyses, the stroke rate (SR), swimming velocity (v), stroke length (SL), and stroke index (SI) were calculated. Surface EMG data were recorded in seven muscles—upper trapezius (UP); latissimus dorsi (LD); pectoralis major (PM); posterior deltoid (PD); anterior deltoid (AD); triceps brachii (TB); and biceps brachii (BB)—for the underwater and recovery phases of the stroke. SL (F = 3.41, p = 0.06, η² = 0.30) and SI (F = 3.29, p = 0.08, η² = 0.29) changed along the covered distances, and SR (F = 1.54, p = 0.24, η² = 0.16) increased, especially in the last 1000 m (32.5 ± 3.0 cycles-min⁻¹). AD was highly activated in recovery, showing statistical differences from the beginning (p ≤ 0.01) to the end of the race (p = 0.03). The TB muscle was mostly recruited in the underwater phase, from the start (p ≤ 0.01) to the finish (p = 0.03), showing a significant difference in each lap, with a large effect. LD showed significant differences in muscle activation, from 1000 m (p ≤ 0.01) with a huge effect, to 5000 m (p ≤ 0.01), with a large effect. These results suggested that the UT and AD muscles had higher activity in recovery than the underwater phase, and TB and LD were higher in the underwater phase. Full article

Soil microbial communities are important for nutrient cycling regulation in forest ecosystems. However, limited knowledge exists regarding the characteristics of these microbial communities in Masson pine (Pinus massoniana Lamb.) plantations of different stand ages. In this study, four planted Masson pine stands (8-year-old, 12-year-old, 22-year-old, and 38-year-old stands) and one natural broadleaved forest stand (as a control) with three replications, were selected in the Laoshan Forest Farm, Qiandao Lake Town, Zhejiang Province, China. Soil physicochemical properties were measured and their effects on soil microbial communities were studied. Amplicon-based high-throughput sequencing was employed to process raw sequence data for soil microbes. It is worth noting that significant differences (p < 0.05) in soil bacterial genera were observed among different stand age groups. Total nitrogen (TN), total phosphorus (TP), total potassium (TK), available potassium (AK), soil organic carbon (SOC), and soil bulk density (BD) were identified as the primary factors influencing bacterial community distribution (p < 0.05). Available nitrogen (AN), SOC, TN, and TK showed significant correlations with soil fungal communities (p < 0.05). These findings underscore the crucial role of soil physicochemical properties in shaping soil microbial community composition in Masson pine plantations. Full article

Glacial lake outburst floods (GLOFs) represent increasingly common and high-magnitude geohazards across the cryosphere of the Tibetan Plateau, particularly under ongoing climate warming and glacier retreat. This study combines multi-temporal remote sensing imagery and detailed Flo-2D hydrodynamic modeling to investigate the erosive dynamics of the 2020 Jinwuco GLOF in Southeastern Tibetan Plateau. Key conclusions include: (1) The 2.35 km-long flood routing channel exhibits pronounced non-uniformity in horizontal curvature, channel width, and cross-sectional shape, significantly influencing flood propagation; five representative cross-sections divide the channel into six distinct segments. (2) Prominent lateral erosion occurred proximally to the dam, attributable to extreme erosive forces and high sediment transport capacity during peak discharge, with horizontal channel curvature further amplifying local impact and erosion. (3) Erosion rates were highest near the dam and in downstream narrow segments, while mid-reach sections with greater width experienced lower erosion. (4) Maximum flow depths reached 28.12 m in topographically confined reaches, whereas peak velocities occurred in upstream and downstream curved sections. (5) The apparent critical erosive shear stress of bank material is controlled not only by soil strength but also by flood dynamics and pre-existing channel morphology, indicating strong feedback between flow dynamics, channel morphology, and critical erosive shear stress of bank material. This study provides a generalized and transferable framework for analyzing GLOF-related erosion in data-scarce high-altitude regions, offering critical insights for hazard assessment, regional planning, and risk mitigation strategies. Full article

The Qinghai Lake Basin (QLB), as a key component of the ecological security barrier on the Tibetan Plateau, is crucial for regional sustainable development due to the stability of its alpine agro-pastoral ecosystems. This study aims to systematically analyze the spatiotemporal evolution patterns and underlying driving mechanisms of eco-environmental quality (EEQ) in the QLB from 2001 to 2022. Based on Google Earth Engine (GEE) and long-term MODIS data, we constructed a Remote Sensing Ecological Index (RSEI) model to evaluate the EEQ dynamics. Geodetector (GD) was applied to quantitatively identify key driving factors and their interactions. The findings reveal: (1) The mean RSEI value increased from 0.46 in 2001 to 0.51 in 2022, showing a fluctuating improvement trend with significant transitions toward higher ecological quality grades; (2) spatially, a distinct “high-north-south, low-center” pattern emerged, with excellent-grade areas (4.77%) concentrated in alpine meadows and poor-grade areas (5.10%) mainly in bare rock regions; (3) 47.81% of the region experienced ecological improvement, whereas 16.34% showed degradation, predominantly above 3827 m elevation; and (4) GD analysis indicated natural factors dominated EEQ differentiation, with temperature (q = 0.340) and elevation (q = 0.332) being primary drivers. The interaction between temperature and precipitation (q = 0.593) exerted decisive control on ecological pattern evolution. This study provides an efficient monitoring framework and a spatially explicit governance paradigm for maintaining differentiated management and ecosystem stability in alpine agro-pastoral regions. Full article

The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R² = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R² = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems. Full article

Managing and analyzing data in data lakes for big data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The increasing need to process sensitive data emphasizes the relevance of secure-by-design approaches that integrate encryption techniques and governance frameworks to protect personal and confidential information. This study proposes a protocol that combines the capabilities of Databricks and format-preserving encryption to improve data security and accessibility in data lakes without compromising usability or structure. The protocol was developed using a design science methodology, incorporating findings from a systematic literature review and validated through expert feedback and proof-of-concept experiments in banking environments. The proposed solution integrates multiple layers, data ingestion, persistence, access, and consumption, leveraging the processing capabilities of Databricks and format-preserving encryption to enable secure data management and governance. Validation results indicate the protocol is effectiveness in protecting sensitive data, with promising applicability in regulated industries. This work contributes to addressing key challenges in big data security and lays the groundwork for future developments in data governance and encryption techniques. Full article

The large lakes and reservoirs of the northeastern Tibetan Plateau play a key role in regional water resources, yet their influence on terrestrial water storage (TWS) changes at different spatial scales remains unclear. This study employed the constrained forward modeling (CFM) method to correct leakage errors in level-2 spherical harmonic (SH) coefficients from the Gravity Recovery and Climate Experiment and its follow-on missions (GRACE/GRACE-FO) at three spatial scales: two circular regions covering 90,000 km² and 200,000 km², respectively, and a 220,000 km² region based on the shape of mass concentration (Mascon). TWS changes derived from SH solutions after leakage correction through CFM were compared with level-3 Mascon solutions. Individual water storage components, including lake and reservoir water storage (LRWS), groundwater storage (GWS), and soil moisture storage (SMS), were quantified, and their relationships with precipitation were assessed. From 2003 to 2022, the CFM method effectively mitigated signal leakage, revealing an overall upward trend in TWS at all spatial scales. Signals from Qinghai Lake and Longyangxia Reservoir dominated the long-term trend and amplitude variations of LRWS, respectively. LRWS explained more than 47% of the TWS changes, and together with GWS, accounted for over 85% of the changes. Both CFM-based and Mascon-based TWS changes indicated a consistent upward trend from January 2003 to September 2012, followed by declines from November 2012 to May 2017 and October 2018 to December 2022. During the decline phases, GWS contributions increased, while LRWS contributions and component exchange intensity decreased. LRWS, SMS, and TWS changes were significantly correlated with precipitation, with varying time lags. These findings underscore the value of GRACE/GRACE-FO data for monitoring multiscale TWS dynamics and their climatic drivers in lake- and reservoir-dominated regions. Full article

We developed a forecast model for cyanobacteria bloom formation in two contrasting inland lakes in Australia by combining in situ sampling and continuous remote sensing hyperspectral reflectance (HydraSpectra) with hydrodynamic and algal growth models. Cyanobacterial distribution of a buoyant species was simulated with an algal growth model, driven by forecasted meteorological data, and modeled temperature stratification and mixing dynamics from a one-dimensional, vertical k-epsilon turbulence hydrodynamic model. The cyanobacteria model was re-initialized daily with measured cell counts derived from the hyperspectral reflectance data. Simulations of cyanobacterial concentrations (cell counts) reflected the dynamic mixing behavior in the lakes with daily phases of near-surface accumulation and subsequent daily mixing due to wind or night-time cooling. To determine the surface concentration of cyanobacteria on sub-daily time scales, it was demonstrated that the combined use of high-resolution water temperature profiles, HydraSpectra reflectance data, and a hydrodynamic model to quantify the mixing dynamics is essential. Overall, the model results demonstrated a prototype for a cyanobacteria short-term forecast model. Having these tools in place allows us to quantify the risks of cyanobacterial blooms in advance to inform options for lake management. Full article

Tear osmotic pressure, tear volume, and quality were measured before and after subjects without dry eye syndrome ate fried chicken slices for one week. By analyzing the data, we explored the causes of temporary dry eye syndrome. A 2 mL volume of normal saline was added to fluorescent tear test paper, which was applied to the conjunctiva of the subject, and the tear breakup time and tear lake height were measured with a digital slit lamp. The tear test paper was placed on the outside of the subject’s lower eyelid to test their tear volume. A total of 29 subjects, aged between 20 and 55 years old, ate fried foods for one week. The amount of tears produced before and after they ate fried chicken slices was tested using paired samples t-tests. The tear volume in the left and right eyes decreased at a significance level of p < 0.05. The tear membrane breakup time before and after treatment was analyzed using fluorescent reagents and digital slit lamps. The paired-sample t-test results showed that there was a statistically significant difference (p < 0.05). Eating fried chicken slices for a week significantly contributed to dry eyes, regardless of gender and age. Full article

Water scarcity, soil salinization, and desertification threaten sustainable agricultural ecosystems of Hetao irrigation district, Yellow River Basin (YRB). Precise quantification of soil water dynamics and plant water consumption processes is essential for the agricultural sustainability of the irrigation district. Therefore, this study mainly focused on the crop coefficients and water consumption processes of three representative plant types in the Hetao irrigation district, each corresponding to a specific land system: Helianthus annuus (cultivated land), Tamarix chinensis (wasteland), and Phragmites australis (lake). The SIMDualKc model was calibrated and validated based on situ observation data (soil water content and yield) during 2018 (conventional conditions), 2023 and 2024 (deep water-saving conditions). Results show strong agreement between simulated and observed soil moisture and crop yields. The results indicate that the process curves of K_cb (basal crop coefficient) and K_cbadj (adjusted crop coefficient) nearly overlapped for the three plant types in 2018 and 2023. However, under the deep water-saving project implemented in 2024, the K_cbadj process curves for all three plant types exhibited a significant reduction (approximately 15%). Soil evaporation fractions (E/ET_cadj) were stable at 19–30% during the 2018, 2023, and 2024. The contribution of capillary rise to ET reached 38.61–43.18% in cultivated land (Helianthus annuus), 41.52–48.93% in wasteland (Tamarix chinensis), and 38.08–46.57% in lake boundary areas (Phragmites australis), which underscores the significant role of groundwater recharge in sustaining plant water consumption. Actual-to-potential transpiration ratios (T_a/T_p) during 2023–2024 decreased by 3–11% for Helianthus annuus, 5–12% for Tamarix chinensis, and 23% for Phragmites australis compared to T_a/T_p values in 2018. Capillary rise decreased approximately 10% during the whole system. Deep water-saving practices increased the groundwater depth and restricted groundwater recharge to plants via capillary rise, thereby impairing plant transpiration and growth. These findings provide scientific support for sustainable agriculture and ecological security in the Yellow River Basin. Full article

The acquisition of plant ecological indicators, such as leaf area index and leaf area density values, typically relies on labor-intensive field sampling and measurements, which are often time-consuming and hinder large-scale application. As different plant ecological indicators are closely related to plants’ geometric characteristics, the development of dynamic correlation and prediction methods for relevant indicators has become an important research topic. However, existing 3D plant models are mainly used for visualization purposes, which cannot accurately reflect the plant’s growth process or geometric characteristics. This study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation. With the established plant model, a method for calculating and analyzing ecological indicators, including the leaf area index, leaf area density, aboveground biomass, and aboveground carbon storage, was then proposed. A method for exporting the model-generated data into ENVI-met v.5.0 to simulate the microclimate environment was also established. Then, by taking Daijia Lake Park as an example, this study utilized site planting construction drawings and field survey data to perform parametric modeling of 21,685 on-site trees from 65 species at three different growth stages using Blender v.4.0 and The Grove plugin v.10. The generated plant model’s accuracy was then verified using the 3D IoU ratio between the models and on-site scanned point cloud data. Plant ecological indicators at various stages were then extracted and exported to ENVI-met for microclimate analysis. The workflow integrates the simulation of plant growth dynamics and their interactions with environmental factors. It can also be used for scenario-based predictions in planting design and serves as a basis for urban green space monitoring and management. Full article