Search Results (92)

In water-scarce regions such as Jordan, accurate tracking of water flows is critical for informed water management. This study applied the Water Accounting Plus (WA+) framework using open-source remote sensing data from the FAO WaPOR portal to develop agricultural water accounting (AWA) for the Amman–Zarqa Basin (AZB) during 2014–2022. Inflows, outflows, and water consumption were quantified using WaPOR and other open datasets. The results showed a strong correlation between WaPOR precipitation (P) and rainfall station data, while comparisons with other remote sensing sources were weaker. WaPOR evapotranspiration (ET) values were generally lower than those from alternative datasets. To improve classification accuracy, a correction of the WaPOR-derived land cover map was performed. The revised map achieved a producer’s accuracy of 15.9% and a user’s accuracy of 86.6% for irrigated areas. Additionally, ET values over irrigated zones were adjusted, resulting in a fivefold improvement in estimates. These corrections significantly enhanced the reliability of key AWA indicators such as basin closure, ET fraction, and managed fraction. The findings demonstrate that the accuracy of P and ET data strongly affects AWA outputs, particularly the estimation of percolation and beneficial water use. Therefore, calibrating remote sensing data is essential to ensure reliable water accounting, especially in agricultural settings where data uncertainty can lead to misleading conclusions. This study recommends the use of open-source datasets such as WaPOR—combined with field validation and calibration—to improve agricultural water resource assessments and support decision making at basin and national levels. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

The dynamics of shallow water areas of inland lakes is closely related to the regional ecology and economy. However, it is still a challenge to extract the natural shallow water area for inland lakes using satellite images due to their rapid changes and various human demands. Therefore, we developed a new remote sensing-based method applied in Hongze Lake (one of the largest freshwater lakes in China) to first delineate the lake from the SWIR1 band of Landsat OLI imagery using cold spots in the LISA method, and then distinguish deep and shallow water areas from the G band of Landsat OLI images using hot spots with LISA after masking the lake out, and finally extracting the natural shallow water area by masking aquatic farms out from shallow water areas using farm ridge classification from NDWI images and aggregating points of farm ridges. The results show that (1) the method of this study is efficient in extracting the natural shallow water area with limited effects from aquatic vegetation; (2) water inflow (upstream water supply and precipitation) and the area of aquatic farms, the two dominant factors for the temporal changes in natural shallow water area, contributed 38.3% (positively) and 42.2% (negatively) to the decrease in the natural shallow water area during 2013–2022 in Hongze Lake; (3) the natural shallow water area of Hongze Lake decreased significantly every April as paddy rice farms withdrew a large amount of irrigation water from Hongze Lake. Our research provides a new approach to extract the natural shallow water areas of inland lakes from satellite images and demonstrates that the upstream water supply, precipitation, and agriculture demands are the three main reasons for seasonal and temporal variations in natural shallow water areas for inland lakes. Full article

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the LSA using an improved multi-index threshold method, which incorporates a slope mask and threshold adjustment to enhance the boundary delineation accuracy (Kappa coefficient = 0.94). By combining the LSA with high-resolution DEM and the GLOBathy bathymetry dataset, the absolute LWS was reconstructed, fluctuating between 12.3 × 10⁹ and 12.8 × 10⁹ m³. A water balance analysis revealed that inflow runoff (IRO) was the primary driver of LWS changes, contributing 54.57%. The cross-wavelet transform and wavelet coherence analyses showed that the precipitation (PRE) and snow water equivalent (SWE) were key climatic factors that directly influenced the variability of IRO, impacting the interannual water availability in the lake, with PRE having a more sustained impact. Temperature indirectly regulated IRO by affecting SWE and potential evapotranspiration. Furthermore, IRO exhibited different resonance periods and time lags with various atmospheric circulation factors, with the Pacific Decadal Oscillation and North Atlantic Oscillation having the most significant influence on its interannual variations. These findings provide crucial insights into the hydrological behavior of Lake Sarez under climate change and offer a novel approach for studying water storage dynamics in high-altitude landslide-dammed lakes, thereby supporting regional water resource management and ecological conservation. Full article

Efficient separation of blood plasma and Packed Cell Volume (PCV) is vital for rapid blood sensing and early disease detection, especially in point-of-care and resource-limited environments. Conventional centrifugation methods for separation are resource-intensive, time-consuming, and off-chip, necessitating innovative alternatives. This study introduces “Intelligent Microfluidics”, an ML-integrated microfluidic platform designed to optimize plasma separation through computational fluid dynamics (CFD) simulations. The trifurcation microchannel, modeled using COMSOL Multiphysics, achieved plasma yields of 90–95% across varying inflow velocities (0.0001–0.05 m/s). The input fluid parameters mimic the blood viscosity and density used with appropriate boundary conditions and fluid dynamics to optimize the designed microchannels. Eight supervised ML algorithms, including Artificial Neural Networks (ANN) and k-Nearest Neighbors (KNN), were employed to predict key performance parameters, with ANN achieving the highest predictive accuracy (R² = 0.97). Unlike traditional methods, this platform demonstrates scalability, portability, and rapid diagnostic potential, revolutionizing clinical workflows by enabling efficient plasma separation for real-time, point-of-care diagnostics. By incorporating a detailed comparative analysis with previous studies, including computational efficiency, our work underscores the superior performance of ML-enhanced microfluidic systems. The platform’s robust and adaptable design is particularly promising for healthcare applications in remote or resource-constrained settings where rapid and reliable diagnostic tools are urgently needed. This novel approach establishes a foundation for developing next-generation, portable diagnostic technologies tailored to clinical demands. Full article

This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation. Full article

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes. Full article

This study provides a comprehensive analysis of the groundwater potential of hard rock aquifers in five diverse African case study areas: Lake Tana Basin and Beles Basin in northwestern Ethiopia and Mount Meru in northern Tanzania (comprising volcanic aquifers); the Mekelle area in northern Ethiopia and Jifarah Plain in Libya (consisting of sedimentary aquifers). The evaluation of recharge, transmissivity, and water quality formed the basis of qualitative and quantitative assessment. Multiple methods, including water table fluctuation (WTF), chloride mass balance (CMB), physical hydrological modeling (WetSpass), baseflow separation (BFS), and remote sensing techniques like GRACE satellite data, were employed to estimate groundwater recharge across diverse hydrogeological settings. Topographic contrast, fractured orientation, lineament density, hydro-stratigraphic connections, hydraulic gradient, and distribution of high-flux springs were used to assess IGF from Lake Tana to Beles Basin. The monitoring, sampling, and pumping test sites took into account the high hydromorphological and geological variabilities. Recharge rates varied significantly, with mean values of 315 mm/year in Lake Tana Basin, 193 mm/year in Mount Meru, and as low as 4.3 mm/year in Jifarah Plain. Transmissivity ranged from 0.4 to 6904 m²/day in Lake Tana Basin, up to 790 m²/day in Mount Meru’s fractured lava aquifers, and reached 859 m²/day in the sedimentary aquifers of the Mekelle area. Water quality issues included high TDS levels (up to 3287 mg/L in Mekelle and 11,141 mg/L in Jifarah), elevated fluoride concentrations (>1.5 mg/L) in 90% of Mount Meru samples, and nitrate pollution in shallow aquifers linked to agricultural practice. This study also highlights the phenomenon of inter-basin deep groundwater flow, emphasizing its role in groundwater potential assessment and challenging conventional water balance assumptions. The findings reveal that hard rock aquifers, particularly weathered/fractured basalt aquifers in volcanic regions, exhibit high potential, while pyroclastic aquifers generally demonstrate lower potential. Concerns regarding high fluoride levels are identified in Mount Meru aquifers. Among sedimentary aquifers in the Mekelle area and Jifarah Plain, limestone intercalated with marl or dolomite rock emerges as having high potential. However, high TDS and high sulfate concentrations are quality issues in some of the areas, quite above the WHO’s and each country’s drinking water standards. The inter-basin groundwater flow, investigated in this study of Beles Basin, challenges the conventional water balance assumption that the inflow into a hydrological basin is equivalent to the outflow out of the basin, by emphasizing the importance of considering groundwater influx from neighboring basins. These insights contribute novel perspectives to groundwater balance and potential assessment studies, challenging assumptions about groundwater divides. Full article

The Prespa–Ohrid lake system in the southwest Balkan region is the oldest permanent lake system in Europe and a global hotspot of biodiversity and endemism. Its smaller component, Lake Macro Prespa (or simply called Prespa), shared by North Macedonia, Albania and Greece has suffered a dramatic water-level fall (nearly 10 m since the 1950s). It was greater in the periods 1987–1993 and 1998–2004 and has further accelerated in the last 5 years. Analysis of satellite images (remote sensing) revealed that over the period 1984–2020 Prespa Lake lost 18.87 km² of its surface (6.9% of its size, dropping from 273.38 km² to 254.51 km²), with a decline in the volume of water estimated as about 54%, even reaching 56.8% in 2022. The environmental status of the lake has also been compromised and the process of its eutrophication is enhanced. The aim of this study is to summarize the current understanding of the diminishing trend in the water level and the factors that have contributed to it. The lake is highly sensitive to external impacts, including climate change, mainly restricted precipitation and increased water abstraction for irrigation. Importantly, nearly half of its outflow is through karst aquifers that feed Ohrid Lake. Of note, the hydrology and especially hydrogeology of the catchment has not been studied in sufficient detail and accurate data for the present state are missing, largely due to a lack of coordinated investigations by the three neighboring countries. However, recent estimation of the water balance of Prespa Lake, elaborated with the consideration of only the natural sources of inflow (precipitation and river runoff) and outflow (evaporation and loss of water through the karst channels) suggested a negative balance of 53 × 10⁶ m³ annually. Our study also offers an estimated projection for the water level in the future in different climate scenarios based on linear regression models that predict its complete loss before the end of the present century. Full article

Accurately delineating sediment export dynamics using high-quality vegetation factors remains challenging due to the spatio-temporal resolution imbalance of single remote sensing data and persistent cloud contamination. To address these challenges, this study proposed a new framework for estimating and analyzing monthly sediment inflow to rivers in the cloud-prone Minjiang River Basin. We leveraged multi-source remote sensing data and the Continuous Change Detection and Classification model to reconstruct monthly vegetation factors at 30 m resolution. Then, we integrated the Chinese Soil Loss Equation model and the Sediment Delivery Ratio module to estimate monthly sediment inflow to rivers. Lastly, the Optimal Parameters-based Geographical Detector model was harnessed to identify factors affecting sediment export. The results indicated that: (1) The simulated sediment transport modulus showed a strong Coefficient of Determination (R² = 0.73) and a satisfactory Nash–Sutcliffe Efficiency coefficient (0.53) compared to observed values. (2) The annual sediment inflow to rivers exhibited a spatial distribution characterized by lower levels in the west and higher in the east. The monthly average sediment value from 2016 to 2021 was notably high from March to July, while relatively low from October to January. (3) Erosive rainfall was a decisive factor contributing to increased sediment entering the rivers. Vegetation factors, manifested via the quantity (Fractional Vegetation Cover) and quality (Leaf Area Index and Net Primary Productivity) of vegetation, exert a pivotal influence on diminishing sediment export. Full article

Coastal changes in China, notably in the Zhoushan Islands, have primarily been driven by coastal reclamation since the establishment of New China. This study conducted a comprehensive analysis of the Zhoushan Archipelago shoreline spanning four decades, employing remote sensing, aerial photographs, and shoreline data since 1984, along with GIS (Geographic Information System) technology. We assessed shoreline changes using the shoreline change index and shoreline artificialization index, as well as examined the influence of the Yangtze River’s suspended sediment and impoldering activities on Zhoushan’s shoreline. Furthermore, the correlation between local economic development and shoreline development was explored. The results revealed the following key findings: (1) From 1984 to 2018, the Zhoushan Archipelago shoreline decreased by 7.05 km. Temporally, the shoreline change index was −0.08%, with the most significant reduction occurring between 2008 and 2018. Spatially, differences among island groups were not pronounced. (2) The shoreline diversity index consistently increased, indicating greater diversity and complexity in shoreline use over the four decades. (3) The shoreline artificiality index steadily rose, particularly after 2000. It was highest in the south, followed by the center, and lowest in the north. (4) The intensity index of coastal land use continuously increased, with the southern island group having a higher index compared to the Zhoushan Islands. (5) The Yangtze River contributed significantly to sand inflow, influencing shoreline changes and beach shaping in Zhejiang. However, reclamation projects were identified as the primary and direct factor. (6) A positive correlation existed between Zhoushan City’s economic development and the intensity of coastal land use. This study emphasized the need for improving the control over reclamation projects and the better management of coastal protection and use. These measures could optimize resource allocation and establish a more scientific and rational coastal zone pattern. Full article

Macrophytes, which are foundational to freshwater ecosystems, face significant threats due to habitat degradation globally. Habitat suitability models are vital tools used to investigate the relationship between macrophytes and their environment. This study addresses a critical gap by developing a Geographic information system-based HSM tailored for small lakes, which are often overlooked in ecological studies. We included various abiotic predictors to model the potential macrophyte habitat for several small lakes in southern Bavaria (Germany). Key factors such as the distance to groundwater inflow, depth, availability of photosynthetically active radiation (PAR), and littoral slope were identified as significant predictors of macrophyte occurrence. Notably, the HSM integrates remote sensing-based data to derive PAR availability at the growing depths of the macrophytes using Sentinel-2 MSI data. Integration of an MSI-based time series of PAR availability enabled the introduction of a temporal component allowing monitoring and predicting changes in macrophyte habitats over time. The modeled habitat suitability score correlates highly (R = 0.908) with macrophyte occurrence. We see great promise in using habitat modeling for macrophytes as a tool for water management; in particular, the use of Sentinel-2 MSI data for habitat suitability modeling holds promise for advancing water management. By demonstrating the efficacy of GIS- and remote sensing-based HSM, we pave the way for future applications of this innovative approach in ecological conservation and resource management. Full article

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. Full article

Bio-inspired flexible pillar-like wind-hairs show promise for the future of flying by feel by detecting critical flow events on an aerofoil during flight. To be able to characterise specific flow disturbances from the response of such sensors, quantitative PIV measurements of such flow-disturbance patterns were compared with sensor outputs under controlled conditions. Experiments were performed in a flow channel with an aerofoil equipped with a 2D array of such sensors when in uniform inflow conditions compared to when a well-defined gust was introduced upstream and was passing by. The gust was generated through the sudden deployment of a row of flaps on the suction side of a symmetric wing that was placed upstream of the aerofoil with the sensors. The resulting flow disturbance generated a starting vortex with two legs, which resembled a horseshoe-type vortex shed into the wake. Under the same tunnel conditions, PIV measurements were taken downstream of the gust generator to characterise the starting vortex, while further measurements were taken with the sensing pillars on the aerofoil in the same location. The disturbance pattern was compared to the pillar response to demonstrate the potential of flow-sensing pillars. It was found that the pillars could detect the arrival time and structural pattern of the flow disturbance, showing the characteristics of the induced flow field of the starting vortex when passing by. Therefore, such sensor arrays can detect the “footprint” of disturbances as temporal and spatial signatures, allowing us to distinguish those from others or noise. Full article

Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, based on the existing measured the total suspended matter flux (F_TSM) and discharge data for the Irrawaddy River, an inversion model of the total suspended matter concentration (C_TSM) is constructed for the Irrawaddy River, and the C_TSM and F_TSM from 1990 to 2020 are estimated using the L1 products of Landsat-8 OLI/TIRS and Landsat-5 TM. The results show that over the last 30 years, the F_TSM of the Irrawaddy River decreased at a rate of 3.9 Mt/yr, which is significant at the 99% confidence interval. An increase in the vegetation density of the Irrawaddy Delta has increased the land conservation capacity of the region and reduced the inflow of land-based total suspended matter (TSM). The F_TSM of the Irrawaddy River was estimated by fusing satellite data and data measured at hydrological stations. The research method employed in this paper provides a new supplement to the existing hydrological data for large rivers. Full article