Search Results (31)

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient R_ALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (R_ALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10⁻¹⁶) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network. Full article

The KM3NeT Collaboration is building an underwater neutrino observatory at the bottom of the Mediterranean Sea, consisting of two neutrino telescopes, both composed of a three-dimensional array of light detectors, known as digital optical modules. Each digital optical module contains a set of 31 three-inch photomultiplier tubes distributed over the surface of a 0.44 m diameter pressure-resistant glass sphere. The module also includes calibration instruments and electronics for power, readout, and data acquisition. The power board was developed to supply power to all the elements of the digital optical module. The design of the power board began in 2013, and ten prototypes were produced and tested. After an exhaustive validation process in various laboratories within the KM3NeT Collaboration, a mass production batch began, resulting in the construction of over 1200 power boards so far. These boards were integrated in the digital optical modules that have already been produced and deployed, which total 828 as of October 2023. In 2017, an upgrade of the power board, to increase reliability and efficiency, was initiated. The validation of a pre-production series has been completed, and a production batch of 800 upgraded boards is currently underway. This paper describes the design, architecture, upgrade, validation, and production of the power board, including the reliability studies and tests conducted to ensure safe operation at the bottom of the Mediterranean Sea throughout the observatory’s lifespan. Full article

The functional relationship between detention dam inflows and outflows was derived in a closed form in a recent work, which led to a theoretically derived probability distribution (TDD) of the peak outflows from in-line detention dams. This TDD is tested using the generalized extreme value (GEV) as a reference distribution for floods. Full article

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites. Full article

In the present work, the use of optical cameras for turbidity measurements is tested on the Bode River in Germany, which is one of the best-instrumented catchments in Central Germany with a long-term time series on water quantity and quality. Four trap cameras have been installed on monitored cross-sections with the aim to explore the potential of RGB indices for the description of water turbidity. A description of the experimental setup and some preliminary results are introduced. Full article

This study suggests a rapid methodology to delineate areas prone to flood using machine learning techniques. Based on available historically flooded areas, the model employs and combines globally collectible and reproducible conditioning factors to analyze flood susceptibility. The flood inventory map includes historically flooded areas from 1920 that occurred over the study area—Southern Italy. The impact of each factor is examined using correlation attribute evaluation and information gain ratio, while the performances of the model are evaluated by using area under receiving operating characteristics. Findings demonstrate that machine learning models can help in quick flood-prone areas analysis, especially in areas where flood hazard maps are not available. Full article

The design of hydraulic structures needs to account for a trade-off between implementation costs and flood damages, as well as for the impacts on basins hydrological responses over a wide spectrum of events. In this work, a new methodology for dimensioning an in-line detention dam that integrates geomorphic, probabilistic and economic modeling is proposed. It is formulated as an economic optimization problem aimed at minimizing the sum of the construction cost and the cost of the residual flood risk on residential buildings. The optimization procedure was applied to a hypothetical in-line detention dam located upstream of the urban area of Castellammare di Stabia (Naples, Italy). Full article

A further investigation aiming to generate new potential antitumor agents led us to synthesize a new series of twenty-two compounds characterized by the presence of the 7-(3′,4′,5′-trimethoxyphenyl)-[1,2,4]triazolo[1,5-a]pyrimidine pharmacophore modified at its 2-position. Among the synthesized compounds, three were significantly more active than the others. These bore the substituents p-toluidino (3d), p-ethylanilino (3h) and 3′,4′-dimethylanilino (3f), and these compounds had IC₅₀ values of 30–43, 160–240 and 67–160 nM, respectively, on HeLa, A549 and HT-29 cancer cells. The p-toluidino derivative 3d was the most potent inhibitor of tubulin polymerization (IC₅₀: 0.45 µM) and strongly inhibited the binding of colchicine to tubulin (72% inhibition), with antiproliferative activity superior to CA-4 against A549 and HeLa cancer cell lines. In vitro investigation showed that compound 3d was able to block treated cells in the G2/M phase of the cell cycle and to induce apoptosis following the intrinsic pathway, further confirmed by mitochondrial depolarization and caspase-9 activation. In vivo experiments conducted on the zebrafish model showed good activity of 3d in reducing the mass of a HeLa cell xenograft. These effects occurred at nontoxic concentrations to the animal, indicating that 3d merits further developmental studies. Full article

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magni-tude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleocli-matic reconstructions. We aimed to determine the fractal behaviour and the long-range de-pendence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as sug-gested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal dis-tribution and a generalized Hurst–Kolmogorov (HK) dependence structure. Full article

The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m³ m⁻³ and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m³ m⁻³ after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally. Full article

Videos acquired from Unmanned Aerial Systems (UAS) allow for monitoring river systems at high spatial and temporal resolutions providing unprecedented datasets for hydrological and hydraulic applications. The cost-effectiveness of these measurement methods stimulated the diffusion of image-based frameworks and approaches at scientific and operational levels. Moreover, their application in different environmental contexts gives us the opportunity to explore their reliability, potentialities and limitations, and future perspectives and developments. This paper analyses the recent progress on this topic, with a special focus on the main challenges to foster future research studies. Full article

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties. Full article

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability. Full article

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing. Full article

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results. Full article