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During the towing of semisubmersible platforms, waves impact and superpose in front of the platform to form a ridge shaped “water ridge”, which protrudes near the platform and produces a large slamming pressure. The water ridges occur frequently in the towing conditions of semisubmersible platforms. The wave–slamming on the braces and columns of platform is aggravated due to the water ridges, particularly in rough sea conditions. The effect of water ridges is usually ignored in slamming pressure analysis, which is used to check the structural strengths of the braces and columns. In this paper, the characteristics of the water ridge at the braces of a semisubmersible platform are studied by experimental tests and numerical simulations. In addition, the sensitivity of the water ridge to the wave height and period is studied. The numerical simulations are conducted by a Computational Fluid Dynamics (CFD) method, and their accuracy is validated based on experimental tests. The characteristics of the water ridge and slamming pressure on the braces and columns are studied in different wave conditions based on the validated numerical model. It is found that the wave extrusion is the main reason of water ridge. The wave–slamming pressure caused by the water ridge has an approximately linear increase with the wave height and is sensitive to the wave period. With the increase of the wave period, the wave–slamming pressure on the brace and column of the platform increases first and then decreases. The maximum wave–slamming pressure is found when the wave period is 10 s and the slamming pressure reduces rapidly with an increase of wave period.

【Objective】Efficient water use is critical for sustaining crop production in arid regions where water resources are scarce. This study experimentally investigates the effects of different sprinkler irrigation amounts on soil water dynamics and the yield formation of sunflower (Helianthus annuus L.) in double-ridge planting systems in the Hexi inland arid region. 【Method】The experiment was conducted from 2019 to 2021 on a sunflower field at the Minqin Irrigation Experimental Station of the Gansu Provincial Institute of Water Conservancy Sciences. The sunflower cultivar ‘Dwarf Head’ was used as the experimental plant. There were four irrigation treatments, with sprinkler irrigation amounts of 24 mm (S1), 30 mm (S2), 36 mm (S3) and 42 mm (S4), respectively. During the experiment, we measured spatiotemporal variations in soil water content, water use efficiency and yield of sunflower.【Result】Variation in sprinkler irrigation amount had a significant influence on water content in the 0-60 cm soil layer. As the pla

In the central North Pacific Subarctic Gyre, CTD hydrographic measurements were carried out yearly in late June from 1990 to 1998 at 9 stations along 180 degree meridian from 48 degree N to 51.2 degree N. Vertical sections of 9-year means, anomalies for each year and others of potential temperature, salinity, potential density and geostrophic velocity (referred to 3000 m) were calculated based on this data set. Empirical Orthogonal Function (EOF) analysis was adopted in the investigation of spatial characteristics and its temporal variation in vertical sections. The spatial distribution of the 1st mode EOF of velocity shows the westward Alaskan Stream and the eastward Subarctic Current. This mode explains 37.6% of the total variance. Two positive maxims appear in its amplitude in 1991 and 1997, which is similar to the variation in volume transport of the eastward Subarctic Current. These variations are closely related to the vertical movement of Ridge Domain deep water.

Mapping and sampling four sections of the slow‐spreading Reykjanes Ridge provide insight into how tectonic and volcanic activity varies with distance from the Iceland plume. The studied areas are characterized by significant variations in water depth, lava chemistry, crustal thickness, thermal structure, and ridge morphology. For each study area, fault pattern and dimension, tectonic strain, seamount morphology, and density are inferred from 15 m‐resolution bathymetry. These observations are combined with geochemical analysis from glass samples and sediment thickness estimations along Remotely Operated Vehicle‐dive videos. They reveal that (a) tectonic and volcanic activity along the Reykjanes Ridge, do not systematically vary with distance from the plume center. (b) The tectonic geometry appears directly related to the deepening of the brittle/ductile transition and the maximum change in tectonic strain related to the rapid change in crustal thickness and the transition between axial‐high and axial valley (∼59.5°N). (c) Across‐axis variations in the fault density and sediment thickness provide similar widths for the neo‐volcanic zone except in regions of increased seamount emplacement. (d) The variations in seamount density (especially strong for flat‐topped seamounts) are not related to the distance from the plume but appear to be correlated with the interaction between the V‐shape ridges (VSR) flanking the ridge and the ridge axis. These observations are more compatible with the buoyant upwelling melting instability hypothesis for VSR formation and suggest that buoyant melting instabilities create many small magma batches which by‐pass the normal subaxial magmatic plumbing system, erupting over a wider‐than‐normal area. Plain Language Summary Volcanic eruptions and faults growth are two important geologic processes taking place along seafloor spreading centers. Their variations in space and time are displayed in the morphology of the spreading centers. Investigating these morphological variations is key to understanding the deeper processes of the oceanic crust formation. South of Iceland, the Reykjanes Ridge is the location of increased volcanism due to the interaction between the mid‐ocean ridge and the Iceland hotspot. Using high‐resolution seafloor topographic data, chemical analyses of volcanic rock, and videos of the seafloor from a remotely operated vehicle, we investigated how volcanism and faulting change along the ridge. The increase in fault dimensions (height, length) with distance from the plume center is probably the result of the crust and mantle becoming cooler and stiffer and thus able to support larger faults. Fault density and thickness of the sediment covering the lava flows near the ridge axis are used to delimit the region of young volcanism. Seamounts often emplaced beyond that region. A peak in seamount abundance near 60°N suggests that the thick crust here is generated from numerous small batches of magma possibly resulting from a migrating instability in the melt production process beneath the axis. Key Points The distance from the plume center is not the only factor controlling tectonic and volcanic activity along the Reykjanes Ridge Fault dimensions are primarily controlled by the variation of crustal thermal structure with distance from the hotspot Flat‐topped seamount abundances peak where a V‐shaped ridge intersects the axis, consistent with a buoyant upwelling melting instability

Community assembly processes are thought to shape the mean, spread, and spacing of functional trait values within communities. Two broad categories of assembly processes have been proposed: first, a habitat filter that restricts the range of viable strategies and second, a partitioning of microsites and/or resources that leads to a limit to the similarity of coexisting species. The strength of both processes may be dependent on conditions at a particular site and may change along an abiotic gradient. We sampled environmental variables and plant communities in 44 plots across the varied topography of a coastal California landscape. We characterized 14 leaf, stem, and root traits for 54 woody plant species, including detailed intraspecific data for two traits with the goal of understanding the connection between traits and assembly processes in a variety of environmental conditions. We examined the within-community mean, range, variance, kurtosis, and other measures of spacing of trait values. In this landscape, there was a topographically mediated gradient in water availability. Across this gradient we observed strong shifts in both the plot-level mean trait values and the variation in trait values within communities. Trends in trait means with the environment were due largely to species turnover, with intraspecific shifts playing a smaller role. Traits associated with a vertical partitioning of light showed a greater range and variance on the wet soils, while nitrogen per area, which is associated with water use efficiency, showed a greater spread on the dry soils. We found strong nonrandom patterns in the trait distributions consistent with expectations based on trait-mediated community assembly. There was a significant reduction in the range of six out of 11 leaf and stem functional trait values relative to a null model. For specific leaf area (SLA) we found a significant even spacing of trait values relative to the null model. For seed size we found a more platykurtic distribution than expected. These results suggest that both a habitat filter and a limit to the similarity of coexisting species can simultaneously shape the distribution of traits and the assembly of local plant communities.

Permeability index (PI) and magnesium absorption ratio (MAR) are both primary irrigation water quality indicators (IWQI) used to evaluate the efficacy of agricultural water supplies. This is considered a complex environmental issue to reliably forecast IWQI parameters without its appropriate time series and limited input sequences. Hence, this research develops an innovative hybrid intelligence framework for the first time to forecast the PI and MAR indices at the Karun River, Iran. The proposed framework includes a new hybrid machine learning (ML) model based on generalized ridge regression and kernel ridge regression with a regularized locally weighted (GRKR) method. This research developed an optimized multivariate variational mode decomposition (OMVMD) technique, optimized by the Runge-Kutta algorithm (RUN), to decompose the input variables. The light gradient boosting machine model (LGBM) is also implemented to select the influential input variables. The main contribution of the intelligence framework lies in developing a new hybrid ML model based on GRKR coupled with OMVMD. Five water quality parameters from the Karun River at two stations (Ahvaz and Molasani) over 40 years are used to forecast the PI and MAR indices monthly. Statistical metrics confirmed that the proposed OMVMD-GRKR model, concerning the best efficiency in the Ahvaz ( R  = 0.987, RMSE = 0.761, and U95% = 2.108) and Molasani ( R  = 0.963, RMSE = 1.379, and U95% = 3.828) stations, outperformed the OMVMD and simple-based methods such as ridge regression (Ridge), least squares support vector machine (LSSVM), deep random vector functional link (DRVFL), and deep extreme learning machine (DELM). For this reason, the suggested OMVMD-GRKR model serves as a valuable framework for predicting IWQI parameters.

Water-saving cultivation techniques have been attracting increased attention worldwide. Ridge-furrow mulching system (RFMS), as a prospective rainwater harvesting system, has been widely adopted in arid and semi-arid areas. Field experiments were conducted in 2014 and 2015 to compare soil water storage, soil temperature, maize yield, and water use efficiency (WUE) among different ridge/furrow width arrangements in RFMS comprised of three different ridge/furrow ratios, i.e., 40:70 cm (RFMS40), 55:55 cm (RFMS55), and 70:40 cm (RFMS70) and conventional flat planting (CK, without mulching). All these four planting patterns had the same planting density. The RFMS technique not only increased soil temperature of the ridge but also improved soil moisture of the furrow when compared with CK. These positive effects were intensified with increasing ridge/furrow ratio in RFMS. This improvement in RFMS resulted in more stable and earlier seedling establishment. Maize yields were increased by 26.1, 36.4, and 50.3% under RFMS40, RFMS55, and RFMS70 treatments, respectively, when compared with CK across both years. RFMS did not decrease the evapotranspiration significantly, compared with CK. Eventually, WUE were enhanced by 25.7, 38.7, and 53.9% in RFMS40, RFMS55, and RFMS70, respectively, compared with CK. Taken together, our results suggest that increasing ratio of ridge to furrow in the case of RFMS70, can be recommended as high-yielding cultivation pattern for promoting precipitation use efficiency in the rain-fed semi-arid areas.

Developing efficient methods for groundwater level (GWL) prediction is essential for identifying the groundwater flow pattern, characterizing the spatial extent of contaminant plumes, and enhancing water resources management. Recently, significant advances have been made in predicting GWL using machine learning (ML) models, but these do not consider hydrogeological heterogeneities that condition the flow pattern. This study develops and evaluates the applicability of advanced geostatistics and ML models to characterize the spatial variability of the GWL, taking into account the discontinuities induced by complex geological environments and leveraging only piezometer positions and monitored GWL. Geostatistical-based ordinary kriging (G/OK) and kernel ridge regression (KRR) were conducted on joint-faulted coastal aquifer systems in southeastern Tunisia. Geological knowledge was incorporated into the characterization process, achieving better function modeling, and optimizing both geostatistical and ML models. The present work counts among the first ML applications that take into account the spatial variability modeling constrained by geological heterogeneities. The task is especially challenging as actual data points are scarce. The results are evaluated using cross-validation with several error and evaluation metrics. Comparative analyses were performed to assess the consistency with the hydrogeological reality. The proposed approaches generated credible GWL maps that reproduce the regional and local flow patterns. A comprehensive interpretation provides a range of essential insights on the spatial variation of the groundwater flow path and the hydraulic behavior of faults acting as conduits, barriers, or conduit-barriers. The implemented model could be applied to other analogous areas to assess GWL and other hydraulic parameters efficiently.

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Methane concentration and its stable carbon isotope ratio studies were carried out in deep waters of ultraslow‐spreading eastern Southwest Indian Ridge (eSWIR), 63.5°–68°E, to identify hydrothermal plumes and associated biogeochemical processes. High methane concentrations (1.0–37.8 nmol/kg) confirm the presence of 11 hydrothermal plumes along the ∼500 km long section of eSWIR. Among these 11 plumes, eight are reporting for the first time. Detailed investigations near 67.67°E; 26.61°S show the presence of three plumes at various depths and their dispersion >100 km along the ridge in east‐west directions. Stable carbon isotope ratios of these plumes show lighter and heavier isotope enrichment (−40‰ to −3‰). Lighter isotope enrichment may indicate that methane could be derived from a combination of abiotic and thermogenic sources and, heavier isotope enrichment is mainly because of microbial methane oxidation. We report strong evidence for the presence of an active hydrothermal field at 67.67°E; 26.61°S in the eSWIR for the first time. Plain Language Summary Hydrothermal circulation along the mid‐oceanic ridge system supplies large quantities of chemical constituents (e.g., methane) to the ocean. Once the hot fluid (>300°C) mixes with the cold oxygenated seawater (∼2°C), the precipitation of metals leads to the formation of seafloor metal deposits. Unlike metals, gases in the hot fluid escape long distances from the source location. High methane concentrations in hydrothermal plumes helps the microbes to survive in the deep ocean. In the present work, we surveyed ∼500 km length of the eastern Southwest India Ridge to identify new locations of seafloor mineral deposits using a hydrothermal tracer in deep waters called “dissolved methane.” Based on high methane concentrations (>1 nmol/kg) when compared to the background seawater concentrations (0.5 nmol/kg), we found around 11 new locations of possible vent fields. Detailed studies near 67.67°E; 26.21°E show that the methane coming from the field has spread more than 100 km along the ridge. Further, the stable carbon isotope studies of methane in these plumes show that the origin of methane could be a combination of abiotic and thermogenic. However, once methane enters the water column, microbes utilize it for their survival. Key Points High methane concentrations in deep waters along the eastern Southwest Indian Ridge (eSWIR) confirm the presence of multiple hydrothermal plumes Detailed inventigations near 67.67°E; 26.61°E provided evidence for the first active hydrothermal field on the eSWIR Origin of methane in this field could be a combination of abiotic‐thermogenic and it is oxidized by microbes