Surface Salinity Research Articles

Freshwater discharge drives latitudinal changes of phytoplankton composition on the continental shelf off Chilean Patagonia

A high-resolution survey of distribution, abundance and composition of phytoplankton was carried out for the first time in surface waters of the continental shelf off Chilean Patagonia (41–48°S). An Imaging FlowCytobot was used along the survey track to record phytoplankton in the size range of 10–120 μm during the austral spring of 2018. Phytoplankton community structure was complemented with continuous underway measurements of temperature and salinity, and physicochemical parameters of the water column at 35 oceanographic stations. Our results evidenced two main macrozones with distinctive phytoplankton assemblages delimited latitudinally at ~45°S. The northern macrozone was characterized by higher surface temperature and salinity, Si:N ratio > 1, diatoms of the genera Thalassiosira and Chaetoceros, and dinoflagellates accounting for over 70% of the total abundance. The southern macrozone, with lower surface temperature and salinity and Si:N ratio < 1, was characterized by members of the genera Guinardia, Lauderia and Cerataulina, representing over 60% of the total phytoplankton. These changes were attributable to the strong influence of freshwater at latitudes higher than 45°S and the enhanced discharge of meltwaters from Patagonian icefields in the area of the Taitao Peninsula and the Gulf of Penas (47–-48°S). Fresh and cold waters impacted the water column stratification and the availability of dissolved silicic acid with potential effects on phytoplankton composition and diatom cell silicification and, thus, on carbon exportation. Our estimations of phytoplankton carbon were comparable to those observed in Patagonian fjords and the highly productive upwelling ecosystem of central Chile. We suggest that the continental shelf off Patagonia can contribute significantly to strengthen the biological carbon pump through the synthesis, exportation, and sequestration of phytoplankton-based organic carbon in the southeastern Pacific Ocean.

Temperature and nutrients control the presence and distribution of long-chain diols in Swiss lakes

Long-chain diols are biomarkers commonly used in the marine realm to reconstruct several environmental parameters such as sea surface temperature and salinity. However, they are also produced in lacustrine and slow-flowing river environments, a characteristic that has proved to be useful to trace past riverine inputs in coastal sedimentary records. So far, their use in lacustrine settings is sparse as their controls are not well-known. Previous studies in two lakes have shown that long-chain diol distribution is linked to changes in temperature (in a small Spanish alpine lake), but also to water column stratification (in a large deep Swiss lake). To understand the controls on i) the presence of long-chain diols in lakes, and ii) the distribution of long-chain diol isomers, surface sediments from 52 Swiss lakes were studied. Long-chain diols are present in 57% of the lakes, and machine learning (i.e., random forest model) showed that their presence is mainly controlled by mean annual air temperature, sodium and potassium concentrations and area of the lakes. Long-chain diol isomer relative distribution seems to react to temperature, nutrient (here nitrate) and oxygen concentrations in the lakes. This new insight was tested on a short sedimentary core from Lake Zurich, and compared with other biomarker proxies (based on branched and isoprenoid glycerol dialkyl glycerol tetraethers), as well as with historical record of nutrient contents and temperature. Variations in the long-chain diol index (LDI) mirror measured temperature, but also reacted to changes in nutrients and oxygenation in the lake. This study highlights the potential of long-chain diols as a proxy to trace both nutrients and temperature in lakes, potentially on geological timescales.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 μmol/kg to 0.99 and 4.01 μmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Regional Sea Level Response to External Forcings from the Twentieth to the Twenty-First Century

Abstract Using a range of Detection and Attribution Model Intercomparison Project (DAMIP) simulations from phase 6 of Coupled Model Intercomparison Project (CMIP6), we study the response of dynamic sea level (DSL) to external anthropogenic climate forcing [greenhouse gases (GHGs), aerosols, and stratospheric ozone] with a focus on the differences over the twentieth and twenty-first century. In the second half of the twentieth century, the DSL nonuniformity in the Northern Hemisphere (NH) was relatively small due to a cancellation between the effects of increasing GHGs and aerosols. In contrast, the DSL signal in the Southern Hemisphere (SH) over this period was large because stratospheric ozone depletion reinforced the effects of increasing GHGs. In the twenty-first century, the DSL response has been intensified in the NH because the warming effects of diminishing aerosols have acted to reinforce the effects of increasing GHGs. Meanwhile, the distribution of SH DSL has also become uneven although stratospheric ozone recovery has partially offset the effects of rising GHGs. Using a global ocean circulation model, we decompose the changes in the twenty-first century DSL into distinct responses to surface forcings including sea surface temperature, salinity, and wind stress. Our results show that the dipole-like pattern of DSL in the North Pacific can be attributed largely to sea surface warming, while the dipole-like pattern in the North Atlantic is attributed to subpolar surface salinity freshening. The belted pattern of DSL changes in the Southern Ocean is induced by both surface warming and intensifying/poleward-shifting westerly winds.

Ocean parameter and hydrographic measurement around Cirebon port channel

Abstract One of the largest ports in West Java is Cirebon, which serves an important role in supporting the surrounding economy and becoming the alternatives of the biggest port in Java. Numerous loading and unloading activities are recorded with coal as one of the commodities. Sedimentation and water quality is a common issue facing any port. To assess the recent condition of the port, an oceanographic parameter and hydrography survey is one approach. A field survey in the Cirebon port, particularly around the channel and outside part was carried out in February 2023. The bathymetry result at the channel varied within 0 - 10 m depth. The mixed semidiurnal type in this area has a tidal range around 1.1 m. Sea current inside the port has low speed within 0-0.03 m/s meanwhile outside of the port can reach 0.13 m/s. Through the weathering system, the average temperature is 26.3°C, average wind speed of 2.25 m/s dominantly blowing from west, relative humidity around 88.6%, and rain rate also low. In the study, the sea condition of surface temperature, salinity, turbidity, DO, and pH is 28.5°-29.3°C, 29.9-30.1 psu, 3.65-17.68 FTU, 3.69-4.83 mg/L and pH 7.9-8.2, respectively. Based on the national water quality standard for the port, the condition is within threshold for several parameters.

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling

Abstract This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts. Significance Statement The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

Ichthyoplankton Biodiversity in the Indonesian Fisheries Management Area-573 in 2015

Abstract Studying ichthyoplankton is crucial for understanding the impact of fish larvae mortality on the recruitment of adult fish and fishing resources. Fish larvae samples were collected in the southern waters of Java using the Baruna Jaya IV research vessel in September - October 2015. Fish larvae were collected using bongo-net and oceanographic data were collected using Conductivity Temperature Depth (CTD) are Temperature and salinity and chlorophyll-a using Aqua MODIS satellite L3 at 36 stations. The results showed the range of fish larvae abundance was 0 - 2,074 ind/1000 m3 with an average of 174 ind/1000 m3. The fish eggs were found with an abundance of 0 - 1,601 eggs/1000 m3. and an average of 213 eggs/1000 m3. The composition of fish larvae recorded 22 families, there were 5 dominant families namely Scombridae (28.60%), Bregmacerotidae (22.96%), Carangidae (7.93%), Blennidae (7.52%), and Gobiidae (7.52%). The diversity of fish larvae was most prevalent at station 28 (SST 26.7 0C; SSS 34.4 PSU; and CHL-a 0.43 mg/m3) and station 36 (SST 27.4 0C; SSS 34.4 PSU) each found 9 families. The abundance of fish larvae was mostly found at stasion 11 had SST 23.50 0C; SSS 34.56 PSU; and CHL-a 0.33 mg/m3 with the dominance of the family Bregmacerotidae. Scombridae was mostly found at stasion 22 with sea surface temperature (SST) 26.52 0C; sea surface salinity (SSS) 34.28 PSU; and sea surface chlorophyll-a (CHL-a) 0.41 mg/m3 which is in the southern waters of Lombok.

Environmental Influences on Illex argentinus Trawling Grounds in the Southwest Atlantic High Seas

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI &gt; 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation

Abstract The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m2, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.

The Amazon River plume—a Lagrangian view

AbstractHydrographic data, nutrient data and bulk rates of nitrate uptake and primary production were determined in the Amazon River plume (ARP) in the Western Tropical North Atlantic (WTNA) during three cruises in May 2018, June/July 2019, with RV Endeavor and April/May 2021 with RV Meteor. Using daily quasi‐geostrophic surface velocity data from satellite observations, the geographical positions of the stations of observations were transformed onto Lagrangian coordinates to obtain a dynamically coherent and consistent spatial distribution. After the transformation, the observed surface salinity and temperature fields were consistent with the flow fields, the ARP formed a coherent structure and the retroflection of the North Brazil Current became visible. By transforming other surface variables such as nitrate concentration, photosynthetically available radiation, turbidity, bulk rates of nitrate uptake, and primary production onto Lagrangian coordinates, patterns became consistent with the physical variables at the surface. The use of “synchronous” fields as done here by transformation onto Lagrangian coordinates is essential for spatially structured analyses of data collected over tens of days in a highly dynamic region characterized by complex flow fields with low persistence such as the WTNA. Therefore, the use of the Lagrangian method provides a powerful tool for exploring spatial distributions of biologically relevant factors in regions with complex and dynamic flow patterns. These spatial distributions are qualitatively in agreement with satellite images of daily sea surface temperature and composites of monthly mean Chlorophyll a distributions.

Modeling environmental effects on fishery landings: A case study of whitemouth croaker (Micropogonias furnieri) in the Southwestern Atlantic shelf

Estuaries are very productive habitats that serve as spawning and nursery grounds for fisheries species. The Rio de la Plata (RdP) Estuary and its maritime front sustain valuable fisheries for Argentina and Uruguay. Micropogonias furnieri, the coastal fish species historically representing highest catches, has declined landings in recent decades. To enhance our understanding on how natural variability influences this resource and improves its management, we analysed the effect of environmental conditions on M. furnieri’s commercial landings in the long term using generalized linear and additive models (GLMs and GAMs). Explanatory variables, with up to a 10-year lag, included sea surface temperature, turbidity, salinity, wind, river runoff, and climatological indices: El Niño-Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and the Southern Oscillation Index (SOI). Time-lagged analyses for single-covariate models reveal higher deviances explained (DE) at 2, 6, 7, and 8-year lags. Turbidity and salinity, followed by wind and temperature, are the most common variables affecting catches of M. furnieri for GLMs and GAMs. The absence of significant models with only climatological indices or river discharge indicates the impact of large-scale climate fluctuations and river runoff on the RdP Estuary conditions. The best-fitting GLM model includes SAM, turbidity, and wind (DE = 78.8%), whereas the GAM model includes turbidity and wind (DE = 86.4%). Sensitivity of M. furnieri’s catches to climatic signals (ENSO, SAM, SOI) is described for the first time. Our findings provide insights for authorities to implement management measures beyond fishing quotas encompassing environmental shifts in the long-term.

Changes in diazotrophic community structure associated with Kuroshio succession in the northern South China Sea

Abstract. Kuroshio intrusion (KI) is a key process that transports water from the western Pacific Ocean to the northern South China Sea (nSCS), where KI-induced surface water mixing often causes variations in microbial assemblages. Yet, how interannual KIs affect the biogeography of diazotrophs and associated environmental factors remains poorly characterized. Here, by quantifying the degree of KIs in 2 consecutive years, coupled with monitoring the diversity and distribution of nitrogenase-encoding nifH phylotypes with quantitative PCR and high-throughput sequencing, we show that changes in the diazotrophic community structure in the nSCS are highly correlated with KI-induced variations in a range of physicochemical parameters. Specifically, the filamentous cyanobacteria in the genus Trichodesmium were more abundant at stations strongly affected by KI and thereby with a deeper mixed layer and higher surface salinity and temperature; the unicellular N2-fixing cyanobacteria in group B (UCYN-B) were more abundant at stations least affected by KI and correlated with nutrient availability, whereas UCYN-C and the γ-proteobacteria were prevalent at stations moderately affected by KI. The neutral community model further demonstrated that dominant diazotrophic subcommunities were significantly affected by environmental factors in 2017 when KI was stronger compared to 2018 when KI retreated. Our analyses provide insightful evidence for the role of KI in shaping the diazotrophic community structure primarily as a stochastic process, implying a potential region-scale redistribution of diazotrophs and nitrogen budget, given that KIs are projected to intensify in a future warming ocean.

Influence of environmental conditions on the abundance of the striped venus Chamelea gallina in the northern Alboran Sea (Western Mediterranean Sea)

Environmental conditions can easily modulate the abundance of short-lived species, including those of commercial interest. These effects can occur during key periods of their life cycle, such as the spawning and recruitment periods, which are crucial for the stability of natural populations. This study attempts to elucidate the effects of abiotic factors on an important commercial species for artisanal fisheries in the northern Alboran Sea, Chamelea gallina, using CPUE data covering a 16-year period (2004–2020). Generalised Additive Models (GAM) were used to study how the abundance of the species was influenced by a set of environmental variables (sea surface temperature, sea surface salinity, phytoplankton, dissolved oxygen, North Atlantic Oscillation index and pluviometry) at spatial and temporal scale, as well as the spatio-temporal variation considering two areas (eastern and western areas) with different oceanographic conditions. The analysis showed that the abundance of C. gallina showed fluctuations over the historical time series in this ecoregion. The largest catches are observed in the western sector of the northern Alboran Sea, where the local sediment characteristics are more suitable for the species occurrence and the upwelling effect is more pronounced. Results showed that the most influential variables in the abundances during the spawning period were sea temperature, salinity and dissolved oxygen while during the recruitment period were only salinity and dissolved oxygen. Studies on the effects of environmental variables on the life cycle of species are essential to understand complex biological processes such as the spawning and recruitment, especially in commercially exploited species and in highly dynamic environments as the Alboran Sea.

Does climate change increase the risk of marine toxins? Insights from changing seawater conditions.

Marine toxins produced by marine organisms threaten human health and impose a heavy public health burden on coastal countries. Lately, there has been an emergence of marine toxins in regions that were previously unaffected, and it is believed that climate change may be a significant factor. This paper systematically summarizes the impact of climate change on the risk of marine toxins in terms of changes in seawater conditions. From our findings, climate change can cause ocean warming, acidification, stratification, and sea-level rise. These climatic events can alter the surface temperature, salinity, pH, and nutrient conditions of seawater, which may promote the growth of various algae and bacteria, facilitating the production of marine toxins. On the other hand, climate change may expand the living ranges of marine organisms (such as algae, bacteria, and fish), thereby exacerbating the production and spread of marine toxins. In addition, the sources, distribution, and toxicity of ciguatoxin, tetrodotoxin, cyclic imines, and microcystin were described to improve public awareness of these emerging marine toxins. Looking ahead, developing interdisciplinary cooperation, strengthening monitoring of emerging marine toxins, and exploring more novel approaches are essential to better address the risks of marine toxins posed by climate change. Altogether, the interrelationships between climate, marine ecology, and marine toxins were analyzed in this study, providing a theoretical basis for preventing and managing future health risks from marine toxins.

Intrinsic and extrinsic factors associated with the spatio-temporal distribution of infectious agents in early marine Chinook and coho salmon

Understanding the factors driving the spatial distribution of infectious agents in populations is key to predicting infectious agent distributions under future ecological and anthropogenic scenarios. We applied a geostatistical analysis to a data set of 59 infectious agents assayed in thousands of Chinook and coho salmon in their first marine year to identify intrinsic and extrinsic factors associated with the probability and density (infectious agent load) of infection. Meta-analysis of the pathogen-specific geostatistical models indicated that sea surface salinity was the extrinsic factor most frequently associated with infection probability and density for a majority of infectious agents. In addition, agents that were categorized as having a moderate risk of transmission from aquaculture to wild salmon were more likely to occur, and at higher infection densities, in fish collected closer to active aquaculture facilities. Although hypotheses pertaining to other intrinsic and extrinsic factors, including age at ocean entry, known hatchery origin, and sea surface temperature deviation, were not supported by the meta-analysis results, some individual agents demonstrated strong associations with these factors. Our results suggest that climate-change-driven shifts in coastal seawater salinity (and to a lesser extent, temperature) may result in changes to the infection dynamics of several infectious agents. In addition, our results contribute to existing evidence characterizing the risk of infectious agent transmission from netpen aquaculture to free-ranging salmon.

Simulated Sea Surface Salinity Data from a 1/48° Ocean Model

To study the validation process for sea surface salinity (SSS) we have generated one year (November 2011- October 2012) of simulated satellite and in situ “ground truth” data. This was done using the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° simulation, the highest resolution global ocean model currently available. The ground tracks of three satellites, Aquarius, SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were extracted and used to sample the model with a gaussian weighting similar to that of the spaceborne sensor ground footprint. This produced simulated level 2 (L2) data. Simulated level 3 (L3) data were then produced by averaging L2 data onto a regular grid. The model was sampled to produce simulated Argo and tropical mooring SSS datasets. The Argo data were combined into a simulated gridded monthly 1° Argo product. The simulated data produced from this effort have been used to study sampling errors, matchups, subfootprint variability and the validation process for SSS at L2 and L3.

Amazon River Plume in the Western Tropical North Atlantic

Measurements of temperature, salinity, and currents in the Amazon River plume over a section in the open ocean of the western tropical North Atlantic (38°48′ W) are considered. The measurements were carried out using an AML Base X CTD probe in the upper layer and a flow-through system that measures salinity, turbidity, and chlorophyll-a content in seawater while a vessel is on the way. The measurements were supplemented by velocity profiling using shipborne SADCP. Additionally, archived oceanographic data from the World Ocean Database (WOD18), data on satellite altimetry measurements (AVISO), and satellite salinity data from Aquarius and SMOS were used. It is shown that the width of the Amazon River plume is about 170–400 km and the depth of desalination is from 50 to 100 m. Surface salinity decreases compared to the background (36.1) by 0.25 in February and by more than 3.0 in September during the period of maximum development of the plume, which was determined from satellite measurements of surface salinity. Lagrangian modeling of the back-in-time advection of passive markers simulating freshwater particles was carried out. It was shown that the source of freshwater in the measurement area is discharge from the Amazon River. Amazon River freshwater covered a distance of 3300 km in 60–80 days. The estimate of freshwater transport in the plume was 0.02 Sv, which is one order of magnitude smaller than the mean river discharge.

SPATIAL-TEMPORAL DISTRIBUTION OF NARROW-BARRED SPANISH MACKEREL (Scomberomorus commerson) FISHING GROUNDS IN RIAU ARCHIPELAGO WATERS, INDONESIA

Narrow-barred Spanish mackerel (Scomberomorus commerson) is an exportable fish captured by gillnet fishing fleets from Bintan Regency. The fleets are operated in the Riau Archipelago waters but information on their activities is limited. Such information is important to the institution the Agency for Marine and Fisheries Affairs of Archipelagic Riau Province for ensuring the sustainability of the fisheries from both economic and biological perspectives. This study aims to determine the spatial-temporal distributions of the mackerel fishing grounds along the year with reference to the capture data, sea surface temperatures, chlorophyll-a concentrations, and salinities in the last 4 months of 2022. This study analyzed data from a fish export company, the PSDKP office at Tanjungpinang, Bintan, interviews of fishers affiliated with 44 fishing boat samples, and satellite-based marine data. Analysis on the monthly fishing productivity and optimum marine conditions for the mackerels were carried out to determine prospects of five recognized fishing grounds. Besides Lingga Islands, the fishing grounds are highly prospective from August to November. The longest fishing seasons are expected around Bangka Island (11 months). The spatial and temporal patterns show that the north-south-north shifts in fishing grounds that are associated with regional climates, i.e., monsoon winds.

How to consider groundwater flow systems in the Earth's Critical Zone? – Demonstration in the Central Pannonian Basin, Hungary

Study regionCentral Pannonian Basin, Hungary. Study focusCritical Zone (CZ) Science generally focuses on the soil and weathered bedrock in a few or tens of meters depth, thus influence of deeper groundwater on the CZ is understudied. Here we aim to introduce a hydrogeological methodology that can separate normal and abnormal pressure regimes and determine the groundwater flow pattern to characterize the connection of different groundwater flow systems to the CZ. Basin-scale evaluation of about 5500 measured hydraulic data were carried out by p(z) and h(z) profiles, tomographic maps and hydraulic cross sections. New hydrological insights for the RegionThree flow domains were separated and characterized. Namely, i) the uppermost topography-driven flow systems, which penetrate only a few hundred meters, ii) a deep overpressured regime below 1600–2100 m depth, which drives fluids upward; and iii) a newly identified transition zone between the former two, which gains its energy from overpressure dissipation and contains non-renewable water resources. Topography-driven flow systems and discharge areas of the transition zone, where its upwelling saline water contributes to surface salinization, are parts of the CZ. Discharge areas of the transition zone cover about 50% of the Great Hungarian Plain. The overpressured system can only influence the CZ through the transition zone. The approach and methodology can be used in any terrestrial sedimentary basin where a deep overpressured regime exists.

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.