scholarly journals Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

2021 ◽  
Vol 13 (5) ◽  
pp. 881
Author(s):  
Zhiyi Fu ◽  
Fangfang Wu ◽  
Zhengliang Zhang ◽  
Linshu Hu ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
River Discharge ◽  
River Flow ◽  
High Accuracy ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Temporal Heterogeneity ◽  
Surface Salinity ◽  
Circular Distribution ◽  
Heterogeneity Analysis

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

scholarly journals Influence of river discharge on circulation and tidal process in the Java Sea, Indonesia

2021 ◽  
Vol 944 (1) ◽  
pp. 012068
Author(s):  
H Ramadhan ◽  
D Nugroho ◽  
I W Nurjaya ◽  
A S Atmadipoera
Keyword(s):  
River Discharge ◽  
River Flow ◽  
Horizontal Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Hourly Data ◽  
Tidal Data ◽  
Java Sea ◽  
Mean Square Difference

Abstract This study investigates the effect of river discharge in transport and tidal processes in the Java Sea using the Coastal and Regional Ocean Community (CROCO) hydrodynamic model. The model has 20 vertical layers and a horizontal resolution of 1/18 degrees. The oceanic and atmospheric forcing of this model is taken from the global Copernicus Marine Environment Monitoring Service (CMEMS) model and the fifth generation ECMWF atmospheric reanalysis (ERA5) hourly data. Daily Global Flood Awareness System (GloFAS) data has been successfully implemented as river flow data for this study. Two scenarios have been applied, namely, with and without river discharge. This study shows that the two scenarios and the satellite observational data agree in terms of water level with Root Mean Square Difference RMSD) about 4 cm, Sea Surface Temperature with RMSD about 0.29 °C, and Sea Surface Salinity with RMSD about 0.39 psu. The model was also validated using seven tide gauges and produced a good agreement. River discharge increase eastward transport in the eastern part of the Java Sea up to 0.1 Sv (1 Sv= 106 m3s−1). Both scenarios produce similar tidal amplitude and phase and agree well with previous studies and other tidal data sources.

scholarly journals Using Remotely Sensed Sea Surface Salinity and Colored Detrital Matter to Characterize Freshened Surface Layers in the Kara and Laptev Seas during the Ice-Free Season

2021 ◽  
Vol 13 (19) ◽  
pp. 3828
Author(s):  
Marta Umbert ◽  
Carolina Gabarro ◽  
Estrella Olmedo ◽  
Rafael Gonçalves-Araujo ◽  
Sebastien Guimbard ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
River Runoff ◽  
River Discharge ◽  
Active Material ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Optical Data ◽  
Surface Salinity ◽  
The Arctic Ocean

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between −0.72 and −0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km2, with a typical freshwater content of 11.84–14.02 km3. The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km2 and a freshwater content of 10.15–12.44 km3. The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season.

scholarly journals The Potential of Space-Based Sea Surface Salinity on Monitoring the Hudson Bay Freshwater Cycle

2020 ◽  
Vol 12 (5) ◽  
pp. 873 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Daqing Yang ◽  
Ellie Mcleod ◽  
Alexander Fore ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
River Discharge ◽  
Open Water ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Hudson Bay ◽  
Surface Salinity ◽  
Ice Melt

Hudson Bay (HB) is the largest semi-inland sea in the Northern Hemisphere, connecting with the Arctic Ocean through the Foxe Basin and the northern Atlantic Ocean through the Hudson Strait. HB is covered by ice and snow in winter, which completely melts in summer. For about six months each year, satellite remote sensing of sea surface salinity (SSS) is possible over open water. SSS links freshwater contributions from river discharge, sea ice melt/freeze, and surface precipitation/evaporation. Given the strategic importance of HB, SSS has great potential in monitoring the HB freshwater cycle and studying its relationship with climate change. However, SSS retrieved in polar regions (poleward of 50°) from currently operational space-based L-band microwave instruments has large uncertainty (~ 1 psu) mainly due to sensitivity degradation in cold water (<5°C) and sea ice contamination. This study analyzes SSS from NASA Soil Moisture Active and Passive (SMAP) and European Space Agency (ESA) Soil Moisture and Ocean Salinity(SMOS) missions in the context of HB freshwater contents. We found that the main source of the year-to-year SSS variability is sea ice melting, in particular, the onset time and places of ice melt in the first couple of months of open water season. The freshwater contribution from surface forcing P-E is smaller in magnitude comparing with sea ice contribution but lasts on longer time scale through the whole open water season. River discharge is comparable with P-E in magnitude but peaks before ice melt. The spatial and temporal variations of freshwater contents largely exceed the remote sensed SSS uncertainty. This fact justifies the use of remote sensed SSS for monitoring the HB freshwater cycle.

Sea surface salinity variability in response to the Congo river discharge

2015 ◽  
Vol 99 ◽  
pp. 35-45 ◽  
Author(s):  
Yi Chao ◽  
John D. Farrara ◽  
Guy Schumann ◽  
Konstantinos M. Andreadis ◽  
Delwyn Moller
Keyword(s):  
River Discharge ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Congo River ◽  
Salinity Variability

scholarly journals Tracking sea surface salinity and dissolved oxygen on a river-influenced, seasonally stratified shelf, Mississippi Bight, northern Gulf of Mexico

2018 ◽  
Vol 169 ◽  
pp. 25-33 ◽  
Author(s):  
Brian Dzwonkowski ◽  
Severine Fournier ◽  
John T. Reager ◽  
Scott Milroy ◽  
Kyeong Park ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Dissolved Oxygen ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Northern Gulf Of Mexico

An 800-year reconstruction of Elbe River discharge and German Bight sea-surface salinity

The Holocene ◽  
2005 ◽  
Vol 15 (3) ◽  
pp. 429-434 ◽  
Author(s):  
Carolyn Scheurle ◽  
Dierk Hebbeln ◽  
Phil Jones
Keyword(s):  
German Bight ◽  
River Discharge ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Elbe River

scholarly journals SPATIAL AND TEMPORAL ANALYSIS OF SEA SURFACE SALINITY USING SATELLITE IMAGERY IN GULF OF MEXICO

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 219-223 ◽  
Author(s):  
S. Rajabi ◽  
M. Hasanlou ◽  
A. R. Safari
Keyword(s):  
Gulf Of Mexico ◽  
Temporal Analysis ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Loop Current ◽  
Surface Salinity ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
Distribution Maps ◽  
Saline Waters

The recent development of satellite sea surface salinity (SSS) observations has enabled us to analyse SSS variations with high spatiotemporal resolution. In this regards, The Level3-version4 data observed by Aquarius are used to examine the variability of SSS in Gulf of Mexico for the 2012-2014 time periods. The highest SSS value occurred in April 2013 with the value of 36.72 psu while the lowest value (35.91 psu) was observed in July 2014. Based on the monthly distribution maps which will be demonstrated in the literature, it was observed that east part of the region has lower salinity values than the west part for all months mainly because of the currents which originate from low saline waters of the Caribbean Sea and furthermore the eastward currents like loop current. Also the minimum amounts of salinity occur in coastal waters where the river runoffs make fresh the high saline waters. Our next goal here is to study the patterns of sea surface temperature (SST), chlorophyll-a (CHLa) and fresh water flux (FWF) and examine the contributions of them to SSS variations. So by computing correlation coefficients, the values obtained for SST, FWF and CHLa are 0.7, 0.22 and 0.01 respectively which indicated high correlation of SST on SSS variations. Also by considering the spatial distribution based on the annual means, it found that there is a relationship between the SSS, SST, CHLa and the latitude in the study region which can be interpreted by developing a mathematical model.

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