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 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 Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements

2013 ◽  
Vol 30 (11) ◽  
pp. 2689-2694 ◽  
Author(s):  
Nadya T. Vinogradova ◽  
Rui M. Ponte
Keyword(s):  
Sampling Error ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Small Scale ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Ocean Salinity ◽  
Mean Square Difference

Abstract Calibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly <0.1 psu (global median is 0.05 psu), but they can be >0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine small-scale noise in the context of other errors, its values are compared with the overall expected differences between monthly Aquarius SSS and Argo-based estimates. Results indicate that in several ocean regions, small-scale variability can be an important source of sampling error for the in situ measurements.

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 Study of Seasonal Variation of Sea Surface Salinity in Java Sea and its Surrounding Seas using SMAP Satellite

2019 ◽  
Vol 246 ◽  
pp. 012043
Author(s):  
Amirotul Bahiyah ◽  
Anindya Wirasatriya ◽  
Jarot Marwoto ◽  
Gentur handoyo ◽  
D. S. P. Agus Anugrah
Keyword(s):  
Seasonal Variation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Java Sea

scholarly journals Demonstrating the potential of Regional Ocean Model System in simulating the upper ocean characteristic over Arabian Sea: impact of horizontal resolution

2021 ◽  
Author(s):  
Prabha Kushwaha ◽  
Vivek Kumar Pandey
Keyword(s):  
Arabian Sea ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Model System ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Regional Ocean Model System ◽  
Regional Ocean Model ◽  
The Impact

Abstract This study attempted to demonstrate the skill of the regional ocean model system (ROMS) is simulating the hydrographic property of the Arabian Sea (AS). Additionally, the impact of horizontal resolution is investigated. In this regard, ROMS is integrated over AS covering [30˚E-80˚E; 5˚N-30N˚] at two different horizontal resolutions 1/6˚(~ 17km) and 1/4˚(~ 25km) for ten years. The comparison of model results with available observation and reanalysis indicates reasonable resemblances in reproducing the spatial-temporal distribution of surface and subsurface hydrographic property i.e. sea surface temperature (SST), sea surface salinity (SSS), sea surface currents, and subsurface temperature and salinity at both resolutions. The increasing resolution shows minimal improvement, indicating the fact that its not always guaranty to enhance the performance towards increasing resolution for every aspect.

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 Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

2021 ◽  
Vol 13 (15) ◽  
pp. 2995
Author(s):  
Frederick M. Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
High Resolution ◽  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

scholarly journals Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations

2021 ◽  
Vol 13 (3) ◽  
pp. 420
Author(s):  
Jingru Sun ◽  
Gabriel Vecchi ◽  
Brian Soden
Keyword(s):  
Tropical Cyclones ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Translation Speed ◽  
Salinity Stratification ◽  
Left Hand ◽  
In Situ Data ◽  
Salinity Response ◽  
The Right

Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs.

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