salinity variations
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2021 ◽  
Author(s):  
Rachel So ◽  
Tim Lowenstein ◽  
Elliott Jagniecki ◽  
Jessica Tierney ◽  
Sarah Feakins

2021 ◽  
Vol 13 (1) ◽  
pp. 443-453
Author(s):  
Abduldaem S. Alqasemi ◽  
Majed Ibrahim ◽  
Ayad M. Fadhil Al-Quraishi ◽  
Hakim Saibi ◽  
A’kif Al-Fugara ◽  
...  

Abstract Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.


2020 ◽  
Vol 12 (24) ◽  
pp. 10677
Author(s):  
Ronghui Ye ◽  
Jun Kong ◽  
Chengji Shen ◽  
Jinming Zhang ◽  
Weisheng Zhang

Accurate salinity prediction can support the decision-making of water resources management to mitigate the threat of insufficient freshwater supply in densely populated estuaries. Statistical methods are low-cost and less time-consuming compared with numerical models and physical models for predicting estuarine salinity variations. This study proposes an alternative statistical model that can more accurately predict the salinity series in estuaries. The model incorporates an autoregressive model to characterize the memory effect of salinity and includes the changes in salinity driven by river discharge and tides. Furthermore, the Gamma distribution function was introduced to correct the hysteresis effects of river discharge, tides and salinity. Based on fixed corrections of long-term effects, dynamic corrections of short-term effects were added to weaken the hysteresis effects. Real-world model application to the Pearl River Estuary obtained satisfactory agreement between predicted and measured salinity peaks, indicating the accuracy of salinity forecasting. Cross-validation and weekly salinity prediction under small, medium and large river discharges were also conducted to further test the reliability of the model. The statistical model provides a good reference for predicting salinity variations in estuaries.


2020 ◽  
Vol 65 (2) ◽  
pp. 87-94
Author(s):  
Ana-Maria Purcari ◽  
Mirela Cimpean ◽  
Karina Paula Battes

The amphipod species Pontogammarus maeoticus (Sovinskij, 1894) was identified in two locations from the Danube Delta, Romania (Sfântu Gheorghe and Sulina beaches) in July 2019. This is an eurybiont species, able to withstand high salinity variations characteristic to mixing fresh and sea waters. The individuals presented a special character in their morphology, a depression on the basis of pereiopod V. The present paper contributes to the knowledge of existing amphipod fauna from the Danube Delta, in the Black Sea coast area.


2020 ◽  
Vol 34 (4) ◽  
pp. 662-672
Author(s):  
Henrique Douglas dos Santos Borburema ◽  
Ruth Pessoa de Lima ◽  
George Emmanuel Cavalcanti de Miranda

2020 ◽  
Vol 33 (20) ◽  
pp. 8751-8766
Author(s):  
Chao Liu ◽  
Xinfeng Liang ◽  
Don P. Chambers ◽  
Rui M. Ponte

AbstractSalinity is one of the fundamental ocean state variables and has been used to infer important information about climate change and variability. Previous studies have found inconsistent salinity variations in various objective ocean analyses that are based on the Argo measurements. However, as far as we are aware, a comprehensive assessment of those inconsistencies, as well as robust spatial and temporal features of salinity variability among the Argo-based products, has not been conducted. Here we compare and evaluate ocean salinity variability from five objective ocean analyses that are solely or primarily based on Argo measurements for their overlapping period from 2005 to 2015. We examine the salinity variability at the sea surface and within two depth intervals (0–700 and 700–2000 m). Our results show that the climatological mean is generally consistent among all examined products, although regional discrepancies are evident in the subsurface ocean. The time evolution, vertical structure, and leading EOF modes of salinity variations show good agreement among most of the examined products, indicating that a number of robust features of the salinity variability can be obtained by examining gridded Argo products. However, significant discrepancies in these variations exist, particularly in the subsurface North Atlantic and Southern Oceans. Also, despite the increasing number of Argo floats deployed in the ocean, the discrepancies were not significantly reduced over time. Our analyses, particularly those of the discrepancies between products, can serve as a useful reference for utilizing and improving the existing objective ocean analyses that are based on Argo measurements.


2020 ◽  
Author(s):  
Ronghui Ye ◽  
Jun Kong ◽  
Chengji Shen ◽  
jinming zhang ◽  
Weisheng Zhang

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