Prediction of near-bottom water salinity in the Baltic Sea using Ordinary Least Squares and Geographically Weighted Regression models

Łukawska‐Matuszewska, Katarzyna; Urbański, Jacek

doi:10.1016/j.ecss.2014.09.003

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…GWR is a practical technique that can be used to examine the spatial nonstationarity for a continuous‐parameter surface at regional scale by integrating spatial information into a weighting matrix to capture spatial variations with local effects (Brunsdon et al, ; Fotheringham et al, ). GWR can be well applied to water environment modeling and monitoring (Huang et al, ; Łukawska‐Matuszewska & Urbański, ; Majid & Muhammad, ). In this study, a satellite‐based GWR model was developed to address the spatial nonstationarity of the relationship between STA and sea surface parameters.…”

Section: Methodsmentioning

confidence: 99%

Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

Huang

et al. 2018

JGR Oceans

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Accurately retrieving and describing subsurface temperature on a large scale can provide valuable information that can be used for subsurface dynamic and variability studies. This study develops a new satellite‐based geographically weighted regression (GWR) model to estimate a subsurface temperature anomaly (STA) in the upper 2,000 m of the Indian Ocean by combining satellite observations (sea surface height, sea surface temperature, sea surface salinity, and sea surface wind) and Argo in situ data (STA). This model improves the estimation accuracy by considering the significant spatial nonstationarity feature between the surface and subsurface parameters in the ocean. The performance of the GWR model is measured by using Akaike Information Criterion combined with root‐mean‐square error and R2. The results showed that the proposed GWR model can easily retrieve the STA and outperform the ordinary least squares model. The GWR model can also explain the contribution from each variable via a local regression coefficient distribution. The sea surface height from altimetry is the most significant variable for GWR estimation. This study demonstrates the great potential and advantage of the GWR model for large‐scale subsurface modeling and information retrieving. Thus, we have developed a novel approach for investigating subsurface thermal anomaly and variability from satellite observations.

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Section: Methodsmentioning

confidence: 99%

Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

Huang

et al. 2018

JGR Oceans

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“…Bottom water salinity varies greatly, from 2 to 6 in Bothnian Bay to 10 in Gotland Basin, 12 to 15 in Bornholm Basin, 22 in Fehmarn Belt and 32 to 35 in Kattegat (Nordberg et al . ; Łukawska‐Matuszewska & Urbański ; Fig. ).…”

Section: Regional Backgroundmentioning

confidence: 99%

“…Bottom water salinity is indicated in white numbers after Frenzel et al . (); Łukawska‐Matuszewska & Urbański (). [Colour figure can be viewed at www.boreas.dk]…”

Section: Regional Backgroundmentioning

confidence: 99%

“…The input of freshwater via riverine inflow and precipitation plays the major role in shaping the salinity regime of the Baltic Sea (Döös et al . ; Łukawska‐Matuszewska & Urbański ). The denser water‐masses are separated from the fresher surface waters by a halocline (Reissmann et al .…”

Section: Regional Backgroundmentioning

confidence: 99%

“…The exchange of watermasses between the North Sea and the Baltic occurs through the narrow and shallow Danish straits; more saline waters sink and fill the deep basins. The input of freshwater via riverine inflow and precipitation plays the major role in shaping the salinity regime of the Baltic Sea (D€ o€ os et al 2004;Łukawska-Matuszewska & Urba nski 2014). The denser water-masses are separated from the fresher surface waters by a halocline (Reissmann et al 2009).…”

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confidence: 99%

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Late Weichselian to Holocene history of the Baltic Sea as reflected in ostracod assemblages

Stepanova

Obrochta

Krupinski

et al. 2019

Boreas

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The study presents the first description and analysis of ostracod records from three sites cored in different parts of the Baltic Sea during the IODP Expedition 347, Baltic Sea Paleoenvironment. Our data present the first high‐resolution ostracod records from the Late Weichselian and Holocene sediments collected across the Baltic Sea Basin. Using published data on modern ostracod species ecology of the Baltic Sea, we were able to provide ostracod‐based palaeoreconstructions of the history of the region. The stratigraphical framework for the sites is based on radiocarbon‐based age models. The three studied sites reveal different ostracod assemblage successions that reflect environmental changes in the study area. Site M0060, located in the Kattegat area, contains the oldest ostracod assemblages that document a marine environment with very high sedimentation rates during the most recent deglaciation. Between ~13 000 and 7500 cal. a BP a modern‐like near‐shore environment developed. Site M0059 in the southwestern Baltic Sea, Little Belt area, contains assemblages reflecting the transition from a freshwater lake to the brackish Littorina Sea between ~7500 and 7300 cal. a BP. Site M0063 is the deepest location in the central Baltic, Landsort Deep, and yielded very limited ostracod data, but comparison with our organic carbon data allowed us to distinguish the Yoldia Sea, Ancylus Lake and Littorina Sea intervals. The ostracod record correlates well with the organic carbon record with alternation between periods of hypoxia and periods of low oxygen that still supported ostracods.

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Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

Khosravi

Balyani

2018

Environ Model Assess

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Prediction of near-bottom water salinity in the Baltic Sea using Ordinary Least Squares and Geographically Weighted Regression models

Cited by 18 publications

References 19 publications

Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

Late Weichselian to Holocene history of the Baltic Sea as reflected in ostracod assemblages

Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

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