Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes

Liibusk, Aive; Kall, Tarmo; Rikka, Sander; Uiboupin, Rivo; Suursaar, Ülo; Tseng, Kuo Hsin

doi:10.3390/rs12244062

Cited by 14 publications

(4 citation statements)

References 32 publications

(43 reference statements)

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“…The dense network of in situ tide gauges in the Baltic Sea also supports satellite altimetry studies because tide gauges data allow for efficient validation of satellite altimetry data; for example, as a result of the validation of the Sentinel-3A and Sentinel-3B Level-2 altimetry products along the Estonian coast. Liibusk et al [50] found that satellite altimetry can determine the height of the sea level in Estonian coastal areas with an average accuracy of 80 ± 70 mm. Sentinel-3 Level-2 sea level altimetry products and 2019 data were used in this study.…”

Section: Satellite Altimetrymentioning

confidence: 99%

Sea Level Rise and Future Projections in the Baltic Sea

Kapsi,

Kall,

Liibusk

2023

JMSE

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This article aimed to provide an overview of relative and absolute sea level rise in the Baltic Sea based on different studies, where researchers have used data from tide gauges, satellite altimetry, sea level rise, and land uplift models. These results provide an opportunity to get an overview of the sea level rise in the Baltic Sea. However, to better understand the impact of sea level rise on the coastal area of the Baltic Sea, and especially in Estonia, two post-glacial land uplift models, the latest land uplift model NKG2016LU of the Nordic Commission of Geodesy (NKG) and Estonian land uplift model EST2020VEL, were used. These models enabled to eliminate post-glacial land uplift from absolute sea level rise. To determine the relative sea level rise in the coastal area of the Baltic Sea, the rates from land uplift models were compared to ESA’s BalticSEAL absolute sea level rise model. It was found that the relative sea level rise between 1995–2019 was −5 to 4.5 mm/yr (based on NKG2016LU) in the Baltic Sea. In addition, the southern area is more affected by relative sea level rise than the northern part. During the research, it was also found that the IPCC AR5 sea level projections predict a maximum relative sea level rise in the Baltic Sea by the year 2100 of between 0.3 to 0.7 m. As coastal areas in the southern part of the Baltic Sea are predominantly flat, the sea level may reach the real estate properties by the end of the 21st century. In the coastal area of Estonia, the relative sea level rise in the period 1995–2019 was −1.1 to 3.1 mm/yr (based on NKG2016LU) and −0.3 to 3.4 mm/yr (based on EST2020VEL), the difference between the land uplift models is −0.9 to 0.1 mm/y. In Estonia, the west and southwest area are most threatened by sea level rise, where the coast is quite flat. One of the largest cities in Estonia, Pärnu, is also located there. Using the ESA’s sea level and EST2020VEL land uplift models, it was found that the relative sea level rise will be 0.28 m by the year 2100. Based on the large spatial resolution IPCC AR5 sea level projections, the relative sea level rise will be on the same scale: 0.2–0.4 m.

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Section: Satellite Altimetrymentioning

confidence: 99%

Sea Level Rise and Future Projections in the Baltic Sea

Kapsi,

Kall,

Liibusk

2023

JMSE

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“…This is the direct product recorded by the satellite altimetry. The SSH values are provided along the satellites' ground tracks or at regular grids interpolated from the values determined along the satellite tracks, e.g., the Copernicus Marine Environment Monitoring Service provides regular and systematic reference information (data products) on the physical and biogeochemical ocean and sea ice state for the global ocean and European regional seas [50].…”

Section: Monitoring Sea-level Rise For Coastal Resiliencementioning

confidence: 99%

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Montillet

Zhao

et al. 2022

Remote Sensing

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Geodesy is the science of accurately measuring the topography of the earth (geometric shape and size), its orientation in space, and its gravity field. With the advances in our knowledge and technology, this scientific field has extended to the understanding of geodynamical phenomena such as crustal motion, tides, and polar motion. This Special Issue is dedicated to the recent advances in modelling geodetic time series recorded using various instruments. Due to the stochastic noise properties inherent in each of the time series, careful modelling is necessary in order to extract accurate geophysical information with realistic associated uncertainties (statistically sufficient). The analyzed data have been recorded with various space missions or ground-based instruments. It is impossible to be comprehensive in the vast and dynamic field that is Geodesy, particularly so-called “Environmental Geodesy”, which intends to understand the Earth’s geodynamics by monitoring any changes in our environment. This field has gained much attention in the past two decades due to the need by the international community to understand how climate change modifies our environment. Therefore, this Special Issue collects some articles which emphasize the recent development of specific algorithms or methodologies to study particular natural phenomena related to the geodynamics of the earth’s crust and climate change.

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“…The SSH values are provided along the satellites' ground tracks or at regular grids interpolated from the values determined along the satellite tracks, e.g., the Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information (data products) on the physical and biogeochemical ocean and sea ice state for the global ocean and the European regional seas. The CMEMS is implemented and operated by Mercator Ocean, which provides oceanographic products and services for maritime safety, coastal and marine environment, climate and weather forecasting, and marine resource users [38,39].…”

Section: Introductionmentioning

confidence: 99%

“…This is the direct product of satellite altimetry. Additionally, the SSH values are provided along the satellites' ground tracks or at regular grids interpolated from the values determined along the satellite tracks, e.g., CMEMS provides regular and systematic reference information on the physical and biogeochemical ocean and sea ice state for the global ocean and the European regional seas [38,39]. Here, we used a "GLOBAL_REANALYSIS_PHY_001_030" reanalysis data product to obtain global sea surface high monthly grid data with a data resolution of 5' covering the period 1993 to 2019 (26 years in total).…”

Section: Introductionmentioning

confidence: 99%

Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island

Montillet

Fernandes

et al. 2022

Remote Sensing

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Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern California. First, we process global navigation satellite system (GNSS) measurements at 405 stations blanketing the whole coast to generate a profile of vertical land motion (VLM) known to bias century-long tide gauge (TG) measurements recording relative SLR (RSLR). We are then able to estimate the absolute SLR (ASLR) by correcting the SLR with the VLM. Our study emphasizes the relationship between the various tectonic movements (i.e., the Cascadia subduction zone, the San Andreas strike-slip fault system) along the Pacific coast which renders it difficult to accurately estimate the SLR. That is why we precisely model the stochastic noise of both GNSS and tide gauge time series using a combination of various models and information criterions (ICs). We also use the latest altimetry products and sea surface height (SSH) to compare it with ASLR at the same location as the TGs. This study supports previous analysis that the power law + white noise and generalized Gauss–Markov + white noise models are the best stochastic noise models for the GNSS time series. The new coastal profile confirms the large variability of VLM estimates in the Pacific Northwest around the Cascadia subduction zone in agreement with previous studies, and a similar result when the San Andreas fault comes onshore in Central California (San Francisco Bay). Negative RSLR values are mostly located in the Pacific Northwest (Vancouver Island and Olympic Peninsula). We also observe a much bigger variation (about 90%–150%) of the ASLR in the Pacific Northwest which is predominantly due to glacial isostatic adjustment (GIA). Moreover, the comparison between the ASLR and the SSH estimates shows similarities in the center of the studied area (South Washington, Oregon planes, and some parts of Southern California) where the tectonic activity does not significantly influence the TG measurements. Finally, the twentieth-century satellite geocentric ocean height rates show a global mean of 1.5 to 1.9 mm/yr. Our estimates based on ASLR and SSH are within this interval.

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Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes

Cited by 14 publications

References 32 publications

Sea Level Rise and Future Projections in the Baltic Sea

Sea Level Rise and Future Projections in the Baltic Sea

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island

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