Annual and inter-annual sea-level variability from coastal altimetry and tide gauge data

2021 ◽  
Author(s):  
Anara Kudabayeva ◽  
Michael Schindelegger ◽  
Rui M. Ponte ◽  
Bernd Uebbing
Keyword(s):  
Sea Level ◽  
Ocean Circulation ◽  
Tide Gauge ◽  
Low Frequency ◽  
Altimeter Data ◽  
Altimetry Data ◽  
Tide Gauges ◽  
Sea Level Variability ◽  
Short Scale ◽  
The Impact

<p> <span>Accurate long-term measurements of coastal sea level are fundamental for understanding changes in ocean circulation and assessing the impact of low-frequency sea-level variability on, e.g., near-shore ecosystems, groundwater dynamics, and coastal flooding. However, tide gauges are sparsely distributed in space and the extent to which satellite altimetry data can be used to infer the complex patterns of sea level near the coast is a subject of debate. Here, we revisit earlier attempts of connecting tide gauge and altimetry observations of low-frequency sea-level changes across the coastal zone. Our interest lies both in short-scale spatial structures indicative of dynamic decoupling between coastal areas and the deep ocean, and in the benefits of using a reprocessed, coastal altimetry product (X-TRACK) for the analysis. The mean annual cycle is chosen as a first benchmark and more than 200 globally distributed tide gauges are examined. We compute statistics between tide gauge and along-track altimeter series within spatial radii of 20 km (“coastal”) and 134 km of the tide gauge location, and additionally split altimetry data inside the 134-km circle into “shallow” and “deep” groups relative to the 200-m isobaths. Globally averaged RMS (root-mean-square) differences in the “coastal” and “shallow” categories are 1.9 and 2.4 cm for the X-TRACK product, somewhat lower than the corresponding values from the non-optimized Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 4.2 (2.3 and 2.6 cm). Examination of inter-annual sea-level variability from 1993 to 2019 is underway, with initial focus on regions where poor correspondence between satellite and tide gauge sea-level estimates has been noted in the past (e.g., US East Coast and western South America). At most locations analyzed so far, RMS differences decrease and correlations improve as one approaches the coast along the satellite tracks. However, the X-TRACK estimates tend to become erratic within 20–30 km from the tide gauge, suggesting that the usability of classical nadir altimetry measurements for studying short-scale coastal dynamics is still limited despite ongoing reprocessing efforts.</span></p>

scholarly journals Spatio-Temporal Assessment of Land Deformation as a Factor Contributing to Relative Sea Level Rise in Coastal Urban and Natural Protected Areas Using Multi-Source Earth Observation Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2296 ◽  
Author(s):  
Panagiotis Elias ◽  
George Benekos ◽  
Theodora Perrou ◽  
Issaak Parcharidis
Keyword(s):  
Protected Areas ◽  
Sea Level ◽  
Tide Gauge ◽  
Coastal Areas ◽  
Earth Observation ◽  
Coastal Hazards ◽  
Altimetry Data ◽  
Relative Sea Level ◽  
Land Deformation ◽  
The Impact

The rise in sea level is expected to considerably aggravate the impact of coastal hazards in the coming years. Low-lying coastal urban centers, populated deltas, and coastal protected areas are key societal hotspots of coastal vulnerability in terms of relative sea level change. Land deformation on a local scale can significantly affect estimations, so it is necessary to understand the rhythm and spatial distribution of potential land subsidence/uplift in coastal areas. The present study deals with the determination of the relative vertical rates of the land deformation and the sea-surface height by using multi-source Earth observation—synthetic aperture radar (SAR), global navigation satellite system (GNSS), tide gauge, and altimetry data. To this end, the multi-temporal SAR interferometry (MT-InSAR) technique was used in order to exploit the most recent Copernicus Sentinel-1 data. The products were set to a reference frame by using GNSS measurements and were combined with a re-analysis model assimilating satellite altimetry data, obtained by the Copernicus Marine Service. Additional GNSS and tide gauge observations have been used for validation purposes. The proposed methodological approach has been implemented in three pilot cases: the city of Alexandroupolis in the Evros Delta region, the coastal zone of Thermaic Gulf, and the coastal area of Killini, Araxos (Patras Gulf) in the northwestern Peloponnese, which are Greek coastal areas with special characteristics. The present research provides localized relative sea-level estimations for the three case studies. Their variation is high, ranging from values close to zero, i.e., from 5–10 cm and 30 cm in 50 years for urban areas to values of 50–60 cm in 50 years for rural areas, close to the coast. The results of this research work can contribute to the effective management of coastal areas in the framework of adaptation and mitigation strategies attributed to climate change. Scaling up the proposed methodology to a continental level is required in order to overcome the existing lack of proper assessment of the relevant hazard in Europe.

scholarly journals Comparing satellite altimetry and tide gauges observations in the Mediterranean Sea within the framework of singular spectrum analysis

MAUSAM ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 187-198
Author(s):  
HADDAD MAHDI ◽  
TAIBI HEBIB ◽  
MOKRANE MOUSTAFA ◽  
HAMMOUMI HOUSSEYN
Keyword(s):  
Sea Level ◽  
Spectrum Analysis ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Singular Spectrum Analysis ◽  
Altimetry Data ◽  
Tide Gauges ◽  
The Mediterranean ◽  
Satellite Altimetry Data

By considering time series from satellite altimetry and tide gauges that extend back to 1993, Singular Spectrum Analysis (SSA) is applied to investigate and compare the non linear trends of the sea level along the Mediterranean coasts. The major issue of this comparison is to show if the satellite altimetry data could be representative of the local sea level as observed by tide gauges.   The results indicate that the local trends estimated from an in-situ tide gauge and satellite altimetry data show nearly identical positive rates over the period from 1993 to 2017. The differences between the estimated rates of sea level change from in-situ tide gauge and satellite measurements vary, in absolute value, from 0.18 to 4.29 mm/year with an average of 1.55 mm/year.   This result is sufficient to admit, if necessary, on the one hand, the complementarily of the two measurement techniques (satellite altimetry and tide gauges) and, on the other hand, the rise in sea level near the Mediterranean coastal areas.

Observing Sea Level Changes Using Satellite Altimetry and In Situ Data

2020 ◽  
Author(s):  
Nikos Flokos ◽  
Maria Tsakiri
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Coastal Zone ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Altimeter Data ◽  
Atmospheric Effects ◽  
Data Sets ◽  
Altimetry Data

<p>corresponding author: N.Flokos</p><p>[email protected]</p><p>ABSTRACT</p><p>Sea level change is one of the key indicators of climate change with numerous effects such as flooding, erosion of beaches, salt intrusion.  The detailed global picture of sea level and the monitoring of its spatial-temporal changes is performed by Satellite Altimetry (SA). Nowadays, SA data compare well with measurements from the global tide gauge network, but the aim of 0.3 mm/year accuracy in the altimeter derived rate of global mean sea level rise is still not fully met. </p><p>Whilst the precise determination of global and regional sea level rise from SA data is promising, there is however an observational gap in our knowledge regarding the coastal zone. While Tide Gauges (TG) are usually located at the coast, therefore providing coastal sea level measurements, altimeters have difficulties there. Filling this gap becomes important when considering that the impact of sea level rise can be devastating on the coast with effects on society and ecosystems. This makes it even more significant knowing that there are many stretches of the world’s coast that still do not possess in situ level measuring devices.  </p><p>This work aims to discuss the available data and methods that link the SA measurements of sea level rise with TG measurements. Whilst there is rich literature on relevant applications, it is important to have a clear and concise methodology on this.</p><p>Tide gauge data</p><p>Several post processing steps need to be applied to the raw TG data to enrich the raw Sea Surface Heights (SSH) values and make them comparable with SA data. There are several geophysical corrections, such as pressure and wind effects, which can be applied to TG data in order to deduce  Sea Level (SL) and be consistent with altimeter data. High frequency atmospheric effects on TG data are corrected using the Dynamic Atmospheric Correction (DAC) provided by AVISO. One other large uncertainty is the vertical stability of the TG benchmark over time. TG data must be corrected for the Vertical Land Motion (VLM) to enable the comparison of two sea level measurements (TG and SA) and their later integration within the surfaces of the absolute sea heights. The main VLM dataset can be obtained from SONEL database (SONEL 2016) which provides crustal velocities from the continuous GNSS measurements at sites collocated to the TG.</p><p>Satellite altimetry data</p><p>Whilst Satellite Altimetry over the open ocean is a mature discipline, global altimetry data collected over the coastal ocean remain still largely unexploited. This is because of intrinsic difficulties in the corrections and issues of land contamination in the footprint that have so far resulted in systematic flagging and rejection of these data. In this work, the relevant methodology to overcome these problems and extend the capabilities of current and future altimeters to the coastal zone (coastal altimetry) will be discussed and a number of coastal altimetry data sets will be used (eg SARvatore, X-TRACK, RADS etc). Finally, a practical example using real data sets over the Aegean Sea will be presented. </p><p> </p><p> </p>

scholarly journals Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability

2012 ◽  
Vol 19 (1) ◽  
pp. 95-111 ◽  
Author(s):  
R. V. Donner ◽  
R. Ehrcke ◽  
S. M. Barbosa ◽  
J. Wagner ◽  
J. F. Donges ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Spatial Patterns ◽  
Tide Gauge ◽  
Sufficient Information ◽  
Sea Level Variability ◽  
Advantages And Disadvantages ◽  
Long Term Trends ◽  
The Impact

Abstract. The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.

scholarly journals 2-D reconstruction of past sea level (1950–2003) using tide gauge records and spatial patterns from a general ocean circulation model

2009 ◽  
Vol 5 (2) ◽  
pp. 1109-1132 ◽  
Author(s):  
W. Llovel ◽  
A. Cazenave ◽  
P. Rogel ◽  
A. Lombard ◽  
M. Bergé-Nguyen
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Ocean Circulation ◽  
Tide Gauge ◽  
Circulation Model ◽  
Global Ocean ◽  
Tide Gauges ◽  
Ocean Circulation Model ◽  
Tide Gauge Records ◽  
Function Decomposition

Abstract. A two-dimensional reconstruction of past sea level is proposed at yearly interval over the period 1950–2003 using tide gauge records at 99 selected sites and 44-year long (1960–2003) 2°×2° gridded dynamic heights from the OPA/NEMO global ocean circulation model with data assimilation. An Empirical Orthogonal Function decomposition of the reconstructed sea level over 1950–2003 displays leading modes that reflect two main components: a long-term (multi-decadal) but regionally variable signal and interannual fluctuations dominated by the signature of El Nino-Southern Oscillation. Tests show that spatial trend patterns of the 54-year long reconstructed sea level (1950–2003) significantly depend on the length of the gridded OPA/NEMO time series used to compute spatial covariance signal used for the reconstruction (i.e., the length of the gridded OPA/NEMO time series). On the other hand, the interannual variability is well reconstructed, even with ~10-year long of the OPA/NEMO model or satellite altimetry-based sea level grids. The robustness of the results is assessed, leaving out successively each of the 99 tide gauges when reconstructing the sea level signal and then comparing observed and reconstructed time series at the non contributing tide gauge site. The reconstruction performs well at most tide gauges, especially at interannual frequency.

Validation of the ALES Coastal Altimetry Dataset against the Norwegian Tide Gauges

2021 ◽  
Author(s):  
Fabio Mangini ◽  
Antonio Bonaduce ◽  
Léon Chafik ◽  
Laurent Bertino
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Linear Trend ◽  
Tide Gauges ◽  
Sea Level Variability ◽  
Preliminary Results ◽  
In Situ Data ◽  
Coastal Sea

<p>Satellite altimetry measurements, complemented by in-situ records, have made a fundamental contribution to the understanding of global sea level variability for almost 30 years. Due to land contamination, it performs best over the open ocean. However, over the years, there has been a significant effort to improve the altimetry products in coastal regions. Indeed, altimetry observations could be fruitfully used in the coastal zone to complement the existing tide gauge network which, despite its relevance, does not represent the entire coast. Given the important role of coastal altimetry in oceanography, we have recently decided to check the quality of a new coastal altimetry dataset, ALES, along the coast of Norway. The Norwegian coast is well covered by tide gauges and, therefore, particularly suitable to validate a coastal altimetry dataset. Preliminary results show a good agreement between in-situ and remote sensing sea-level signals in terms of linear trend, seasonal cycle and inter-annual variability. For example, the linear correlation coefficient between the inter-annual sea level variability from altimetry and tide gauges exceeds 0.8. Likewise, the root mean square difference between the two is less than 2 cm at most tide gauge locations. A comparison with Breili et al. (2017) shows that ALES performs better than the standard satellite altimetry products at estimating sea level trends along the coast of Norway. Notably, in the Lofoten region, the difference between the sea level trends computed using ALES and the tide gauges range between 0.0 to 0.7 mm/year, compared to circa 1 to 3 mm/year found by Breili et al. (2017). These preliminary results go in the direction of obtaining an accurate characterization of coastal sea-level at the high latitudes based on coastal altimetry records, which can represent a valuable source of information to reconstruct coastal sea-level signals in areas where in-situ data are missing or inaccurate.</p>

Decadal Sea level Variability in the subtropical South Pacific

2020 ◽  
Author(s):  
Frauke Albrecht ◽  
Oscar Pizarro ◽  
Eduardo Zorita
Keyword(s):  
Sea Level ◽  
Ocean Circulation ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
South Pacific ◽  
Ekman Transport ◽  
Cmip5 Models ◽  
Altimetry Data ◽  
Sea Level Variability ◽  
The North

<p>Observational altimetry data and data of 18 phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated to analyze decadal sea level variability for the subtropical South Pacific. The altimetry data covers the period 1993 to 2017. In order to analyze decadal variability yearly means of detrended data are considered. An Empirical Orthogonal Function (EOF) analysis of the Region 20°S to 60°S is performed in to analyze sea level variablility in the subtropics. The tropical region has been omitted in order to avoid the strong El Niño Southern Oscillation (ENSO) signal masking other subtropical variability in the analysis. The first EOF of the altimetry data shows a clear pattern with a North-South dipole explaining 30% of the variance and the corresponding time series shows a decadal periodicity. The decadal variability of this pattern is reproduced by the CMIP5 models. Analyzing model ocean circulation data show consistent decadal variability in the North-South velocity. As a possible forcing zonal (westerly) surface winds are analyzed. Their pattern confirm Ekman transport to the North (South) in the lower (higher) latitudes, leading to a convergence zone and therefore explaining the sea level rise as seen in the EOF pattern, consistently with the Ekman transport a deep compensatory poleward flow is observed.</p>

scholarly journals The Zone of Influence: Matching sea level variability from coastal altimetry and tide gauges for vertical land motion estimation

2020 ◽  
Author(s):  
Julius Oelsmann ◽  
Marcello Passaro ◽  
Denise Dettmering ◽  
Christian Schwatke ◽  
Laura Sanchez ◽  
...  
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Spatial Scales ◽  
Tide Gauges ◽  
Relative Sea Level ◽  
Sea Level Variability ◽  
Vertical Land Motion ◽  
Spatial Coverage ◽  
Coupling Procedure ◽  
Zone Of Influence

Abstract. Vertical land motion (VLM) at the coast is a substantial contributor to relative sea level change. In this work, we present a refined method for its determination, which is based on the combination of absolute satellite alimetry (SAT) sea level measurements and relative sea level changes recorded by tide gauges (TG). These measurements complement VLM estimates based on GNSS (Global Navigation Satellite System) by increasing their spatial coverage. Trend estimates from SAT and TG combination are particularly sensitive to the quality and resolution of applied altimetry data as well as to the coupling procedure of altimetry and tide gauges. Hence, a multi-mission, dedicated coastal along-track altimetry dataset is coupled with highfrequent tide gauge measurements at 58 stations. To improve the coupling-procedure, a so-called `Zone of Influence’ is defined to identify coherent zones of sea level variability on the basis of relative levels of comparability between tide gauge and altimetry observations. Selecting 20 % of the most representative absolute sea level observations in a 300 km radius around the tide gauges results in the best VLM-estimates in terms of accuracies and uncertainties. At this threshold, VLM_SAT-TG estimates have median formal uncertainties of 0.59 mm/year. Validation against GNSS VLM estimates yields a root-mean-square (RMS_VLM) of VLM_SAT-TG and VLM_GNSS differences of 1.28 mm/year, demonstrating the level of accuracy of our approach. Compared to a reference 250 km radius selection of sea level anomalies, the 300 km Zone of Influence improves trend accuracies by 12 % and uncertainties by 28 %. With progressing record lengths, the spatial scales of coastal sea level trend coherency increase. Therefore the relevance of the ZOI for improving VLM_SAT-TG accuracies decreases. Further individual Zone of Influence adaptations offer the prospect of bringing the accuracy of the estimates below 1 mm/year.

scholarly journals Overlapping sea level time series measured using different technologies: an example from the REDMAR Spanish network

2014 ◽  
Vol 14 (3) ◽  
pp. 589-610 ◽  
Author(s):  
B. Pérez ◽  
A. Payo ◽  
D. López ◽  
P. L. Woodworth ◽  
E. Alvarez Fanjul
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Tide Gauge ◽  
Sampling Strategies ◽  
Tide Gauges ◽  
Sea Levels ◽  
Tsunami Detection ◽  
The Impact

Abstract. This paper addresses the problems of overlapping sea level time series measured using different technologies and sometimes from different locations inside a harbour. The renovation of the Spanish REDMAR (RED de MAReógrafos) sea level network is taken here as an example of the difficulties encountered: up to seventeen old tide gauge stations have been replaced by radar tide gauges all around the Spanish coast, in order to fulfil the new international requirements on tsunami detection. Overlapping periods between old and new stations have allowed the comparison of records in different frequency ranges and the determination of the impact of this change of instrumentation on the long-term sea level products such as tides, surges and mean sea levels. The differences encountered are generally within the values expected, taking into account the characteristics of the different sensors, the different sampling strategies and sometimes the different locations inside the harbours. However, our analysis has also revealed in some cases the presence of significant scale errors that, overlapping with datum differences and uncertainties, as well as with hardware problems in many new radar gauges, may hinder the generation of coherent and continuous sea level time series. Comparisons with nearby stations have been combined with comparisons with altimetry time series close to each station in order to better determine the sources of error and to guarantee the precise relationships between the sea level time series from the old and the new tide gauges.

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