Estimates of vertical crustal movements along the coast of Greece, based on mean sea level data

1983 ◽  
Vol 121 (5-6) ◽  
pp. 869-887 ◽  
Author(s):  
E. Lagios ◽  
M. Wyss
Keyword(s):  
Sea Level ◽  
Mean Sea Level ◽  
Crustal Movements ◽  
Level Data ◽  
Vertical Crustal Movements ◽  
Of Greece

Pattern of recent vertical crustal movements in Maritime Canada

1976 ◽  
Vol 13 (5) ◽  
pp. 661-667 ◽  
Author(s):  
Petr Vaníček
Keyword(s):  
New Brunswick ◽  
Sea Level ◽  
Bay Of Fundy ◽  
Vertical Movements ◽  
Crustal Movements ◽  
First Order ◽  
Level Data ◽  
Velocity Surface ◽  
Maritime Canada ◽  
Vertical Crustal Movements

A surface depicting linear vertical movements in Maritime Canada was computed from sea-level data recorded by 8 tide guages and 308 mostly disjoint, relevelled segments of the first-order Canadian levelling network. Owing to the sparsity of the available data and their distribution, the velocity surface must be regarded as indicative of the crude features only. The indications are that there is a west-northwest trending belt of faster subsidence across the eastern end of the Bay of Fundy, and that there may be an area of uplift in northeastern New Brunswick. Although the faster subsidence around the eastern Bay of Fundy seems to be well established now, more data are needed to prove or dispel the existence of the indicated uplift.

scholarly journals NEW MODEL OF THE VERTICAL CRUSTAL MOVEMENTS IN THE AREA OF POLAND

2012 ◽  
Vol 32 (4) ◽  
pp. 83-87 ◽  
Author(s):  
Kamil Kowalczyk
Keyword(s):  
Least Squares ◽  
Sea Level ◽  
Least Squares Collocation ◽  
Land Uplift ◽  
Good Opportunity ◽  
Mean Sea Level ◽  
Crustal Movements ◽  
The Third ◽  
The Mean ◽  
Vertical Crustal Movements

In 2003 the fourth levelling campaign has been finished in Poland. This campaign, together with the previous one carried out in 1974–1982, gave a very good opportunity to determine the land uplift in the area of Poland. The paper describes shortly the third and fourth campaigns, the computation of the relative land uplift, computation of land uplift referred to the mean sea level and modeling the land uplift by the least-squares collocation method. Obtained results are compared with the computation done by the Institute of Geodesy and Cartography in 1986.

Sea level in the Global Geodetic Observing System

2020 ◽  
Author(s):  
Elizabeth Bradshaw ◽  
Andy Matthews ◽  
Kathy Gordon ◽  
Angela Hibbert ◽  
Sveta Jevrejeva ◽  
...  
Keyword(s):  
Sea Level ◽  
Working Group ◽  
Sea Level Change ◽  
Tide Gauges ◽  
Controlled Vocabularies ◽  
Mean Sea Level ◽  
Level Change ◽  
Level Data ◽  
Metadata Standards

<p>The Permanent Service for Mean Sea Level (PSMSL) is the global databank for long-term mean sea level data and is a member of the Global Geodetic Observing System (GGOS) Bureau of Networks and Observations. As well as curating long-term sea level change information from tide gauges, PSMSL is also involved in developing other products and services including the automatic quality control of near real-time sea level data, distributing Global Navigation Satellite System (GNSS) sea level data and advising on sea level metadata development.<br>At the GGOS Days meeting in November 2019, the GGOS Focus Area 3 on Sea Level Change, Variability and Forecasting was wrapped up, but there is still a requirement in 2020 for GGOS to integrate and support tide gauges and we will discuss how we will interact in the future. A recent paper (Ponte et al., 2019) identified that only “29% of the GLOSS [Global Sea Level Observing System] GNSS-co-located tide gauges have a geodetic tie available at SONEL [Système d'Observation du Niveau des Eaux Littorales]” and we as a community still need to improve the ties between the GNSS sensor and tide gauges. This may progress as new GNSS Interferometric Reflectometry (GNSS-IR) sensors are installed to provide an alternative method to observe sea level. As well as recording the sea level, these sensors will also provide vertical land movement information from one location. PSMSL are currently developing an online portal of uplift/subsidence land data and GNSS-IR sea level observation data. To distribute the data, we are creating/populating controlled vocabularies and generating discovery metadata.<br>We are working towards FAIR data management principles (data are findable, accessible, interoperable and reusable) which will improve the flow of quality controlled sea level data and in 2020 we will issue the PSMSL dataset with a Digital Object Identifier. We have been working on improving our discovery and descriptive metadata including creating a use case for the Research Data Alliance Persistent (RDA) Identification of Instruments Working Group to help improve the description of a time series where the sensor and platform may change and move many times. Representatives from PSMSL will sit on the GGOS DOIs for Data Working Group and would like to contribute help with controlled vocabularies, identifying metadata standards etc. We will also contribute to the next GGOS implementation plan.<br>Ponte, Rui M., et al. (2019) "Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level." <em>Frontiers in Marine Science</em> 6(437).</p>

scholarly journals Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

2019 ◽  
Vol 11 (17) ◽  
pp. 4643
Author(s):  
Vivien Lai ◽  
Ali Najah Ahmed ◽  
M.A. Malek ◽  
Haitham Abdulmohsin Afan ◽  
Rusul Khaleel Ibrahim ◽  
...  
Keyword(s):  
Sea Level ◽  
Peninsular Malaysia ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mean Sea Level ◽  
Sea Levels ◽  
Tuning Parameters ◽  
Level Data ◽  
Input Dataset ◽  
Sea Level Oscillations

The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis.

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