Tide Gauge Stations Research Articles

Chronological changes in the Black Sea level at tide gauge station Varna 1928-2023

This paper outlines time series data analysis from Varna tidal station (Black sea, Bulgaria), with the objective being to determine the influences of long-period and short-period tides. Observations series from monthly mean sea level values cover the period 1928-2023. The sea level measurements until 2013 were done with a mechanical float tide gauge. Since 2013 a radar tide gauge was installed in place of the mechanical one. A least squares linear regression model was applied for the analysis of monthly sea level data. As a result of the analysis, mean sea level, trend, seasonal and long-term tidal influences were established. In order to determine the influence of short-term tides, the method of least squares harmonic analysis was applied. In this method, observed sea level values are considered to be represented as a sum of tidal harmonics operating at previously known frequencies. The observation data include hourly values for the period 06.2013-12.2023. Some of the advantages of the applied method are that it allows to analyze series with interruptions in the observations, as well as assist to easily determine tides with close frequencies. From the analysis was determined the influence of: the annual and semi-annual tides, as well as about 68 short-period tides.

Comprehensive comparative analysis of reconstructed sea level datasets in the China Seas: insights from tide gauge and satellite altimetry

At present, there are many reconstructed datasets at the global scale. To test the applicability of these datasets in the China seas, the study comprehensively analyzes the reliability and accuracy of reconstructed sea level datasets in capturing nuanced temporal patterns of sea level changes in the China Seas. This study applied analysis methods or indicators such as time series, Taylor plots, correlation coefficients, growth rates, and standard deviations. Ocean Data Assimilations (ODAs) outperform Tide Gauge Reconstructions (TGRs) in terms of correlation with measured data in the nearshore, while TGRs exhibit superior capability in capturing oceanic sea level variability. Although the ODAs and TGRs both suffer from the underestimation of sea level variability in China as well as in neighboring seas, the TGRs perform better than the former. ODAs show inconsistency in reflecting the rate of sea level rise, but they, particularly the China Ocean Reanalysis (CORA), demonstrate a better correlation with satellite altimetry datasets. Meanwhile, both of them can reflect the Pacific Decadal Oscillation (PDO) well. TGRs, relying on oceanic tide gauge stations, suffer from poor correlation with tide gauge stations due to limited coverage. Reconstruction discrepancies are attributed to methodological differences and data assimilation techniques. Future studies should explore alternative variables like sea surface temperature and so on to enhance sea-level reconstruction, especially in regions with sparse tide gauge coverage.

Flood Vulnerability Assessment in Paradip Coastal Area Using Weighted Overlay Index

Paradip, a prominent coastal city on India's eastern seaboard, presents a unique combination of environmental challenges and urban pressures. It has the largest tidal range among major regional tidal gauge stations, including Vishakhapatnam and Chennai. The city experiences an annual sea level rise of 2.33 mm, with projections indicating an increase of 0.183 feet by 2047 and 0.76 feet over the next century. The city's unique environmental and urban issues are exacerbated by its placement within the Mahanadi River's deltaic deposits, floodplains, and coastal marine deposits along the Bay of Bengal. The shoreline of Paradip is topographically uniform, with ground elevations ranging from 3.6 to 5.7 meters. According to India's Wind and Cyclone Hazard Map, Paradip is in the "Very High Damage Risk Zone," indicating the presence of significant wind speeds along the coast. The paper digs into these vulnerabilities and their possible influence on the coastal community, emphasizing the city's need to strike a balance between urban growth and ecological preservation. One of the main problems is flooding. Rainwater enters government buildings and impoverished areas because of encroachments on natural drainage systems. Dryland capable of residential, agricultural, and other economic activities is submerged by overflowing water which causes loss of life, property, and infrastructure. This study evaluates flood vulnerabilities in Paradip, a coastal city, using GIS and a weighted overlay index. By integrating satellite imagery, digital elevation models (DEM), and historical data on rainfall and soil types, we developed a detailed flood vulnerability map that categorises areas into high, moderate, and low risk. ArcGIS Pro 2.7.3 software and a weighted overlay index were utilized for the collection, processing, and display of spatial and attribute data. spatial analysis was the process used to produce the flood vulnerability map. The results provide essential insights for urban planning and flood mitigation strategies, with direct implications for improving disaster preparedness and climate resilience. The flood vulnerability map was then compared to the masterplan of Paradip which was proposed for 2030, to know the actual vulnerable area and to defy some major which were proposed by Paradip development authority. The study illustrates how remote sensing and GIS techniques can be effectively utilized to generate flood vulnerability data essential for informed decision-making.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Storm frequency, magnitude, and cumulative storm beach impact along the US east coast

Abstract. This study extracted historical water level data from 12 National Oceanographic and Atmospheric Administration tide gauge stations, spanning the period from the early 20th century to 2022 from central Maine to southern Florida, in order to determine if temporal and spatial trends existed in the frequency and magnitude of storms along the US Atlantic Ocean coast. We used the Storm Erosion Potential Index (SEPI) to identify and quantify storms. We then use the timing and magnitude of those storms to determine the cumulative effect of storm clustering and large-magnitude storms on sandy beaches using the cumulative storm impact index (CSII) empirical model. The results from this study showed (1) no appreciable increase in storm frequency at any of the stations (except for sheltered stations susceptible to storm tide augmentation), (2) statistically significant but modest increases in storm magnitudes over time for 8 of the 12 tidal stations, (3) regional differences in storm magnitudes (SEPI) and cumulative storm impacts (CSII) characteristic of more frequent extratropical storms (temporal clustering) in the north and less frequent tropical storms in the south, and (4) a 4- to 10-year recovery period for regional beach recovery.

Estimation of tidal energy potential in the Vietnam East Sea: A comprehensive analysis using semi-empirical tide models

This study assesses the tidal energy potential in the Vietnam East Sea (VES), a region with complex hydrodynamics and significant interactions with the Western Pacific Ocean. A rigorous validation of six prominent semi-empirical global ocean tide models (FES2014, DTU16, EOT20, GOT4.10c, HAMTIDE12, OSU12.V1) was conducted against an extensive network of 34 tide gauge stations in the VES region. The validation identified FES2014 as the superior model, exhibiting the closest agreement with observations due to its advanced data assimilation techniques, high-resolution grids, and robust hydrodynamic representation influenced by the complex VES bathymetry. Leveraging the FES2014 model, the influential role of the semi-diurnal component (M2) in dictating energy distribution is prominently evident. Evaluation results of tidal amplitudes revealed three emerging hotspots: the Batang Lupar estuary (Malaysia), Anpu Gang (China), and Bac Lieu (Vietnam), with tidal amplitudes up to 326.2 cm. The Anpu Gang exhibited the highest theoretical potential tidal energy of 14.90 Wh/m2, making it the most promising site for localized tidal power development. Although moderate compared to globally renowned sites, the identified VES hotspots merit consideration for small-to-medium scale projects tailored to local conditions. While limited to potential energy assessments, this study provides a crucial baseline for the VES region, highlighting opportunities for sustainable tidal energy exploitation.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

Assessment of Low-Cost Tide Gauges to Meet GLOSS 1-cm Precision and Accuracy Standards: A Case Study on Pramuka Island, Indonesia

The expansion of the tide gauge network along the coasts is essential for better monitoring of sea-level dynamics. Owing to climate change, the urgency has been exacerbated, especially during the last two decades. However, densification a challenging task because of the lack of affordability of the sensor, especially in the Global South. Further, the precision and accuracy requirements of 1-cm imposed by the Global Sea Level Observing System (GLOSS) is too restrictive, particularly for low-cost tide gauge sensors. Here, we evaluated the performance of a low-cost DIY tide gauge in meeting these standards. Three sets of sea level observations from IR-TIDES, a DIY tide gauge sensor observed in 2016 and 2018, were subjected to a performance test in terms of precision and accuracy in comparison with a global tide model and two neighbouring established tide gauges. All three datasets were estimated to have an 8- cm standard deviation as a metric for the precision level. In terms of accuracy, the IR-TIDES datasets had a standard deviation of 25 cm and a correlation coefficient of 0.616. Overall, IR-TIDES demonstrated sufficient precision while still lacking accuracy, partially meeting the GLOSS quality standard. These findings could strengthen the confidence level of a low-cost DIY tide gauge, especially for use as a back-up and redundant sensor for an established tide gauge station after ad-dressing the limitations.

Regional tidal modeling on the European coast using tide gauges and satellite altimetry

The study investigates sea-level measurements along the coastal area of Europe for the 60-year (1961–2020) time span. Linear and quadratic modeling of tide gauge measurements showed an almost positive rate of trend of sea-level rise (0.09 to 3.6 mm/yr) and low acceleration (−0.05 to 0.40 mm/yr2). A least-squares harmonic estimation tidal modeling was carried out to estimate frequency (cycles per day) for a certain period. The smaller and higher tidal frequencies of these stations indicate their stability in terms of their surface variation. We used the 1993–2020 satellite altimetry data from the nearest grid points of the tide gauge station. The correlation coefficient between observed and satellite altimetry (lowest 0.53 and highest 0.93) varies at each station. This happens because of many factors that can affect the large difference in the sea-level trend between the satellite-derived and tide gauge results. Finally, to implement a global reference system for physical heights, the offshore topographic slope direction and slope range with contour spacing from the sea to the associated coastline were analyzed using bathymetry data. The abrupt change in slope from the coastline toward the sea can be seen toward the east, west, and southeast on the European coast. This is also an important factor that affects the variation of sea level.

Machine learning models applied to altimetry era tide gauge and grid altimetry data for comparative long-term trend estimation: A study from Shikoku Island, Japan

Estimation of sea level trends is essential for understanding sea level rise dynamics. In this study, the performance of traditional Ordinary Least Squares (OLS) linear trend forecasting is compared with modern machine learning techniques, specifically Random Forests (RF) and Least Squares Support Vector Machines (LS-SVM).These methods are applied to 50 years of long-term tide gauge (TG) data from six tide gauge stations off the coast of Shikoku Island, Japan, and CMEMS Grid Altimetry data from 1993 to the present. The analysis uses OLS, RF, and LS-SVM to estimate trends from both data sets and compares the results. The objective is to determine the consistency and accuracy of RF and LS-SVM methods compared to the OLS method. The results indicate that machine learning algorithms (LS-SVM) effectively estimate sea level trends, offering potential improvements in precision for both long-term and medium-term analyses. Shikoku Island's coastal sea level trend is determined as 2.91±1.44 mm/yr using TG data and 3.00±1.52 mm/yr using CMEMS Grid Altimeter data with the OLS approach. Using the LS-SVM approach, the trend is found as 2.96±1.58 mm/yr with TG data and 3.02±1.60 mm/yr with CMEMS Grid Altimetry data. The novelty of this study lies in its thorough comparison of traditional and machine learning approaches for sea level trend estimation, providing valuable insights for future predictions of the sea level rise.

Extracting waterline and inverting tidal flats topography based on Sentinel-2 remote sensing images: A case study of the northern part of the North Jiangsu radial sand ridges

Muddy tidal flats with landforms vary widely over space and time. The evolution of tidal flats topography has had a significant impact on the region. Therefore, there is a need to develop methods for rapidly and accurately characterizing tidal flats topography. This study applied thresholding segmentation (TS), machine learning, and two image classification methods to Sentinel-2 remote sensing images to extract the instantaneous waterline of the northern part of the North Jiangsu Radial sand Ridges (NJRSR). Tidal level data at specific time intervals were obtained using the Delft3D and TPXO9 models, after which tidal flats topography inversion was conducted. As a result, varied among the different classification methods, with the K-Nearest Neighbors (KNN) algorithm producing the optimal waterline extraction. Furthermore, the accuracy of tidal level simulation was improved by employing Delft3D software and constructing a scaled-down model of the study area. Comparisons with Delft3D model simulations of tidal levels with observations at three tide gauge stations yielded accuracies of 17.5 cm, 18.5 cm and 17.9 cm, this result showed that the establishment of the tidal level model can enhance the accuracy of tidal flats Digital Elevation Model (DEM) inversion. In fact, the combined application of the KNN algorithm and Delft3D model within retrieval of tidal level provided the most accurate topographic inversion, with an average error of 0.297 m, which was reduced to 0.292 m after median filtering, and the results indicated a trend of southward erosion in the tidal flats in the study area, with a turning point roughly located in the southern part of Doulong Port. The results of this study can help improve the future monitoring of dynamic changes in intertidal wetland topography and the conservation and development of tidal flats areas.

Analysis of the Data Presented by Tide Gauge Stations in Albania

This study focuses on the comprehensive monitoring of tide gauges along the coastal regions of the Republic ofAlbania, aiming to assess sea level variations and understand the dynamics of the coastal environment. Tide gaugesplay a crucial role in providing essential data for coastal management, climate change studies, and disasterpreparedness. The research involves the deployment and maintenance of tide gauges at strategic locations along theAlbanian coastline, coupled with an in-depth analysis of the collected data. The monitoring process includescontinuous data acquisition from tide gauges, incorporating advanced sensors and telemetry systems to ensure realtime and accurate measurements. The acquired data are analyzed to identify patterns, trends, and anomalies in sealevel variations. Additionally, the study investigates the influence of meteorological and oceanographic factors on tidegauge readings, enhancing the understanding of the complex interactions shaping coastal dynamics. Furthermore, theresearch explores the implications of observed sea level changes on coastal ecosystems, infrastructure, andcommunity resilience. It provides valuable insights into potential threats such as coastal erosion, saltwater intrusion,and flooding. The findings contribute to the development of effective adaptation and mitigation strategies to addressthe challenges posed by rising sea levels.

Improving Coastal Storm Surge Monitoring Through Joint Modeling Based on Permanent and Temporary Tide Gauges

AbstractWith climate change, there will be higher requirements for monitoring storm surges (SSs) in nearshore areas. However, this capability is limited by the sparseness of tide gauge (TG) stations. Establishing and maintaining a permanent, high‐spatial coverage, in situ TG network is complex and expensive. Here, we propose a joint modeling method developed from the all‐site modeling data‐driven framework by importing temporary TGs into coastal regions with insufficient permanent TG stations. The assessments show that this method can significantly optimize the capability of extreme SS monitoring during typhoons and hurricanes. Moreover, the evaluation based on Coupled Model Intercomparison Project Phase 6 data indicates that it will monitor extreme SSs more effectively during 2025–2050 compared with only using existing permanent in situ TGs (reducing root mean square error and absolute mean bias by ∼50%). The joint modeling method provides an applicable and sustainable solution for optimizing the SS monitoring capability in coastal areas.

The effects of non-local observations on the adjoint estimation of local model parameters: An example of Manning’s n coefficient in a tidal model over the Bohai, Yellow, and East China Seas

Accurate parameter estimation is a key issue for enhancing the performance of hydrological models. With the rapid development of satellite technology, integrating satellite data and numerical modelling for parameter estimation has gained more prominence. The limited spatial coverage of altimeters necessitates areas without direct local observations to rely only on information from nearby non-local observations. However, the relation is far from clear between non-local observations and the estimation of local model parameters. In this study, a two-dimensional adjoint tidal model over the Bohai, Yellow, and East China Seas (BYECS) is developed, assimilating observations from TOPEX/Poseidon, Jason-1, and Jason-2 altimeters in conjunction with tide-gauge stations. In twin experiments, the feasibility and reliability of utilizing non-local observations are emphasized by varying synthetic observations. The experiments also reveal how the number and distance of non-local observations affect the ill-posedness of local parameter estimation. The estimation errors can be reduced by increasing observations within a ‘critical distance’, beyond which the accuracy does not improve further. Subsequently, the robustness of ‘critical distance’ is discussed under different prescribed parameter distributions, sizes of predefined optimized area, and random observational errors, indicating that the ‘critical distance’ is approximately 1.0°-2.0°. These results provide a positive reference for practical experiment. In BYECS, the order of Manning’s n coefficient ranges from 10-4 to 10-1, with an average value of 0.0246 in shallow waters. The spatial distribution reveals 9 high-value areas, primarily modulated by a concurrence of sandy sediment and weak tidal currents. This study offers valuable insights into refining the adjoint model for physical parameter estimation, particularly in anticipation of the expanded spatial coverage of SWOT. Compared to only assimilating T/P series altimeters, incorporating SWOT reduces discrepancies by over 38%. Additionally, the spatial Manning’s n coefficient provides guidance for enhancing tidal simulation in other models.

Evaluating of the accuracy of the DTU22MDT mean dynamic topography model based on the tidal gauge data in Vietnam’s coastal areas

In this study, the accuracy of the latest mean dynamic topography model (MDT), DTU22MDT, is determined based on data collected from 14 tide gauge stations along the coast of Vietnam, from the North to the South. The process of assessing the accuracy of the DTU22MDT model is carried out carefully and precisely, involving determining the model’s height at tide gauge stations, quality checks of the data, and evaluation of the model’s accuracy. The research results indicate that, compared to the local sea surface in Vietnam, the DTU22MDT model has a higher height of approximately 0.7 meters. This model represents the global MDT with the highest accuracy in Vietnam, with a mean square error of about 9.0 cm. This information provides a basis for applying the model in scientific research and practical applications, such as constructing a national height system, planning the economic development of coastal areas, generating specialized maps, designing structures, and other activities in the maritime regions of Vietnam. The method used to evaluate the accuracy of the MDT in this study can be applied to other research areas and to different MDTs.

Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction.

Tsunami waveform inversion using Green’s functions with advection effects: application to the 2003 Tokachi–Oki earthquake

We explored nonlinear effects within the context of tsunami waveform inversion, wherein Green's functions were linearly superimposed to estimate earthquake slips. We focused on these effects while developing a source model for the 2003 Tokachi–Oki earthquake off Hokkaido, Japan. A source model for this earthquake was developed based on linear tsunami waveform inversion using Green’s functions and tsunami waveforms observed at tide gauge stations. Subsequently, tsunami waveforms from the source were simulated at the stations using nonlinear long-wave theory and compared with those estimated by inversion. The comparisons demonstrated that the waveforms had a non-negligible discrepancy that was attributed to advection effects, even for the primary wave used in the inversion at the two stations. This result strongly suggests that advection effects should be considered in the source modeling of the 2003 earthquake based on tsunami waveforms observed by tide gauges. Based on these results, a new tsunami waveform inversion technique that incorporates linearly approximated advection effects and maintain the framework of linear tsunami waveform inversion using Green’s functions is proposed and applied. The proposed method successfully mimicked the advection effects during the 2003 tsunami, reproduced better tsunami waveforms, and developed a source model for the 2003 earthquake using these effects. The peak slip amount and seismic moment were greater in the source model with advection effects than those without the effects. This finding suggests that the values in the source models developed for other earthquake events without considering these effects may have been underestimated.Graphical abstract

Asymmetry response of storm surges along the eastern coast of the Taiwan Strait

Spatiotemporal variation of storm surges in the Taiwan Strait (TWS) is studied using water level datasets from 14 tidal gauge stations located in the TWS from summer to fall of 2016. The effects of bathymetry on storm surges and tropical cyclone (TC)-induced continental shelf wave (CSW) are explored. By comparing water level response along the east coast of TWS, it is found that storm surges are asymmetric on the north and south sides of the Zhan-Yun Ridge (ZYR), regardless of the different categories and tracks of TCs passing by. Observations indicate that the ZYR could modulate the storm surges and the CSW propagation; ZYR can not only amplify the storm surges that generally peak around the ZYR, but also block the CSW propagation by dramatically dissipating its kinetic energy as revealed by the dispersion relation for the first mode CSW. Moreover, local wind work and the remote forcing, which are induced by TCs and cold fronts, respectively, can also modulate the synoptic variations of water level in the TWS.

Magnetogravimetric study on the Scotia Plate, in the South Atlantic Ocean for the characterization of tsunamis

The marine and coastal environments of the Scotia Sea regions in the Southern Atlantic Ocean and Antarctica are vulnerable to the potentially disastrous effects of seismic activity along the Scotia Arc. This paper presents a magnetogravimetric study of the Scotia Plate for tsunami characterization. The influence of earthquakes on the Geomagnetic Field (GMF) is investigated using data from INTERMAGNET network observatories. A tectonic model is evaluated using gravity data from NOAA and seismic refraction data from Lamont-Doherty Earth Observatory. The study also assesses the impact on water level (WL) measured at Intergovernmental Oceanographic Commission (IOC) tide gauge stations. Cross Wavelet Transform (XWT) is applied, and a frequency analysis of the GMF is conducted to identify specific frequencies during seismic events. A 2D tectonic model is constructed for the North Scotia Ridge using gravimetric and seismic data to characterize structural boundaries that may be activated during seismic events. Water level records collected from 6 tide gauge stations in the region are filtered and analyzed to identify tsunamis at each station. The results reveal anomalous frequencies in the frequency analysis of the horizontal component of the GMF during the November 25, 2013 earthquake, with high data correlation from different observatories in the study area for periods of 0.5 and 1 hour. Gravimetric modeling delineates faults activated during seismic activity and edges of structures potentially activated due to the transcurrent and compressional nature of the margin. WL anomalies up to 1.30 m are obtained following earthquakes with a magnitude greater than 8. The propagation speed in the study area averaged 460 km/h, consistent with the expected speed for those depths, except for Puerto Argentino, which exceeded them in 50%.

On the selection of multi-satellite altimetry for empirical tide modelling over Indonesia

ABSTRACT Multi-satellite altimetry has been employed in tidal modelling as its accuracy has been proven acceptable in constructing good-quality models. However, the modelling process often fails to consider the varying temporal resolution and observation periods among satellites, which can affect the accuracy of the model due to aliasing and Rayleigh effects. In Indonesia, we investigate and propose a satellite selection procedure for tidal modelling utilizing the response method, validated with along-track data obtained from both older and newer generations of ERM (Exact Repeat Mission)-type altimetry satellites, including the TOPEX/Jason series (TOPEX, Jason-1, Jason-2, Jason-3, and SENTINEL-6A), ENVISAT, ERS-2, GFO, SENTINEL-3B, and SENTINEL3A. The most optimal satellite combination identified is the TOPEX/Jason series, GFO, and ENVISAT, as they contribute significantly less error compared to ERS-2, SENTINEL-3A, and SENTINEL-3B, with errors exceeding 50%, primarily due to aliasing and Rayleigh effects. This combination of three satellites with tide gauge stations achieves higher precision (11.31 cm) than the current global tidal model. This study provides empirical evidence that the accuracy of tidal models is not solely dependent on the number of altimetry satellites utilized, but implementing effective satellite selection procedures is also crucial to enhance the precision of a model.