Tide Gauge Data Sets Research Articles

Nonlinearities detection in river-tide interaction in Río Negro hydrographic lower basin (Argentina) using higher-order spectra

The nonlinear interactions between river discharge, astronomical tidal wave, and local geomorphology during storm passage or water release from upstream dams can produce severe floods in the Río Negro lower basin (Argentina). For this reason, this paper aims to detect and study nonlinear processes in this area. The watercourse hydrodynamics was described using hourly water level data from three limnigraphs during 2003 – 2021 and flow time series. The tide gauge dataset was employed to describe the influence of tidal cycles on the hydrological regimen. Nonlinear processes' impact on the astronomical tidal cycle and river discharge was analyzed using Harmonic Analysis, and Fourier higher-order spectra, also it was complemented with the selection of two study cases. Harmonic Analysis results showed that the tidal wave entry upstream of the Río Negro modulates its hydrological regime, presenting the water column semidiurnal variations. Also, high-order spectral analysis detected nonlinear interactions in the signal in storm conditions with an energetic redistribution among the linear tidal constituents toward shallow water harmonics. Additionally, nonlinear interactions provoked a delay in the tidal ebb phase with a consequential extension of flooding duration time. This type of study contributes to the knowledge of the flood mechanisms activated during a storm.

Estimating Trends of Sea-Level Fluctuations from Tide Gauge Data Analysis along Bangladesh Coast

The present study tries to determine the nature and magnitudes of relative sea-level to estimate present and future trends of mean sea-level (MSL) changes based on historical tide gauge data of Cox's Bazaar, Charchanga, Hiron Point, Khepupara and Sadarghat stations of Bangladesh. Annual PSMSL tide gauge dataset of Cox's Bazaar station shows 2.72 mm/year sea-level rise while CPA provided 'Metric' dataset of Sadarghat station demonstrate 3.93 mm/year sea-level rise along Chittagong coast. Accordingly, the sea­ levels are supposed to be increased 10.54 cm, 24.11 cm and 37.69 cm than the 1988 MSL (213.70 cm) at Cox's Bazaar by the years 2050, 2100 and 2150, respectively. Moreover, based on long-term PSMSL tide dataset of Charchanga, Hiron Point and Khepupara stations, the estimated rise of sea-levels along the respective coasts are 8.15.mm/year, 3:56 mm/year and 18.66 mm/year, respectively. Finally, 7.4 :mm/year average rate of sea-level rise has been calculated from all the studied datasets under present study. However, disregarding the exceptionally high rate (18.66 m.m/year) at Khepupara station determined average rate of sea level rise along Bangladesh coast is 4.59 mm/year which seems to be consistent with the global average. Consequently, the predicted sea level along Bangladesh coast are approximately 22 cm, 45 cm and 68 cm above their average MSL (210.4 cm) in the years 2050, 2100 and 2150, respectively. The Chittagong Univ. J. B. Sci. 8(1&2): 75-86, 2013

Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)

Abstract. We present a new 3D unstructured-grid global ocean model to study both tidal and nontidal processes, with a focus on the total water elevation. Unlike existing global ocean models, the new model resolves estuaries and rivers down to ∼8 m without the need for grid nesting. The model is validated with both satellite and in situ observations for elevation, temperature, and salinity. Tidal elevation solutions have a mean complex root-mean-square error (RMSE) of 4.2 cm for M2 and 5.4 cm for all five major constituents in the deep ocean. The RMSEs for the other four constituents, S2, N2, K1, and O1, are, respectively, 2.05, 0.93, 2.08, and 1.34 cm). The nontidal residual assessed by a tide gauge dataset (GESLA) has a mean RMSE of 7 cm. For the first time ever, we demonstrate the potential for seamless simulation on a single mesh from the global ocean into several estuaries along the US West Coast. The model is able to accurately capture the total elevation, even at some upstream stations. The model can therefore potentially serve as the backbone of a global tide surge and compound flooding forecasting framework.

Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning

Sea level rise (SLR) in small island countries such as Kiribati and Tuvalu have been a significant issue for decades. There is an urgent need for more accurate and reliable scientific information regarding SLR and its trend and for more informed decision making. This study uses the tide gauge (TG) dataset obtained from locations in Betio, Kiribati and Funafuti, Tuvalu with sea level corrections for vertical land movement (VLM) at these locations from the data obtained by the Global Navigation Satellite System (GNSS) before the sea level trend and rise predictions. The oceanic feature inputs of water temperature, barometric pressure, wind speed, wind gust, wind direction, air temperature, and three significant lags of sea level are considered in this study for data modeling. A new data decomposition method, namely, successive variational mode decomposition (SVMD), is employed to extract intrinsic modes of each feature that are processed for selection by the Boruta random optimizer (BRO). The study develops a deep learning model, namely, stacked bidirectional long short-term memory (BiLSTM), to make sea level (target variable) predictions that are benchmarked by three other AI models adaptive boosting regressor (AdaBoost), support vector regression (SVR), and multilinear regression (MLR). With a comprehensive evaluation of performance metrics, stacked BiLSTM attains superior results of 0.994207, 0.994079, 0.988219, and 0.899868 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for Kiribati, and with values of 0.996806, 0.996272, 0.992316, and 0.919732 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for the case of Tuvalu. It also shows the lowest error metrics in prediction for both study locations. Finally, trend analysis and linear projection are provided with the GNSS-VLM-corrected sea level average for the period 2001 to 2040. The analysis shows an average sea level rate rise of 2.1 mm/yr for Kiribati and 3.9 mm/yr for Tuvalu. It is estimated that Kiribati and Tuvalu will have a rise of 80 mm and 150 mm, respectively, by the year 2040 if estimated from year 2001 with the current trend.

A newly reconciled dataset for identifying sea level rise and variability in Dublin Bay

Abstract. We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

ANCHORS: A multi‐decadal tide gauge dataset to monitor Australian relative sea level changes

AbstractHere, we describe a new national tide gauge‐based sea level dataset for monitoring sea level changes around Australia, and the novel homogenization methodology used to develop it. Homogenization is a two‐step process that involves the detection of jumps or steps in the data (inhomogeneities) followed by a correction applied to remove the change. This new dataset is called the Australian National Collection of Homogenized Observations of Relative Sea Level (ANCHORS). ANCHORS provides coastal sea levels at hourly resolution with homogenization performed on annual means. ANCHORS is intended to be a national tide gauge‐based sea level dataset to monitor changes in mean sea level and resultant coastal flood frequency changes around Australia. Whilst sea levels are rising around Australia in response to global mean sea level changes, coastal water levels represent a notable gap in the suite of in situ high‐quality datasets available for researchers, governments and industry. Tide gauge datasets are well suited to impact‐based assessments of changes in flood frequency, duration and risk. In this regard, ANCHORS will sit alongside existing satellite‐ and numerical modelling‐based sea level datasets to provide enhanced insights into the physical impacts of sea level rise on Australian coastlines. We have adapted established homogenization techniques to develop a national collection of homogenous long tide gauge records. The dataset comprises 38 tide gauge records from around Australia, varying in length from 34 to 123 years, with an average of 57 years. Finally, we discuss limitations and potential applications for ANCHORS and how these have informed the design of this coastal sea level dataset.

Rates of Natural Subsidence along the Texas Coast Derived from GPS and Tide Gauge Measurements (1904–2020)

AbstractThis study investigated the rate of natural subsidence along the Texas coast using multidecadal to century tide gauge (TG) and global positioning system (GPS) data sets. The rates of land s...

Evaluation and Correction of Elevation Angle Influence for Coastal GNSS-R Ocean Altimetry

The elevation angle influence on coastal GNSS-R ocean code-based altimetry for GPS signals (L1 C/A and L5) and BDS B1 signals is investigated, and the corresponding correction method is presented. The study first focuses on the coastal ocean altimetry method, including the general experiment geometry and the code delay estimation using the single-point tracking algorithm. The peak power and the maximum first derivative are used as the location of the specular point. Then, the sensitivity of the height retrieved using the above coastal ocean altimetry method to elevation angle is analyzed based on the Z-V model. It can be seen that the elevation angle has a significant influence on the height retrieval, which will affect the precision of the coastal GNSS-R ocean altimetry. Finally, two correction methods, the model-driven method and the data-driven method, are proposed. The coastal altimetry experiments demonstrate that the correction methods can correct the elevation angle influence, and the data-driven method is more effective. The experimental results show that, after correcting the elevation angle influence, the code-based altimetry precision of the GPS L1 C/A signal, L5 signal, and BDS B1 signal can be up to the meter level, decimeter level (less than 4 decimeters), and meter level with respect to a reference tide gauge (TG) data set, respectively, without smoothing over time. These results provide information to guide the sea surface height retrieval using coastal GNSS-R, especially multi-satellite observation and GNSS signal with a higher chipping rate.

Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis

Non-stationary extreme value analysis is a powerful framework to address the problem of time evolution of extremes and its link to climate variability as measured by different climate indices CI (like North Atlantic Oscillation NAO index). To model extreme sea levels (ESLs), a widely-used tool is the non-stationary Generalized Extreme Value distribution (GEV) where the parameters (location, scale and shape) are allowed to vary as a function of some covariates like the month-of-year or some CIs. A commonly used assumption is that only a few CIs impact the GEV parameters by using a linear model, and most of the time by focusing on two GEV parameters (location or/and the scale parameter). In the present study, these assumptions are revisited by relying on a data-driven spline-based GEV fitting approach combined with a penalization procedure. This allows identifying the type (non- or linear) of the CI influence for any of the three GEV parameters directly from the data, and evaluating the significance of this relation, i.e. without making any a priori assumptions as it is traditionally done. This approach is applied to the monthly maxima of sea levels derived from eight of the longest (quasi century-long) tide gauge dataset (Brest, France; Cuxhaven, Germany; Gedser, Denmark; Halifax, Canada; Honolulu, US; Newlyn, UK; San Francisco, US; Stockholm, Sweden) and by accounting for four major CIs (the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation, the Niño 1 + 2 and the Southern Oscillation indices). From this analysis, we show that: (1) the links between CIs and different parameters of a GEV distribution fitted to ESL data are most of the time linear, but some of them present significant non-linear shapes; (2) multiple CIs should be considered to predict ESLs, and (3) the CI influence of the GEV distribution is not limited to the location parameter. These results are useful to understand current modes of variability of ESLs, and ultimately to improve coastal resilience through more precise extreme water level assessments.

The decadal sea level variability observed in the Indian Ocean tide gauge records and its association with global climate modes

Decadal sea level variability of the Indian Ocean is studied using long-term tide gauge observations of 17 stations. The existence of close association between Indian Ocean sea level and the global decadal climate modes, such as Pacific Decadal Oscillation (PDO) and/or Atlantic Multidecadal Oscillation (AMO) is revealed in most of the tide gauge data sets. An anti-phase sea level evolution with PDO index is identified in the regions such as west coast of India, east coast of India and the western coast of Australia. Moreover, the PDO related sea level variability is found to be stronger at the tide gauge stations along the west coast of India compared to that along the east coast of India, ruling out the remote forcing from the equatorial Indian Ocean. On the other hand, the tide gauge station at Mumbai with more than 100 years of data shows an in-phase relationship with AMO index on the decadal timescales, whereas such relationship is very weak at the tide gauge station Fremantle where similar long-term data is available. The analysis of sea level pressure (SLP), rainfall and winds over the tropical Indian Ocean indicates existence of strong relationship with AMO especially over the Arabian Sea region, strongly suggesting that the variability observed at the tide gauge station Mumbai may have larger spatial signatures. During the positive phase of AMO, the Arabian Sea region is characterized by large fall in SLP and a strong cross equatorial flow towards the Indian land mass, which are primarily responsible for the positive sea level anomalies (SLAs) at the Mumbai tide gauge station and the adjacent regions and positive rainfall over India.

Sea-Level Variability in the Gulf of Naples and the “Acqua Alta” Episodes in Ischia from Tide-Gauge Observations in the Period 2002–2019

This work presents an 18-year-long (2002–2019) tide-gauge dataset collected on the Island of Ischia (Gulf of Naples, Southern Tyrrhenian Sea) that can contribute to the analysis of the basic features of sea-level variability in this region. Analysis of tidal constituents shows that the Gulf of Naples is characterized by the absence of any amphidromic system. In this area, sea-level changes due to the astronomical component of the tide are generally limited to ±20 cm with respect to the mean sea level, but the impact of this variability is enhanced by global sea-level increase and the effect of regional atmospheric perturbations that might also triple sea-level variations. The effects of these events, whose frequency has increased in recent decades, has been dramatic in coastal areas where intense social and economic activity occurs, e.g., in Ischia. On interannual time scales, the results indicate that the relative sea-level rise in Ischia has a magnitude of 3.9 mm/year. Special attention is dedicated to the “acqua alta” episodes and to their linkage with long-term sea-level trends and atmospheric forcing.

Coastal Sea-Level Measurements Based on GNSS-R Phase Altimetry: A Case Study at the Onsala Space Observatory, Sweden

The characterization of global mean sea level is important to predict floods and to quantify water resources for human use and irrigation, especially in coastal regions. Recently, the application of global navigation satellite system reflectometry (GNSS-R) for water level monitoring has been successfully demonstrated. This paper focuses on the retrieval of sea surface height within a field experiment that was conducted at the Onsala Space Observatory (OSO) using the phase-based altimetry method. A continuous phase tracking algorithm, which relies on the GNSS amplitude and phase observations is proposed and works even under rough sea conditions at OSO’s coast. Factors impacting the phase-based altimetry model, i.e., atmospheric propagation effects of the GNSS signals and influence of the GNSS-R observation instrument, are discussed. The relationship between the yield of coherent GNSS-R compared to the overall recorded events and the wind speed is investigated in detail. Ground-based sea-level measurements from June 10 to July 3, 2015 demonstrate that altimetric information about the reflecting water surface can be obtained with a root mean square error of 4.37 cm with respect to a reference tide gauge (TG) data set. The sea surface changes, derived from our field experiment and the reference TG, are highly correlated with a correlation coefficient of 0.93. The altimetric information can be retrieved even when the sea surface is very rough, corresponding to wind speeds up to 13 m/s. Moreover, the use of inexpensive conventional GNSS antennas shows that the system is useful for future large-scale sea level monitoring applications including numerous low-cost coastal ground stations.

California sea level rise: Evidence based forecasts vs. model predictions

The extreme predictions of the Intergovernmental Panel on Climate Change (IPCC) have been the inspiration of hundreds of papers by local panels proposing ever-increasing alarming messages. The latest analyses are on the effect on the surf spots of California of a tidal range added to a sea level rise of 1.67 m. We show that the sea level rises estimate by a local panel for California as well as the IPCC for the entire world are up to one order of magnitude larger than what is extrapolated from present sea level rise rates and accelerations based on tide gauge data sets (California-8, Permanent Service on Mean Sea Level PSMSL-301, Mitrovica-23, Holgate-9, National Oceanic and Atmospheric Administration NOAA-199 and US-71). These extrapolations are consistent with present temperature warming rates and accelerations of different global temperature data sets (University of Alabama in Huntsville UAH and Remote Sensing Systems RSS) and IPCC Assessment Report (AR) 5 Representative Concentration Pathway (RCP) 8.5 sensitivity. As the evidence from the measurements does not support the IPCC expectations or the even more alarming predictions by the local California panel, these claims and the subsequent analyses are too speculative and not suitable for rigorous use in planning or policy making.

Differences between mean tide level and mean sea level

This paper discusses the differences between mean tide level (MTL) and mean sea level (MSL) as demonstrated using information from a global tide gauge data set. The roles of the two main contributors to differences between MTL and MSL (the M4 harmonic of the M2 semidiurnal tide, and the combination of the diurnal tides K1 and O1) are described, with a particular focus on the spatial scales of variation in MTL–MSL due to each contributor. Findings from the tide gauge data set are contrasted with those from a state-of-the-art global tide model. The study is of interest within tidal science, but also has practical importance regarding the type of mean level used to define land survey datums. In addition, an appreciation of MTL–MSL difference is important in the use of the historical sea level data used in climate change research, with implications for some of the data stored in international databanks. Particular studies are made of how MTL and MSL might differ through the year, and if MTL is measured in daylight hours only, as has been the practice of some national geodetic agencies on occasions in the past.

Spatial sea-level reconstruction in the Baltic Sea and in the Pacific Ocean from tide gauges observations

<p>Exploiting the Delaunay interpolation, we present a newly implemented 2-D sea-level reconstruction from coastal sea-level observations to open seas, with the aim of characterizing the spatial variability of the rate of sea-level change. To test the strengths and weaknesses of this method and to determine its usefulness in sea-level interpolation, we consider the case studies of the Baltic Sea and of the Pacific Ocean. In the Baltic Sea, a small basin well sampled by tide gauges, our reconstructions are successfully compared with absolute sea-level observations from altimetry during 1993-2011. The regional variability of absolute sea level observed across the Pacific Ocean, however, cannot be reproduced. We interpret this result as the effect of the uneven and sparse tide gauge data set and of the composite vertical land movements in and around the region. Useful considerations arise that can serve as a basis for developing sophisticated approaches.</p>

Modeling sea-level change using errors-in-variables integrated Gaussian processes

We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The input data to our model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. These data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. The model we propose places a Gaussian process prior on the rate of sea-level change, which is then integrated and set in an errors-in-variables framework to take account of age uncertainty. The resulting model captures the continuous and dynamic evolution of sea-level change with full consideration of all sources of uncertainty. We demonstrate the performance of our model using two real (and previously published) example data sets. The global tide-gauge data set indicates that sea-level rise increased from a rate with a posterior mean of 1.13 mm$/$yr in 1880 AD (0.89 to 1.28 mm$/$yr 95% credible interval for the posterior mean) to a posterior mean rate of 1.92 mm$/$yr in 2009 AD (1.84 to 2.03 mm$/$yr 95% credible interval for the posterior mean). The proxy reconstruction from North Carolina (USA) after correction for land-level change shows the 2000 AD rate of rise to have a posterior mean of 2.44 mm$/$yr (1.91 to 3.01 mm$/$yr 95% credible interval). This is unprecedented in at least the last 2000 years.

Considerations for estimating the 20th century trend in global mean sea level

AbstractRecent efforts in reconstructing historical sea level change have led to a range of published estimates for the global mean sea level trend over the last century. Disagreement in these estimates can be attributed to two factors: (1) differences in analysis and/or reconstruction techniques and (2) differences in tide gauge selection and quality control of the data. Here the impact of tide gauge selection is explored by calculating global mean trends using three different tide gauge data sets that have been utilized in recent reconstruction studies. The inclusion of tide gauge records that are affected by unresolved internal variability and/or unaccounted for vertical land motion are found to significantly impact the estimates of the long‐term trend in global mean sea level. In conclusion, several guidelines are presented regarding the selection of tide gauges for use in historical reconstructions focused on estimating the 20th century global mean sea level trend.

Tidal notches in Mediterranean Sea: a comprehensive analysis

Recent works (Evelpidou et al., 2012) suggest that the modern tidal notch is disappearing worldwide due sea level rise over the last century. In order to assess this hypothesis, we measured modern tidal notches in several of sites along the Mediterranean coasts. We report observations on tidal notches cut along carbonate coasts from 73 sites from Italy, France, Croatia, Montenegro, Greece, Malta and Spain, plus additional observations carried outside the Mediterranean. At each site, we measured notch width and depth, and we described the characteristics of the biological rim at the base of the notch. We correlated these parameters with wave energy, tide gauge datasets and rock lithology.Our results suggest that, considering ‘the development of tidal notches the consequence of midlittoral bioerosion’ (as done in Evelpidou et al., 2012) is a simplification that can lead to misleading results, such as stating that notches are disappearing. Important roles in notch formation can be also played by wave action, rate of karst dissolution, salt weathering and wetting and drying cycles. Of course notch formation can be augmented and favoured also by bioerosion which can, in particular cases, be the main process of notch formation and development.Our dataset shows that notches are carved by an ensemble rather than by a single process, both today and in the past, and that it is difficult, if not impossible, to disentangle them and establish which one is prevailing. We therefore show that tidal notches are still forming, challenging the hypothesis that sea level rise has drowned them.

Preliminary analysis of acceleration of sea level rise through the twentieth century using extended tide gauge data sets (August 2014)

This work explores the potential for extending tide gauge time series from the Permanent Service for Mean Sea Level (PSMSL) using historical documents, PSMSL ancillary data, and by developing additional composite time series using near neighbor tide gauges. The aim was to increase the number, completeness, and geographical extent of records covering most or all of the twentieth century. The number of at least 75% complete century-scale time series have been approximately doubled over the original PSMSL data set. In total, over 4800 station years have been added, with 294 of these added to 10 long Southern Hemisphere records. Individual century-scale acceleration values derived from this new extended data set tend to converge on a value of 0.01 ± 0.008 mm/yr2. This result agrees closely with recent work and is statistically significant at the 1 sigma level. Possible causes of acceleration and errors are briefly discussed. Results confirm the importance of current data archeology projects involving digitization of the remaining archives of hard copy tide gauge data for sea level and climate studies.

How frequent is storm-induced flooding in the central part of the Bay of Biscay?

This study analyzes historical archives to produce a database of storm-induced coastal flooding in the French central part of the Bay of Biscay since 1500AD. From this new database, 46 coastal floods have been reported for the last 500years (1 event every 11yr on average), which demonstrates the high vulnerability of this region to coastal flooding. The limitations of historical archives prevent concluding to a change in storminess over the period. The six largest coastal floods that occurred since 1900 are further investigated because numerous informations are available in terms of meteorological conditions as well as maximum water levels reached. Associated storm surges are also modeled using a simplified methodology, relying on a fully coupled wave and circulation modeling system directly forced by the available meteorological data. The analysis of modeling results and historical archives reveals firstly that several meteo-oceanic settings can lead to extensive coastal flooding in the studied area. The analyzed events allow distinguishing three main settings: (1) a small storm surge associated with the highest astronomical tidal levels, (2) a large storm surge, induced either by extreme winds or a particular sea-state and associated with high astronomical tidal levels and, (3) a large storm surge enhanced by extreme wave conditions. Second, maximum water levels reached during these 6 coastal floods were shown to be higher than the 100years return period water level estimated in several recently published studies. This inconstancy can be explained mainly by the lack of extreme water level records in tide gauge datasets used in these previous studies. To a slightest degree, this study questions the validity of classical statistical approaches when analyzing extreme water levels data resulting from contrasting meteo-oceanic settings. It is concluded that historical archives combined with storm surge modeling could be a valuable approach to better estimate the return period of extreme sea levels and improve the understanding on the vulnerability of mixed-energy coastal environments such as the study area.