Why we must tie satellite positioning to tide gauge data

Accurate measurements of changes in sea and land levels with location and time require making precise, repeated geodetic ties between tide gauges and satellite positioning system equipment

A global classification of coastal flood hazard climates associated with large-scale oceanographic forcing

Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.A.R., F.J.M. and P.C. acknowledge the support of the Spanish ‘Ministerio de Economia y Competitividad’ under Grants BIA2014-59643-R and BIA2015-70644-R. This work was critically supported by the US Geological Survey under Grant/Cooperative Agreement G15AC00426 and from the US DOD Strategic Environmental Research and Development Program (SERDP Project RC-2644) through the NOAA National Centers for Environmental Information (NCEI). Dynamic atmospheric corrections (storm surge) are produced by CLS Space Oceanography Division using the Mog2D model from Legos and distributed by Aviso, with support from CNES (http://www.aviso.altimetry.fr/). Marine data from global reanalysis are provided by IHCantabria and are available for research purposes upon request at [email protected]

Sea-level rise in Venice: historic and future trends (review article)

The city of Venice and the surrounding lagoonal ecosystem are highly vulnerable to variations in relative sea level. In the past ∼150 years, this was characterized by an average rate of relative sea-level rise of about 2.5 mm/year resulting from the combined contributions of vertical land movement and sea-level rise. This literature review reassesses and synthesizes the progress achieved in quantification, understanding and prediction of the individual contributions to local relative sea level, with a focus on the most recent studies. Subsidence contributed to about half of the historical relative sea-level rise in Venice. The current best estimate of the average rate of sea-level rise during the observational period from 1872 to 2019 based on tide-gauge data after removal of subsidence effects is 1.23 ± 0.13 mm/year. A higher – but more uncertain – rate of sea-level rise is observed for more recent years. Between 1993 and 2019, an average change of about +2.76 ± 1.75 mm/year is estimated from tide-gauge data after removal of subsidence. Unfortunately, satellite altimetry does not provide reliable sea-level data within the Venice Lagoon. Local sea-level changes in Venice closely depend on sea-level variations in the Adriatic Sea, which in turn are linked to sea-level variations in the Mediterranean Sea. Water mass exchange through the Strait of Gibraltar and its drivers currently constitute a source of substantial uncertainty for estimating future deviations of the Mediterranean mean sea-level trend from the global-mean value. Regional atmospheric and oceanic processes will likely contribute significant interannual and interdecadal future variability in Venetian sea level with a magnitude comparable to that observed in the past. On the basis of regional projections of sea-level rise and an understanding of the local and regional processes affecting relative sea-level trends in Venice, the likely range of atmospherically corrected relative sea-level rise in Venice by 2100 ranges between 32 and 62 cm for the RCP2.6 scenario and between 58 and 110 cm for the RCP8.5 scenario, respectively. A plausible but unlikely high-end scenario linked to strong ice-sheet melting yields about 180 cm of relative sea-level rise in Venice by 2100. Projections of human-induced vertical land motions are currently not available, but historical evidence demonstrates that they have the potential to produce a significant contribution to the relative sea-level rise in Venice, exacerbating the hazard posed by climatically induced sea-level changes

Tide Gauge Benchmark Monitoring Working Group Technical Report 2022

editorial reviewedApplications of the Global Navigation Satellite Systems (GNSS) to Earth Sciences are numerous. The International GNSS Service (IGS), a voluntary federation of government agencies, universities and research institutions, combines GNSS resources and expertise to provide the highest–quality GNSS data, products, and services in order to support high–precision applications for GNSS–related research and engineering activities. This IGS Technical Report 2022 includes contributions from the IGS Governing Board, the Central Bureau, Analysis Centers, Data Centers, station and network operators, working groups, pilot projects, and others highlighting status and important activities, changes and results that took place and were achieved during 2022

Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix

The sea level at Port-aux-Francais, Kerguelen Island, from 1949 to the present

International audienceRelative sea level rise at Kerguelen Island over the last 55 years has been investigated using a combination of historical and recent tide gauge data. The best estimate of relative sea level trend from data sets spanning 38 years is estimated to be 1.1 +/- 0.7 mm year(-1). We have tried to quantify the error budget due to some of the possible sources of uncertainty. As expected, the main source of uncertainty comes from oceanic interannual variability, preventing an accurate estimate of sea level trend over short record lengths. However, our values are reasonably consistent with other reported southern hemisphere sea level trends for similar time periods