Sea Level Forecasts Research Articles

Forecasting of absolute dynamic topography using deep learning algorithm with application to the Baltic Sea

Accurate sea-level forecasting is crucial for navigation, engineering and coastal conservation. One of the major obstacles in obtaining accurate sea-level data, both at coastal and offshore areas, has been in defining a realistic vertical datum. The Baltic Sea countries, however, have collaborated in calculating a high-resolution regional geoid model (NKG2015). This now paves the way for determining accurate sea-levels over the entire sea. Accordingly, this study explores the application of a deep learning two-dimensional Convolutional Neural Network (Conv2D) technique along with using essential inputs (e.g. accurate dynamic topography (DT), wind speed and direction, surface pressure and temperature). The method was tested for a three-year 2017–2019 period in the Baltic Sea. The evaluation was based on two statistical criteria: spatial root mean squared error (RMSE) and R-squared (R2). Results revealed that the proposed Conv2D model allows predicting the DT of the Baltic Sea, with an R2 of 0.91. The spatial RMSE plot also confirms accurate DT predictions for most of the Baltic Sea points, with the discrepancies distributed within ±4 cm. Spatially, some larger RMSE values (∼10 cm) were obtained at particular locations in south-eastern Baltic Sea, which may be due to the input sources utilized. Examination of sea level maxima also showed that the Conv2D model reproduced the maxima events in most scenarios (9 of 10 and 8 of 11 in Gulf of Finland and Riga, respectively) with residuals that varied up to 7 cm–18 cm. For the higher residuals, the Conv2D tended to underestimate the sea level. This suggests the importance of considering the necessary inputs (e.g waves) for forecasting storm surges and that the Conv2D model can still be improved. External validation was also performed using along-track sea level satellite altimetry data, with differences between forecasted model and satellite being within 5 cm. This confirms the validity of the forecasted model and the occurrences of model biases to be minimum. The method utilized shows the potential to contribute toward operational sea level forecasting in the Baltic Sea.

OVERVIEW OF NATURAL HYDROMETEOROLOGICAL PHENOMENA, OBSERVED ON THE TERRITORY OF THE REPUBLIC OF KAZAKHSTAN IN 2021

The review provides information on the weather conditions, a brief description of the synoptic processes that caused the occurrence of natural meteorological phenomena, hydrometeorological and agrometeorological conditions, the state of the water surface of the Caspian Sea in 2021 (January- December) on the territory of the Republic of Kazakhstan. Data from 209 meteorological stations of the Republican Hydrometeorological Fund of RSE "Kazhydromet" were used to prepare a review of the weather condition. The climatic norms of variables were calculated according to the recommendations of the WMO as the average long-term value for the period 1981...2010. Air temperature anomalies are defined as deviations of the observed value from the norm; precipitation anomalies were considered in fractions (percentages) of the norm. The basis for the selection of criteria for natural hydrometeorological phenomena was based on the guiding documents of the RSE "Kazhydromet" "Instructions to meteorological stations and posts. The procedure for transmitting information about dangerous and natural hydrometeorological phenomena", "Methodological guidelines for making a forecast of sea level, run-up phenomena and a bulletin on the Caspian Sea", "Guidance on the forecasting service".

Reconstruction of the palaeo‐sea level of Britain and Ireland arising from empirical constraints of ice extent: implications for regional sea level forecasts and North American ice sheet volume

ABSTRACTReconstructions of palaeo‐sea level are vital for predicting future sea level change and constraining palaeo‐ice sheet reconstructions, as well as being useful for a wide array of applications across Quaternary Science. Previous reconstructions of the palaeo‐sea level of Britain and Ireland relied on a circular tuning of glacio‐isostatic models: input ice sheet thicknesses and extents were iteratively altered to fit relative sea level data. Here we break that circularity by utilizing new data from the BRITICE‐CHRONO project, which constrains the position of the British–Irish ice sheet margin through time, and we compare derived glacio‐isostatic modelling to the rich relative sea level record. We test a combination of plausible ice thickness scenarios which account for the uncertainty of ice margin position over the North Sea, demonstrating the region where regional sea level data could distinguish between different glaciation scenarios. Our optimal reconstruction is then combined with several global‐scale reconstructions. As the signal of the British–Irish Ice Sheet is constrained, we demonstrate how the relative sea level record of Britain and Ireland can be used to test reconstructions of far‐field ice sheets (e.g. Antarctica, Eurasia and the Laurentide). The derived palaeo‐topography data are likely to be useful for multiple disciplines. Finally, our improved method of sea level reconstruction impacts predictions of contemporary vertical land motion.

Variational inference of ice shelf rheology with physics-informed machine learning

AbstractFloating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the rigidity (related to viscosity) of the ice that constitutes it. Ice rigidity is highly heterogeneous and must be calibrated from spatially continuous surface observations assimilated into an ice-flow model. Realistic uncertainties in calibrated rigidity values are needed to quantify uncertainties in ice sheet and sea-level forecasts. Here, we present a physics-informed machine learning framework for inferring the full probability distribution of rigidity values for a given ice shelf, conditioned on ice surface velocity and thickness fields derived from remote-sensing data. We employ variational inference to jointly train neural networks and a variational Gaussian Process to reconstruct surface observations, rigidity values and uncertainties. Applying the framework to synthetic and large ice shelves in Antarctica demonstrates that rigidity is well-constrained where ice deformation is measurable within the noise level of the observations. Further reduction in uncertainties can be achieved by complementing variational inference with conventional inversion methods. Our results demonstrate a path forward for continuously updated calibrations of ice flow parameters from remote-sensing observations.

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

AbstractMultisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting.

Towards a pan-European coastal flood awareness system: Skill of extreme sea-level forecasts from the Copernicus Marine Service

European coasts are regularly exposed to severe storms that trigger extreme water-level conditions, leading to coastal flooding and erosion. Early Warning Systems (EWS) are important tools for the increased preparedness and response against coastal flood events, hence greatly reducing associated risks. With this objective, a proof-of-concept for a European Coastal Flood Awareness System (ECFAS) was developed in the framework of the H2020 ECFAS project, which capitalizes on the Copernicus products. In this context, this manuscript evaluates for the first time the capability of the current Copernicus Marine operational ocean models to forecast extreme coastal water levels and hence to feed coastal flood awareness applications at European scale. A methodology is developed to focus the assessment on storm-driven extreme sea level events (EEs) from tide-gauge records. For the detected EEs, the event peak representation is validated, and the impact of forecast lead time is evaluated. Results show satisfactory performance but a general underprediction of peak magnitudes of 10% for water levels and 18% for surges across the detected EEs. In average, the models are capable of independently flagging 76% of the observed EEs. Forecasts show limited lead time impact up to a 4-day lead time, demonstrating the suitability of the systems for early warning applications. Finally, by separating the surge and tidal contributions to the extremes, the potential sources of the prediction misfits are discussed and consequent recommendations for the evolution of the Copernicus Marine Service forecasting models towards coastal flooding applications are provided.

Problems of forecasts of natural hazards in Kazakhstan: extreme conditions of large water bodies

An overview of previous studies of the changes and forecasting of Caspian Sea level is presented. Examples of abrupt sea level changes at the historical period and for the observation period since 1900 are considered. A retrospective analysis of sea level forecasts, projects and activities initiated by these forecasts is carried out. Particular attention is paid to the long-term forecasts of fluctuations in the level of the Caspian Sea, which are currently published.

Deep learning of sea-level variability and flood for coastal city resilience

Due to climate change, it is important to study the relationship between floods and sea-level rise in coastal city resilience. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation, and sea-level rise from satellite altimetry are investigated for dynamic sea-level variability. An annual SST increase of 0.1C° is observed around the Gothenburg coast. Also in the middle of the North Sea, an annual increase of about 0.2C° is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation between SST and SSH near the Gothenburg coast. In the next step, dynamic sea-level variability is predicted with a convolution neural network and long short term memory. Root mean square error of wind speed, precipitation, SST, and mean sea-level forecasts are ±0.84 m/s, ±48.75 mm, ±3.48C° and ±24 mm, respectively. The 5-year trends of mean seal level show a significant increase from 28 mm/year to 46 mm/year in the last 5 year periods and the rate of increase has doubled. In the final step, the water rise of 5–10 m in Gothenburg city was simulated, and in the worst scenario, more than 50 % of the city will be damaged.

Метод и технология краткосрочного прогноза изменений уровня моря в юго-западной части Берингова моря

The main characteristics of the method to forecast sea level with a lead time of 72 hours for the southwestern part of the Bering Sea are considered. Spatiotemporal variations in storm surges within the Bering Sea and adjacent areas are calculated using a two-dimensional numerical hydrodynamic model of the coupled water and ice dynamics. The model forcing is implemented using the data on surface air pressure and wind, which are predicted by the WRF-ARW atmosphere model. Tidal elevations are calculated only at coastal points, 11 major tidal harmonic constituents are used. Information about the distribution of the ice cover within the study object is formed based on the Global Forecast System data. A technological line of the forecasting method operates in automatic mode. The results of the comparison of sea level forecasts and observations at Nikol’skoye, Ossora, and Korf (Tilichiki) tide gages indicate that the developed forecast method complies with the requirements of the Russian Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet). Keywords: forecast method, sea level, Bering Sea, numerical hydrodynamic model, storm surge, tide, ice cover, forecast skill scores

Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cantconsequences in coastal operations, such as naval engineering and navigation. Conventional sealevel predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elementsand require long-term historical sea level data. In this paper, two deep learning approachesare applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), andthe second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (InexpensiveDevice for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained themodel for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1stMay to 1st August. We compared forecasting results with RNN and LSTM with the results of theconventional method, namely tidal harmonic analysis. The LSTM’s results showed better performancethan the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 andan RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodatenon-tidal harmonic data such as sea level anomalies.

Ocean Circulation Model Applications for the Estuary-Coastal-Open Sea Continuum

Coastal zones are among the most variable environments. As such, they require adaptive water management to ensure the balance of economic and social interests with environmental concerns. High quality marine data of hydrographic conditions e.g., sea level, temperature, salinity, and currents are needed to provide a sound foundation for the decision making process. Operational models with sufficiently high forecasting quality and resolution can be used for a further extension of the marine service toward the coastal-estuary areas. The Limfjord is a large and shallow water body in Northern Jutland, connecting the North Sea in the West and the Kattegat in the East. It is currently not covered by the CMEMS service, despite its importance for sea shipping, aquaculture and mussel fisheries. In this study, we use the operational HIROMB-BOOS Model (HBM) to resolve the full Baltic-Limfjord-North Sea system with a horizontal resolution of 185.2 m in the Limfjord. The study shows several factors that are essential for successfully modeling the coastal-estuary system: (a) high computational efficiency and flexible grids to allow high resolution in the fjord, (b) an improved short wave radiation scheme to model the thermodynamics and the diurnal variability of the temperature in very shallow waters, (c) high resolution atmospheric forcing, (d) adequate river forcing, and (e) accurate bathymetry in the narrow straits. With properly resolving these issues, the system is able to provide high quality sea level forecast for storm surge warning and hydrography forecasts: temperature, salinity and currents with sufficiently good quality for ecosystem-based management. The model is able to simulate the complex spatial and temporal pattern of sea level, salinity and temperature in the Limfjord and to reproduce their diurnal, seasonal and interannual variability and stratification rather well. Its high computational efficiency makes it possible to model the transition from the basin-scales to coastal- and estuary-scales seamlessly. In total, The HBM model has been successfully extended, to include the complex estuary system of the Limfjord, and shows an adequate model performance with regards to sea level, salinity and temperature predictions, suitable for storm surge warning applications and coastal management applications.

Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models

This study tried to demonstrate the role of time series models in modeling and forecasting process using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0)12 with drift model was selected to be the best fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0)12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.

NATIONAL NONTIDAL SEA LEVEL FORECASTS ON A COASTAL WAVEGUIDE

An approach to reduce gridded forecast data to novel waveguide coordinates is demonstrated; informed by the literature on coastally trapped waves. This does not produce new forecasts per se, but reduces data to a useful model-independent physically ordered array. Discussion is limited to the Australian mainland and forecast systems currently maintained in national operations. Heterogenous forecast models are considered with regard to the development of "seamless" sea level services across timescales.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/jlJO_dxHwuw

A copula approach for sea level anomaly prediction: a case study for the Black Sea

Forecasting future sea levels is of great importance in terms of the conservation of coastal areas, monitoring and forecasting coastal ecosystems, and the maintenance and planning of coastal structures. In addition, highly accurate sea level forecasts allow adequate water management policies and coastal infrastructures to be developed. Today, many methods, such as harmonic analyses, artificial neural networks and support vector machines, are used to predict sea level anomalies. In this study, a novel approach based on Copula functions is presented for the prediction of sea level anomalies. The primary purpose of this study is to examine the applicability and capability of Copula-based prediction models in predicting short-term variations in the sea level. The minimum 95% correlations and minimum 22 mm RMSE values in sea level anomaly predictions during the testing period indicate that the Copula approach can be a powerful tool in the prediction of sea level anomalies.

Improving storm surge forecast in Venice with a unidimensional Kalman filter

This article presents the application of a post-processing method to correct forecasts produced by a hydrodynamical storm surge model. The model runs over the Mediterranean Sea and into the Venice lagoon. It provides storm surge forecasts in the Mediterranean Sea and total sea-level forecasts inside the Venice lagoon. Sea level observations outside and inside the lagoon are used to correct these forecasts in some locations, by means of a unidimensional Kalman filter. This method, not very known in ocean sciences, is explained here in details. Its advantages consist of easy implementation, low computational times and in the need of few input data. In the present application, the forecast improves up to about two days ahead, even if the best results are obtained in the first 6 hours. The method does not require any long database to be calibrated and can be used also to provide hourly updates to the initial forecast. In the case of extreme events, the possibility of hourly updates is essential to keep a small error in the short-term forecast. These qualities make this method very useful for improving the prediction of a deterministic model which, normally, is performed only once or twice a day. Finally, its generality makes it adaptable for use with other variables and in association with non-local methods of data assimilation.

Sea Level Anomaly Forecasts on a Coastal Waveguide

Abstract An approach to reduce forecast data to coastal waveguide coordinates is described and demonstrated, informed by the literature on coastally trapped waves (CTWs). All discussion is limited to the Australian mainland but the approach is generally relevant to regions where CTWs influence sea level, including the Americas and Africa. The approach does not produce new forecasts, but aims to focus forecaster attention on aspects of sea level forecasts prominent on the long Australian coast. The approach also explicitly addresses spatial issues associated with measuring coastal paths. Coastal paths are scale dependent and forecast models discretize the coastal boundary differently. A well-defined coastal path is required for the quantitative application of CTW concepts such as propagation distance and offshore direction. The relevance of coastally trapped signals and remote forcing is documented in the oceanographic literature, but is effectively unknown to the general public and rarely mentioned in press reports of sea level events such as nuisance flooding. Routine presentation of forecast guidance in waveguide coordinates could contribute to the transfer of oceanographic research understanding into forecast narratives. In addition, the approach can facilitate quantitative forecast evaluations that target CTW properties. Two ocean forecast systems are contrasted in this framework for the Australian mainland. One year of daily forecasts are compared, with indications that model baroclinicity is of practical relevance.

СЦЕНАРНЫЕ ПРОГНОЗЫ КОЛЕБАНИЙ УРОВНЯ КАСПИЯ С УЧЕТОМ КЛИМАТИЧЕСКИХ И ТЕХНОГЕННЫХ ВОЗДЕЙСТВИЙ НА ВОДНЫЙ БАЛАНС МОРЯ

The paper deal with the scenario method for predicting long-term fluctuations in the Caspian Sea level. The scenario forecasts are included: Model of the sea level fluctuations mechanism, Models of the main components of the sea water balance, Morphometric characteristics of sea, Dependence between the outflow of water into the Kara-Bogaz-Gol Bay and the sea level, Assessments of possible direct technogenic impacts on the sea water balance, for example, the withdrawals of water from the inflow into the sea. The Caspian Sea level long-term fluctuations are considered by us as the output process of a non-linear hydrological system. The mechanism of the Caspian level fluctuations contains two negative and one positive feedback. The dependences of the Caspian sea area and the outflow from the sea into the Kara-Bogaz-Gulf bay on the sea level form negative feedbacks. Positive feedback is formed by the nonlinear dependence of evaporation from the sea area on the sea level. Under certain conditions, the effect of this dependence can lead to the sea level bimodal probability distribution density. A necessary (but not sufficient) condition for bimodality is the absence of free seawater inflow from the sea into Kara-Bogaz-Gol. The results of scenario forecasts of the Caspian Sea level developed within the framework of the Caspian Federal Targeted Program (1994-95) are presented. The linear and nonlinear models of the sea level fluctuations used in scenario forecasts are discussed. Failure to take into account of any kind of the feedbacks leads to incorrect predictive estimates of the sea level fluctuations statistical characteristics.

Contribution of future wide-swath altimetry missions to ocean analysis and forecasting

Abstract. The impact of forthcoming wide-swath altimetry missions on the ocean analysis and forecasting system was investigated by means of OSSEs (observing system simulation experiments). These experiments were performed with a regional data assimilation system, implemented in the Iberian–Biscay–Ireland (IBI) region, at 1∕12∘ resolution using simulated observations derived from a fully eddy-resolving free simulation at 1∕36∘ resolution over the same region. The objective of the experiments was to assess the ability of different satellite constellations to constrain the ocean analyses and forecasts, considering both along-track altimeters and future wide-swath missions; consequently, the capability of the data assimilation techniques used in the Mercator Ocean operational system to effectively combine the different kinds of measurements was also investigated. These assessments were carried out as part of a European Space Agency (ESA) study on the potential role of wide-swath altimetry in future versions of the European Union Copernicus programme. The impact of future wide-swath altimetry data is evident for investigating the reliability of sea level values in OSSEs. The most significant results were obtained when looking at the sensitivity of the system to wide-swath instrumental error: considering a constellation of three nadir and two “accurate” (small instrumental error) wide-swath altimeters, the error in ocean analysis was reduced by up to 50 % compared to conventional altimeters. Investigating the impact of the repetitivity of the future measurements, the results showed that two wide-swath missions had a major impact on sea-level forecasting – increasing the accuracy over the entire time window of the 5-day forecasts – compared with a single wide-swath instrument. A spectral analysis underlined that the contributions of wide-swath altimetry data observed in ocean analyses and forecast statistics were mainly due to the more accurate resolution, compared with along-track data, of ocean variability at spatial scales smaller than 100 km. Considering the ocean currents, the results confirmed that the information provided by wide-swath measurements at the surface is propagated down the water column and has a considerable impact (30 %) on ocean currents (up to a depth of 300 m), compared with the present constellation of altimeters. The ocean analysis and forecasting systems used here are those currently used by the Copernicus Marine Environment and Monitoring Service (CMEMS) to provide operational services and ocean reanalysis. The results obtained in the OSSEs considering along-track altimeters were consistent with those derived from real data (observing system experiments, OSEs). OSSEs can also be used to assess the potential of new observing systems, and in this study the results showed that future constellations of altimeters will have a major impact on constraining the CMEMS ocean analysis and forecasting systems and their applications.

River Runoff and Probabilistic Forecast of the Caspian Sea Level

The main components ofthe Caspian Sea water balance and water level are assessed. Stochastic models of time series of the water balance components are proposed ustng morphometric dependences specified by creating and processing digital elevation models for the flooded areas. The sea level forecast is obtained by the method of simulation modeling based on algorithms for the generation of Markovian random sequences with non-Gaussian marginal distributions.

Sea level oscillations in Japan and China since the start of the 20th century and consequences for coastal management - Part 2: China pearl river delta region

This study examines low-frequency (>10 years) sea level variations in the Pearl River Delta (PRD) Region. The low-frequency sea level variability is relevant to regional sea level forecasting and flood risk management. Linear and parabolic fittings are applied to the monthly average mean sea levels (MSL) measured by tide gauges. A spectral analysis is performed of the time series of the MSL and the time series of the monthly climate indices of Pacific Decadal Oscillation (PDO) and El Niño–Southern Oscillation (ENSO)/NINO. Based on the analysis of a composite record of different tide gauges, the PRD Region sea levels, despite being complicated by the river discharge, have reduced similarity with either ENSO, NINO or PDO indices. The PRD Region sea levels have low frequency similarities with the sea level pattern in the Western North Pacific (e.g. Hosojima, Japan) and Western South Pacific (e.g. Sydney, Australia), while at higher frequencies the similarities reduce. One strong low frequency fluctuation of periodicity quasi-20 years is very clear and another of a longer periodicity of quasi-60 years is clear. There is then a relevant fluctuation of about 12 years of periodicity, of reduced amplitude compared to the quasi-20 years fluctuation. In the medium frequency range (<10 years), there are several components detected, but the intensity of the fluctuations is largely reduced compared to the fluctuation of 12 years periodicity. Regionally, the sea levels in the PRD region and Japan show no significant acceleration from 1900 to present, but only oscillations. This result is consistent with the other coastal area of the world where long-term tide gauges are located. Policy making, and management, should therefore focus on adaptive measures linked to the monitoring by tide gauges and Global Navigation Satellite System (GNSS) of relative sea level rise and land subsidence. Extreme sea level rise warnings based on predictions by never validated models, or speculations, that are defocusing coastal management from every other relevant situation, should be discharged.