Magdalena Idzikowska

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

The aim of this study was to estimate trends and multiscale variability in sea level along the southern coast of the Baltic Sea based on tide gauge and altimetry data, using Harmonic Analysis (HA) and Continuous Wavelet Transform (CWT). Particular consideration was given to the influence of time series length, data type, seafloor depth and distance from the coastline on the stability and consistency of the estimated trends and amplitudes. The results indicated that, for coastal stations, trends derived from tide gauge data averaged 2.2 mm/yr for the 1993–2024 period and 2.0 mm/yr for the 1951–2025 series, with lower estimation errors for series with an extended time range of data. Satellite altimetry data indicated a higher rate of sea level rise, averaging 4.3 mm/yr, and higher spatial consistency, particularly at virtual stations away from the coast. As the distance from the coastline increased, a more stable trend and a decrease in the influence of local hydrodynamic processes were observed. A comparison of methods demonstrated that Harmonic Analysis significantly improves the consistency of trends derived from altimetry and tide gauge data compared to classical linear regression—the correlation coefficient increased from 0.72–0.80 to 0.92–0.95. CWT confirmed the reliability of these results, while also allowing the identification of temporal modulation in cycle amplitudes and periods of increased signal nonstationarity. The results confirm that a correct interpretation of sea level changes requires that we take both long-term trends and natural variability across various timescales into account. The proposed approach provides a universal tool for analyzing nonstationary environmental signals. It can support risk assessment in coastal zones and the development of adaptation strategies in the context of climate change.

This paper presents a probabilistic approach to the analysis of non-stationary sea level measurement series in the southern Baltic Sea based on tide gauge data for Swinoujscie, Kolobrzeg, Ustka, Wladyslawowo and Gdansk stations. Harmonic analysis (HA), Continuous Wavelet Transform (CWT), AR(1) autoregressive model and Monte Carlo uncertainty propagation were applied to identify trends, multiscale variability and the stochastic structure of the measurement data. The results indicate a spatially consistent sea level rise trend of 1.8–2.2 mm/yr, modulated by multiscale periodic variability and short-term stochasticity. The model used allows for a probabilistic forecast of sea level changes. In addition, the analysis of extremes using the Gumbel distribution indicates an increase in the probability of extreme sea levels along the southern Baltic coast. The proposed methodology extends conventional sea level analysis by integrating probabilistic interpretation, classical uncertainty estimation, and multiscale signal analysis, thereby providing a useful tool for coastal hazard and flood risk assessment and climate change adaptation in coastal regions.

Seafloor morphology forms regional sea level rise (SLR), affecting ocean circulation. Although many studies have examined global sea level rise, there remains a lack of analyses that show how seafloor morphology modifies the rate and character of regional SLR. Previous studies have rarely investigated the geophysical interactions between seafloor morphology and sea level modulation, leaving a gap in explaining the spatial variability of sea level trends and accelerations. The aim of the study is to assess the impact of seafloor morphology on the regional rate and character of Sea Level Rise (SLR) in the western Pacific, in the Japan Trench region. Seafloor morphology, through its interactions with gravity and circulation processes, is a major factor in how SLR trends and accelerations are determined across different locations. The analysis is based on hybrid datasets: numerical models, bathymetric data, and altimetric time series of sea level anomalies (SLA) from 1993 to 2023. SLR trends, seasonal and nodal cycles were determined at 78 virtual stations. Regional rates of sea level changes were estimated using linear regression, harmonic analysis, Continuous Wavelet Transform (CWT), and Kalman filtering. Future SLR was simulated using a modified Monte Carlo method with an AR(1) autoregressive model and a block bootstrap technique. The results indicated that SLR trends are positively correlated (r ≈ 0.9) with mean dynamic topography (MDT) and negatively correlated with depth (r ≈ –0.4), confirming the impact of ocean circulation and seafloor morphology on regional SLR. The strong, positive correlation of trends with the amplitude of the 18.61-year nodal cycle (r > 0.8) indicates the important role of long-term tidal components. The highest SLR accelerations (up to 1.7 mm/yr²) were observed in locations of seamounts and subduction zones, while in the ocean trench, the rate of change stabilized or inversed locally. The results confirm the research hypothesis—the regional rate of sea level rise depends on the morphology of the seafloor and the associated geophysical and dynamic processes. The results have wide global application, supporting the implementation of the UN Sustainable Development Goals, the development of marine protection and management policies, infrastructure planning and coastal safety.

This study presents an analysis of regional sea level variability on various seafloor structures. The main aim of this paper was to determine the regional trends of sea level changes (time span of 29 years) in the area of the ocean trench and submarine canyon, in the area of seamounts and corrugations, and then to compare the models of the seafloor with the models of the sea surface. We used hybrid datasets, including satellite altimetric time series, multibeam bathymetric soundings, GEBCO products, free-air gravity anomaly models, and mean dynamic ocean topography models. Radar remote sensing and spaceborne radar technologies are essential in capturing the long-term dynamics of sea surface variability regarding seafloor topography. The values of regional sea level change trends in the seamounts and corrugation region are two times higher (from 2.56 ± 0.10 mm/year to 7.66 ± 0.18 mm/year) than in the trench and canyon region (1.75 ± 0.01 mm/year to 3.65 ± 0.07 mm/year). In the region of trench and canyon, i.e., on narrow and deep forms of the seafloor, the values of regional trends are stable and regular. In the region of seamounts and corrugations, where the depth is more diverse, regional trend values are higher and irregular. Study results show that regional sea level fluctuations can be the consequence of the diversified structure of the seafloor. The region of the trench and canyon, although characterized by high susceptibility to climatic phenomena, presents lower amplitudes of sea level changes than the subregion of seamounts and corrugations, where the bathymetry of the seafloor is more complex.