Assessment and Prediction of Sea Level Trend in the South Pacific Region

Abstract

Sea level rise is an important and topical issue in the South Pacific region and needs an urgent assessment of trends for informed decision making. This paper presents mean sea level trend assessment using harmonic analysis and a hybrid deep learning (DL) model based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Neighbourhood Component Analysis (NCA) to build a highly accurate sea level forecasting model for three small islands (Fiji, Marshall Island and Papua New Guinea (PNG)) in the South Pacific. For a 20-year period, the estimated mean sea level rise per year from the harmonic computation is obtained: 112 mm for PNG, 98 mm for Marshall Island and 52 mm for Fiji. The DL procedure uses climate and environment-based remote sensing satellite (MODIS, GLDAS-2.0, MODIS TERRA, MERRA-2) predictor variables with tide gauge base mean sea level (MSL) data for model training and development for forecasting. The developed CEEMDAN-CNN-GRU as the objective model is benchmarked by comparison to the hybrid model without data decomposition, CNN-GRU and standalone models, Decision Trees (DT) and Support Vector Regression (SVR). All model performances are evaluated using reliable statistical metrics. The CEEMDAN-CNN-GRU shows superior accuracy when compared with other standalone and hybrid models. It shows an accuracy of >96% for correlation coefficient and an error of <1% for all study sites.

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

“…Using geodetic observations, several studies [42,43] have estimated the relative sealevel rise using tide gauges (TGs). However, TGs cannot measure the absolute sea-level change, but the height of the sea surface relative to crustal reference points that may move with tectonic activity or local subsidence.…”

Investigation of Tidal and Subtidal Variations in Sea Level Close to the Southeastern Coast of Sakhalin Island Using Two-Year Time Series

“…In the recent paper [Koohestani et al, 2023] the authors analyzed the appearance of internal solitary waves in the Bay of Oman caused different mechanisms including variations of atmospheric pressure, wind, currents, etc. Note also two more publications that relate to the study of long-term changes in sea level and the identification of a trend in rising sea levels [Goryachkin et al, 1999;Raj et al, 2022]. Among domestic authors, we note investigations with the participation of E. G. Morozov to study the temporal and spatial variability of hydrological processes and internal waves in a wide range of scales, such as, for example, [Morozov et al, 2020].…”

Comparative analysis of PCS and ICA on trend estimation of sea-level change from tide gauge observations

“…Geodetic observations play a pivotal role in estimating relative sea level rise (RSLR) through tide gauges (TG) (Hannah and Bell, 2012;Raj et al, 2022), which present the sea surface height relative to the crustal reference. However, due to local geodynamic factors such as tectonic activity and subsidence, the observations may encompass various movements, exhibiting linear or non-linear behaviors over extended periods(e.g., glacial isostatic adjustment, inter-seismic strain accumulation), (Camargo et al, 2022;Montillet et al, 2018;Santamaría-Gómez et al, 2012;Wang et al, 2021).…”