Multiscale Analysis and Prediction of Sea Level in the Northern South China Sea Based on Tide Gauge and Satellite Data

Abstract

Under the influence of global warming, the problem of sea-level rise is becoming increasingly prominent. The northern part of the South China Sea (SCS) is low lying, with intense economic development, and densely populated. These characteristics make the region extremely sensitive to the consequences of rising sea levels. This study aims to reveal the trends of sea-level changes in the northern SCS and provide scientific insights into the potential flooding risks in low-lying areas. To achieve this, the Ensemble Empirical Mode Decomposition (EEMD) method is used to analyze the water level time series data from three tide gauges along the coast of Hong Kong. This analysis reveals the multidimensional change characteristics and response mechanisms of the sea level in the SCS. The findings reveal distinct seasonal, interannual, decadal, and interdecadal variations in sea-level changes. Furthermore, we explore the impact of the El Niño-Southern Oscillation (ENSO) on sea-level changes in the study area, finding a 6-month lagged correlation between the sea level and ENSO. Spatially, the rate of sea-level change is faster in nearshore areas than in the open ocean and higher in the northern regions than in the southern regions. The Multifractal Detrended Fluctuation Analysis (MF-DFA) method is employed to analyze the sea-level change time series, revealing long-range correlations and multifractal characteristics. In addition, we propose a sea-level prediction method that combines EEMD with Long Short-Term Memory (LSTM) neural networks and conducts empirical research on sea-level changes in the northern South China Sea. The results indicate that the EEMD-LSTM model outperforms the standalone LSTM model in terms of predictive accuracy, effectively eliminating noise from signals and providing a valuable reference. In summary, this research delves into the multiscale characteristics and influencing factors of sea-level changes in the northern SCS, proposing an improved sea-level prediction method that integrates EEMD and LSTM. The findings lay the groundwork for evaluating the risks of sea-level rise in low-lying regions of the northern SCS and inform future response strategies.

Monitoring Total Suspended Solids and Chlorophyll-a Concentrations in Turbid Waters: A Case Study of the Pearl River Estuary and Coast Using Machine Learning

“…The basin area is about 4.5 × 10 5 km 2 [31], and its annual average flow can reach 3.5 × 10 11 m 3 , with an annual sediment transport of 8.87 × 10 7 t [32]. The PRE lies in the northern part of the South China Sea and is influenced by subtropical monsoon climate [33]. The runoff in this area changes significantly with the seasons.…”

Characterizing Tidal Currents and Guangdong Coastal Current Over the Northern South China Sea Shelf Using Himawari‐8 Geostationary Satellite Observations

“…Yang et al. (2013) simulated the NSCS tidal currents characteristics of M2, S2, K1, and O1. Verification of model tidal currents however is still a challenge due to the limited direct current observations.…”

“…Using eight moorings, R. Li et al (2014) characterized the tidal currents over the NSCS shelf and verified the model performance in reproducing diurnal currents. Yang et al (2013) simulated the NSCS tidal currents characteristics of M2, S2, K1, and O1. Verification of model tidal currents however is still a challenge due to the limited direct current observations.…”