Methodology and Case Study of Sea Level Prediction Based on Secular Tide Gauge Data
Duan Xiao-fen · Beijing Daxue Xuebao. Zirankexueban · 2014
Based on the periodic, trending, and stochasticcharacteristics of secular tide gauge data, a predictive methodology using stochastic-dynamic model was present to the sea level change research. The periodic term was resolved by wavelet and spectrum analysis. Stepwise regression was applied to the trending term analysis. The residual sequence was fitted by autoregression moving average model. Least-squares iteration method was applied for parameter estimation ofthe superposition model, which was composed of significant period model, trending term model and the residual sequenceautoregression moving average model. The stochasticdynamic model is applied to 57 years' monthly mean sea level data from Tanggu tide gauge for case study. The results show that the predictive methodology based on stochastic-dynamic model is feasible and efficient in sea level change prediction. Considering the high accuracy of modeling and predicting, this methodology can be used as a reference for future studies in sea level change.