Abstract: 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.

Key words: sea level, prediction, tide gauge data, stochasticdynamic model, methodology and case study

摘要： 在充分考虑长时间序列潮位具有周期性、趋势性和随机性特征的基础上, 建立一套基于随机动态预测模型的海平面变化分析方法。模型中的周期项模拟首次采用小波分析与谱分析相结合的方法; 趋势项采用逐步回归法拟合; 残差序列采用自回归移动平均混合模型进行拟合; 三项叠加建立随机动态预测模型, 参数的确定采用非线性最小二乘迭代法。应用塘沽验潮站57年的月平均海平面高度数据进行案例分析, 通过实测数据验证和预测精度统计学检验, 表明此方法对海平面变化的模拟与预测具有较高精度, 可为海平面上升预测研究提供有效可行的借鉴与范例。

关键词: 海平面, 预测, 验潮数据, 随机动态模型, 方法与案例