Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data

Abstract

Sea level rise is a threat to coastal habitation and is corroborating evidence for global warming. The present study investigated the combined use of quantitative forecasting methods for sea level rise using exponential smoothing state space models (ESMs) and an autoregressive integrated moving average (ARIMA) model fed with sea level data over 17 years (1994–2010). Two levels of ESMs were employed: double (model levels with trend) and triple (model levels, trend and seasonal decomposition). The overall data analysis revealed the better performance of ARIMA in terms of index of agreement (d = 0.79), root-mean-square error (RMSE = 32.8 mm) and mean absolute error (MAE = 25.55 mm) than the triple ESM (d = 0.76; RMSE = 39.86 mm; MAE = 35.02 mm) and double ESM (d = 0.14; RMSE = 52.71 mm; MAE = 45.99 mm) models. The present study results suggest that the rate of Arabian Sea level rise is high, and if this is not taken into consideration many coastal areas may become subject to climate-change-induced habitat loss in future.