Long‐term prediction of precipitation and hydrologic extremes with nonstationary oscillation processes

Abstract

Nonstationary oscillations in climatic variables and indices have been the focus of many studies. Since climate indices or their associated hydrometeorological variables might contain nonstationary oscillation processes, it would be useful to be able to divide the intrinsic nonstationary oscillation into a finite number of components. Those components can then be used to predict the future system evolution. In the current study nonstationary oscillations of certain time series are extracted using a decomposition analysis called the empirical mode decomposition (EMD). In EMD the most important components are modeled with a nonstationary oscillation resampling (NSOR) technique. To predict a long‐term oscillation pattern, a time series with a long record is required. The normalized regional precipitation of eastern Canada is one such series. In a second example, the future evolution of extreme streamflows at two stations from the province of Quebec, Canada, is studied by using the long‐term patterns of climatic indices. Results indicate that the future long‐term patterns are well‐modeled with the NSOR and EMD. However, the indirect approach to finding the interconnection sometimes gives rise to a high prediction uncertainty.