Hydrological Data Projection Using Empirical Mode Decomposition: Applications in a Changing Climate

Abstract

This paper demonstrates the effectiveness and superiority of Empirical Mode Decomposition (EMD) in projecting non-stationary hydrological data. The study focuses on daily Sea Surface Temperature (SST) sequences in the Ni & ntilde;o 3.4 region and uses EMD to forecast the probability of El Ni & ntilde;o events. Applying the Mann-Kendall test at the 5% significance level reveals a significant increasing trend in SST changes in this region, particularly noticeable after 1980. This trend is associated with the occurrence of El Ni & ntilde;o and La Ni & ntilde;a events, which have a recurrence interval of approximately 8.4 years and persist for over a year. The modified Oceanic Ni & ntilde;o Index (ONI) proposed in this study demonstrates high forecast accuracy, with 97.56% accuracy for El Ni & ntilde;o and 89.80% for La Ni & ntilde;a events. Additionally, the EMD of SST data results in 13 Intrinsic Mode Functions (IMFs) and a residual component. The oscillation period increases with each IMF level, with IMF7 exhibiting the largest amplitude, fluctuating between +/- 1 degrees C. The residual component shows a significant upward trend, with an average annual increase of 0.0107 degrees C. These findings reveal that the EMD-based data generation method overcomes the limitations of traditional hydrological models in managing non-stationary sequences, representing a notable advancement in data-driven hydrological time series modeling. Practically, the EMD-based 5-year moving process can generate daily sea temperature sequences for the coming year in this region, offering valuable insights for assessing El Ni & ntilde;o probabilities and facilitating annual updates.