Wavelet regression: An approach for undertaking multi-time scale analyses of hydro-climate relationships

TL;DR

This paper introduces wavelet regression, a novel method combining wavelet analysis and regression to uncover multi-time scale hydro-climate relationships in complex, non-stationary data.

Contribution

The paper develops a new integrated approach called wavelet regression that reveals multi-scale hydro-climate patterns and relationships more effectively than existing methods.

Findings

Reveals hydro-climate patterns at different time scales.

Quantitatively describes hydro-climate relationships.

Provides a way to discover hidden patterns in stochastic data.

Abstract

Previous studies showed that hydro-climate processes are stochastic and complex systems, and it is difficult to discover the hidden patterns in the all non-stationary data and thoroughly understand the hydro-climate relationships. For the purpose to show multi-time scale responses of a hydrological variable to climate change, we developed an integrated approach by combining wavelet analysis and regression method, which is called wavelet regression (WR). The customization and the advantage of this approach over the existing methods are presented below: (1) The patterns in the data series of a hydrological variable and its related climatic factors are revealed by the wavelet analysis at different time scales. (2) The hydro-climate relationship of each pattern is revealed by the regression method based on the results of wavelet analysis. (3) The advantage of this approach over the existing methods is that the approach provides a routing to discover the hidden patterns in the stochastic and non-stationary data and quantitatively describe the hydro-climate relationships at different time scales.