Hydrologic Regionalization Using Wavelet Based Multi-Scale Entropy Method

Abstract:

Catchment regionalization is an important step in estimating hydrologic parameters of ungauged basins. This paper proposes a multiscale entropy method using wavelet transform and K-means based hybrid approach for clustering of hydrologic catchments. Multi-resolution wavelet transform of a time series reveals structure which is often obscured in streamflow records, by permitting gross and small features of a signal to be separated. Wavelet-based Multiscale Entropy (WME) is a measure of randomness of the given time series at different timescales. In this study, streamflow records observed during 1951–2002 at 530 selected catchments throughout the United States are used to test the proposed regionalization framework. Further, based on the pattern of entropy across multiple scales, each cluster is given an entropy signature which provides an approximation of the entropy pattern of the streamflow data in each cluster. The test for homogeneity reveals that the proposed approach works very well in regionalization.

Keywords-Hydrologic regionalization, Ungauged catchments, Wavelet Transform, K-means clustering, Multiscale entropy.