Configurational entropy theory for streamflow forecasting

Abstract

This study develops configurational entropy theory (CET) for monthly streamflow forecasting. The theory is comprised of three main parts: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. Comparison with the Burg entropy theory (BET) shows that CET yields higher resolution spectral density with more accurate location of spectral peaks. Cepstrum analysis yields more accurate parameters than the Levinson algorithm in the autoregressive (AR) method and the Levinson–Burg algorithm in BET. CET is tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CET captures streamflow seasonality and satisfactorily forecasts both high and low flows. High flows are satisfactorily forecasted with the coefficient of determination (r2) higher than 0.92 for one year ahead of time, with r2 higher than 0.85 for two years ahead of time, and up to 60 months ahead with r2 higher than 0.80. However, low flows are forecasted with r2 higher than 0.50 for one year ahead time. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately (r2 = 0.84) than downstream streamflow (r2 = 0.75). Residuals of forecasted values relative to observed values are found to follow normal distribution.