Abstract
Monthly streamflow has elements of stochasticity, seasonality, and periodicity. Spectral analysis and time series analysis can, respectively, be employed to characterize the periodical pattern and the stochastic pattern. Both Burg entropy spectral analysis (BESA) and configurational entropy spectral analysis (CESA) combine spectral analysis and time series analysis. This study compared the predictive performances of BESA and CESA for monthly streamflow forecasting in six basins in Northwest China. Four criteria were selected to evaluate the performances of these two entropy spectral analyses: relative error (RE), root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency coefficient (NSE). It was found that in Northwest China, both BESA and CESA forecasted monthly streamflow well with strong correlation. The forecast accuracy of BESA is higher than CESA. For the streamflow with weak correlation, the conclusion is the opposite.
Highlights
Accurate streamflow forecasting is important for developing measures to flood control, river training, navigation, reservoir operation, hydropower generation plan and water resources management
The relative error provides the average magnitude of differences between observed values and predicted values relative to observed values
The coefficient of determination is defined as the square of the coefficient of correlation
Summary
Accurate streamflow forecasting is important for developing measures to flood control, river training, navigation, reservoir operation, hydropower generation plan and water resources management. Time series models, such as autoregressive (AR) or autoregressive moving average (ARMA) models, as proposed by Box and Jenkins [1], are generally used for monthly streamflow forecasting [2,3,4]. These models assume that streamflow time series is stochastic and are linear which limits their application [5]. Huo et al [24]
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