Stochasticity, nonlinearity and forecasting of streamflow processes

By Wen Wang. 2006

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Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, methodology): “Both original N-S efficiency and seasonally adjusted Nash–Sutcliffe (SANS) efficiency proposed by Wang (2006) were computed for the estimation of the model performance over the entire calibration and validation period, covering all four seasons and years.”
Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm (2021, background): “However, these models are mostly built upon the assumption that the process follows a normal distribution, however the streamflow process is generally non-linear and stochastic in nature [13].”