Abstract
Abstract. Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R2) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.
Highlights
The description of hydrological processes is the basis of hydrological modeling and simulation
These hydrologic models can be classified as stochastic and deterministic models according to their mathematical property, classified as conceptual and physically based models according to the physical processes involved in modeling, or classified as lump and distributed models according to the spatial description of the watershed process (Refsgaard, 1996; Moglen and Beighley, 2002)
The results show that the four IMF components (IMF1–4) of the NINO3.4 index data series respectively display quasi-3-year, quasi6-year, quasi-11-year and quasi-28-year periodic fluctuation (Fig. 9), whereas the four IMF components (IMF1–4) of the annual runoff (AR) series in the Kaidu River respectively show quasi-3-year, quasi-6-year, quasi-11-year and quasi-27-year cyclic variay = 1.0005x + 0.0046
Summary
The description of hydrological processes is the basis of hydrological modeling and simulation. Many models have been developed for describing hydrological processes over the past decades. From different perspectives, these hydrologic models can be classified as stochastic and deterministic models according to their mathematical property, classified as conceptual and physically based models according to the physical processes involved in modeling, or classified as lump and distributed models according to the spatial description of the watershed process (Refsgaard, 1996; Moglen and Beighley, 2002). The Soil, Water, Atmosphere and Plant (SWAP) model has been intensively validated during the past 2 decades (van Dam et al, 1997; Gusev and Nasonova, 2003; Kroes et al, 2000; Gusev et al, 2011; Ma et al, 2011). According to the investigation by Gassman et al (2007), there have been hundreds of published articles including SWAT applications, reviews of SWAT components, or other studies of SWAT in the past decades
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