Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

We investigated the nonlinearity of runoff response to global mean temperature (GMT) change in the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at the river basin scale globally. Results show that changes in long‐term mean annual runoff are nonlinear with GMT rise over most extended subtropical basins, suggesting that estimation of future runoff change derived from the linear scaling relations would be biased. As for the interannual variability, nonlinearities are apparent mainly in central and western Asia, southern and western Africa, most of Europe, and Australia when GMT increases beyond 1.5°C. This suggests that impacts of climate change under 1.5°C GMT rise on runoff variability should not be simply scaled from that under a 2°C warming world. Our results highlight the contrasting response of areal runoff to GMT rise across global major river basins and reveal the threshold of GMT increment at which the nonlinear runoff response is projected to emerge.

Relative contributions from environmental factors to daily actual evapotranspiration (ETa) across a variety of climate zones is a widely open research question, especially regarding the roles played by soil water content ((SWC); water supply) and net radiation ((Rn); energy supply) in controlling ETa. Here, the boosted regression tree method scheme was employed to quantify environmental controls on daily ETa using the global FLUXNET dataset. Similar to the general trend suggested by the Budyko theory at annual scales, the results showed that the relative control of SWC on daily ETa increased with increasing aridity index (Φ); however, Rn played a major role at most FLUXNET sites (roughly Φ < 4), indicating that Rn could be a leading control on daily ETa even at water-limited sites. The variability in the relative controls of SWC and Rn also partly depended on factors affecting water availability for daily ETa (e.g. vegetation characteristics and groundwater depth). Our study showed that other than SWC and Rn, the net effect of environmental controls (particularly leaf area index) on daily ETa was more important at drier sites than at relatively humid sites. This suggests that near-surface hydrological processes are more sensitive to vegetation variations due to their ability to extract deep soil water and enhance ETa, especially under arid and semi-arid climatic conditions. Our findings illustrate how environmental controls on daily ETa change as the climate dries, which has important implications for many scientific disciplines including hydrological, climatic, and agricultural studies.

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