Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction accuracy, a novel hybrid model based on the empirical mode decomposition (EMD) for annual runoff forecasting is proposed and applied in this paper. Firstly, the original annual runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one trend term based on the EMD, which makes the series stationary. Secondly, it will be forecasted by a least squares support vector machine (LSSVM) when the IMF component possesses chaotic characteristics, and simulated by a polynomial method when it does not. In addition, the reserved trend term is predicted by a Gray Model. Finally, the ensemble forecast for the original runoff series is formulated by combining the prediction results of the modeled IMFs and the trend term. Qualified rate (QR), root mean square errors (RMSE), mean absolute relative errors (MARE), and mean absolute errors (MAE) are used as the comparison criteria. The results reveal that the EMD-based chaotic LSSVM (EMD-CLSSVM) hybrid model is a superior alternative to the CLSSVM hybrid model for forecasting annual runoff at Shangjingyou station, reducing the RMSE, MARE, and MAE by 39%, 28.6%, and 25.6%, respectively. To further illustrate the stability and representativeness of the EMD-CLSSVM hybrid model, runoff data at three additional sites, Zhaishang, Fenhe reservoir, and Lancun stations, were applied to verify the model. The results show that the EMD-CLSSVM hybrid model proved its applicability with high prediction precision. This approach may be used in similar hydrological conditions.

The exchange between shallow groundwater and soil water is unusually strong during freeze-thaw periods. The purpose of this study is to determine the effect of four different groundwater table depths (GTDs) and two soil textures on soil water moisture migration and phreatic evaporation during a freeze-thaw period using lysimeters. The results show that a high-moisture zone (HMZ) formed at a depth of 25–35 cm for sandy loam with a soil moisture content of 52%, while no obvious HMZ formed for fine sand when the GTD was 0.5 m. When the GTD was 2.0 m, a HMZ formed at a depth of 50–70 cm for sandy loam at the highest soil moisture content of 22%, while a HMZ formed at a depth of 60–80 cm for fine sand with a soil moisture content of 10%. The cumulative phreatic evaporation increased by a power function on freezing days during the freezing period. The total phreatic evaporation for sandy loam declined linearly with the increasing of GTD, and with the largest evaporation value of 73.6 mm for fine sand when the GTD was 1.0 m during the freeze-thaw period. The research would be significant for water resource assessment, the conversion of farmland water, and the prevention of saline land.

Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP₁, HSP₂, HSP₃), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP₁ stage floods and great floods.

Reducing soil evaporation is important to alleviate water shortages in arid and semi-arid regions. The objective of this work was to reveal the effect of straw mulch on soil evaporation based on field experiments during a freeze–thaw period in Northern China. Four soil surface mulch treatment modes were investigated: Bare soil (BS), 1 cm thick straw mulch with 100% coverage rate (J1), 2 cm thick straw mulch with 100% coverage rate (J2), and 2 cm thick straw mulch with 50% coverage rate (J3). Principal component analysis was used to analyze the major factors influencing soil evaporation in three freeze–thaw stages. The results show that cumulative soil evaporation decreased with increased straw mulch thickness and coverage rate. The effect of straw mulching on soil evaporation was obvious during the stable freezing period, and soil evaporation with straw mulch treatments was reduced by 49.0% to 58.8% compared to BS treatment, while there was little difference for straw mulch treatments in the thawing stage. The relationship between cumulative soil evaporation under different straw mulch modes and time was well fitted by the power function. In the unstable freezing stage, the major factors for all treatments influencing soil evaporation were surface soil temperature and water surface evaporation; in the stable stage, they were solar radiation and relative humidity, and in the thawing stage, they were solar radiation and air temperature. The research results can provide a basis for addressing soil water storage and moisture conservation and restraining ineffective soil evaporation in arid and semi-arid areas.