The combination of wavelet analysis methods with data-driven models is a prevalent approach to conducting hydrological time series forecasting, but the results are affected by the accuracy of the wavelet decomposition of the series. The choice of decomposition level is one of the key factors for the wavelet decomposition. In this paper, the data of daily precipitation and streamflow time series measured in the upper reach of the Heihe River Basin in Northwest China were used as an example, and the influence of the decomposition level on wavelet-based hydrological time series forecasting was investigated. The true components of the precipitation series were identified, and the modeling results using 10 decomposition levels and two decomposition types were compared. The results affirmed that the wavelet-based modeling performance is sensitive to the choice of decomposition level, which is determined by the time series analyzed, but has no relation with the decomposition type used. The essence of the choice of decomposition level is to reveal the complex variability of hydrological time series under multi-temporal scales, and first knowing the true components of series could guide the choice of decomposition level. Through this study, the relationship among original series’ characteristics, the choice of decomposition level, and the accuracy of wavelet-based hydrological time series forecasting can be more clearly understood, and it can be an improvement for wavelet-based data-driven modeling.

In this study, a mathematical expression describing the relationship between infiltration rate and soil moisture content was deduced based on the Horton equation in order to study the relationship between infiltration rate and soil moisture content during the rainfall infiltration process. Specific experimental data on infiltration processes were used to verify the validity of the equations and to analyze the main factors affecting the infiltration rate. The results indicate the following: (1) The experimental data demonstrated a high degree of accuracy. The volumetric error of soil moisture increase and cumulative infiltration was 3.5% and the coefficient of determination (R²) was 0.87 in the 42 tests. (2) The equation obtained in this paper can well describe the relationship between infiltration rate and soil moisture content; the R² of the fitted results was greater than 0.80 in more than 80% of the experiments. (3) The relationship between infiltration rate and soil moisture content is mainly influenced by initial soil moisture content; the higher the initial soil moisture content, the lower the initial infiltration rate, the faster the infiltration rate decreases with soil moisture content, and the lower the “relative stable infiltration rate” in the process of infiltration.

Urban flooding is a global water disaster resulting from the expansion of urban impervious surfaces and the strengthening of extreme precipitation events, especially in China. Nonetheless, few studies have focused on the spatial distributions of urban flooding characteristics and their variations in the context of climate change. In this study, eight critical metrics (i.e., maximum flooding volume, total overloaded manholes with different flooding volumes or durations, total flooding volume, mean and maximum flooding durations, maximum inundation area, and depth) are adopted to characterize the urban flood events. The impacts of climate change on these metrics are assessed for two periods, the 2030s (2020–2049) and 2070s (2060–2089), and compared with those in the baseline period (1976–2005). The Future Science City Park in Beijing, China, is selected as our study area. The results show that all four flood events are well simulated, with both efficiency coefficients and correlation coefficients being over 0.8. The number of overloaded manholes and the total flooding volume are projected to increase 19.3%–44.8% and 171%–716% under 20‐year rainfall events due to climate change in the two future periods. The spatial distribution of overloaded manholes with different increased flooding volumes is projected to expand to almost the whole area from the region with lowland and limited drainage capacity. Furthermore, the maximum inundation area and depth are projected to increase obviously. This study will be helpful for designing and improving the drainage system, controlling urban flooding, and adapting to climate change.

Accurate and prompt flood forecasting is essential for effective decision making in flood control to help minimize or prevent flood damage. We propose a new custom deep learning model, IF-CNN-GRU, for multi-step-ahead flood forecasting that incorporates the flood index ($I_F$) to improve the prediction accuracy. The model integrates convolutional neural networks (CNNs) and gated recurrent neural networks (GRUs) to analyze the spatiotemporal characteristics of hydrological data, while using a custom recursive neural network that adjusts the neural unit output at each moment based on the flood index. The IF-CNN-GRU model was applied to forecast floods with a lead time of 1–5 d at the Baihe hydrological station in the middle reaches of the Han River, China, accompanied by an in-depth investigation of model uncertainty. The results showed that incorporating the flood index $I_F$ improved the forecast precision by up to 20%. The analysis of uncertainty revealed that the contributions of modeling factors, such as the datasets, model structures, and their interactions, varied across the forecast periods. The interaction factors contributed 17–36% of the uncertainty, while the contribution of the datasets increased with the forecast period (32–53%) and that of the model structure decreased (32–28%). The experiment also demonstrated that data samples play a critical role in improving the flood forecasting accuracy, offering actionable insights to reduce the predictive uncertainty and providing a scientific basis for flood early warning systems and water resource management.

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.