The rainfall-runoff process (RRP) is an important part of hydrologic process. There is an effective measure to study RRP through artificial rainfall simulation. This paper describes a study on three growing stages (jointing stage, tasseling stage, and mature stage) of spring maize in which simulated rainfall events were used to study the effects of various factors (rainfall intensity and slope gradient) on the RRP. The RRP was tested with three different rainfall intensities (0.67, 1.00, and 1.67 mm/min) and subjected to three different slopes (5°, 15°, and 20°) so as to study RRP characteristics in semiarid regions. Regression analysis was used to study the results of this test. The following key results were obtained: (1) With the increase in rainfall intensity and slope, the increasing relationship with rainfall duration, overland flow, and cumulative runoff, respectively, complied with logarithmic and quadratic functions before reaching stable runoff in each growing stage of spring maize; (2) The runoff coefficient increased with the increase in rainfall intensity and slope in each growing stages of spring maize. The relationship between runoff coefficient, slope, rainfall intensity, rainfall duration, antecedent soil moisture, and vegetation coverage was multivariate and nonlinear; (3) The runoff lag time decreased with the increase in rainfall intensity and slope within the same growing stage. In addition, the relationship between runoff lag time, slope, rainfall intensity, antecedent soil moisture, and vegetation coverage could also be expressed by a multivariate nonlinear equation; (4) The descent rate of soil infiltration rate curve increased with the increased rainfall intensity and slope in the same growing stage. Furthermore, by comparing the Kostiakov, Horton, and Philip models, it was found that the Horton infiltration model was the best for estimating soil infiltration rate and cumulative infiltration under the condition of test.

Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored catchments. The parameters for the ungauged regions are estimated by regionalization. River water stage data and reservoir water stage data from Shunchang, and reservoir water stage data from Zherong are used to validate the model. The model performs well and the average Nash–Sutcliffe efficiency (NSE) is above 0.8 for the five catchments. The validation shows the difference in the timing of flood peaks using the two types of water stage data is less than 1 h. The rising and declining trends of the floods correspond to the observed trends over the entire validation process. Furthermore, the flash flood warning system was effectively applied in flash flood event on 28 September 2016 in Zherong. Thus, the CNFF-HM with regionalization is effective in forecasting flash floods for ungauged mountainous regions.

Prediction of the groundwater dynamics via models can help better manage the groundwater resources and guarantee their sustainable use. Three types of data-driven models are built for groundwater depth prediction in the plain of Shijiazhuang, the capital of Hebei Province in North China. The data-driven models include the Power Function Model (PFM), Back-Propagation Artificial Neural Network (BPANN) and Support Vector Machines (SVM) with two kernel functions of linear kernel function (LKF) and radial basis function (RBF). Five classes of factors (including 12 indices) are considered as potential model input variables. The Gamma Test (GT) is adopted in this study to help identify the relative importance of the input indices and tackle the tricky issue of the optimal input combinations for the data-driven models. The established models are evaluated in both fitting and testing procedures based on the root mean squared error (RMSE) and Nash-Sutcliffe efficiency (E) for different input combination schemes. The results show that SVM (RBF) performs the best. It is interesting to find that the natural factors (i.e., precipitation and evaporation) are less relevant to the groundwater depth variations. The methods used in this study have much significance for groundwater depth prediction in areas lacking hydrogeological data.

The performance evaluation of a city’s flood control system is essentially based on accurate storm designs, where a particular challenge is the development of the joint distributions of dependent rainfall variables. When it comes to the research design for consecutive rainfall, the analytical investigation is only focused on the maximum of consecutive rainfalls, and it does not consider the probabilistic relations between the first day of rainfall and the overall rainfall included in consecutive rainfall events. In this study, the copula method is used to separate the dependence structure of multi-day rainfall from its marginal distribution and analyse the different impacts of the dependence structure and marginal distribution on system performance. Three one-parameter Archimedean copulas, including the Clayton, Gumbel, and Frank families, are fitted and compared for different combinations of marginal distributions that cannot be rejected by statistical tests. The fitted copulas are used to generate rainfall events for a system performance analysis, including the conditional probability and design values for different return periods. The results obtained in this study highlight the importance of taking into account the dependence structure of one-day and multi-day rainfall in the context of storm design evaluations and reveal the different impacts of the dependence structure and the marginal distributions on the probability.

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.