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.

Crude oil pollution of soil is a serious environmental issue, and bioremediation using plants and microorganisms is a natural and sustainable method for its restoration. Pot incubation of a two-factor randomized block (plants with two levels, and crude oil with three levels) was designed to investigate the rhizosphere bacterial community of Suaeda salsa (L.) Pall. Crude oil contamination of soil was studied at different levels: 2 g/kg (low), 4 g/kg (medium), and 6 g/kg (high) levels. In this study, the physicochemical properties of the collected rhizosphere soil were analyzed. Moreover, the soil bacteria were further identified using the 16S rRNA gene. The effects of S. salsa and crude oil and their interaction on the physiochemical properties of the soil and crude oil degradation were found to be significant. Crude oil significantly influenced the diversity and evenness of bacteria, while the effects of S. salsa and interaction with crude oil were not significant. Proteobacteria were found to be dominant at the phylum level. Meanwhile, at the genera level, Saccharibacteria and Alcanivorax increased significantly in the low and medium contamination treatment groups with S. salsa, whereas Saccharibacteria and Desulfuromonas were prevalent in the high contamination treatment group. High crude oil contamination led to a significant decrease in the bacterial diversity in soil, while the effects of S. salsa and its interaction were not significant. Despite the highest abundance of crude oil degradation bacteria, S. salsa reduced crude oil degradation bacteria and increased bacteria related to sulfur, phosphorus, and nitrogen cycling in the low and high contamination group, whereas the opposite effect was observed for the medium contamination treatment group. The abundance of most crude oil degradation bacteria is negatively correlated with crude oil content. Nitrogen cycling bacteria are sensitive to the total nitrogen, total phosphorus, ammonia nitrogen, and nitrate nitrogen, and pH of the soil. Sulfur cycling bacteria are sensitive to aromatic hydrocarbons, saturated hydrocarbons, and asphaltene in soil. This research is helpful for further studying the mechanism of synergistic degradation by S. salsa and bacteria.

To predict the consequences of environmental change on the biodiversity of alpine wetlands, it is necessary to understand the relationship between soil properties and vegetation biodiversity. In this study, we investigated spatial patterns of aboveground vegetation biomass, cover, species diversity, and their relationships with soil properties in the alpine wetlands of the Gannan Tibetan Autonomous Prefecture of on the Qinghai-Tibetan Plateau, China. Furthermore, the relative contribution of soil properties to vegetation biomass, cover, and species diversity were compared using principal component analysis and multiple regression analysis. Generally, the relationship between plant biomass, coverage, diversity, and soil nutrients was linear or unimodal. Soil pH, bulk density and organic carbon were also significantly correlated to plant diversity. The soil attributes differed in their relative contribution to changes in plant productivity and diversity. pH had the highest contribution to vegetation biomass and species richness, while total nitrogen was the highest contributor to vegetation cover and nitrogen–phosphorus ratio (N:P) was the highest contributor to diversity. Both vegetation productivity and diversity were closely related to soil properties, and soil pH and the N:P ratio play particularly important roles in wetland vegetation biomass, cover, and diversity.

The characteristics of rainfall-runoff are important aspects of hydrological processes. In this study, rainfall-runoff processes and soil moisture dynamics at different soil depths and slope positions of grassland with two different row spacings (5 cm and 10 cm, respectively, referred to as R5 and R10) were analyzed, by means of a solution of rainfall simulation experiments. Bare land was also considered as a comparison. The results showed that the mechanism of runoff generation was mainly excess infiltration overland flow. The surface runoff amount of R5 plot was greater than that of R10, while the interflow amount of R10 was larger than that of R5 plot, although the differences of the subsurface runoff processes between plots R5 and R10 were little. The effects of rainfall intensity on the surface runoff were significant, but not obvious on the interflow and recession curve, which can be described as a simple exponential equation, with a fitting degree of up to 0.854–0.996. The response of soil moisture to rainfall and evapotranspiration was mainly in the 0–20 cm layer, and the response at the 40 cm layer to rainfall was slower and generally occurred after the rainfall stopped. The upper slope generally responded fastest to rainfall, and the foot of the slope was the slowest. The results presented here could provide insights into understanding the surface and subsurface runoff processes and soil moisture dynamics for grasslands in semi-arid 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.

Reclaimed water is useful for replenishing dried up rivers in North China, although changes in water quality could be an issue. Therefore, it is essential to understand the spatio-temporal variation and the controlling factors of water quality. Samples of Yongding River water were collected seasonally, and 24 water quality parameters were analyzed in 2015. All waters were alkaline, and nitrate-nitrogen was the main form of nitrogen, while phosphorus was mostly below detection level. The water quality parameters varied in time and space. Cluster analysis showed a distinct difference between winter and the other seasons and between the natural river section and the section with reclaimed water. Based on the analysis of Gibbs plots, principal component analysis, and ionic relationships, the water chemistry was controlled by dissolution of rocks in natural river section, the quality of replenished water, the effects of dilution, and the reaction of aqueous chemistry in the reclaimed water section. The positive oxidation environment in most of the river water was conducive to the formation of nitrate-nitrogen by nitrification, and not conducive to denitrification.