Mulched drip irrigation has been widely used in agricultural planting in arid and semi-arid regions. The dynamics and distribution of soil salinity under mulched drip irrigation greatly affect crop growth and yield. However, there are still different views on the distribution and dynamics of soil salinity under long-term mulched drip irrigation due to complex factors (climate, groundwater, irrigation, and soil). Therefore, the soil salinity of newly reclaimed salt wasteland was monitored for 9 years (2008–2016), and the effects of soil water on soil salinity distribution under mulched drip irrigation have also been explored. The results indicated that the soil salinity decreased sharply in 3–4 years of implementation of mulched drip irrigation, and then began to fluctuate to different degrees and showed slight re-accumulation. During the growth period, soil salinity was relatively high at pre-sowing, and after a period of decline soil salinity tends to increase in the late harvest period. The vertical distribution of soil texture had a significant effect on the distribution of soil salinity. Salt accumulated near the soil layer transiting from coarse soil to fine soil. After a single irrigation, the soil water content in the 30–70 cm layer under the cotton plant undergoes a ‘high–low–high’ change pattern, and the soil salt firstly moved to the deep layer (below 70 cm), and then showed upward migration tendency with the weakening of irrigation water infiltration. The results may contribute to the scientific extension of mulched drip irrigation and the farmland management under long-term mulched drip irrigation.

Analyzing the encounter frequency of high–low runoff and sediment yield is important for the appropriate dispatching of runoff–sediment resources, as well as river regulation. However, there have been no reports on the utilization of the pair-copula function in analyzing the runoff–sediment characteristics from a probabilistic perspective and conducting probability control on the runoff–sediment yields of different hydrologic stations. This paper builds marginal distribution functions on the basis of kernel distribution theory. In addition, this paper builds the joint distribution functions through pair-copula functions in order to analyze the encounter probability and the compensation characteristics of high–low runoff and sediment at different stations on the Weihe River in China, as well as the origins of runoff–sediment, to conduct probability control of river runoff–sediment resource allocation. The results show that, in different periods, the synchronous probability of high–low runoff of the Weihe River’s Xianyang and Huaxian Stations, and the Jinghe River’s Zhangjiashan Station differ, while that of high–low sediment at the three stations changes little—remaining at around 54%. Therefore, the sediment and runoff of the Weihe River apparently have different origins. In years of high and low runoff, if the runoffs of the Xianyang and Zhangjiashan Stations can be kept within a certain range, then the runoff of the Huaxian Station will be in a particular range, at a certain probability. Sediment at the Huaxian Station can be controlled, in a similar way. These results are of great significance for the water and sediment management department of the Weihe river, in order to reasonably allocate water and sediment resources.

The relationship between the water table and driving factors is a reliable theoretical reference for the reasonable planning of surface water resources and the water table. Previous research has neglected the distribution and probabilities of the water table. However, this paper analyzes the relationship between the water table and driving factors from a statistical perspective by correcting the variables and introducing the Kernel Distribution Estimation and the Copula Function. The average data of the buried depth of the phreatic water, annual irrigation volume of the surface water, and precipitation in the Jinghui Irrigation District in China from 1977 to 2013 were adopted. We precisely obtained the two-dimensional (2D) and three-dimensional (3D) Joint Distribution Function of each driving factor and the marginal distribution of the water table, calculate the conditional probability in different ranges, and exactly predict the design value of surface water irrigation giving set conditions. Eventually, we emphasize the importance of probability analysis and prediction in groundwater planning.