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Recharge sources such as precipitation, mountain front recharge, mountain block recharge and confined water are the sources usually considered in quantitative studies of groundwater recharge. Changes in recharge processes caused by irrigation practices need to be fully considered for the accurate budgeting and management of water resources. Here, we put forward a conceptual framework for evaluating the shallow groundwater recharge process in arid irrigated agricultural areas using hydrochemical and stable isotope techniques, combined with an assessment of hydrogeological conditions and quantitative models. In general, the recharge effect of atmospheric precipitation on shallow groundwater in arid areas is relatively small. The contributions made by recharge sources in the studied river irrigated area, from greater to smaller, were confined groundwater (46.98 %), river water (45.48 %) and precipitation (7.55 %). The original range in groundwater recharge levels caused by river leakage also appeared to have expanded in response to the establishment of canal irrigation networks. Lateral groundwater flow and confined groundwater were the main recharge sources of shallow groundwater in areas fed by well irrigation and well-/spring-water irrigation (not taking into account any groundwater irrigation leakage). However, had the recharge of shallow groundwater by groundwater irrigation leakage, which reached 19.8-41.1 %, not been counted as contributing to actual groundwater recharge, the recharge contributions made by lateral groundwater flow and confined groundwater to shallow groundwater would have been significantly overestimated. This is because the groundwater recharge process has been modified by the various irrigation measures employed in arid agricultural areas, leading to a redistribution effect in groundwater recharge source contributions. This study provides a new perspective and intuitive data support for the development and utilization of water resources in arid regions.

Debris flow hazards can occur easily in loess regions, due to the particular characteristics of loessic material. Some of them have historically caused considerable damage to both the natural and the human environment. Little research has been conducted into the impact pressures caused by debris flows varying with densities and weights in loess regions. Flume experiments were conducted to estimate the impact pressures of debris flows, and the maximum impact pressure was measured. Moreover, hydrodynamic and hydrostatic models were improved by using these experimental results. Finally, after combining these results with a dimensionless analysis and Buckingham’s π theorem, the Froude number and the Reynolds number were able to be introduced in order to construct a comprehensive dimensionless equation for debris flows. The results showed that the velocity ranged from 1.23 to 3.62 m/s when the debris flow density increased from 1100 to 2300 kg/m3 and the mixture weight rose from 100 to 500 kg. The debris flow depth was between 2.7 and 13.4 cm, and the maximum impact pressure ranged from 1.23 to 28.41 kPa. In addition, the empirical parameters of hydrodynamic and hydrostatic models were modified and valued at 5.08 and 9.48, respectively, which were significantly different from the empirical parameters for earth-rock areas. Specifically, the modified hydrodynamic model and modified hydrostatic model were observed to perform very well for debris flows with comparatively high Froude debris flow numbers. The maximum dimensionless impact pressure was expressed as a power function of both the Froude number and the Reynolds number. A comprehensive maximum dimensionless impact pressure formula for debris flows was coupled with the Froude number and the Reynolds number and expressed as a power function. Results indicated that the modified model and the comprehensive approach can both be applied to the loess regions of China and can provide a better understanding of loess debris flow mechanisms, as well as feed into engineering design work and risk assessments in loess regions affected by debris flows.

In the southwestern Tengger desert in northwestern China, nitrate circulation processes in the unsaturated zone and associated driving factors were studied using water chemistry and stable isotope techniques. At unvegetated sand sites, NO3− content increases from the south to north in the desert. Additionally, a negative correlation (R2 = 0.90, P

Slope failure is a one of major process that causes severe landform variation and environment variation, and slope failure has become a major hidden danger to human settlement and urban construction in this vast loess region. The physical model of slope failure as induced by artificial rainfall was constructed in the field, and monitored the pore water pressure (PWP), earth stress (ES), volumetric water content (VWC), electrical conductivity (EC), and temperature (T) of the soil using this physical simulation. The surface morphology of slope started to occur in the slope as a result of erosion caused by rainfall and rainwater infiltration at the beginning of the experiment; concurrently, PWP, ES, VWC, and EC were increased gradually. Meanwhile, the saturated weight of the soil rose. In the middle of the experiment, PWP, ES, VWC, and EC were increased rapidly as the artificial rainfall continued, and the ratio of soil pore the soil fell. The slope landform was obviously occurred during the experiment, when it was noted that PWP, ES, VWC, and EC of the soil rapidly decreased. Afterwards, slope failure evolved into a debris flow; eventually, the landform was entirely changed in the slope. The soil became more compact toward the end of the experiment, and PWP, ES, VWC, and EC were slowly increased; these factors indicated that the loess slope was temporarily stable. This study could potentially be used to provide the relevant parameters for numerical simulations of landform variation in loess regions, and provide reference for regional land use planning and environmental development.

The unsaturated zone is a significant feature of the nitrogen cycle in arid areas connecting atmospheric precipitation and surface water to groundwater. To measure NO3−, Cl−, and NH4+ concentrations, precipitation samples (39.9592°N, 94.3302°E) were collected at the western edge of the Mingsha sand dunes. Soil profiles were taken along the northern edge of the dunes to determine NO3− and Cl− concentrations and δ18O-NO3− and δ15N-NO3− levels in soil water. The mNO3−/Cl−, mNH4+/Cl− and NH4+-N/NO3−-N values in precipitation showed seasonal variations, reflecting the influence of distinct anthropogenic sources. The δ15N-NO3− and δ18O-NO3− of the LT profile ranged from 2.38 to 7.84‰ and 14.72 to 57.5‰, respectively, and that of GC profile ranged from − 1.36 to 2.35‰ and 5.07 to 21.3‰, respectively. These results suggest that the main sources of vadose zone nitrogen in the profiles are NO3− fertilizer and NH4+ in fertilizer and precipitation, all of which are influenced by agricultural sources. Obvious nitrification and the mixing of different sources were noted, but the profiles showed no evidence of denitrification. The findings of this study show that migration of NO3− and Cl− in the desert vadose zone is influenced by heavy rainfall events, vegetation, and evaporation. Heavy rainfall events promote leaching of NO3− and Cl− into the deep vadose zone, while evaporation at the surface prevents leaching and vegetation hinders the downward migration of NO3− and Cl−. Migration of NO3−-N and Cl− in sand dunes is affected by the solute gradient concentration difference of the soil water, moving from areas of higher concentrations to lower concentration. The NO3−-N and Cl− accumulations were 1.02 kg N ha− 1 and 3.45 kg ha− 1, respectively, throughout autumn. These were larger than could be attributed to the input flux of atmospheric precipitation, especially in the near-surface soil layer, likely illustrating the influence of atmospheric dry deposition.

To enable photocatalytic self-cleaning cotton fabrics working under visible light irradiation, platinum (IV) chloride modified TiO2 (Pt-TiO2) and N-TiO2 (Pt-N-TiO2) nanosols are synthesized through a low temperature precipitation-peptization method. According to the characterizations of XRD, DRS and TEM, all nanoparticles are anatase nanocrystallites in the sizes of less than 10 nm, while N-TiO2 nanoparticles have better crystallization and smaller sizes. However, the cotton fabrics functionalized with Pt-TiO2 display significantly enhanced photocatalytic activity for methyl orange degradation and coffee stain removal under both solar simulator and visible light irradiation, while the performance of that coatings of Pt-N-TiO2 is poor. Further XRF and XPS results indicate that surface species on N-TiO2 block the adsorption of PtCl62− anions, whereas these anions strongly attach on the surface of TiO2 nanoparticles, and accordingly enable functionalized cotton fabrics efficient visible light driven activities based on a mechanism of charge transfer from ligand to metal (CTLM) excitation.

For landslides characterized with “step-like” deformation curves, the accelerations of the deformation during the rainy season are destructive for both residents and infrastructure; therefore, it is essential to perform displacement prediction. The aim of this study is to present a computational intelligence approach that adopts ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM) method optimized by particle swarm optimization (PSO) to conduct displacement prediction. First, the cumulative displacement was decomposed by the EEMD method to obtain the trend and periodic components. The trend displacement was predicted by setting previous displacement data as an input variable. The external triggering factors were also decomposed by EEMD into several subsequences. Subsequences with periodic characteristics were selected as the input datasets to forecast the periodic displacements using an ELM model optimized by PSO (PSO-ELM). Finally, the total displacement was obtained by adding the two predictive components to validate the model performance. The Baishuihe landslide in the Three-Gorges area of China was selected as an example; long-term monitoring records from monitoring site ZG118 were utilized to validate the model. The results revealed that the prediction accuracy can be improved by deleting any random components in the triggering factors. The correlation coefficient and the root mean square error between the measured and predicted displacements were 0.996 and 7.62 mm, respectively, thus indicating satisfactory calculation accuracy for the trained model. Therefore, under the premise of available monitoring data, the PSO-ELM model was effective in forecasting landslide displacements with step-like curve in this region.

Atmospheric N deposition patterns play an important role in the nitrogen cycles of arid ecosystems. This study observed a significant spatiotemporal variation in inorganic N concentrations in precipitation from 2013 to 2016 at eight sampling sites in the Hexi Corridor, northwestern China. Mean mNO3–/Cl– and mNH4+/NO3– values were 1.62 and 0.98 from late autumn to early spring, respectively, indicating that inorganic N in precipitation is most likely principally controlled by NO3– in response to coal-fired heating and low wind speeds. However, mean mNO3–/Cl– and mNH4+/NO3– were 0.91 and 2.69 from late spring to early autumn, respectively, suggesting that the primary contributor is NH3 from agricultural soils and fertilizers. Mean annual mNH4+/NO3– values in the eastern sectors of the Hexi Corridor ranged from 0.57 to 1.55, but those in the central and western sectors ranged from 1.53 to 3.59. The ratios of NH4+-N deposition flux to total wet deposition of inorganic N flux were > 50% in the Corridor’s central and western sectors, but

Evaluating the flood discharge and deposition process of a debris flow is important for risk assessment, management, and design of possible supporting works for geo-hazard mitigation. The movement and deposition process of a typical debris flow gully in southern Gansu province, China, was simulated using the Soil Conservation Service-curve number (SCS-CN) approach and a two-dimensional finite model (FLO-2D PRO model) coupled with geographic information systems. Runoff volumes and depths were obtained by the use of the SCS-CN model using different precipitations and different intervals. The deposition, velocity, impact force, and influence zone of the debris flow were simulated with the FLO-2D PRO model based on the results of the SCS-CN method. Simulation results for a storm that occurred on 12 October 2010 suggest a maximum flow velocity of 23.1 m/s, a maximum deposition depth of 27.9 m, and a hazard zone of about 0.414 km2. These results were consistent with measured results from the documented debris flow. Verification demonstrated that model results could be used to help predict disaster-causing debris flows, thus helping to protect the lives, property, and economy of the local population.