Spatial Variations and Influencing Factors of River Networks in River Basins of China

Analysis of the spatial variations in river networks and the related influencing factors is crucial for the management and protection of basins. To gain insight into the spatial variations and influencing factors of river networks between large basins, in this study, three river basins from north to south in China (Songhua River Basin, Yellow River Basin and Pearl River Basin) were selected for investigation. First, based on a digital elevation model, different river networks with six drainage accumulation thresholds of three basins were extracted using ArcGIS. The optimal networks were determined through fitting the relationship between the accumulation threshold and related drainage density. Then, we used two indicators, drainage density and water surface ratio, to characterize the spatial variations of three basins. Finally, Pearson’s correlation coefficients were calculated between those two indicators and natural/human influencing factors. The results showed that drainage density and water surface ratio decreased from north to south in China and were negatively correlated with natural/human influencing factors. Drainage density was more influenced by natural factors than by human factors, while the opposite was true for water surface ratio. These findings may provide some basis for the management and protection of the river network.

Geological forward-prospecting using Tunnel-seismic-while-drilling (TSWD) method: A case study of the water supply project at Songhua River, China

Unexpected geological heterogeneities in the tunnel lines often cause safety issues and economic losses during excavation using Tunnel Boring Machines (TBMs). Therefore, collecting geological information ahead of the excavation front is important to prevent geological disasters and ensure excavation safety. To predict the geological conditions ahead of the TBM-excavated tunnel in the water supply project at the Songhua River in China, the tunnel-seismic-while-drilling (TSWD) method was adjusted and then applied. First, common source gather (CSG) data were acquired to record TBM drilling noise (pilot signal and seismic waves); a processing approach (including normalization, cross-correlation, wave-field separation, and migration) was used to image the major relevant geological heterogeneities ahead of the excavation front. A synthetic study from a numerical model with a fault verified the feasibility of the modified TSWD method to identify and image geological interfaces. Then this method was applied to field data resulting from a TBM tunnel excavation project. The field test of the modified TSWD in the TBM tunnel revealed the fractured zones, fault boundaries, and a pre-excavated tunnel ahead, providing the required geological information for TBM tunneling and preventing geologically caused safety issues.

Using Dual Isotopes of Nitrate to Identify the Source of Nitrogen Pollution in the Songhua River Basin, Northeast China

Nitrogen pollution is a severe problem in the Songhua River Basin (SHR) in China. Samples were collected from 36 sections of the SHR during the high, low, and flat seasons of the river, and the main sources of nitrogen in the water were qualitatively analyzed with isotope data for nitrogen and oxygen of nitrate. The contribution rates of each major pollution source were quantitatively analyzed using the Iso Source mass balance model. The results from these experiments indicate that the values for δ15N-NO3 and δ18O-NO3 in the flat flow season range from 1.52‰ to 14.55‰ and −14.26‰ to 2.03‰, respectively. The values for δ15N-NO3 and δ18O-NO3 in the low flow season range from 6.66‰ to 15.46‰ and −5.82‰ to 65.70‰, respectively. In the low flow season, nitrogen comes from the input of domestic and manure sewage (53%) and soil organic N (45%). The values of δ15N-NO3 and δ18O-NO3 in the high flow season range from 2.07‰ to 14.24‰ and −3.99‰ to 8.03‰, respectively. In the high flow season, nitrogen comes from soil organic nitrogen (41%), domestic and manure sewage (32%), and nitrogen fertilizer (27%), which are the main sources of nitrogen pollution in the SHR. The conclusions from the qualitative and quantitative analysis of nitrogen sources in the SHR can provide a scientific basis for the source control and treatment of nitrogen pollution.

Method to identify composition and production phases of spring runoff in high-latitude mid-temperate regions: a case study in the Second Songhua River Basin, China

Simulation and forecasting of runoff play an important role in the early warning and prevention of drought and flood disasters. To improve the accuracy of spring runoff simulations, it is important to identify spring runoff production patterns under the combined effect of snow and frozen soil. Based on the theory of the hydrological cycle, three important parameters, which include surface and subsurface runoff, precipitation and temperature, were selected for this study. The trend analysis, statistical analysis and Eckhardt's recursive numerical filtering method were used to qualitatively identify the production patterns of spring runoff, the start and end dates and stage periods of the production patterns. Based on the qualitative identification results, the contribution of each production runoff to the total annual runoff and the total annual spring runoff is quantitatively assessed. The results of the study show that the spring runoff production patterns in the Second Songhua River Basin can be divided into snowmelt runoff, frozen soil conditions of snowmelt–rainfall runoff and rainfall runoff under frozen soil conditions; the snowmelt production is from 21 March, the frozen soil conditions production is from 21 April and the frozen soil ablation ended on 15 June; the shortest phases of each production pattern last 28, 20 and 18 days and the longest last 31, 26 and 24 days. This research provides the basis for improving the principles of production runoff calculation in spring runoff simulation methods.

NPA-Cu2+ Complex as a Fluorescent Sensing Platform for the Selective and Sensitive Detection of Glyphosate

Glyphosate is a highly effective, low-toxicity, broad-spectrum herbicide, which is extensively used in global agriculture to control weeds and vegetation. However, glyphosate has become a potential threat to human and ecosystem because of its excessive usage and its bio-concentration in soil and water. Herein, a novel turn-on fluorescent probe, N-n-butyl-4-(3-pyridin)ylmethylidenehydrazine-1,8-naphthalimide (NPA), is proposed. It efficiently detected Cu2+ within the limit of detection (LOD) of 0.21 μM and displayed a dramatic turn-off fluorescence response in CH3CN. NPA-Cu2+ complex was employed to selectively and sensitively monitor glyphosate concentrations in real samples accompanied by a fluorescence turn-on mode. A good linear relationship between NPA and Cu2+ of glyphosate was found in the range of 10–100 μM with an LOD of 1.87 μM. Glyphosate exhibited a stronger chelation with Cu2+ than NPA and the system released free NPA through competitive coordination. The proposed method demonstrates great potential in quantitatively detecting glyphosate in tap water, local water from Songhua River, soil, rice, millet, maize, soybean, mung bean, and milk with mild conditions, and is a simple procedure with obvious consequences and no need for large instruments or pretreatment.