Estimation of non-point source pollution loads with flux method in Danjiangkou Reservoir area,China

The estimation of non-point source pollution loads into the Danjiangkou Reservoir is highly significant to environmental protection in the watershed. In order to overcome the drawbacks of traditional watershed numerical models, a base flow separation method was established coupled with a digital filtering method and a flux method. The digital filtering method has been used to separate the base flows of the Hanjiang,Tianhe, Duhe, Danjiang, Laoguan, and Qihe rivers. Based on daily discharge, base flow, and pollutant concentration data, the flux method was used to calculate the point source pollution load and non-point source pollution load. The results show that:(1) In the year 2013, the total inflow of the six rivers mentioned above accounted for 95.9% of the total inflow to the Danjiangkou Reservoir. The total pollution loads of chemical oxygen demand(CODMn) and total phosphorus(TP) from the six rivers were 58.20 103 t and 1.863 10~3 t, respectively, and the non-point source pollution loads were 39.82 10~3 t and 1.544 10~3 t, respectively, indicating that the non-point source pollution is a major factor(with a contribution rate of 68.4% for CODMnand 82.9% for TP).(2) The Hanjiang River is the most significant contributor of pollution loads to the Danjiangkou Reservoir, and its CODMnand TP contribution rates reached 79.3% and 83.2%, respectively. The Duhe River took the second place.(3) Non-point source pollution mainly occurred in the wet season in 2013, accounting for 80.8% and 90.9% of the total pollution loads of CODMnand TP, respectively. It is concluded that the emphasis of pollution control should be placed on non-point source pollution.

Evaluation, prediction, and protection of water quality in Danjiangkou Reservoir, China

The water quality in the Danjiangkou Reservoir has attracted considerable attention from the Chinese public and government since the announcement of the Middle Route of the South to North Water Diversion Project （SNWDP）, which commenced transferring water in 2014. Integrated research on the evaluation, prediction, and protection of water quality in the Danjiangkou Reservoir was carried out in this study in order to improve environmental management. Based on 120 water samples, wherein 17 water quality indices were measured at 20 monitoring sites, a single factor evaluation method was used to evaluate the current status of water quality. The results show that the main indices influencing the water quality in the Danjiangkou Reservoir are total phosphorus （TP）, permanganate index （CODM,）, dissolved oxygen （DO）, and five-day biochemical oxygen demand （BODs）, and the concentrations of TP, BODs, ammonia nitrogen （NH3--N）, CODM,, DO, and anionic surfactant （Surfa） do not reach the specified standard levels in the tributaries. Seasonal Mann--Kendall tests indicated that the CODMn concentration shows a highly significant increasing trend, and the TP concentration shows a significant increasing trend in the Danjiangkou Reservoir. The distribution of the main water quality indices in the Danjiangkou Reservoir was predicted using a two-dimensional water quality numerical model, and showed that the sphere of influence from the tributaries can spread across half of the Han Reservoir if the pollutants are not controlled. Cluster analysis （CA） results suggest that the Shending River is heavily polluted, that the Jianghe, Sihe, and Jianhe rivers are moderately polluted, and that they should be the focus of environmental remediation.

Spatiotemporal patterns of non-point source nitrogen loss in an agricultural catchment

Non-point source nitrogen loss poses a risk to sustainable aquatic ecosystems. However, non-point sources, as well as impaired river segments with high nitrogen concentrations, are difficult to monitor and regulate because of their diffusive nature, budget constraints, and resource deficiencies. For the purpose of catchment management, the Bayesian maximum entropy approach and spatial regression models have been used to explore the spatiotemporal patterns of non-point source nitrogen loss. In this study, a total of 18 sampling sites were selected along the river network in the Hujiashan Catchment. Over the time period of 2008e2012, water samples were collected 116 times at each site and analyzed for non-point source nitrogen loss. The morphometric variables and soil drainage of different land cover types were studied and considered potential factors affecting nitrogen loss. The results revealed that, compared with the approach using the Euclidean distance, the Bayesian maximum entropy approach using the river distance led to an appreciable 10.1% reduction in the estimation error, and more than 53.3% and 44.7% of the river network in the dry and wet seasons, respectively, had a probability of non-point source nitrogen impairment. The proportion of the impaired river segments exhibited an overall decreasing trend in the study catchment from 2008 to 2012, and the reduction in the wet seasons was greater than that in the dry seasons. High nitrogen concentrations were primarily found in the downstream reaches and river segments close to the residential lands. Croplands and residential lands were the dominant factors affecting non-point source nitrogen loss, and explained up to 70.7%of total nitrogen in the dry seasons and 54.7% in the wet seasons. A thorough understanding of the location of impaired river segments and the dominant factors affecting total nitrogen concentration would have considerable importance for catchment management.