[Nitrogen non-point source pollution identification based on ArcSWAT in Changle River].

The ArcSWAT (Soil and Water Assessment Tool) model was adopted for Non-point source (NPS) nitrogen pollution modeling and nitrogen source apportionment for the Changle River watershed, a typical agricultural watershed in Southeast China. Water quality and hydrological parameters were monitored, and the watershed natural conditions (including soil, climate, land use, etc) and pollution sources information were also investigated and collected for SWAT database. The ArcSWAT model was established in the Changle River after the calibrating and validating procedures of the model parameters. Based on the validated SWAT model, the contributions of different nitrogen sources to river TN loading were quantified, and spatial-temporal distributions of NPS nitrogen export to rivers were addressed. The results showed that in the Changle River watershed, Nitrogen fertilizer, nitrogen air deposition and nitrogen soil pool were the prominent pollution sources, which contributed 35%, 32% and 25% to the river TN loading, respectively. There were spatial-temporal variations in the critical sources for NPS TN export to the river. Natural sources, such as soil nitrogen pool and atmospheric nitrogen deposition, should be targeted as the critical sources for river TN pollution during the rainy seasons. Chemical nitrogen fertilizer application should be targeted as the critical sources for river TN pollution during the crop growing season. Chemical nitrogen fertilizer application, soil nitrogen pool and atmospheric nitrogen deposition were the main sources for TN exported from the garden plot, forest and residential land, respectively. However, they were the main sources for TN exported both from the upland and paddy field. These results revealed that NPS pollution controlling rules should focus on the spatio-temporal distribution of NPS pollution sources.

[Bivariate statistical model for calculating phosphorus input loads to the river from point and nonpoint sources].

Based on the hydrological difference between the point source (PS) and nonpoint source (NPS) pollution processes and the major influencing mechanism of in-stream retention processes, a bivariate statistical model was developed for relating river phosphorus load to river water flow rate and temperature. Using the calibrated and validated four model coefficients from in-stream monitoring data, monthly phosphorus input loads to the river from PS and NPS can be easily determined by the model. Compared to current hydrologica methods, this model takes the in-stream retention process and the upstream inflow term into consideration; thus it improves the knowledge on phosphorus pollution processes and can meet the requirements of both the district-based and watershed-based wate quality management patterns. Using this model, total phosphorus (TP) input load to the Changle River in Zhejiang Province was calculated. Results indicated that annual total TP input load was (54.6 +/- 11.9) t x a(-1) in 2004-2009, with upstream water inflow, PS and NPS contributing to 5% +/- 1%, 12% +/- 3% and 83% +/- 3%, respectively. The cumulative NPS TP input load during the high flow periods (i. e. , June, July, August and September) in summer accounted for 50% +/- 9% of the annual amount, increasing the alga blooming risk in downstream water bodies. Annual in-stream TP retention load was (4.5 +/- 0.1) t x a(-1) and occupied 9% +/- 2% of the total input load. The cumulative in-stream TP retention load during the summer periods (i. e. , June-September) accounted for 55% +/- 2% of the annual amount, indicating that in-stream retention function plays an important role in seasonal TP transport and transformation processes. This bivariate statistical model only requires commonly available in-stream monitoring data (i. e. , river phosphorus load, water flow rate and temperature) with no requirement of special software knowledge; thus it offers researchers an managers with a cost-effective tool for quantifying TP pollution processes in both district and watershed scales.