Bivariate Statistical Model for Calculating Phosphorus Input Loads to the River from Point and Nonpoint Sources
区域点源和非点源磷入河量计算的二元统计模型

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 hydrological 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 water 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·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·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 and managers with a cost-effective tool for quantifying TP pollution processes in both district and watershed scales.