Rain Pattern Deeply Reshaped Total Phosphorus Load Pattern in Watershed: A Case Study from Northern China

Abstract

Excessive phosphorus in aquatic systems poses a threat to ecosystem stability and human health. Precipitation has a profound influence on the phosphorus biogeochemical process; however, it has been inadequately considered at the watershed scale. In this study, the Bayesian latent variable regression model was utilized to quantify the impact of rainfall on the concentration of total phosphorus using daily monitoring data from 2019 to 2021. The results revealed a piecewise linear relationship between total phosphorus concentration and precipitation. It was further inferred that the breakpoint (The total rainfall during a single rainfall event equal to 39.4 ± 0.45 mm) represented the tipping point for the transformation of the primary river runoff generation mechanism. Subsequently, the excess phosphorus load caused by rainfall events was estimated in the Shahe basin by combining the regional nutrient management approach with the results of the Bayesian latent variable regression model. The results indicated that rainfall events were one of the most significant sources of TP loads from 2006 to 2017, accounting for 28.2% of the total. Non-artificial land, including farmland, forests, and grasslands, serves as the primary source of the excess phosphorus load resulting from rainfall events. This study provides insights into how to quantify the phosphorus load resulting from rainfall events at the basin scale, which is valuable for phosphorus management. Environmental managers should prioritize the regulation of phosphorus in non-artificial land moving forward. Implementing hierarchical management based on calibrated curve numbers and soil phosphorus content could prove to be an efficient approach for regulating phosphorus in the watershed.