Comprehensive experimental design for carbon, nitrogen, and phosphorus loss characteristics from slopes under simulated rainfall

Current undergraduate environmental engineering education faces pedagogical bottlenecks as laboratory courses on nonpoint source pollution monitoring and prevention lag behind practical needs. This gap is attributable to the failure of traditional steady-state and constant-flow experiments to reproduce the nonstationary, nonlinear, and multifactor-coupled environmental processes associated with real nonpoint source pollution. Meanwhile, natural rainfall monitoring is severely constrained by temporal randomness and the difficulty of deploying field instruments, nearly preventing experimental teaching under real-world conditions. These limitations hinder the cultivation of students’ ability to solve “complex engineering problems” as required by accreditation standards. To address these issues, this study designs a comprehensive experiment to investigate the characteristics of carbon, nitrogen, and phosphorus losses from slopes using an artificial rainfall simulation system. The aim is to integrate the whole chain of “rainfall–soil erosion–pollutant release and transport–pollutant prediction and control” into experimental teaching, thereby providing a solid foundation for cultivating multi-skilled talents with engineering practice and innovative skills.

The experiment employs a high-precision multinozzle downward-spray rainfall simulator and a variable-slope runoff trough, using typical sandy yellow loam soil from the upper reaches of the Yangtze River as the test subject. An orthogonal array of “rainfall intensity × slope gradient” is constructed to systematically investigate the characteristics of runoff generation, sediment yield, and the associated carbon, nitrogen, and phosphorus losses from the slope. The synergistic relationships among runoff, sediment, and nutrient transport are revealed, and predictive estimations of carbon, nitrogen, and phosphorus losses in runoff are further performed.

Experimental results for the yellow loam slope showed that the runoff rate exhibited a distinct three-stage temporal evolution under all treatment combinations: rapid increase, slow rise, and eventual stabilization. Cumulative runoff per unit area increased significantly with rainfall intensity but decreased with increasing slope gradient, following the order 6°>12°>18°. The sediment yield rate peaked in the early stage and then declined rapidly to a stable level; total sediment yield increased with rainfall intensity, while its response to slope followed an inverted U-shaped nonlinear trend, with the critical slope angle identified at approximately 12°. Carbon loss was dominated by sediment-associated total carbon (STC), with the highest total carbon load (5286.23 mg/m²) occurring under the combination of high rainfall intensity (90 mm/h) and moderate slope (12°). Sediment-associated total nitrogen (STN) accounted for more than 70% of the total nitrogen loss, and the peak total nitrogen load appeared under the moderate intensity and moderate slope combination. Sediment-associated total phosphorus (STP) constituted 70%–96% of the total phosphorus loss, and the peak total phosphorus loss occurred under moderate intensity or under the 12° slope. Correlation analysis revealed that sediment yield was significantly positively correlated with STN, STP, total nitrogen, and total phosphorus. The correlation coefficients exceeded 0.90 in all cases, confirming that sediment serves as the primary carrier for carbon, nitrogen, and phosphorus export. Furthermore, the prediction models for STC, STN, and STP trained using artificial neural networks achieved coefficient-of-determination values exceeding 0.7, demonstrating their capability to effectively support field-scale load estimation.

Practical implementation demonstrates that the experiment achieves a deep integration of macroscopic hydrodynamic erosion with microscopic pollutant migration. This integration significantly enhances students’ understanding of complex environmental engineering problems, deepens their mastery of core courses such as environmental monitoring and pollution control engineering, and strengthens their engineering practice ability and innovative thinking. Moreover, the experimental module can be readily extended to incorporate emerging research frontiers such as microplastic transport and pesticide wash-off. It can also be combined with virtual simulation to establish a “virtual–physical–application” integrated teaching model. This approach provides a scalable and comprehensive experimental platform for cultivating innovative talents in resource and environmental sciences.