Assessment of influencing factors on non-point source pollution critical source areas in an agricultural watershed

• Published in: Ecological indicators Vol. 141; p. 109084
• Main Authors: Wang, Shuhui, Wang, Yunqi, Wang, Yujie, Wang, Zhen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-08-2022; Elsevier
• Subjects: AnnAGNPS model; Boosted regression tree; Critical source areas; Non-point source pollution; Watershed planning; Critical source areas; Watershed planning; Non-point source pollution; AnnAGNPS model; Boosted regression tree
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2022.109084

•Boosted regression tree model is robust in charactering nonlinear non-point source pollution generation and transport processes.•The nonlinear responses of critical source areas to influencing factors was evaluated quantitatively.•The thresholds for influencing factors can provide supportive information for watershed management.•Land use and fertilizer application have higher importance in determining the occurrence of critical source areas.•Machine learning techniques have great potential for predicting critical source areas under climate change.
Critical Source Areas (CSAs) are areas that contribute disproportionate high levels of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of the complex interaction between the factors related to the sources and transport processes of NPS pollution. A systematic understanding of how these influencing factors affect CSAs is essential for successful watershed management. In this study, we applied a statistical data mining technique boosted regression tree model to quantify the contribution of eight influencing factors (soil type, slope, elevation, RUSLE LS factor, RUSLE K factor, runoff, fertilizer application rate and land use) on two types of CSAs (TN-CSAs and TP-CSAs), as well as the marginal effects and potential thresholds of influencing factors on the occurrence of CSAs. Results show that land use (37.35%, 25.03%), fertilizer application (36.93%, 57.83%) and soil type (17.59%, 13.70%) have higher importance in determining the occurrence of TN-CSAs and TP-CSAs; and the incidence of TN-CSAs is positively correlated with most factors before the threshold for each influencing factor, after which the marginal effect largely leveled off or dropped slightly; TP-CSAs have essentially the same characteristics as TN-CSAs, but TP-CSAs are more likely to occur in areas with an annual runoff of around 244.92 mm. In addition, this study discussed the application of machine learning techniques in predicting CSAs under climate change without physical-based models, as well as a preliminary watershed management planning for NPS pollution control in the study watershed. These results provided important information for nutrient management regulations.