[Estimation of Cropland Nitrogen Runoff Loss Loads in the Yangtze River Basin Based on the Machine Learning Approaches]

Yu-Fu Zhang; Zhe-Qi Pan; Ding-Jiang Chen

doi:10.13227/j.hjkx.202208129

[Estimation of Cropland Nitrogen Runoff Loss Loads in the Yangtze River Basin Based on the Machine Learning Approaches]

Huan Jing Ke Xue. 2023 Jul 8;44(7):3913-3922. doi: 10.13227/j.hjkx.202208129.

[Article in Chinese]

Authors

Yu-Fu Zhang^{1

2}, Zhe-Qi Pan¹, Ding-Jiang Chen^{1

2

3}

Affiliations

¹ College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China.
² Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, China.
³ Zhejiang Provincial Key Laboratory of Agricultural Resource and Environment, Zhejiang University, Hangzhou 310058, China.

PMID: 37438290
DOI: 10.13227/j.hjkx.202208129

Abstract

A quantitative understanding of cropland nitrogen (N) runoff loss is critical for developing efficient N pollution control strategies. Using correlation analysis, a structural equation model, variance decomposition, and machine learning methods, this study identified the primary influencing factors of total N (TN) runoff loss from uplands (n=570) and paddy (n=434) fields in the Yangtze River Basin (YRB) and then developed a machine learning-based prediction model to quantify cropland N runoff loss load. The results indicated that runoff depth, soil N content, and fertilizer addition rate were the major influencing factors of TN runoff loss from uplands, whereas TN runoff loss rate from paddy fields was mainly regulated by runoff depth and fertilizer addition rate. Among the four used machine learning methods, the prediction models based on the random forest algorithm presented the highest accuracy (R²=0.65-0.94) for predicting upland and paddy field TN runoff loss rates. The random forest algorithm based model estimated a total cropland TN loss load in the YRB of 0.47 Tg·a^-1 (upland:0.25 Tg·a^-1; paddy field:0.22 Tg·a^-1) in 2013, with 58% of TN runoff loss load derived from the midstream and downstream regions. The models predicted that TN runoff loss loads from croplands in YRB would decrease by 2.4%-9.3% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. To mitigate cropland N nonpoint source pollution in YRB, it is essential to integrate efficient water, fertilizer, and soil nutrient managements as well as to consider the midstream and downstream regions as the high priority area. The machine learning-based modeling method developed in this study overcame the difficulty of identifying the functional relationships between cropland TN loss rate and multiple influencing factors in developing relevant prediction models, providing a reliable method for estimating regional and watershed cropland TN loss load.

Keywords: Yangtze River Basin; cropland; machine learning; nitrogen loss; nonpoint source pollution; runoff.

Publication types

English Abstract