Use of interpretable machine learning to identify the factors influencing the nonlinear linkage between land use and river water quality in the Chesapeake Bay watershed. (July 2022)

• Record Type: Journal Article
• Authors: Zhang, Zhenyu; Huang, Jinliang; Duan, Shuiwang; Huang, Yaling; Cai, Juntao; Bian, Jing
• Is Part Of: Ecological indicators. Volume 140(2022)
• Publication Date: 2022-07
• Subjects: Land-use -- Water quality -- Nonlinearity -- Threshold effect -- Potomac River basin; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108977
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: We used RFR models and SHAP method to identify the factors that determined the nonlinearity of linkage between land use and water quality. The machine learning method is effective to simulate and understand the linkage between land use and stream water quality. The threshold effects of land use patterns on water quality were obvious for TN and NH4 + -N. The wetland may become major source of P export under specific conditions. Abstract: Understanding the relationship between land use and water quality is essential for effective watershed management. However, it remains challenging to identify such a relationship owing to its nonlinearity. We developed an interpretable machine learning method that integrated the random forest regression (RFR) model with the Shapley Additive exPlanations (SHAP) method to explore the relationship between water quality and land use in the Potomac River Basin (PRB), the second largest tributary entering Chesapeake Bay from 2006 to 2019. The water quality of the 26 sub-watersheds, classified into five types (natural, forested, agricultural, mixed, and urbanized), was investigated using statistical methods and scenario analysis. The results showed that the models employed were effective in predicting the water quality. The mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS), R 2 coefficient of determination (R 2 ), and Kling-Gupta efficiency (KGE) were 0.011–0.159 mg/L, 0.019–0.219 mg/L, −0.14–0.64%, 0.79–0.99, and 0.69–0.98, respectively, during the training period, which were 0.010–0.201 mg/L, 0.017–0.292 mg/L, −1.87–0.41%, 0.82–0.99, and 0.80–0.97, respectively, during the testing period. The threshold effects of land use patterns were obvious for water quality indicators with high concentrations (i.e., TN and NH4 + -N). In contrast, the water quality at low concentrations (i.e., TP and NO3 – -N) may be more sensitive to wetland or barren land with changing climate. Agricultural activities and urbanization could be the dominant factors determining nutrient export to the PRB. Meanwhile, the typical 'sink' for the nutrient such as wetland may change into the 'source' for different nutrient. This study provides an in-depth understanding of how riverine nutrient export responds to the land use gradient in the Chesapeake Bay watershed.