An integrated SWAT-XGBoost-SHAP framework identifies key drivers of critical source areas during critical periods in a small watershed.
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| Název: | An integrated SWAT-XGBoost-SHAP framework identifies key drivers of critical source areas during critical periods in a small watershed. |
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| Autoři: | Shen Z; School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China., Zhou H; School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China. zhouhp@cczu.edu.cn., Wang Y; School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China., Pan J; Changzhou Branch of Jiangsu Province Hydrology and Water Resources Investigation Bureau, Changzhou, 213022, China., Xue Y; School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China. xyg@cczu.edu.cn. |
| Zdroj: | Environmental monitoring and assessment [Environ Monit Assess] 2026 Apr 10; Vol. 198 (5). Date of Electronic Publication: 2026 Apr 10. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
| Výrazy ze slovníku MeSH: | Environmental Monitoring*/methods , Non-Point Source Pollution*/statistics & numerical data , Non-Point Source Pollution*/analysis , Water Pollutants, Chemical*/analysis, Phosphorus/analysis ; Nitrogen/analysis ; Soil/chemistry ; Machine Learning ; Boosting Machine Learning Algorithms |
| Abstrakt: | Non-point source (NPS) pollution has emerged as a critical environmental issue, significantly impacting water quality and ecosystem health at the watershed scale. The identification of critical periods (CPs) and critical source areas (CSAs) is fundamental for formulating effective watershed management strategies. However, the identification of effective management measures remains challenging, primarily due to the complex interplay between diverse pollution sources and dynamic environmental factors. To address this challenge, this study proposes an integrated framework that synergistically combines the Soil and Water Assessment Tool (SWAT) model, the eXtreme Gradient Boosting (XGBoost) machine learning algorithm, and the SHapley Additive exPlanations (SHAP) approach. The framework aims to quantitatively analyze the driving factors responsible for the formation of CSAs of NPS pollution in small watersheds during CPs. SWAT simulated nutrient loads, identifying CPs and CSAs via load-time and load-area curves. XGBoost modeled factor relationships, and SHAP quantified each driver's contribution. Applied to a small watershed, results showed CPs (months 2, 6, 7) contributed 59% of total nitrogen (TN) and 65% of total phosphorus (TP) loads. Within CSAs, 56.2-66.2% of TN/TP loads originated from just 35.2-36.8% of the area. Fertilizer application amount (mean |SHAP|= 1.91) and the proportion of cultivated land (mean |SHAP|= 0.52) were identified as the predominant drivers governing the formation of CSAs. Operationally, the identified thresholds (e.g., runoff 30 mm, fertilizer 100 kg/ha) serve as objective tipping points that trigger the implementation of targeted best management practices (BMPs). Despite limitations in the temporal scope of monitoring data and potential model parameter uncertainties, this framework provides a robust scientific basis and a novel methodology for precision watershed management. (© 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
| Competing Interests: | Declarations. Ethical approval: All authors have read, understood, and have complied, as applicable, with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Consent to participate: Informed consent was obtained from all individual participants included in the study. Competing interests: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: Critical periods; Critical source areas; Non-point source pollution; SHapley Additive exPlanation; XGBoost |
| Substance Nomenclature: | 27YLU75U4W (Phosphorus) N762921K75 (Nitrogen) 0 (Water Pollutants, Chemical) 0 (Soil) |
| Entry Date(s): | Date Created: 20260410 Date Completed: 20260410 Latest Revision: 20260410 |
| Update Code: | 20260411 |
| DOI: | 10.1007/s10661-026-15274-5 |
| PMID: | 41963745 |
| Databáze: | MEDLINE |
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