Modeling Nitrate at Domestic and Public-Supply Well Depths in the Central Valley, California

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• 作者：Bernard T. Nolan ; JoAnn M. Gronberg ; Claudia C. Faunt ; Sandra M. Eberts ; Ken Belitz
• 刊名：Environmental Science & Technology
• 出版年：2014
• 出版时间：May 20, 2014
• 年：2014
• 卷：48
• 期：10
• 页码：5643-5651
• 全文大小：468K
• 年卷期：v.48,no.10(May 20, 2014)
• ISSN：1520-5851

文摘

Aquifer vulnerability models were developed to map groundwater nitrate concentration at domestic and public-supply well depths in the Central Valley, California. We compared three modeling methods for ability to predict nitrate concentration >4 mg/L: logistic regression (LR), random forest classification (RFC), and random forest regression (RFR). All three models indicated processes of nitrogen fertilizer input at the land surface, transmission through coarse-textured, well-drained soils, and transport in the aquifer to the well screen. The total percent correct predictions were similar among the three models (69鈥?2%), but RFR had greater sensitivity (84% for shallow wells and 51% for deep wells). The results suggest that RFR can better identify areas with high nitrate concentration but that LR and RFC may better describe bulk conditions in the aquifer. A unique aspect of the modeling approach was inclusion of outputs from previous, physically based hydrologic and textural models as predictor variables, which were important to the models. Vertical water fluxes in the aquifer and percent coarse material above the well screen were ranked moderately high-to-high in the RFR models, and the average vertical water flux during the irrigation season was highly significant (p < 0.0001) in logistic regression.