Development of a multi-level factorial data assimilation approach for groundwater contaminant sources identification

• Published in: Journal of hydrology (Amsterdam) Vol. 636; p. 131233
• Main Authors: Yan, Xueman, An, Yongkai, Lu, Wenxi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Data assimilation; Ensemble Kalman filter; Groundwater contamination sources; Individual and interactive effects; Multi-level factorial analysis; Individual and interactive effects; Multi-level factorial analysis; Ensemble Kalman filter; Groundwater contamination sources; Data assimilation
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2024.131233

•A novel approach is developed for groundwater contaminant sources identification.•The approach integrates ensemble Kalman filtering and multi-level factorial analysis.•The individual and interactive effects of sources of uncertainty are revealed.•The performance of the approach is deeply evaluated by two cases. The ensemble Kalman filter (EnKF) is recognized as a widely used approach for groundwater contamination sources (GCSs) parameters identification. The identification accuracy is affected by the quantification of sources of uncertainty (such as random disturbances on model inputs and measurements) in the assimilation process, but few studies have revealed the contributions of different sources of uncertainty on identification accuracy. To this end, this study developed an innovative multi-level factorial data assimilation (MFDA) approach, which integrates ensemble Kalman filtering and multi-level factorial analysis, to comprehensively reveal the impact of sources of uncertainty on the GCSs parameters identification accuracy for the first time. Not only did it explore the direct impact of individual sources of uncertainty on the GCSs parameters identification accuracy, more importantly, it also revealed the composite ones from multi-parameter interactions among these sources of uncertainty. The developed MFDA approach quantified the contributions of multiple sources of uncertainty, including model input data error (i.e. precipitation), model parameter error (i.e. hydraulic conductivity), contaminant concentration measurements error, and ensemble size. And the feasibility of the developed approach was verified by two hypothetical numerical case studies. The results indicated that: i) it was critically important to reduce the contamination concentration measurement errors to improve GCSs parameter identification accuracy. ii) model parameter and model input data were not sensitive to the identification accuracy. iii) the interaction between sources of uncertainty significantly affects the identification accuracy, which illustrated the interaction cannot be ignored in the identification process. Overall, this study highlights that the developed MFDA approach represents a promising solution for improving GCSs parameters identification accuracy.