HGS RESEARCH HIGHLIGHT – Evaluating backward probability model under various hydrogeologic and hydrologic conditions
Hwang, H.-T., Neupauer, R. M., Jeen, S.-W., Steinmoeller, D. T., Sudicky, E. A., Lee, S.-S., & Lee, K.-K. (2022). Evaluating backward probability model under various hydrogeologic and hydrologic conditions. In Journal of Contaminant Hydrology (Vol. 244, p. 103909). Elsevier BV. https://doi.org/10.1016/j.jconhyd.2021.103909
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This article co-authored by Aquanty scientists Dr. Hyoun-Hae Hwang, Dr. Derek Steinmoeller, and Dr. Ed Sudicky (alongside researchers at the University of Colorado Boulder, Jeonbuk National University and Seoul National University) evaluates the reliability of a backward-in-time solute transport probability model under different groundwater flow conditions.
The purpose of this technique is to improve our ability to trace contaminants in groundwater flow systems back to their source, with increased computational efficiency and accuracy compared to other source identification techniques. Should the backward probability model prove effective it may represent a key to faster and more effective contaminated site remediation strategies should a spill occur.
Evaluating backward probability model under various hydrogeologic and hydrologic conditions
This paper provides a framework for testing the backward probability model under both transient saturated flow conditions and transient variably saturated flow conditions The viability of the framework was tested through comparison against two simple (homogeneous) synthetic test problems from published studies, and two tests “involving more complex conditions that involved comparison to results obtained with [a] forward probability model.”
The results of the backward probability model were highly correlated with previously published results (based on a forward probability model) for the simple test cases, although the results for the two complex studies (which introduced heterogeneity in the porous media and reactive solute transport) were less correlated with forward probability models. Nevertheless, “the backward probability model can estimate contaminant source release times with reasonable accuracy under variably-saturated transient flow conditions.”
This is an important study as groundwater contamination has serious negative effects on human health and ecosystems. To effectively and efficiently remediate contamination, we need to know where it came from. The appeal of the backward probability model compared to other numerical approaches for identifying contaminant sources it is that it can potentially do so with improved computational efficiency and accuracy.
HydroGeoSphere was instrumental in the research presented here, as it is one of the only commercially available modelling platforms that supports fully 3-dimensional variably saturated groundwater flow and solute transport.
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Abstract:
Contaminant source identification improves the understanding of contaminant source characteristics including location and release time, which can lead to more effective remediation and water resources management plans. The backward probability model can provide probabilities of source locations and release times under various contaminant properties and hydrogeologic conditions. The backward probability model has been applied to numerous synthetic and real contamination sites for locating possible contaminant sources, but it is also important to evaluate the reliability of the backward probability model through rigorous verification analyses. Here, we present a model verification framework for the backward probability model using a stepwise approach from simple to complex model settings: comparison with previous studies, transient saturated flow under various hydrogeologic conditions, and transient variably-saturated flow conditions. As a simple condition, one-dimensional homogeneous problems under steady-state and transient flow conditions were verified by comparing with previous studies. Model verifications with complex conditions were conducted by comparing forward and backward probability simulation results. The verification results demonstrate that the backward probability model performs well for homogeneous problems. For heterogeneous problems, the backward probability model results in slightly different backward travel times due to differences in solute decay and boundary conditions assigned for both forward and backward probability simulations, but the backward travel time at the maximum probability can be reproduced well.