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Arash Massoudieh

Publications and source records attributed to Arash Massoudieh.

2 recordsLinked to original sources

Complexity of groundwater age mixing near a seawater intrusion zone based on multiple tracers and Bayesian inference

Aquifer flow systems near seawater interfaces can be complicated by density-driven flows and the formation of stagnation zones, which inevitably introduces uncertainty into groundwater age-dating. While age-dating has proved effective to understand the seawater intrusion and aquifer salinization process in coastal aquifers, further efforts are needed to propagate model and data uncertainty to the uncertainty associated with the inferred age distributions. This study was performed in a coastal aquifer located close to the Yellow Sea, South Korea, where there is a decreasing trend of groundwater levels due to recent heavy exploitation, raising a warning of induced seawater intrusion. We inferred the groundwater age distributions in wells around the intrusion zone and estimated the uncertainty associated with the inference based on multiple age tracers including 3 H, tritiogenic 3 He, radiogenic 4 He, CFC-11, CFC-12 and CFC-113 using Bayesian inference. We examined various models representing the age distributions including traditional parametric Lumped Parameter Models (LPMs) and two non-parametric “shape-free” models. The results showed that the mean ages at the study site ranged from 10.9 to 522.5 y. Complex, multimodal distributions of ages occurred near a seawater intrusion area and upland recharge zones, implying converging paths of a wide range of different ages in those regions. In particular, the age distributions estimated near the seawater intrusion interface were characterized by heavy-tailed mixing structures with elevated concentrations of 4 He. This likely indicates density-driven upward flow at the seawater intrusion interface, forcing old groundwater rich in 4 He into the shallow aquifer. The Bayesian inference estimated large uncertainties particularly for the old age distributions, which was attributed partly to the gradual accumulation of 4 He in groundwater. The Bayesian inference improved understanding of flow dynamics at a complex seawater interface and identified opportunities to further reduce uncertainty of old water age estimates that characterize upwelling groundwater near the interface.

Jaancheon basin

Sediment source apportionment in Laurel Hill Creek, PA, using Bayesian chemical mass balance and isotope fingerprinting

A Bayesian chemical mass balance (CMB) approach was used to assess the contribution of potential sources for fluvial samples from Laurel Hill Creek in southwest Pennsylvania. The Bayesian approach provides joint probability density functions of the sources' contributions considering the uncertainties due to source and fluvial sample heterogeneity and measurement error. Both elemental profiles of sources and fluvial samples and 13 C and 15 N isotopes were used for source apportionment. The sources considered include stream bank erosion, forest, roads and agriculture (pasture and cropland). Agriculture was found to have the largest contribution, followed by stream bank erosion. Also, road erosion was found to have a significant contribution in three of the samples collected during lower-intensity rain events. The source apportionment was performed with and without isotopes. The results were largely consistent; however, the use of isotopes was found to slightly increase the uncertainty in most of the cases. The correlation analysis between the contributions of sources shows strong correlations between stream bank and agriculture, whereas roads and forest seem to be less correlated to other sources. Thus, the method was better able to estimate road and forest contributions independently. The hypothesis that the contributions of sources are not seasonally changing was tested by assuming that all ten fluvial samples had the same source contributions. This hypothesis was rejected, demonstrating a significant seasonal variation in the sources of sediments in the stream.

Pennsylvania