Assessing the Worth of Noble Gas Tracer Concentrations in Alluvial River‐Aquifer Systems Using Data Space Inversion

Abstract Alluvial systems are dynamic, structurally complex environments. Groundwater that is extracted from these systems is widely used for domestic consumption. Numerical models play an important role in the management of this extraction. To provide support for decision‐making, models must assimilate data that inform the spatial and temporal dynamics of the interactions between alluvial surface water (SW) and groundwater (GW). In theory, assimilation of noble gas tracer data into numerical models that solve the advection‐dispersion equation as it pertains to gas tracer concentrations can reduce the uncertainties of management‐salient predictions made by these models. To date, however, this has not been undertaken; nor has its decision‐support efficacy been tested. A modeling framework is introduced whereby data space inversion, a highly efficient method for evaluation of post‐data‐assimilation uncertainties of complex simulators, is used to evaluate benefits accrued through assimilation of noble gas tracer concentrations (i.e., 222 Rn; 37 Ar; 4 He) along with more classical observations. The ability of noble gas concentrations to reduce the uncertainties of predictions made using an integrated surface‐subsurface hydrological model is rigorously evaluated in simulation experiments that span a range of plausible hydraulic and hydrological conditions. Outcomes of these experiments demonstrate that measurements of 222 Rn and 4 He concentrations are able to constrain temporally‐variable predictions, such as the magnitudes of SW‐GW exchange fluxes, as well as solute breakthrough curves that reflect the residence times of potential pollutants following an aquifer contamination event. 37 Ar concentrations can also provide important information on SW‐GW interactions, especially the residence times of bank filtrate.