Multiphase, Multicomponent Parameter Estimation for Liquid and Vapor Fluxes in Deep Arid Systems Using Hydrologic Data and Natural Environmental Tracers

Abstract

Multiphase, multicomponent numerical models of long‐term unsaturated‐zone liquid and vapor movement were created for a thick alluvial basin at the Nevada Test Site to predict present‐day liquid and vapor fluxes. The numerical models are based on recently developed conceptual models of unsaturated‐zone moisture movement in thick alluvium that explain present‐day water potential and tracer profiles in terms of major climate and vegetation transitions that have occurred during the past 10 000 yr or more. The numerical models were calibrated using borehole hydrologic and environmental tracer data available from a low‐level radioactive waste management site located in a former nuclear weapons testing area. The environmental tracer data used in the model calibration includes tracers that migrate in both the liquid and vapor phases (δD, δ18O) and tracers that migrate solely as dissolved solutes (Cl), thus enabling the estimation of some gas‐phase as well as liquid‐phase transport parameters. Parameter uncertainties and correlations identified during model calibration were used to generate parameter combinations for a set of Monte Carlo simulations to more fully characterize the uncertainty in liquid and vapor fluxes. The calculated background liquid and vapor fluxes decrease as the estimated time since the transition to the present‐day arid climate increases. However, on the whole, the estimated fluxes display relatively little variability because correlations among parameters tend to create parameter sets for which changes in some parameters offset the effects of others in the set. Independent estimates on the timing since the climate transition established from packrat midden data were essential for constraining the model calibration results. The study demonstrates the utility of environmental tracer data in developing numerical models of liquid‐ and gas‐phase moisture movement and the importance of considering parameter correlations when using Monte Carlo analysis to characterize the uncertainty in moisture fluxes.